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Health Adjusted Life Expectancy Among Adult HIV/AIDS Patients in Kenya: A Comparative
Study of Nyanza and Central Regions
By
John Njuguna Njenga
A PhD Thesis Submitted in Fulfilment of the Requirements of the Award for the
Degree of Doctor of Philosophy in Population Studies of the University of Nairobi.
November, 2016
DECLARATION
This PhD thesis is my original work and has not been presented for an award of a degree in this
or any other university.
CANDIDATE: JOHN NJUGUNA NJENGA
SIGNATURE:……………………………………..Date:…………………….…………
This PhD thesis has been submitted with our approval as University Supervisors:
PROF. MURUNGARU KIMANI
SIGN………………..………………………………………………………………………
Date…………………………………………………………………………………………
PROF. LAWRENCE IKAMARI
SIGN………………………………………………………………………………………..
Date…………………………………………………………………………………………..
i
DEDICATION
To my mother for her commitment, undying love and dedication.
ii
ACKNOWLEDGEMENTS
I would like to acknowledge the following for making it possible for me to complete this thesis:
Consortium for Advanced Research Training in Africa (CARTA) for partially funding my study
and providing me with many opportunities to interact with experts from all over the world
through joint advanced seminars and support to attend international conferences. I am grateful to
Population Studies and Research Institute of University of Nairobi (PSRI) fraternity for giving
me a conducive environment for doing my PhD. More specifically, I am forever indebted to my
supervisors Prof Murungaru Kimani and Prof Lawrence Ikamari for their guidance and patience
throughout the process. I am also grateful to my CARTA cohort 2 fellows for the many sessions
we held giving feedback on each other’s work and your valuable feedback that went a long way
in making my work better each day.
This study would not have been completed without the support of facility and county health
managers who allowed me to collect data in their respective jurisdictions. I would like to
sincerely thank my research assistants who did the actual work of enrolling participants into the
study and followed them up to ensure they were interviewed for the second round of data
collection. I am also grateful to my wife and children for bearing with me when I was away for
many days or when I was not able to be with them when they needed me. To all of you, may God
bless you mightily.
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ABSTRACT
Introduction of advanced management and treatment of HIV/AIDS has seen life expectancy of
people living with HIV/AIDS (PLWHA) increase over the years to almost the level of the
general population. Little is however known what proportion of this life is spent in different
health statuses as measured by health adjusted life expectancy (HALE) and if this measure
differs across sub populations in Kenya. This longitudinal study set out to achieve five objectives
namely: to assess health related quality of life (HRQOL) of adult HIV/AIDS patients newly
started on HIV care and treatment in Nyanza and Central regions of Kenya; to compare transition
probabilities from baseline health states to health states at one year follow-up for adult
HIV/AIDS patients in Nyanza and Central regions of Kenya; to compare HALE among adult
HIV patients in Nyanza and Central Kenya regions; to determine factors associated with HALE
for adult HIV/AIDS patients in Nyanza and Central regions of Kenya; and to compare HALE
results estimated using Sullivan and multistate life table (MSLT) approaches.
Data were collected in two waves among adult HIV patients aged 15 years and above newly
diagnosed with HIV in six public health facilities in Nyanza and Central regions of Kenya.
Demographics, socio-economic, biomedical and self-reported health related quality of life
(HRQOL) information. Two summary measures of health; physical health summary (PHS)
measure and mental health summary (MHS) measure were obtained from HRQOL measures
which were then categorized into different health thresholds and using both Sullivan and MSLT
approaches, the number of years spent in each threshold (HALE) was obtained.
The findings of the study showed that there were significant differences in health adjusted life
expectancy between Nyanza and Central regions. Life expectancy adjusted for various MHS
statuses was lower than that adjusted for various PHS statuses. The proportion of life spent in
good health status was higher among male than female, was higher among those initially in good
health statuses than those initially in poor health statuses and higher among those in Central than
in Nyanza region. HALE estimates obtained using Sullivan method were higher for proportion of
life spent in poor health statuses compared to estimates obtained using MSLT approach.
The findings of the study clearly demonstrate PLWHA in Kenya spend substantial proportion of
their lives in poor health states and regional differences persist. Different methodological
approaches provide different estimates of health adjusted life expectancy. There is need for
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further studies to explain observed regional differences as well as further comparison of results
obtained using the two approaches since Sullivan approach may have overestimated proportion
of life spent in poor health status due to use of baseline HRQOL estimates.
v
Table of Contents
DECLARATION ............................................................................................................................. i
DEDICATION .................................................................................................................................ii
ACKNOWLEDGEMENTS ............................................................................................................iii
ABSTRACT ....................................................................................................................................iv
List of Acronyms and Abbreviations ............................................................................................ viii
List of Tables .................................................................................................................................. xi
List of Figures ................................................................................................................................ xv
CHAPTER ONE ............................................................................................................................. 1
INTRODUCTION .......................................................................................................................... 1
1.1 Background ...................................................................................................................... 1
1.2 Problem Statement ........................................................................................................... 6
1.3 Research Questions .......................................................................................................... 7
1.4 Research Objectives ......................................................................................................... 7
1.5 Rationale and Justification ............................................................................................... 8
1.6 Scope and Limitation of the Study ................................................................................. 10
1.7 Organization of the Thesis ............................................................................................. 13
CHAPTER TWO .......................................................................................................................... 14
LITERATURE REVIEW ............................................................................................................. 14
2.1 Introduction .................................................................................................................... 14
2.2 Trends and levels in life expectancy .............................................................................. 14
2.3 Trends in life expectancy in sub Saharan Africa and the effect of HIV/AIDS .............. 15
2.4 Increasing life expectancy among people living with HIV/AIDS ................................. 16
2.5 Concerns over increasing life expectancy ...................................................................... 18
2.6 HIV/AIDS and health related quality of life .................................................................. 19
2.7 HIV/AIDS and health adjusted life expectancy ............................................................. 20
2.8 Factors associated with health adjusted life expectancy ................................................ 21
2.9 Measurement of HRQOL and HALE............................................................................. 22
2.10
Key limitations in the measurement of health expectancies ....................................... 25
2.11
HIV/AIDS and regional inequalities in Kenya ........................................................... 27
2.12
Conceptual frameworks .............................................................................................. 28
2.13
Definition of concepts used in the study .................................................................... 30
2.14
Conclusion ................................................................................................................. 30
CHAPTER THREE ...................................................................................................................... 32
METHODOLOGY ....................................................................................................................... 32
3.1 Introduction .................................................................................................................... 32
3.2 Study design ................................................................................................................... 32
3.3
Study settings ................................................................................................................ 34
3.4 Summary measures of population health and sources of data ....................................... 35
3.5 Data collection tools ....................................................................................................... 36
3.6 Data collection process................................................................................................... 37
3.7 Description of characteristics of patients assessed in the study ..................................... 38
3.8 Confidentiality considerations........................................................................................ 44
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3.9 Data quality assurance measures .................................................................................... 44
3.10
Data analysis methods ............................................................................................... 45
CHAPTER FOUR ......................................................................................................................... 57
CHARACTERISTICS OF STUDY PARTICIPANTS AND FACTORS ASSOCIATED WITH
HEALTH RELATED QUALITY OF LIVE MEASURES .......................................................... 57
4.1 Introduction .................................................................................................................... 57
4.2. Basic characteristics of study participants ..................................................................... 58
4.4.
One year follow-up outcomes of study participants and their association with
baseline HRQOL measures ....................................................................................................... 79
4.5. Conclusion...................................................................................................................... 85
CHAPTER FIVE .......................................................................................................................... 87
HEALTH ADJUSTED LIFE EXPECTANCY AND ASSOCIATED FACTORS ...................... 87
5.1 Introduction .................................................................................................................... 87
5.2. Calculation of HALE using Sullivan approach .............................................................. 88
5.3. Estimation of MSLT functions using SPACE programme ............................................ 98
5.4. Estimation of health adjusted life expectancy (HALE) using SPACE programme ..... 109
5.5 Conclusion ........................................................................................................................ 127
CHAPTER SIX ........................................................................................................................... 129
SUMMARY, CONCLUSION AND RECOMMENDATIONS ................................................. 129
6.1 Introduction .................................................................................................................. 129
6.2 Summary of results....................................................................................................... 129
6.3 Conclusion.................................................................................................................... 136
6.4 Implications of the study .............................................................................................. 136
6.5 Recommendations ........................................................................................................ 138
References ................................................................................................................................... 140
Appendices .................................................................................................................................. 148
Appendix 1: Demographic questionnaire ............................................................................... 148
Appendix 2: MOS-HIV questionnaire ................................................................................... 152
Appendix 3: Consent form for the HALE study ..................................................................... 158
Appendix 4: Adapted SPACE code ........................................................................................ 159
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List of Acronyms and Abbreviations
ADL
Activities of Daily Living
AIDS
Acquired Immunodeficiency Syndrome
ALE
Active Life Expectancy
ART
Anti-Retroviral Therapy
ARV
Anti-Retroviral Drugs
BMI
Body Mass Index
BP
Bodily Pain
CARTA
Consortium for Advanced Research Training in Africa
CBS
Central Bureau of Statistics
CDC
Centre for Disease Control and Prevention
CD4
Cluster of Differentiation 4 (White Blood Cells)
CDH
County Director of Health
CF
Cognitive Functioning
CIA
Central Intelligence Agency
CTX
Cotrimoxazole
DALY
Disability Adjusted Life Years
DFLE
Disability Free Life Expectancy
DHIS
District Health Information System
EHEMU
European Health Expectancy Monitoring Unit
EQ-5D
EuroQol five dimensions
ERC
Ethics Review Committee
EV
Energy/Vitality
GHP
General Health Perception
GSMLT
Gibbs Sampler for Multistate Life Tables
HAART
Highly Active Antiretroviral Therapy
(HA)LE
(Health Adjusted) Life Expectancy
HD
Health Distress
HeaLYs
Health Life Years
HH
Household
HIV
Human Immunodeficiency Virus
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HIVQUAL
HIV Quality
HLE
Healthy Life Expectancy
HPTN
HIV Prevention Trails Network
HRQOL
Health Related Quality of Life
HT
Heath Transition
IADL
Instrumental Activities of Daily Living
ICAP
International Centre for AIDS Programme
IMaCH
Interpolated Markov Chain
KAIS
Kenya AIDS Indicator Survey
KDHS
Kenya Demographic and Healthy Survey
KNBS
Kenya National Bureau of Statistics
KNH
Kenyatta National Hospital
LTFU
Lost to Follow-up
MH(S)
Mental Health (Summary)
MNLR
Multinomial Logistic Regression
MoH
Ministry of Health
MOS-HIV
Medical Outcome Study HIV
MSLT
Multi State Life Tables
NACC
National AIDS Control Council
NASCOP
National AIDS and STI Control Programme
OI
Opportunistic Infection
PF
Physical Functioning
PHS
Physical Health Summary
PLWHA
People Living with HIV and AIDS
PMTCT
Prevention of Mother to Child Transmission
PSRI
Population Studies and Research Institute
PSU
Primary Sampling Unit
QL
Quality of Life
RF
Role Functioning
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REVES
Reseau Esperance de vie en Sante Re (International Network on Health
Expectancy)
SAS
Statistical Analysis System
SF-12V2
Short Form 12 Survey–version 2
SPACE
Stochastic Population Analysis of Complex Events
SPSS
Statistical Package for Social Science
STI
Sexually Transmitted Infection
TB
Tuberculosis
TO
Transferred Out
UNAIDS
Joint United Nations Programme on HIV/AIDS
UNDP
United Nations Development Programme
UoN
University of Nairobi
WHO
World Health Organization
WHOQOL HIV
WHO Quality of Life HIV
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List of Tables
Table 3.1: Characteristics assessed and their coding .................................................................. 40
Table 3.2: Recoding of dummy variables .................................................................................... 43
Table 3.3: Variables used in SPACE Programme ......................................................................... 55
Table 3.4: Demonstration of process of obtaining weights for study participants ................... 56
Table 4.1: Distribution of study participants by various background characteristics n=393 ...... 60
Table 4.2: Other background characteristics of study participants by region ............................. 62
Table 4.3: Health related characteristics of study participants .................................................... 64
Table 4.4: Baseline and one year follow-up average measures of different dimensions of
health related quality of life (out of a possible 100) ................................................................. 66
Table 4.5: Factors associated with both baseline and one year follow-up measure of general
health perception ......................................................................................................................... 67
Table 4.6: Factors associated with baseline and one year follow-up measure of physical health
perception .................................................................................................................................... 68
Table 4.7: Factors associated with baseline and one year follow-up measure of role
functioning.................................................................................................................................... 69
Table 4.8: Factors associated with baseline and one year follow-up measure of social
functioning.................................................................................................................................... 70
Table 4.9: Factors associated with baseline and one year follow-up measure of cognitive
functioning.................................................................................................................................... 71
Table 4.10: Factors associated with baseline and one year follow-up measure of pain .......... 72
Table 4.11: Factors associated with baseline and one year follow-up measure of mental
health ............................................................................................................................................ 73
Table 4.12: Factors associated with baseline and one year follow-up measure of vitality ...... 74
Table 4.13: Factors associated with baseline and one year follow-up measure of distress ..... 75
Table 4.14: Factors associated with baseline and one year follow-up measure of quality of life
....................................................................................................................................................... 76
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Table 4.15: Factors associated with baseline and one year follow-up measure of health
transition ...................................................................................................................................... 77
Table 4.16: Factors associated with baseline and one year follow-up measure of physical
health summary ........................................................................................................................... 78
Table 4.17: Factors associated with baseline and one year follow-up measure of mental
health summary ........................................................................................................................... 79
Table 4.18: One year status of study population ........................................................................ 80
Table 4.19: Baseline factors associated with being dead at one year follow-up relative to
being alive..................................................................................................................................... 81
Table 4.20: Baseline factors associated with being lost to follow-up at one year follow-up
relative to being alive................................................................................................................... 83
Table 4.21: Baseline factors associated with being transferred out at one year follow-up
relative to being Alive .................................................................................................................. 84
Table 5.1: Proportion of participants in various statuses of physical health across different
factors ........................................................................................................................................... 90
Table 5.2: Proportion of participants in various statuses of mental health across different
factors ........................................................................................................................................... 92
Table 5.3: Life expectancy adjusted for physical health for various categories of study
participants ................................................................................................................................... 94
Table 5.4: Life Expectancy adjusted for mental health for various categories of study
participants ................................................................................................................................... 95
Table 5.5: Comparison of LE adjusted for physical health across different categories of
participants ................................................................................................................................... 97
Table 5.6: Comparison of LE adjusted for mental health across different categories of
participants ................................................................................................................................... 98
Table 5.7: Number of participants interviewed in each of the two data collection waves .... 100
Table 5.8: Proportion of participants in different states of PHS at both baseline and at one
year follow-up ............................................................................................................................ 101
Table 5.9: Transition probabilities to poor PHS health status at one year follow-up ............. 102
xii
Table 5.10: Transition probabilities to good PHS health status at one year follow-up .......... 103
Table 5.11: Transition probabilities to death PHS health status at one year follow-up from
different statuses at baseline .................................................................................................... 104
Table 5.12: Proportion of participants in different states of MHS at both baseline and at one
year follow-up ............................................................................................................................ 105
Table 5.13: Transition probabilities to poor MHS health status at one year follow-up from
different statuses at baseline .................................................................................................... 106
Table 5.14: Transition probabilities to good MHS health status at one year follow-up from
different statuses at baseline .................................................................................................... 107
Table 5.15: Transition probabilities to death at one year follow-up from different MHS
statuses at baseline .................................................................................................................... 108
Table 5.16: Life expectancy in various statuses of PHS among all study participants regardless of
their baseline PHS status ............................................................................................................ 110
Table 5.17 Life expectancy in various statuses of PHS among male study participants
regardless of their baseline PHS status ..................................................................................... 111
Table 5.18: Life expectancy in various statuses of PHS among female study participants
regardless of their baseline PHS status ..................................................................................... 112
Table 5.19: Life expectancy in various statuses of PHS among all study participants in poor
baseline PHS health status .......................................................................................................... 113
Table 5.20: Life expectancy in various statuses of PHS among male study participants in poor
baseline PHS status .................................................................................................................... 114
Table 5.21: Life expectancy in various statuses of PHS among female study participants in
poor baseline PHS status ........................................................................................................... 115
Table 5.22: Life expectancy in various statuses of PHS among all study participants in good
baseline PHS status ..................................................................................................................... 116
Table 5.23: Life expectancy in various statuses of PHS among male study participants in good
baseline PHS status .................................................................................................................... 117
Table 5.24: Life expectancy in various statuses of PHS among female study participants in
good baseline PHS status ........................................................................................................... 118
xiii
Table 5.25: Life expectancy in various statuses of MHS among all study participants regardless
of their baseline MHS health status .......................................................................................... 119
Table 5.26: Life expectancy in various statuses of MHS among male study participants
regardless of their baseline MHS status .................................................................................... 120
Table 5.27: Life expectancy in various statuses of MHS among female study participants
regardless of their baseline MHS status .................................................................................... 121
Table 5.28: Life expectancy in various statuses of MHS among all study participants in poor
baseline MHS status ................................................................................................................... 122
Table 5.29:Life expectancy in various statuses of MHS among male study participants in poor
baseline MHS status ................................................................................................................... 123
Table 5.30: Life expectancy in various statuses of MHS among female study participants in
poor baseline MHS status .......................................................................................................... 124
Table 5.31: Life expectancy in various statuses of MHS among all study participants in good
baseline MHS status ................................................................................................................... 125
Table 5.32: Life expectancy in various statuses of MHS among male study participants in good
baseline MHS status ................................................................................................................... 126
Table 5.33: Life expectancy in various statuses of MHS among female study participants in good
baseline MHS status.................................................................................................................... 127
xiv
List of Figures
Figure 2.1: Classification System of Health Attributes. ............................................................... 28
Figure 2.2: Ferrans et al 2005 HRQOL Conceptual Framework .................................................. 29
Figure 2.3: Operational Framework Adapted for the Study ......................................................... 29
Figure 4.1: Distribution of Participants by Different Age Groups ............................................... 59
Figure 5.1: Trends in Probability of Transitioning from Poor PHS Status at Baseline to Good
Status at 1 Yea Follow-up ......……….…………..…………………………………………… 91
Figure 5.2: Trends in Probability of Transitioning from Poor PHS Status at Baseline to Death at
1 Yea Follow-up ………………………………….…………………………………………… 93
Figure 5.3: Trends in Probability of Remaining in Poor MHS Status at 1 Yea Follow-up across
Different Ages …………………………….……………………………………………………. 95
Figure 5.4: Trends in Probability of Transitioning from Poor MHS Status at Baseline to Good
Status at 1 Yea Follow-up …………………………………………………………………… 96
Figure 5.2: Trends in Probability of Transitioning from Poor MHS Status at Baseline to Death at
1 Yea Follow-up ………………….………………………………………………………… 97
xv
CHAPTER ONE
INTRODUCTION
1.1
Background
Life expectancy (LE) is an important measure of wellbeing of a population. It highlights mortality patterns
(Romero et al., 2005) and reflects the overall mortality level of a population (WHO, 2006). The measure
summarizes the mortality patterns prevailing in the population across all ages (Behrman et al., 2011). Life
expectancy measures the number of remaining years that an individual can expect to live if the current age
specific mortality levels remain the same in future. It has been measured for many countries and has had
various key uses including monitoring the impact of different health interventions. It has been used as a
health indicator (Lancet, 2008), and has been utilized to evaluate the general state of health of a population
(Romero et al., 2005). This measure has been estimated for almost all countries and over the years, it has
been increasing especially in developed countries (Romero et al., 2005).
Due to its use as a health indicator, increase in LE has been interpreted to mean that there has been an
improvement in the health of the population. World Health Organization (WHO) estimated that in 2009, life
expectancy at birth globally was 68 years with that of high income countries being 1.4 times higher than that
of low income countries. Life expectancy has been changing since world war II with some countries
experiencing an increase of almost twice their LE in the 1940s (Lehmijoki, 2009).
Although higher life expectancy values have been equated to better health, there are concerns on whether
this measure alone is sufficient as an indicator of the health of the population (BMJ 2012; Campsmith et al.,
2003; Delate & Coons, 2001). Recent studies on longevity and health have concluded that the positive
tendencies of prolonged life are not accompanied by similar trends in the extension of healthy life; i.e. a
long life does not necessarily mean a healthy life (Manuel and Schultz, 2004; van Baal et al., 2006). Life
expectancy estimates are insensitive to the health status of the population since they provide no indication of
1
the quality of life, only the quantity (Wolfson, 1996). They do not address the impact on quality of life
through disability caused by chronic diseases (Baal et al., 2006). It has been argued that with increased life
expectancy, the proportion of years of life with degenerative chronic diseases, disabilities and socioeconomic disadvantages also increase (Manuel et al., 2004). This concern has given rise to new concepts
with some schools of thought arguing that rise in LE has led to expansion of morbidity where people live for
long in poor health thus increasing the prevalence of some health conditions while others have said that
morbidity has been compressed to the last days of one’s life. WHO has summarized these concerns in a
statement that ‘adding years to life is an empty victory without adding life to years (Manuel et al., 2004).
Empirical studies have demonstrated the validity of these concerns. A study conducted in Canada
demonstrated that between 1990 and 1992, men were expected to spend 11 percent of their life expectancy
in poor health while for women, the proportion spent in poor health was 14 percent (Wolfson, 1996). A
similar study conducted in Brazil found that at age 60, men expect to lose 21 percent of their remaining life
to poor health while women lose 26 percent to poor health (Romero et al., 2005). This means that although
women have a longer life expectancy, a higher proportion of this life is spent in poor health than for men.
Life expectancy treats all years lived by an individual equally while ignoring the fact that some of these
years may have been lived in poor health and others in total disability. It is thus arguable that mortality
measurements alone are insufficient to adequately evaluate the state of health, quality of life in a population,
or the comparative impact of health interventions.
The rise in the average age at death in developed countries has brought the realization that longevity should
be accompanied with improvements in health-related quality of life (HRQOL), (Manuel et al., 2004).To
address this, summary measures of population health that expand upon the concept of life expectancy and
take account of health status by combining information on both length and quality of life have been
developed (Wolfson, 1996). The concept of a health indicator which combines information on mortality and
2
morbidity was first proposed by Sanders in 1964 and later expanded by Sullivan in 1966, (Mathers and
Robine, 1997; Lynch and Brown, 2010).
Over the past few years, a lot of effort has been made in developing measures that not only combine
mortality and morbidity but also consider concepts relative to the well-being and the quality of life of a
population (Wolfson, 1996; Mathers, 1999). These measures fall into two categories; positive measures of
health expectancy which measure life expectancy adjusted for years lived in health states worse than full
health and measures of health gap which quantify the difference between the actual health of a population
and some stated norm (Murray et al., 2000). Within each category, there are many different possible
measures, which despite measuring different aspects of health, share a number of similarities including
information on mortality, non-fatal health outcomes, and health state valuations (Murray et al., 2000).
Healthy life expectancies (HLE) have been the most common measures. The measures are calculated by
weighting the total years lived according to health status with years lived in good health being given higher
weight than years lived in poor health. The difference between LE and HLE is that while LE measures the
number of years one expects to live, HLE represent the burden of ill health
(Wolfson, 1996). Health
adjusted life expectancy (HALE) is one such measure of healthy life expectancy. This measure uses health
related quality of life (HRQOL) information as data input to evaluate health states. While LE is the average
number of years a person is expected to live regardless of their health state, HALE is life expectancy
weighted or adjusted for the level of HRQOL (Loukine, 2011). One of the key uses of HALE is in
comparing the state of health between different populations (Romero, 2005).
HALE has been measured in many countries and for different health conditions. These include HALE for
diabetic patients in Canada (Manuel et al., 2004), in Brazil (Romero et al., 2005), in Denmark using cohorts
defined only by their risk factors - obesity and smoking (Baal et al., 2006) and for patients with
3
hypertension in Canada (Loukine, 2011). The hypertension study in Canada found that the difference
between LE and HALE increased with age while it was lower in men than in women. Another study was
conducted in Japan (Yong and Saito, 2009) which measured trends between 1986 and 2004. This study
showed that in early years before 1995, there were gains in years of good self-rated health whereas
thereafter, the gains were in years of poor self-rated health.
On its part, WHO has estimated healthy life
expectancy for 191 member states using information from health interview surveys and from the Global
Burden of Disease Study (Mathers et al., 2006). Japan was found to have the highest healthy life expectancy
of 74.5 years while the lowest 10 countries were all in Sub Saharan Africa. Other similar studies have been
conducted in many parts of the world including the USA (Campsmith et al., 2003).
Despite its importance, methodological concerns in calculation of HALE abound. There has been debate
whether to use cross sectional information as proposed by Sullivan in 1966 or use longitudinal information
(Mathers et al., 1999). Cross sectional information assumes that current health status of a person will not
change while longitudinal methods allow for transition from one health state to another. Use of dichotomous
measures of health (healthy or not healthy) has also been criticized as being insensitive to the severity of the
ill health being experienced by patients (Romero, 2005; Mathers et al., 1999). Other concerns have been
about the use of subjective measures especially self-reported health status which may not be comparable
across populations due to differences in instruments used (Mathers et al., 1999).
To address these concerns, disease specific validated tools have been developed and used (Campsmith et al,.
2003). Use of validated tools in research ensures that the study provides reliable and valid conclusions about
the concept being measured while ensuring uniformity and comparison at the same time (Kimberlin et al.,
2008). For HIV/AIDS, a 35 item tool known as MOS-HIV has been developed and used across the world to
measure HRQOL among HIV/AIDS patients (Campsmith et al., 2003). This tool allows the weighting of
4
HRQOL from severe to mild and this addresses the two problems of lack of uniformity across different
populations and the dichotomous nature of health status obtained when other tools are used.
Similar to other health conditions, the concept of HALE can be extended to patients living with HIV and
AIDS (Mathers et al., 2006). Research has shown that with the introduction of chronic management of
HIV/AIDS, LE for people living with HIV/AIDS (PLWHA) has been increasing steadily to a level where it
is almost the same as that of the general population (Ashford, 2006). With PLWHA living longer, the
burden of ill health for these patients as measured by the difference between LE and HALE is not known.
Measurement of HALE among PLWHA would not only provide information on burden of ill health due to
HIV but also point out differences at sub population level in this outcome of population health.
This study was premised on the observation that there has been tremendous improvement in LE among
PLWHA in developed countries (Lancet, 2008) but little is known if this is the case in Kenya. While
appreciating the increase in LE among PLWHA, little is known how this compares with HALE and between
populations. Information on how HALE and LE among PLWHA compare across sub populations is
important both to guide development of programmes and allocation of resources for optimal outcome.
The aim of the study was to use Kenya as a case study to compare how LE and HALE differ across
different subpopulations of PLWHA and assess demographic and socio-economic determinants of this
difference. The study compared Nyanza and Central regions1 of the country. These regions have
consistently shown differences in major health indicators including HIV prevalence, and general life
expectancy. With treatment of HIV/AIDS being free in all government health facilities, the study sought to
1
Kenya no longer has a regional administrative structure. Instead, counties have replaced regions. Now there is the National
government and 47 county governments. In this study the Central region comprise of Kiambu and Nyandarua counties. While
the Nyanza region comprises Kisumu County, However, the study sites cannot be categorized as county level facilities as they
accommodate patients from across several counties since they act as referral health facilities hence comparison at regional level
5
determine if inequality in health indicators in these two regions extends to HALE among PLWHA and help
unearth underlying factors responsible for this.
1.2
Problem Statement
Studies have shown that although people living with HIV/AIDS have low life expectancy than people with
no HIV infection, the introduction of advanced management and treatment of the infection has seen LE of
people living HIV/AIDS increase over the years to almost the level of the general population (Lancet, 2008;
Ashford, 2006). Other studies have also shown that since the introduction of ART, mortality rates among
PLWHA have become much closer to the general mortality rates (Campsmith et al., 2003; Krishnan et al.,
2008; Nakagawa et al.,2012; Mills et al., 2011; 2012).
While these studies have shown improved clinical outcomes of PLWHA, information from the perspective
of the patients themselves on how chronic management of HIV/AIDS has affected their lives is rare.
Persons infected with HIV are not only concerned with the treatment ability to extend life but also with the
quality of the life they are able to lead (Delate and Coons, 2001). Self-reported health related quality of life
(HRQOL) is one such measure that assesses patients’ perspective and it has not been clear how HIV/AIDS
therapy has impacted on it (Liu et al., 2006) with some studies showing positive association (Lehmijoki,
2009) and others showing no association (Campsmith et al., 2003). Where information on HRQOL has been
made available, little has been done to extend this knowledge to establish the health adjusted life expectancy
(HALE) and determine the burden of ill health among HIV/AIDS patients.
In Kenya, it is not known how long people living with HIV/AIDS can expect to live and what proportion of
this life is lived in good health. With inequalities recorded across Kenyan population in almost all health
indicators, it is not known if the same pattern can be expected for HALE among HIV/AIDS patients and
what factors explain differences if any across the sub populations. Previous attempts elsewhere to identify
6
these factors have failed to recognize the time dependent nature of some of the possible factors in modeling
their impact on HALE. There has also been overuse of cross sectional information despite its inherent
limitation in ignoring transition from one health state to another. With this knowledge gap, it is difficult to
address factors that act as impediment in improving non clinical health outcomes among HIV/AIDS patients
either at programme implementation or policy levels.
The focus of this study was to assess and compare HALE among PLWHA using both Sullivan and MSLT
methods for different sub populations in Kenya and provide information to different stakeholders on what
factors are associated with this measure for the purpose of programme improvement.
1.3
Research questions
The study aimed at answering the following questions:
1. How does health related quality of life (HRQOL) of adult HIV/AIDS patients newly started on HIV
care in Nyanza region compare with that of similar patients in Central region?
2. How does health adjusted life expectancy of adult HIV/AIDS patients newly started on HIV care in
Nyanza region compare with that of patients in Central region?
3. What socio-economic and demographic factors are associated with health adjusted life expectancy
among adult HIV/AIDS patients newly started on HIV care in Nyanza and Central regions?
4. How do HALE estimates obtained using Sullivan and MSLT approaches compare?
1.4
Research objectives
The overall objective of the study was to estimate and compare health adjusted life expectancy of adult
HIV/AIDS patients in Nyanza and Central regions of Kenya. The specific objectives were:
1. To determine health related quality of life of adult HIV/AIDS patients in different waves of data
collection in Nyanza and Central Kenya;
7
2. To determine transition probabilities from one health state to another across different time periods
for adult HIV/AIDS patients in Nyanza and Central regions;
3. To determine health adjusted life expectancy among adult HIV/AIDS patients in Nyanza and Central
Kenya regions;
4.
To determine factors associated with health adjusted life expectancy for adult HIV/AIDS patients in
Nyanza and Central Kenya regions;
5. To compare HALE results estimated using Sullivan and MSLT approaches.
1.5
Rationale and justification
The study sought to provide information on other aspects of HIV/AIDS treatment outcome other than
clinical. The outcome of interest articulated the concerns of the patients themselves and addressed their
health care needs based on their own opinion. With health being defined as not just the absence of a disease
but as encompassing the total physical, mental and social wellbeing of an individual, health adjusted life
expectancy becomes an important measure that gives this definition its true meaning. Increased investment
that has been witnessed in the management of HIV/AIDS calls for continued evaluation of not only
reduction of mortality among the patients but also reduction in burden of ill health as a result of HIV/AIDS
and progress towards this can be achieved by measuring HALE. One major goal of HIV/AIDS management
is to ensure patients reap maximum benefits of a decentralized and highly subsidized health care system
and it would be of interest to key players to not only know if this is being achieved but also if the benefits
vary across subpopulations or regionally. Due to the very chronic nature of HIV/AIDS condition and the
cost involved in its management, the health care system should aim at identifying and addressing factors
that perpetuate inequalities in health outcomes of people living with HIV/AIDS. Kenya as a country has had
its fair share of inequalities in many health indicators across her population. For a programme like
HIV/AIDS management that aims at addressing barriers to equitable access to quality health care for all,
comparing health outcomes of this system would boost achievement of this goal. Comparing HALE across
8
different populations provides an opportunity and new ground to having a better understanding of how the
programme could be improved for the benefit of all.
The study sought to provide comparative information on HALE among PLWHA in two regions that have in
the past shown substantial differences across all health indicators. The study also sought to shade light on
what combination of socio-economic and demographic factors are associated with HALE among HIV/AIDS
patients and gives policy makers and programme developers and implementers information to guide
improvement of HIV/AIDS programmes. The study adds to the existing body of knowledge on the impact
of HIV care and treatment programmes as well as identifying factors that could be addressed in order to
have better outcomes for HIV/AIDS patients. The findings of the study have far reaching impact in
informing the national HIV/AIDS programme on potential areas of focus in offering care and treatment
services that not only help patients live longer but also help them live healthier lives.
Nyanza and Central regions of Kenya have been identified as two regions that have big disparities in health
indicators. Central has been performing better than Nyanza in terms of health facility network, doctor
patient ratio, and even in health seeking behavior among its residents. Life expectancy in Central is higher
than in Nyanza and the two regions have different mortality levels and different prevalence of HIV with
Nyanza having the highest prevalence of HIV in the country (NASCOP, 2014). In the past, Nyanza region
has seen concerted efforts by many players to establish intervention programmes to address the epidemic
while Central region has not attracted as much interest in comparison as demonstrated in levels of spending
in the two regions (MoH 2014). Despite standardization of care and treatment guidelines across the country,
service delivery models as well as minimum package of care differ across the two regions. Access to some
advanced laboratory tests required by HIV/AIDS patients differs with Nyanza region enjoying a wellestablished laboratory infrastructure while Central still has to send samples to Nairobi for testing. These
perceived differences make the two regions appropriate for studying and comparing.
9
The study contributes significantly in the existing body of knowledge in methodological approaches by
demonstrating feasibility of applying complex methods in measuring health adjusted life expectancy in the
African context, their appropriateness and limitations. The study also highlights limitations on data sources
that make application of different methods difficult in developing countries that do not have well developed
statistical systems. The study is among the first in Africa to make an attempt in measuring health adjusted
life expectancy among PLWHA and comparing it across subpopulations. The study takes HIV/AIDS related
research into a new level and subsequent studies are expected to improve on this one both in terms of data
sources, data quality and methodological approaches. Another significant importance of this study is that it
attempts to put together several methods in estimating not only transition probabilities but also multistate
life table functions. Most of the literature available today deals with bits and pieces of the multistate life
table calculations and this study brings these aspects together in a single write-up.
1.6
Scope and limitation of the study
The study was conducted among HIV/AIDS patients newly enrolled for HIV care and treatment services in
Nyanza and Central regions of the republic of Kenya. The patients were sampled among adult patients (15
years and above) at the time of enrolment for HIV care in three public health facilities in each of the two
regions. The three health facilities were purposively selected to represent the region since they were found
to hold approximately 25 percent of all the HIV/AIDS patients enrolled into care in the region. The facilities
were also seen as better representatives of the region since their catchment included both rural and urban
residents and were also located in cosmopolitan areas.
The study did not recruit a comparison group from the general population and hence the results do not
provide information as to what the impact of HIV infection is on health adjusted life expectancy of Kenyan
population. Exclusion of a comparison group from the general population was mostly due to difficulties
involved in recruiting such a group and following them for 1 year with limited resources. While HIV/AIDS
10
patients have a motivation to remain in the study as they have regular appointments with clinicians, there are
no motivations for the general population to come back for subsequent follow-up data collection. Following
such a group at the community level would have been very difficult and expensive in terms of time and
money. The results of this study are therefore only limited to people living with HIV/AIDS and should not
be interpreted as the impact of HIV/AIDS on the health adjusted life expectancy.
Despite the lack of a control group, inclusion of other factors such as major opportunistic infections, TB
status, age and sex among HIV/AIDS patients provided a good control within the study target population
(Nakagawa et al., 2013). The inclusion of these factors as controls made it possible to assess the regional
differences in HALE which was the main focus of the study. Self-reported health status should also be
interpreted with caution especially in relation to the impact of HIV/AIDS care and treatment on the same.
The results obtained are not an evaluation of the effectiveness of care and treatment programmes especially
as assessed through clinical outcomes but rather the perception of patients on what they think about their
own health.
Biased reporting of health status by study participants may also have affected self-reported health status in
the subsequent visits as they may have responded to questions based on what they thought was the expected
health status given that they had already received some services meant to improve their health and
wellbeing. Due to limitation of financial resources, the study only sampled 6 health facilities, 3 per region.
The 6 health facilities were purposively sampled to represent high volume facilities that hold almost 25
percent of all HIV/AIDS patients enrolled for care in the 2 regions. Sample size calculation ensured that
patients sampled were a true representation of people living with HIV who were expected to enroll into the
health facilities for management of HIV/AIDS. Although some of the variables could change within the
follow-up period such as WHO stage, marital status, co-morbidity and others, the methods used for data
analysis could only accommodate these variables taken at one point in time thus the role of time dependent
11
covariates in the estimated HALE was not assessed despite this being one of the identified gaps in the
previous studies. The role of environmental factors such as change in policy or differences in infrastructure
and services offered by different health facilities was also not investigated and this calls for further research.
Regression models were however applied for other outcomes proxy to HALE to estimate contribution of the
identified time dependent covariates.
Another limitation of the study was that there were no specific life tables for Nyanza and Central regions.
There were also no life tables available for the people living with HIV/AIDS. To address this limitation, an
assumption was made that life expectancy among people living with HIV/AIDS in Kenya is similar to that
of general population and that it does not vary across different regions. This assumption led to the use of
WHO 2012 life table for Kenya in estimation of HALE using cross sectional data.
Small sample size was another limitation of this study. Although Sullivan approach can work with a sample
size smaller than 500, SPACE programme requires a larger dataset than the sample size used in this study in
order to obtain robust results on the different health states. Limited number of expected patients who could
be enrolled in the health facilities within a given time period and associated cost made it difficult to get a
larger sample of patients. Care was however taken to reduce bias that could be introduced by smaller sample
size by ensuring that the sample size was comparable to samples used in similar studies conducted in the
past.
Post stratification weighting of data were done in one step only. Only two factors; sex and age were
considered during weighting while other factors of interest such as education and income were not
considered since detailed information on how these factors were distributed in the target population was not
available. While it would have been appropriate to consider these factors, the researcher assumed that some
of the factors were correlated sex and education, income and age etc.
12
1.7
Organization of the thesis
This thesis is divided into 6 chapters. The first chapter gives a background to the study and defines the
problem statement, the objectives of the study and the research questions. This chapter also provides a
rationale for the study and enumerates key assumptions and limitations of the study.
Chapter two is a review of literature relevant to the area of study. The literature reviewed include levels and
trends in LE and the impact of HIV on LE, concerns regarding use of LE as a measure of a population
wellbeing, and other measures used to assess the wellbeing of a population including health expectancies.
The chapter introduces HALE and how it is measured, limitations in measuring HALE and factors
associated with HALE especially among people living with HIV. Lastly the chapter describes the
conceptual and operational framework that guided the study and the definition of key terms used in the
study.
Chapter three describes research methods used including design of the study, sampling, data collection,
ethical issues, data quality, how various measures were assessed, description of statistical software used and
mathematical background of measures that were assessed.
Chapters four and five present the results of the study. The results include characteristics of study
participants, measures of health related quality of life, health statuses, factors associated with these health
statuses, transition probabilities to various health statuses, measures of health adjusted life expectancy and
factors associated with these measures. Chapter six which is the final chapter provides a summary of the
study results, their implications and recommendations based on the findings.
13
CHAPTER TWO
LITERATURE REVIEW
2.1
Introduction
This chapter reviews literature on the relevant studies conducted on the topic of interest. The review covers
topics related to levels and trends in life expectancy globally, regionally and locally and the major concerns
raised as life expectancy changes. The review also examines the contribution of HIV/AIDS in changes in
life expectancy and how these changes have been affected by programmes put in place to manage the
condition. In addressing the concerns raised on whether prolonged life expectancy is being matched with
improved quality of life, the literature review also examines how HIV/AIDS has affected health related
quality of life of infected people and how this has in turn affected health adjusted life expectancy and factors
associated with this.
This review gives perspective to the study. It helps acknowledge work that has been undertaken in
measuring health adjusted life expectancy among people living with HIV while identifying gaps in
knowledge or inconsistent conclusions. In particular, the review focuses on the work done in Sub Saharan
Africa. The focus on Sub Saharan Africa is deliberate as the region has the highest burden of HIV/AIDS in
the world despite it having insufficient resources to effectively manage the disease (Ashford, 2006).
2.2
Trends and levels in life expectancy
Life expectancy is an important measure of the wellbeing of a population. It is a reflection of the overall
mortality level of a population (WHO, 2006). The measure summarizes the mortality patterns prevailing in
the population across all ages. In turn, mortality is one of the measures used in conceptualization of health, a
key characteristic of population wellbeing, (Behrman et al., 2011). Life expectancy measures the number of
remaining years that an individual can expect to live if current age specific mortality rates are sustained. In
14
2009, WHO estimated that on average, life expectancy at birth was 68 years globally with that of high
income countries being 1.4 times that of low income countries. Life expectancy has been changing since
World War II with some countries experiencing an increase of almost twice their life expectancy in the
1940s (Lehmijoki, 2009). In 1999, life expectancy at birth was estimated to be 64.5 years globally, an
increase of about 6 years from the 1979 estimate (Mathers et al., 2001a). This change has however not been
uniform across all countries. According to WHO, some countries have experienced stagnation in life
expectancy increase while others have even experienced a decrease (www.who.org). Decrease in life
expectancy has been more prominent in sub Saharan Africa between 1990 and 2000 and this has been linked
to HIV/AIDS (Lehmijoki, 2009).
2.3
Trends in life expectancy in sub Saharan Africa and the effect of HIV/AIDS
Life expectancy in Africa rose steadily between 1950 and 1990 but suddenly plateaued and began to decline
as a result of HIV/AIDS (Ashford, 2006). In most Southern Africa countries, life expectancy at birth had
reduced by between 15-20 years due to HIV/AIDS by 1999 (Mathers et al., 2001a). The United Nations
Development Programme (UNDP) projected that between 2000 and 2005, life expectancy in Sub Saharan
Africa was going to decline by between 17 years in Kenya and 34 years in Botswana (Poston & Micklin,
2005) due to HIV/AIDS. Another projection showed that in 2010, life expectancy in Africa would be 10-30
years lower than it would have been without HIV/AIDS (Quinn, 1996). The average life expectancy in
Africa in the period 2000 – 2005 was estimated to be 50.6 years with HIV/AIDS and this would have been
56.9 years if there was no HIV/AIDS (Dorling et al., 2006). HIV/AIDS has led to an increase in mortality
among young adults in Africa and in some countries such as Zimbabwe and Botswana, life expectancy has
reduced by 10-20 years to the levels of 1950-55 ( McMichael et al., 2004). In Kenya, life expectancy has
reduced by 13 years from 62 years to 49 years as a result of HIV and AIDS (Kaloustian et al., 2006). This
compares with that of UK where life expectancy of people with HIV is estimated to be 13 years lower than
that of the general population (May et al., 2011).
15
2.4
Increasing life expectancy among people living with HIV/AIDS
Several studies aimed at determining the life expectancy of HIV/AIDS patients in the era of ART have been
conducted in many parts of the world (Lohse et al., 2007; Ashford, 2006; Fang et al., 2007). These studies
have shown that people with HIV/AIDS have lower life expectancy than people with no HIV/AIDS
infection though this is changing with time. Low life expectancy among HIV/AIDS patients was worse in
the early days of HIV pandemic when there was no known treatment for HIV (Lohse et al., 2007). Life
expectancy after HIV infection has been shown to increase since the introduction of ART albeit at different
rates across different populations to almost the same level to that of the general population (Ashford,
2006). The increase in survival time for people with HIV/AIDS has been attributed to the advancement
made in the management and treatment of the infection (Fang et al., 2007; Lohse et al., 2007). In one study,
the mean survival time of HIV/AIDS patients enrolled in care was found to be associated with the timing of
diagnosis of the virus with those being diagnosed when the virus is at an advanced stage having lower
survival time than those diagnosed when the infection has not advanced to AIDS condition (Fang et al.,
2007). A cohort study conducted in several developed countries showed that there has been a steady rise in
life expectancy among patients taking a combination of antiretroviral (Lancet, 2008). This study showed
that the average number of years remaining to be lived at age 20 years was about two-thirds of that in the
general population in these countries. In Denmark, a 25 year old HIV infected youth was expected to live
only an extra 8 years in 1995 which increased to 33 years in 2005 as compared to 51 years in a 25
uninfected person (Lohse et al., 2007). Between 1996 and 2008, life expectancy of people with HIV
increased in the UK by over 15 years (May et al., 2011). In a study conducted in America, results showed
that since treatment of HIV/AIDS disease started in the US, over 3 million years of life have been saved in
that country alone as a direct result of care of HIV/AIDS patients (Rochelle et al., 2006). Other studies
have also shown that since the introduction of ART, mortality rates among HIV/AIDS infected persons have
become much closer to the general mortality rates (Krishnan et al., 2008). One such study showed that
16
compared to general population, excess mortality due to HIV reduced from 40.8 per 1000 person years pre
ART era to 6.1 per 1000 person years in ART era in industrialized countries with good access to HIV/AIDS
treatment (Krishnan et al., 2008). In another study conducted in USA, the investigators showed that the
average life expectancy after HIV diagnosis increased from 10.5 to 22.5 years from 1996 to 2005 (Kathleen
et al., 2010).
Despite all these studies that have clearly shown the benefits of ART, very few studies have been conducted
in Africa and therefore it is difficult to generalize that these benefits apply to the African populations here in
Africa. Among the few studies conducted in Africa was one conducted in Uganda and published in the
Annals of Internal Medicine in August 2011. The study found that the life expectancy of a 20 year old HIV
infected person in Uganda was 26.7 years and that of a 35 year old person was 27.9 years (Edward et al.,
2011). The same study also showed that HIV/AIDS patients could expect to live a near normal life (Mills et
al., 2011) if put on care and treatment programmes. Despite this, life expectancy is not uniform among all
patients with sex being a major determinant (Mills et al., 2011).
The situation in Kenya is no different though there is paucity of data on this. According to the CIA World
fact book, life expectancy in Kenya reached its lowest level of 44.94 years in 2004 immediately after the
introduction of free ART in public health facilities (CIA, 2014). Since then, this has been rising steadily and
in 2011, life expectancy stood at 59.48 years. This rise has been attributed to ART. ART reduces the
likelihood of opportunistic infections and patients receiving ART are thus expected to be healthier as well as
experience less hospitalization. Despite increased survival, HIV infected persons still have a higher risk of
death at a rate of 3-15 times more than the general population mostly due to the degree of
immunodeficiency (Lohse et al., 2007).
17
2.5
Concerns over increasing life expectancy
As the life expectancy of a population increases, questions emerge as to whether these additional years of
life and other benefits accruing from improved health interventions are being accompanied by healthy life.
Health demographers and policy makers have recognized the limitation of assessing population health status
through measures of mortality or life expectancy alone without considering morbidity (Poston et al., 2005).
There have been concerns that increased life expectancy may not always come with improved health related
quality of life (Douglas and Susan, 2004). There is a big concern of having a large proportion of population
living in poor health which will subsequently increase burden on the society and health care services if
increasing life expectancy is not matched with improved healthy life. WHO has stated that “adding years to
life” is an empty victory without “adding life to years” thus leading to the expansion of the definition of
health as ‘‘a state of complete physical, mental and social well-being.’’ (Douglas et al., 2004). This means
that celebrating the success of HIV/AIDS management in adding years to HIV/AIDS patients’ lives should
be accompanied by the assessment of the quality of life of these additional years. Increased life expectancy
leads to changes in disability (Murray et al., 1997) and several theories explain this change in disability
(Molla et al., 2003). One of the theories talks about compression of morbidity where it is argued that
reduction in mortality will lead to reduction in morbidity. The other theory predicts that reduced mortality
will lead to high prevalence of disability while the third theory says that prevalence of severe disability will
reduce but prevalence of mild disability will increase as life expectancy increases especially for people with
chronic conditions. The debate on what the effect of increased life expectancy is on the health of a
population has been going on since 1980s’ and this debate has been fueled more by contradictory empirical
evidence (Poston et al., 2005).
In an attempt to address this, summary measures of population health have been introduced including
healthy life expectancy. Healthy life expectancy among people suffering from various chronic diseases or
18
living under different circumstances has been assessed across the world. This has been done using various
approaches and methods. In 2004, a study conducted in Ontario, Canada found out that people with diabetes
were expected to live 12.8 and 12.2 years less than men and women without diabetes respectively (Douglass
et al., 2004). The same study showed that diabetic patients had a health-adjusted life expectancy of 58.3 and
62.7 years for men and women respectively, far much less than that of men and women without diabetes.
The difference between life expectancy and health-adjusted life expectancy for diabetic patients was 6.4 and
8.0 years for men and women respectively meaning that a male diabetic patient lived 6.4 years of their
expected life in poor health. This is a clear indication that life expectancy alone without looking at the
health status of the population is not enough to warrant celebrating improvement in population wellbeing.
2.6
HIV/AIDS and health related quality of life
People living with HIV/AIDS are not only concerned about the ability of treatment to extend their lives but
also the quality of life they are able to lead (Delate et al., 2001). In the recent past, health related quality of
life (HRQOL) has become an important aspect of HIV/AIDS treatment (Howard et al., 2003). People living
with HIV/AIDS have been found to have lower HRQOL scores than that of the general population as well
as that of patients suffering from other chronic conditions such as cancer or depression (Howard et al.,
2003). HRQOL is an important measure in the process of assessing health adjusted life expectancy.
Measuring variability in HRQOL of HIV/AIDS patients is relevant for allocation of resources and in
development of health policy (Hays et al., 2000). HRQOL enhances communication between the health
care worker and the patient and it should be done frequently in clinical settings and not just in research
settings (Howard et al., 2003). Through assessment of HRQOL, patients are able to emphasize areas of
HRQOL that are of great concern to them (Howard et al., 2003).
Understanding HRQOL also helps in defining continuum of care for people living with HIV/AIDS (Murray
et al., 1997). In a study in Columbia, the scores of HRQOL of adult HIV/AIDS patients were found to be
19
between 60.5 and 90.2 (Arias et al., 2011). Physical functioning among people with HIV/AIDS is worse
compared to patients with other chronic diseases and emotional well-being is significantly worse than in
general population (Hays et al., 2000; Sikkema et al., 2015). Among people living with HIV, higher levels
of mental disorders are associated with higher levels of stigma (Yi et al., 2015). A study in Malawi found
out that compared to non-infected persons, HIV/AIDS patients have lower physical, mental and social
functioning (Mathers et al., 2006). The effect of HIV care and treatment and more so ART on HRQOL has
been unclear (Liu et al., 2006). Some studies have shown that HIV/AIDS care and treatment increases
quality of life among HIV/AIDS patients (Lehmijoki, 2009). In the contrary, one study found that treatment
of HIV/AIDS was not associated with HRQOL outcomes of HIV/AIDS patients (Campsmith et al., 2003).
While HRQOL for HIV/AIDS patients has been assessed in many developed countries, such data are few in
Africa (Pitt et al., 2009). A Study to review literature on studies conducted in Sub Saharan Africa on
HRQOL in adults with HIV/AIDS showed that only a few such studies have been conducted that show how
patients’ HRQOL respond to HIV/AIDS treatment over time (Robberstad and Olsen, 2010). The study
recommended that more knowledge on HRQOL indices was needed for population groups outside South
Africa. In a study undertaken in South Africa, 67 percent of patients initiating ARV were found to improve
on health related quality of life in the first 48 weeks of starting HAART (Pitt et al., 2009). In 2009, an
evaluation of HIV/AIDS programme in Kenya was conducted and it showed that health related quality of
life of HIV/AIDS patients improved over time after a 3 month of follow-up (Harding et al., 2009).
2.7
HIV/AIDS and health adjusted life expectancy
Health adjusted life expectancy (HALE) is a composite index that combines both mortality and morbidity
information (Loukine et al., 2011). It is a summary measure of population health and it is mostly calculated
from health related quality of life information (Loukine et al., 2011). This measure has been used since 2001
as an indicator of health of a population after the indicator was altered from disability adjusted life
expectancy to reflect all states of health in the computation of healthy life expectancy (Law et al., 2003). It
20
is comparable directly with life expectancy and can be compared across populations. Although it has been
shown that HIV/AIDS affects health related quality of life which is the measure that is used in assessing
health adjusted life expectancy, the later has been rarely studied among HIV/AIDS patients. HALE for the
general population has been studied and in one such study, HALE at birth was 56.8 years globally (Mathers
et al., 2001a). Among 191 countries assessed in 1999, the bottom 38 countries were all in Africa. This low
figure was associated with HIV/AIDS (Mathers et al., 2001a)
2.8
Factors associated with health adjusted life expectancy
Factors associated with health adjusted life expectancy have been assessed using various approaches. While
HALE is measured using information on HRQOL, there has been inconsistency on the association between
sex and HALE on one hand, and sex and HRQOL on the other hand especially when comparing global and
local estimates. Some studies have shown that women have higher health adjusted life expectancy (Mathers
et al., 2001a; Harding et al., 2009). A study conducted in 1999 showed that HALE was 57.8 years for
women and 55.8 years for men globally (Mathers et al., 2001a). In a more recent study in Kenya, it was
concluded from the research that sex was not a determinant of quality of life (Harding et al., 2009). In a
study on aging conducted in Tanzania, better health status was associated with sex, marital status and age
but no association was shown between health status and education and socio-economic status (Sankoh et al.,
2010). In this study, men were shown to report better health status compared to women, results that were
contrary to what the Kenyan study found but consistent with a study conducted in Uganda (Scholten et al.,
2011). The Uganda study however only examined a few determinants. In the Ugandan study, majority of the
factors examined were clinical other than socio-economic and demographic. The inclusion of clinical factors
in a study emphasizes the need to control for covariates in conducting regression analysis of the association
between health adjusted life expectancy and socio-economic and demographic factors. Another study in
Nigeria showed that sex and age had no significant influence on health related quality of life in HIV/AIDS
patients with post herpetic neuralgia (Saidu et al., 2009). Wealth status had an impact on health change in
21
this study. The association between health status and age was consistent with empirical knowledge on
physiological process of aging. Apart from the impact of physiological process of aging on health status,
HIV/AIDS has been found to confound this as most older people will be diagnosed for HIV at an advanced
stage of the infection since they are not regarded as being at risk of getting infected (George et al.,, 2009).
This perception leads health care workers not to be keen in assessing older people for HIV whenever they
visit a health facility. Economic and social support factors have been found to be associated with HRQOL
(Arias et al., 2011; Cruz et al., 2007). Other studies have shown the relationship between health related
quality of life and gender, age and socio-economic factors as well as how disability free survival decreases
with age, (Mathers et al.,1999). Studies have also shown that although women tend to live longer than men,
they report poorer health, (Luy & Minagawa, 2014, Oksuzyan, Juel et al, 2008). Possible explanation for
this is that women are more likely to report health problems more frequent but the conditions they suffer
from are less severe (Luy & Minagawa, 2014). The differences could also be due to genetic factors, immune
system response, disease pattern or simply under-reporting of health problems by men (Oksuzyan et al,
2008).
One key characteristic of HIV is co-morbidity with other conditions such as TB. Adjusting for dependent
co-morbidity in calculation of HALE is important to avoid overestimation of the severity of population
average health state (Mathers et al., 2006). Co-morbidity with TB has been shown to be negatively
associated with quality of life (Deribew et al., 2009) . In a study conducted in Kenya, co-infection of
HIV/AIDS patients with TB was found not to have any impact on mental health change but had impact on
physical health change (Harding et al., 2009).
2.9
Measurement of HRQOL and HALE
Many measures of population health expectancy and methods for calculating these measures have been in
use (Romero, 2005; Mathers et al., 1999; Campsmith et al., 2003). Estimates of health expectancies are
22
affected by the quality of the measure and the study design, the operational definitions of the health
measures and the various methods used to calculate the measure (Poston et al., 2005). Efforts to harmonize
the measures and methods have been promoted by International Network on Healthy Life Expectancy
(REVES), a scientific organization recognized by WHO (Poston et al., 2005). Two key methodological
issues that have been identified that need to be addressed are whether or not to use longitudinal as opposed
to cross sectional data and how to differentiate changes in environmental factors from changes at individual
level that may affect health status. These methodological issues affect assumptions made, the type of
modeling to be done and the interpretation of results. Summary measures of population health combine both
mortality and morbidity measures in line with the WHO 1974 definition of health as a state of complete
physical, mental and social wellbeing (Manuel and Schultz, 2004). Mortality accounts for longevity while
morbidity accounts for state of health. Morbidity is measured in terms of person years of life in good health
while mortality is measured in person years of life (Molla et al., 2008). Summary measures of population
health fall into two categories; measures of health gap (DALYs and HeaLYs) and measures of health
expectancies (Manuel and Schultz, 2004). Health expectancies are based on such concepts as disease,
impairment, disability, handicap and self- perceived health (Molla et al., 2008). These measures of health
expectancies add a quality dimension to the quantity of life lived. As there are many dimensions of health,
there are many health expectancies. Some of the measures are healthy life years which is based on limitation
of daily activities (disability free life expectancy), healthy life expectancy based on self- rated health and
life expectancy free of certain diseases (EHEMU, 2007).
Healthy life expectancy is calculated from measures of heath related quality of life and the later is measured
variably. Models that have been used include health belief model and quality of life tree (Ogbuji et al.,
2010). One of the most common tools that has been used among HIV/AIDS patients is MOS-HIV
(Campsmith et al., 2003). The tool has been validated for use in the African context. It has been found to be
23
a valid and a reliable instrument to measure HRQOL in HIV/AIDS patients (Henderson et al., 2010). This
tool has been compared with another tool, SF-12v2, and the two tools have been found to give similar
results in measuring HRQOL in adult HIV/AIDS patients (Ion et al., 2011). Other tools commonly used
include HIV symptom scale and quality of life scale, WHOQOL HIV, EQ-5D (Deribew et al., 2009; Ogbuji
et al., 2010). Dimensions of HRQOL measured by these tools are overall health, pain, physical functioning,
role functioning, social functioning, mental health, energy/fatigue, and cognitive functioning (Campsmith et
al., 2003).
Although several studies have been conducted to measure health related quality of life in HIV/AIDS
patients, there has been very little study conducted to estimate health adjusted life expectancy of these
patients. One key challenge in conducting such a study has been whether this measure has the same value
for all patients (Bermudez et al., 2008). In almost all the studies conducted analysis of time dependent
covariates such as marriage, ART status, education attainment and co-infection have not been considered.
The effect of these factors on health adjusted life expectancy is expected to vary over time and this needs to
be put into consideration when analyzing data instead of treating them as variables that remain constant at
individual level.
In estimating survival of HIV infected persons, several methods have been applied. Statistical analysis using
Kaplan-Meier method has been used. Using this method, one study in Taiwan showed that ‘the 5-year
survival rate was 58 percent in patients who had already developed AIDS at diagnosis (AIDS group), and 89
percent in those who had not (non-AIDS group). Extrapolation yielded an expected mean survival time of
10.6 years after diagnosis for the AIDS group, and 21.5 years after diagnosis for the non-AIDS group (Fang
et al., 2007). Markov models which take into consideration transition from one health state to another have
also been used to estimate long term survival of HIV/AIDS patients. Using this approach, a study carried
24
out in US showed 4 to 6-year survival benefit of ART over pre-ART therapies (Palella et al., 2003). Another
study that investigated the effect of late initiation of ART and premature discontinuation of the same on life
expectancy losses found out that compared to uninfected persons of similar risk profile, a 33 year old HIV
infected person lost a total of 3.3 years of life due to these two factors (Elena et al., 2009 ).
The basic model used in calculating healthy life expectancy incorporates measures of both morbidity and
mortality (Molla et al., 2003). The traditional life table technique does not provide measures of morbidity.
This has however been modified to include measures of morbidity so that healthy life expectancy is
obtained by partitioning the remaining years of life into healthy and unhealthy years using health data.
Sullivan method was initially introduced in the early 1970s and it uses cross sectional data (Jagger et al.,
2006). This method assumes that one’s health status does not change and it may therefore either
underestimate or overestimate the measure. It has however been shown that the method produces relatively
accurate results, it is simple and is easy to interpret (Jagger et al., 2006). Over the years, the Sullivan method
has been modified and multistate life table method has been developed to address the limitations of the
earlier method which did not allow for members of a population to transition from one state of health to
another. This method is known as increment- decrement method and it uses panel data (longitudinal data) as
opposed to cross sectional data. Under this method, health status can transition to six states namely: from
healthy to healthy (retention), healthy to unhealthy, healthy to dead, unhealthy to unhealthy (retention),
unhealthy to dead and unhealthy to healthy (Cruz et al., 2007). To model the outcomes, multinomial
logistic regression model has been applied with four health states being used as possible outcomes(Cruz et
al., 2007; Wagener et al., 2001).
2.10
Key limitations in the measurement of health expectancies
Measurement of health expectancies has a number of limitations. The most important limitation is biasness
in self-reported health which makes it difficult to compare groups as one is not sure as to whether the
25
observed differences among groups are real or are due to different health expectations (Pitt et al., 2006).
Self-reported health status is more subjected to social factors where individuals in a population are inclined
in reporting what would be generally expected of them depending on their social status (Pitt et al., 2006).
Although models exist that demonstrate the process of change in health (Verbruggle and Jette, 1994), some
of the available methods used in measuring healthy life expectancy have been driven more by the available
data other than any clear theoretical concepts (Molla et al., 2003). Another major issue in the measurement
of healthy life expectancy stems from unharmonious way in which healthy life expectancy has operationally
been defined. This affects the methods and the study designs.
In almost all studies on healthy life expectancy, two life table approaches have been used. These are single
decrement life tables popularly known as Sullivan methods and decrement-increment tables known as
multistate life tables. The Sullivan life table approach uses cross sectional data while multistate life table
approach relies on longitudinal data. It has been argued that the Sullivan method gives estimates close to
those obtained using the multistate life tables if there are smooth and relatively regular changes in disability
prevalence over the longer term (Mathers et al., 1997; Poston et al., 2005). While this fact is not contested,
the question remains as to how one would know whether changes in disability prevalence over time are
smooth without empirical data. This can only be obtained if one has longitudinal data thus making
multistate life table the preferred option in measuring healthy life expectancy. While appreciating work
done by many scholars in measuring healthy life expectancy using multistate life table approach, (Grace et
al., 2007; Zachary, 2005), none has attempted to address methodological issues raised in the Handbook of
Population (Poston et al., 2005). Limitations identified in the application of longitudinal methods include
gaps due to attrition yet the impact of attrition is either ignored in the final analysis or is regarded as one of
the health outcomes of people being followed though it is not. Some researchers have actually used lost to
26
follow-up as a fifth health status thus ending up either underestimating or overestimating the measures of
interest (Cruz et al., 2007). Another observation with the application of multistate life table method is that
functional changes are always associated with biomedical processes hence associated with changes in health
(Poston et al., 2005). It has been argued that functional changes e.g. ability to perform a given task may not
necessarily be a factor of improving health but may be technological advancement that makes it possible to
cope with that situation. Changes in environmental and social factors may also lead to changes in functional
ability. Despite this limitation, the multistate life table methods have advantage over Sullivan method since
they allow transition of health status (Wagener et al., 2001; Molla et al., 2003; Zachary, 2005) and they give
more accurate estimates (Cruz et al., 2007; Wagener et al., 2001).
Understanding these dynamics in methodological approaches is important since although the study focused
more on measuring healthy life expectancy among HIV/AIDS patients, it identified the need for more
advanced research to adjusted healthy life expectancy due to changes in environmental, social and
technological factors as well as how to adjust for lost to follow-up.
2.11
HIV/AIDS and regional inequalities in Kenya
According to KDHS 2003 and 2008/09 surveys, Nyanza and Central regions were found to be almost at par
on HIV knowledge, prevention methods including prevention of mother to child transmission (CBS et al.,
2004; KNBS et al., 2010). The surveys however showed big differences between the two regions in such
areas as stigma, misconception on HIV, testing rates, risk behaviors and prevalence with Central region
having lower HIV prevalence than Nyanza region. Comparing the two regions, the gap between them in the
above areas reduced substantially between the two surveys except for HIV prevalence where Nyanza
remained significantly higher.
27
2.12
Conceptual frameworks
A health state classification system adapted from CDC literature reveals that in assessing health related
quality of life, it is important to consider not only the breadth but also the depth of the health issues (Keppel
et al., 2002). The breadth of health issues considers the domains of health status while the depth considers
the functional abilities affected by the various health domains as shown in figure 2.1. For example, the
physical functioning domain will affect such functions as physical activity, self-care, mobility and role
performance. In turn, self-care functions that will be affected include dressing, eating and bathing. The
implication of this is that while improvement of health may affect these limitations positively,
environmental and technological changes may make it easier to cope with the limitations. As an example,
mobility may be made easier through provision of wheelchair or use of motorized means of transport and
while a patient may report improvement in physical functioning, this may actually not be as a result of
improved health but rather a reflection of the ease of performing reported activities due to technological
changes. These are factors that need to be put into consideration when measuring socio-economic and
demographic correlates of healthy life expectancy.
Figure 2.1: Classification System of Health Attributes.
Source: CDC (2001)
28
Based on this classification system, three main health related quality of life conceptual models have been
used extensively in research. The three are Wilson and Cleary model, Ferrans and colleagues model and
World Health Organization (WHO) model (Ferrans et al., 2005). Ferrans model is a revised version of
Wilson and Cleary model and it has been recommended for use in research since it has both individual and
environmental characteristics. WHO model is not specific to health and was therefore not appropriate for
use in this study. Figure 2.2 refers to the Ferrans et al model.
Figure 2.2: Ferrans et al 2005 HRQOL Conceptual Framework
Source: Daria ( 2008)
For this study, the following operational framework was adapted.
Variables
Intermediate
Demographic and
socio-economic
Variables – Age, sex,
marital status, wealth,
,
education,
Environmental
Variables-clinical,
Ownership of items,
treatment supporters etc
Initial and followup health states
measured
through Health
Related Quality
of Life (HRQOL)
Impact
Health Adjusted
Life Expectancy
Figure 2.3: Operational Framework Adapted for the Study
29
2.13
Definition of concepts used in the study
Health-Related Quality of Life: Many definitions of health related quality of life (HRQOL) exist (Nilsson,
2012). For this study, HRQOL is defined as physical, mental and social wellbeing and functioning, in line
with the comprehensive WHO definition of health (adapted from Nilsson, 2012). HRQOL is the patientreported part of the WHO definition of health.
MOS-HIV: This is a health status instrument in HIV research developed as part of Medical Outcomes
Study that measures health related quality of Life. It is a 35-item questionnaire that measures 11 dimensions
of health of a patient and it has been shown to be internally consistent.
Health Adjusted Life Expectancy: HALE is a measure of population health that takes into account
mortality and morbidity. It adjusts overall life expectancy by the amount of time lived in less than perfect
health. This is calculated by subtracting from the life expectancy a figure which is the number of years lived
with disability multiplied by a weighting to represent the effect of the disability.
2.14
Conclusion
This literature review has revealed several things that are important for the study. First the review clearly
indicates that HIV/AIDS is an important factor to consider in understanding the changes taking place across
populations in terms of life expectancy. HIV/AIDS has had a negative effect on life expectancy and this has
been worse in Sub Saharan Africa. This trend has however changed in recent years as new improved
methods for the management of HIV/AIDS have been implemented. Life expectancy has risen tremendously
and compared to the general population, HIV infected persons can expect to have near normal life
expectancy though this benefit is not enjoyed by most of the patients in sub Saharan Africa. Health related
quality of life for HIV/AIDS patients has also been measured and has been shown to improve as patients
30
remain in care and treatment programmes. Though many factors have been associated with health related
quality of life, it is not clear which factors have stronger association.
The literature has also shown that health adjusted life expectancy among HIV/AIDS patients in sub Saharan
Africa has received little attention and thus remains largely unknown. Several approaches have been
adopted in measuring HRQOL and subsequently HALE and some of these have had serious limitations that
can be addressed by adopting more rigorous though time consuming methods. Some tools that have been
used may not be appropriate for the African context. The tool mostly used in Africa and which has been
validated is the MOS-HIV short form and this is the tool that has been used in this study. In conclusion,
although life expectancy in HIV/AIDS patients is on the increase globally, this is not so clear in the Kenyan
context. Even if this was the case for Kenya, there is still a gap as to whether the added years are lived in
good or poor health as perceived by the patients themselves. It is also not known what factors could be
associated with HRQOL and HALE for HIV/AIDS patients in Kenya. The essence of this study was
therefore to assess both HRQOL and HALE in HIV/AIDS patients in Kenya using both cross sectional and
longitudinal approaches, and develop a predictor model of HALE while controlling for covariates. This
study was different from an earlier study conducted in Kenya (Harding et al., 2009) in that the study had a
longer follow-up period, and the study measured health adjusted life expectancy and not just health related
quality of life of people living with HIV.
31
CHAPTER THREE
METHODOLOGY
3.1
Introduction
This section defines summary measures of health that were assessed in this study, describes the study
design, key variables that were considered, sources of data, the methods and materials that were used to
assess the summary measures of health and their correlates, the target population and inclusion and
exclusion criteria. The section describes in details how each of the study objectives was measured. It gives
details on the study design, sources of data, tools used for data collection and their validation, study area, the
sampling methods, field data collection, ethical considerations and data analysis.
3.2
Study design
This was a prospective cohort observational study. Participants were recruited into the study in the first
three months since commencement of the study when their baseline information was collected. The
participants were followed up for 12 months when the second wave data collection was done. This interval
between successive data collection was a tradeoff between having many waves that would have made the
analysis very complex and having very long intervals that could have increased bias as number of events
that may not be captured would increase with long intervals.
3.2.1. Sampling and sample size
Study participants were recruited into the study at the same time they were being enrolled for HIV care. The
study was conducted in Nyanza and Central regions of Kenya in public health facilities where HIV/AIDS
patients access care and treatment services for free. In each region, 3 of the high volume facilities were
sampled purposively. The three facilities in each region account for about 25 percent of the total patients
enrolled in the two regions. The sample size for each region was calculated using the following formula
(adapted from formula suggested by Charan, (Charan & Biswas, 2013)):
32
Where
Z = confidence level (95%)
e= sampling error (5%)
p = Expected response rate of new patients being enrolled into care.
N = Expected number of new patients aged 15 years and above enrolling into HIV care in the sampled
health facilities in a three month period for each region.
The expected response rate for new patients being enrolled into HIV care was assumed to be 50 percent.
The expected number of newly enrolled patients aged 15 years and above for a 3 month period was obtained
from DHIS 2 for the period January-March 2014. DHIS 2 is an online national reporting platform that
captures all service delivery data from all the health facilities in Kenya (Manya et al., 2012). For the
reference period, number of new patients enrolled into care for the three health facilities from Central region
was 302 while that of Nyanza facilities was 292. Using the above formula, the calculated sample size for
Nyanza was 163 patients and for Central was 170 patients which gave a combined sample size of 333.
Assuming an attrition rate of 20 percent through transfer out and being lost to follow-up, the sample size
was increased to 399. This was considered an adequate sample to give the study a strong statistical power.
Previous similar studies to assess health related quality of life for HIV/AIDS patients have used almost
similar sample size (Jia et al., 2005). Swindells et al cited in Jia et al study followed a cohort of 138 patients
in assessment of changes in quality of life of HIV/AIDS patients (Jia et al., 2005). A study conducted in
33
Netherlands that followed a cohort of HIV/AIDS patients on HAART to assess if HRQOL is associated with
survival of patients had 570 participants (Marion et al., 2010).
3.2.2
Inclusion and exclusion criteria
Study participants included all HIV/AIDS patients aged 15 years and above who were newly enrolled into
HV care and treatment services in the sampled facilities. The two factors that were put into consideration in
arriving at this target population were: in Kenya, HIV care and treatment programme recognizes a person
aged 15 years and above as an adult and the tools used for data collection for this study could only be
applied to people within this age bracket. Younger persons are generally not expected to respond to
questions related to physical and mental limitations as an adult would and would therefore require a
different method of capturing their disability. Only patients enrolled into HIV care and treatment services
within the first 3 months since commencement of the study were recruited. The study participants excluded
patients transferring in from other facilities, and patients previously on care and treatment but then
discontinued and were now restarting the services. The last inclusion criterion was willingness of patients to
participate in the study.
3.3
Study settings
Before promulgation of the new constitution in 2010, Kenya was divided into 8 administrative units which
were the provinces. Even after promulgation of the constitution, provincial administration remained active
until the general election of 2013 where county governments became effective. This study was
conceptualized before the county governance units became effective and the aim was to estimate and
compare health adjusted life expectancies at provincial levels for two provinces; Nyanza and Central. In
Nyanza, three health facilities in Kisumu County were purposefully selected due to the expected high
number of new HIV/AIDS patients enrolling for HIV care. In Central, the selection of the facilities was also
done purposefully and three facilities were selected. The facilities were Ahero, Katito and Nyakach in
34
Nyanza and Thika, Ruiru and Nyahururu in Central. All the 6 facilities are public health facilities run and
managed by the Ministry of Health. The facilities enroll people living with HIV for chronic care under
guidelines developed by NASCOP. At a minimum, all people living with HIV receive counseling at the
hospital, get medication to prevent and treat opportunistic infections, get package of laboratory tests to
monitor their progress and are put on ARV once they are deemed eligible.
Despite similarities in the package of care provided across the two regions, there are a number of distinct
differences. The two regions have different HIV prevalence rates. According to KAIS 2014 report, HIV
prevalence in Kenya is estimated at 5.6 percent, a decrease from 7.6 percent recorded in 2007 (NASCOP,
2014). Prevalence among women is higher than in males, 6.9 percent and 4.2 percent respectively.
Prevalence in Nyanza was estimated at 15.1 percent and 3.8 percent in Central.
3.4
Summary measures of population health and sources of data
Two summary measures of health for adult HIV/AIDS patients were assessed in this study: health related
quality of life and health adjusted life expectancy. Health related quality of life was measured using the selfreported health status by HIV/AIDS patients at 2 points in time during the one year follow-up. The first data
collection was undertaken at the enrolment into HIV care, and the second one after 12 months during
scheduled clinic visit.
Health adjusted life expectancy was assessed by first weighting the different states of health as measured by
health related quality of life and then calculating life table functions using both Sullivan and multistate life
table approaches. The main assumption in calculation of multistate life table functions is that the transition
from one health state to another followed a Markov process where the future health status is independent of
past status given the present status. Sullivan method of measuring HALE was also used both at baseline and
at 12 month follow-up for comparison purposes.
35
Both primary and secondary data collection was done. Primary data were collected by directly interviewing
patients and secondary data were obtained by abstracting information from existing medical records
maintained at health facilities. Information obtained by interviewing patients included their socio-economic
status, distance from home, number of dependants and their self-reported health status. Data abstracted from
existing records included CD4 counts, presence of opportunistic infections and TB status.
3.5
Data collection tools
The study collected data on two main dimensions: self-reported health related quality of life and factors
expected to be associated with this measure. Since the focus of the study was to develop a predictor model
based on socio-economic and demographic correlates, independent factors were organized into two main
categories: predictor variables and control variables. Predictor variables included age, sex, marital status,
number of dependants, level of education, wealth status, and type of treatment supporter. Control variables
included whether or not patients were started on ART immediately after enrolment, type of treatment, type
of health facility, level of CD4 at enrolment, co-morbidity with other common illnesses such as TB at
enrolment, and clinical manifestation of the disease as measured by WHO stage. All the independent
variables were collected at baseline. Although some of the variables could change within the follow-up
period such as WHO stage, marital status, co-morbidity and others, the methods used for data analysis could
only accommodate these variables taken at one point in time.
Four main tools were used for data collection: a screening tool for filtering patients who met the inclusion
criteria, a demographic tool for capturing all socio-economic, demographic and biomedical information of
the participants, a tool to collect data on major events such as death, transfer out, and lost to follow-up, and
a validated tool to measure health related quality of life. The facility characteristic tool was used to gather
information on cadre of staff, services available to patients and the cost of accessing these services.
36
Health related quality of life was assessed using MOS – HIV questionnaire. The MOS-HIV is a 35-item
validated questionnaire that includes eleven dimensions of health related quality of life and is used
specifically for people living with HIV/AIDS. The eleven dimensions include general health perceptions
(GHP), bodily pain (BP), physical functioning (PF), role functioning (RF), social functioning (SF), mental
health (MH), energy/vitality (EV), cognitive functioning (CF), health distress (HD), overall quality of life
(QL) and health transition (HT) (Ion et al., 2011). Two health summary scores were generated from this
tool: physical (PHS) and mental (MHS).
MOS-HIV questionnaire was not translated to maintain its validity. Research assistants were however taken
through thorough training to ensure they were comfortable using the questionnaire and that they could ask
the questions as accurately as possible.
Samples of these tools are provided as appendices 1 and 2.
3.6
Data collection process
Patients were recruited from the national HIV/AIDS care and treatment programme run by Ministry of
Health through National AIDS and STI Control Programme (NASCOP) and other partners. Specifically, the
study was conducted in facilities currently supported by Center for Health Solutions - Kenya and ICAP
through funding from CDC Kenya. The programme has a well-established information management and
defaulter tracing systems and any patient recruited in the study and moved to other locations within the
study period was easily traced and subsequent information collected. Information about patients dying
within study period was provided by their treatment supporters who are normally identified when the
patients are being enrolled into care and treatment programme. The primary data collected by interviewing
patients was supplemented by routinely collected information usually recorded when patients make their
monthly or bimonthly routine follow-up visits e.g. TB status, CD4 count among others.
37
Twelve (12) study assistants were engaged during data collection waves, 2 for each health facility. The
study assistants were recruited from among peer educators who among other things are charged with the
responsibility of providing peer education to HIV/AIDS patients and tracing patients who default. They
underwent a-thorough two day training on the tools and also had one day field work in a facility not
sampled for the study to pre-test the tools before the actual data collection could start. The researcher also
supplemented funds for defaulter tracing by giving research assistants monthly airtime money so as to
ensure that patients who did not turn up when scheduled to come were traced and their true status
established. This airtime supplemented the existing defaulter tracing system which has been in place since
inception of HIV care and treatment programme.
Once data collection had started, the researcher maintained close contact with the lead peer educator in each
facility by proving them with extra airtime to call whenever there were problems. The researcher made field
visits every 2 weeks to monitor progress and pick filled up questionnaires, and address any issues identified.
Data entry was done in a central place into a database using EpiData 3.1 software. The database was
protected and was only accessible to the data entry clerk and the researcher for the purpose of ensuring
confidentiality of patient information.
3.7
Description of characteristics of patients assessed in the study
Apart from measuring health related quality of life of study participants and their outcome status at the end
of one year follow-up, assessments were also conducted on the several characteristics that could be used to
explain these measures such a age and sex. Sex was coded as 1 for male and 2 for female and age was
collected in full completed years. At analysis level, age was categorized into the following five age groups:
15-24 years, 25-34 years, 35-44 years, 45-54 years and 55 and above years. The wide age groups were as a
result of few patients in each single year ages. Each observation was categorized as either wave one
(baseline) or wave 2 (follow-up) of data collection.
38
Since it is already hypothesized that there were differences in key health indicators among patients in
Nyanza and Central, the two regions were taken as strata with Nyanza being coded 1 and Central 2. The
facilities within each of these strata were taken as primary sampling units and were coded thus; Ahero 1,
Katito 2, and Nyakach 3 in strata 1 and , Ruiru 4, Thika 5 and Nyahururu 6 in strata 2. Patients were put into
3 categories of marital status; never married 1, previously married 2 to include divorced, separated and
widowed and currently married 3 to include married monogamous, married polygamous and cohabiting.
Religious affiliation was coded as protestants 1, Catholics 2, Muslims 3 and others 4 to include atheists,
traditionalists and those who said others. Educational level was measured in terms of completed level of
schooling with no education coded as (1), primary level (2) and post primary (3). Rural and urban
residences were coded as (1) and (2) respectively while distance from patient’s home to the health facility
enrolled in was coded as <=2 km (1), 3-5 km (2), 6-10 km (3) and above 10km (4).
Other key variables studied were occupation coded as not employed (1), student (2), informal employment
(3), formal employment (4) and business (5). Income was coded as 0-10000 shillings per month (1) and
above 10000 per month (2). Patients who had no dependants were coded as (1), 1-2 dependants (2), 3-5
dependants (3) and 6-9 dependants (4). Number of days between HIV diagnosis and enrolment into HIV
care was also measured and coded as 1 if enrolled on the same day of diagnosis, 2 if enrolled within 1 week
of diagnosis, 3 if enrolled within 1 month and 4 if more than 1 month. Participants were also given a list of
household items and asked to state if they or anyone in the household owned any of those items. The items
were radio, phone, TV, car, fridge, and a bicycle. Any participant who said yes to any of these items was
categorized as owning a house hold item (1) and if they did not own any item they were categorized as not
owning any household item (2). A couple of other factors were studied which included whether or not a
patients had treatment supporter, relationship with treatment supporter, WHO stage of the disease, CD4
39
count, TB status, pregnancy status, whether or not a patient was on CTX, if a patient had an opportunistic
infection and BMI. The coding for these variables is shown in table 3.1.
Table 3.1: Characteristics assessed and their coding
1
2
3
4
5
6
7
Variable
Response
Response
code
Sex
Male
1
Female
2
15-24
1
25-34
2
35-44
3
45-54
4
55+
5
Wave 1
1
Wave 2
2
Nyanza
1
Central
2
Ahero
1
Katito
2
Nyakach
3
Ruiru
4
Thika
5
Nyahururu
6
Never married
1
Previously married
2
Currently married
3
Protestant
1
Catholic
2
Muslim
3
Others
4
Age
Data collection wave
Strata (region)
PSU (Facility)
Marital status
Religion
40
Table 3.1 continued
8
9
Variable
Response
Response code
Education
No education
1
Primary
2
Post-Secondary
3
Rural
1
urban
2
<=2km
1
3-5 km
2
6-10 km
3
More than 10 Km
4
Not employed
1
Student
2
Informal employment
3
Formal employment
4
Business
5
0-10000 KES per month
1
More than 10000 KES per month
2
None
1
1-2
2
3-5
3
6-9
4
Yes
1
No
2
Residence
10 Distance from Home
11 Occupation
12 Income
13 Dependants
14 Own household item
15 Number of days between diagnosis Same day
and enrolment
Within a week
16 Patient has treatment support
1
2
Within a month
3
More than a month
4
Yes
1
No
2
41
Table 3.1 continued
Variable
17 Relationship with supporter
18 WHO Stage of the disease
19 Tb Status
20 Pregnancy status
21 Patient on CTX
22 BMI
23 Patient status at one year follow-up
42
Response
Response code
Parent
1
Child
2
Spouse
3
Sibling
4
Other
5
Stage 1
1
Stage 2
2
Stage 3
3
Stage 4
4
No signs
1
Presumptive TB Case
2
On TB Treatment
3
Not Screened for TB
4
Post-partum (still breast feeding)
1
Not pregnant
2
Pregnant
3
Not Applicable (Male)
4
Yes
1
No
2
<18.5
1
18.5 – 24.9
2
25.0 – 29.9
3
30and above
4
Alive
2
Dead
3
Lost to Follow-up (LTFU)
4
Transferred (TO)
5
Although patients were also asked to name the ethnic group they belonged, the variable was dropped at data
analysis as it was found to be highly correlated with the region (strata) with over 99 percent of patients in
Nyanza being Luos and over 95 percent of patients in Central being Kikuyus. The effect then of ethnic
group could as well be measured using region.
In the multiple linear regression to determine factors associated with health related quality of life in the
various health domains, new dummy variables were created from the categorical variables to enable them
be used in the multiple linear regression, with HRQOL scores for each health dimension being the
dependent variable. As an example, marital status with three categories (never married, previously married
and currently married) was recoded into two dummy variables; previous married and currently married with
never married being the reference category. The recoding of new dummy variables was done as shown in
the table 3.2.
Table 3.2: Recoding of dummy variables
Variable
Previously married (d1)
Currently married (d2)
Never married
0
0
Previously married
1
0
Currently married
0
1
The other variables were recoded in a similar manner. At one year follow-up, patients who were either lost
to follow-up or had transferred out were excluded from analysis since their HRQOL scores were not known.
Patients who were dead were given a score of zero for each of the dimensions including the two health
summary measures.
43
3.8
Confidentiality considerations
The ethical clearance was obtained from KNH/UoN-Ethics and Research Committee (KNH/UoN-ERC)
before the start of the study. This approval was necessary since the study involved human subjects and extra
data were collected beyond routinely collected medical statistics. Further, authority to conduct research in
respective counties was obtained from County Directors of Health (CDH). Facility in-charges also gave the
go ahead to the researcher to collect data from their facilities.
Written informed consent was sought from each of the patients included in the study. Patients were given a
written form to read and where possible seek clarification before signing. For those who could not read, the
form was read to them by the research assistant. The form explained the purpose of the study, the benefits
patients could expect by participating in the study, their rights to stop participating in the study at any time
and the length of the study. Participants were not given any financial incentives of whatever nature since all
follow-up data collection coincided with their scheduled clinic visit. To ensure confidentiality of patient
information, no filled study tool was taken from the facility by any other person other than the researcher.
Once taken out, the questionnaires were kept in a locked cabinet and will be archived for long enough as
required and then they will be destroyed appropriately. No identifying information was on the
questionnaires; all patients were coded in a way that the information on the questionnaires could not be used
to trace back the patient or their records at the facility. The consent form is attached as appendix 3.
3.9
Data quality assurance measures
Several methods were employed to ensure quality of the data. At the start of the study, research assistants
were trained for 2 days on how to conduct interviews with the patients. The training culminated with a mock
interview session in the field to provide them with practical skills on the use of tools. During data collection,
the researcher made several visits to the field to supervise data collection and provide any technical
assistance. For the data that was to be abstracted from existing medical records such as CD4 count and
44
presence of opportunistic infections, the researcher regularly sampled a few filled questionnaires randomly
and counterchecked the primary data sources for accuracy. At data entry level, the data clerk ensured all the
entered questionnaires were marked appropriately and the researcher sampled them to ensure all the
information was entered correctly. Data were also checked for validity and consistency. As an example,
male patients were expected to have ‘N/A’ as response to the question of pregnancy status. Patients who
indicated they were not married were also not expected to have a spouse as their treatment supporter. These
checks were inbuilt in the data entry platform with pop up alerts to data entry clerk whenever inconsistency
was detected.
3.10
Data analysis methods
3.10.1 Weighting health status
Once the data were collected, each patient was given a health related quality of life score in each of the 11
dimensions in a scale of 0-100. The scoring was done by recoding the original scores so that a higher score
indicated a better health status and vice versa across all the dimensions. The transformation of scores to a 0100 scale was to allow comparison of data across all the dimensions. Recoding was done at data analysis
level and not at data entry level. Out of range values were corrected by checking the original questionnaire
and where this was not possible, these values were regarded as missing values.
The formula that was used for linear transformation of the scores to a 0-100 scale was:
D = (100/ (T-L))*(Raw Score-L)
Where
D = HRQOL score of the health dimension being measured e.g. cognitive functioning dimension
T = the top of the range for the sum of untransformed item scores (highest possible score in a given
dimension)
L = the lowest possible score of the untransformed scale and
Raw Score = is the actual score for an individual in the dimension (sum of the item scores in this dimension)
45
As an example, a patient who scored 21 in the cognitive functioning dimension which has 4 items with a
maximum score of 24 and a minimum score of 4 had a transformed score of:
Cognitive Functioning = (100/ (24-4))*(21-4) = 85
From these transformed scores, two summary measures, Physical Health Summary (PHS) and Mental
Health Summary (MHS) scores were obtained in three steps:
i) The scores for each of the 10 dimensions excluding the health transition dimension were
standardized to Roche patient population by calculating the z-scores transformations
ii) The z-scores were multiplied by some established scoring coefficients for each of the dimensions
and then summed up
iii) The summed up scores were transformed to have a mean of 50 and a standard deviation of 10 using
the formulae:
a. Physical Health Summary Score = 50+ (PHS*10)
b. Mental Health Summary Score = 50 +(MHS*10)
The transformation of the scores into a standardized scale was to make the scores comparable to those of
other studies done using MOS-HIV (Delate et al., 2001). Using the transformed scores for each of the
dimensions and the two summary measures, each patient was assigned to one of the four non absorbing
HRQOL states. These states were low (l), moderate (m), good (g) and very good (vg) and were established
using baseline quartile values generated through descriptive statistics. Dead (d) was the fifth state which was
an absorbing state i.e. patients cannot transition from a death state to any other state. At baseline, the count
of patients in dead state was zero.
3.10.2 Adjusting health weights due to age
The physiological process of aging was expected to affect health related quality of life such that in the
absence of HIV, a 15 year old person would generally be expected to have a better HRQOL score than a 65
year old. The effect of age was adjusted for in the model by using age as a covariate.
46
3.10.3 Adjusting for HIV unrelated deaths
In the course of patient follow-up, it was expected that some would die due to other causes unrelated to HIV
e.g. accidents. Patients who succumbed to these other causes were not regarded to have transitioned to the
absorbing state, dead, but rather their final health status was imputed from the average status of other
patients with similar characteristics.
3.10.4 Correlates of baseline self-reported health status
The four levels of self-reported health statuses were treated as being measured on an ordinal scale with low
status being the worst and very good the best. To fit a predictor model of baseline self-reported health status,
ordered (ordinal) logit regression also known as proportional odds model was used with predictor variables
being age, sex, marital status, level of education, and type of treatment supporter. The model was adjusted
for possible confounders which included baseline CD4 count, co-infection with TB, and pregnancy status.
Low health status was coded 1, moderate status as 2, good as 3 and very good as 4.
The ordinal logit regression is a modification of binary logistic regression where instead of considering the
probability of an individual event, the probability of that event and all other events ordered before it is
considered.
In this study;
Θ1 = odds of being in the low health status =number in the lower health status / number in moderate, good or
very good health statuses
Θ2 = odds of being in the moderate or low health statuses = number in the moderate or lower health statuses
/ number in good or very good health statuses
Θ3 = odds of being in the good, moderate or lower health statuses = number in the good, moderate or lower
health statuses / number in very good health status
47
The last health status category has no odds associated with it since the probability of being in that category
or any other category is 1.
The ordinal logistic model was then specified as
ln(θj) = αj + βkXk
where j = 1,2,3 and k is equal to the number of predictor variables
3.10.5 Length of time spent in a specific baseline health status and its predictors
The multistate life table methods advanced by Kuo, Suchindran and Koo for describing complex transitions
to various health states using right censored event history data were used (Kuo et al., 2008). There were
five possible health states that patients belonged to based on self-reported health related quality of life. The
five were low (l), moderate (m), good (g), very good (g) and dead (d). These states were determined as
described earlier. At the baseline, no patient belonged to the dead (d) category but it was possible for
patients to transition to this state in the course of the twelve months follow-up. The dead (d) state was an
absorbing state i.e. once a patient entered this state they could not leave the state.
Suppose Xt is the health state at time t occupied by a patient chosen at random, t= 1, 12 months and X = l,
m, g, vg, d. The health states were assumed to follow a Markov process where the probability of occupying
state j at time t+1 was dependent on the state i occupied at time t i.e. the future probability of being in a state
only depended on the present state and not on the past state.
Given that we have multistate, the probabilities of being in the different states at time t were presented in
form of a matrix as well as the initial (baseline) state occupancy distribution.
If we let N be the initial state occupancy distribution, then N will be a row vector matrix given by N = [l m g
vg d=0] where the four elements were calculated from the survey data and d=0 since all patients were alive
at the start of the study. As patients were followed up and their health status assessed, they transitioned from
48
one state to the other with a possibility of retaining their current status and this was calculated using the
formula:
qˆ (t, t+u) =dij (t, t+u) / [ni(t) –0.5ci (t, t +u)], 0<t<t=u,
where dij(t, t + u) is the number of transitions from state i to state j between the time interval t and t
+ u; ni(t) is the number of individuals who remain in original state i at time t; and ci(t, t + u) is the
number of individuals in original state i at time t who were censored between time interval t and t +
u.
The assumption was that the censoring was uniform between time interval t and t+u. The set of all transition
probabilities from time t to t+u formed a transition probability matrix Q(t, t+u) where its (i, j)th element was
qij (t, t+u). There were several of these transition probability matrices for each of the intervals.
The expected length of stay in state j between times s and t, given occupancy of state i at time s was
estimated by using the following formula, (Kuo et al., 2008).
i
where the E(ti ti+1) was obtained by partitioning the E(s,t) matrix so that the top n1 rows and the first n1
columns were for the transient states:
Partitioning the Q(ti,ti + 1) matrix in the same manner, we obtain the following 2 equations
49
3.10.6 Number of transitions from baseline health status to other health statuses after 1 year and its
predictors
If we let M(s,t) be the matrix of the expected number of visits made to a transient state j, (j=l, m, g, vg)
between the time interval s and t, then M(s,t) can be partitioned as above to give the matrix
The partitioned matrixes can be computed using the following 2 equations
where
B11(ti,ti+1)
is
obtained
from
the
matrix
, (where h= (ti+1 – ti ) ), by
replacing the diagonal elements with 0 (zero).
3.10.7 Relationship between baseline self-reported health status and treatment outcomes
The relationship between two treatment outcomes and the baseline self-reported health status were assessed.
Two treatment outcomes were considered as discussed in the subsequent sub-sections.
3.10.7.1 Retention in the HIV/AIDS care and treatment programme
Logistic regression analysis techniques were applied to assess the relationship between the baseline selfreported health status retention in the programme. Retention was defined as either being active in the
programme at the end of the 12 month follow-up period or having been transferred to another facility within
the follow-up period. Patients who died or got lost to follow-up were censored. Factors that were adjust for
and are known to affect retention included age, sex, distance from health facility to home and type of
treatment supporter.
50
3.10.7.2 Probability of dying within the follow-up period
The probability of dying within the follow-up period given the baseline self-reported health status was
modeled using binomial logistic regression while adjusting for other factors such as age, sex, baseline CD4,
new opportunistic infections, co-infection with HIV and adherence to treatment.
3.10.8 Calculation of health adjusted life expectancy and its differentials
Health adjusted life expectancy (HALE) was calculated using multistate life tables, MSLT. MSLT was
constructed using event-history data with right censoring (Kuo et al., 2008).
Recalling the functions of single decrement life table, we have;
All persons in a cohort start in a single state say birth cohort and the cohort decreases through only one
cause say death. In this case, if we start with l0 births, after n years, we have the original cohort which can be
represented by l0+n given by
l0+n = px .l0
where px is the probability of surviving between the time interval t=o and t=n.
dx is the number of people dying within the interval
mx which is age specific mortality rate is given by
Lx is defined as total person years lived within the time interval i.e.
px Is also given by
The other important functions of single decrement life table are Tx which is defined as the cumulative person
years lived beyond age x and is given by the formula
of a person aged x years and is given by the formula
51
and ei defined as the expected life given
Similarly, the functions of a multistate life table remain the same but matrix calculus is applied instead of
the conventional calculus. There are also a few additional columns in a multistate life table to depict the
various states that individuals can transition to.
In the study, the original cohort was in four states namely, low status l, moderate status m, good status g and
very good status vg as described earlier. A fifth status dead (d) was added although it was not expected that
any patient would initially be in this status.
Instead of now having a single value for lx, we can represent the values of lx for all statuses in a single vector
matrix called lx given by
lx = [llx mlx glx vglx dlx]
Where
llx
are patients in state l at time x
mlx
are patients in state m at time x
glx
are patients in state g at time x
vglx
are patients in state vg at time x
d lx
are patients in state d at time x
The matrix lx+n will then be given by multiplying matrices lx and px where px is a transition probability
matrix from one state to another and is given by the formula
While in single state tables mx was a single value and was only due to one cause e.g. death, in multistate life
tables, mx is a matrix and is a combination of decrement from state i due to both deaths and transition from
52
state
i
to
other
states
in
the
interval
where symbol
x
and
x+n
i.e.
and
is added to emphasizes exit through death.
The matrix mx can be represented as
in which each kth diagonal element contains the total rate of exit out of state k—that is, the mortality
rate plus the sum
of the rates of moving to another state and each k-lth- off-diagonal element
contains the rate of moving from state l to state k preceded by a minus sign. Matrices Lx and Tx can similarly
be obtained.
From these
can be obtained where ex is a matrix such that its k-lth element expresses the
remaining lifetime in region k to an individual residing in state l at age x.
Estimation of probability transition matrices was not obtained the same way survival probability is done in
the ordinary single decrement life table. Instead, survival methods were applied using the Kaplan-Meier
estimator, a non parametric estimator for right-censored event history data (Poston et al., 2005). The
rationale behind this was that at time x+n, patients in state say g (good) may be more than those in the same
state at time x due to transition from other states into this state. This means that the quotient l x+n /lx may fall
53
outside the [0 1] range and is actually not a probability at all. The use of this approach allowed for
estimation of transition probability matrices for various covariates and also for small samples of each of
these covariates. Standard data smoothing techniques were applied to address the problem of erratic changes
with age of survival probability due to sampling and other stochastic variations.
3.10.9 Data analysis approaches and software
Both Sullivan and MSLT approaches were applied in estimation of HALE for two reasons; triangulation of
information for conclusive results regarding differences in estimates between the two regions and second, to
compare if the estimates obtained by the two approaches were similar. Use of only one approach would
make any observed regional differences in HALE inconclusive since it would not be clear if the differences
were true or only due to the approach employed. Although the two approaches have substantial differences
in the type of data input, there have been arguments that the two give relatively similar results and it would
be good to test if the same applies to HALE for PLWHA in the era of advanced management of the
condition. Given the relative ease of using Sullivan approach, a finding of comparable results from the two
approaches would be of advantage to the Kenyan HIV/AIDS programme as it would mean that routine
measurement of HALE could be done using data collected at only one point in time which would be feasible
in terms of finances and analysis.
SPACE (Stochastic Population Analysis for Complex Events) programme, a package of SAS programmes
developed by a team led by Cai (Cai et al., 2010), was used to estimate MSLT functions. SPACE has
advantages over other programmes such as IMaCH and GSMLT software for calculating MSLT functions in
that it is capable of estimating many more statistics other than just the health adjusted life expectancy (Cai et
al., 2010). It also calculates standard errors of these statistics hence it can be used to compare
subpopulations. Another advantage of the programme over the other programmes is its ability to analyze
data for more than 2 health outcomes and for covariates with more than two levels. It can also accommodate
54
more than 2 covariates in a single analysis. SPACE package has a set of six programmes and for this study,
MSLT_SIMxCOV package was used. This package estimates age-specific state-dependent transition
probabilities using the multinomial logistic regression with one or more covariates and calculates MSLT
functions using micro simulation. The programme first calculates the point estimates from the full analysis
data sample. It then generates a large number of bootstrap samples and calculates the MSLT functions for
each of the samples. The standard errors of the original point estimates are the standard deviations of these
bootstrap estimates. This means that the programme is appropriate even for small samples especially when
number of persons within each age group is small.
The SPACE programme only accepts data waves that are at least 1 year apart. The one year follow-up
period is considered adequate for changes in HRQOL measure to change in newly enrolled HIV/AIDS
patients. The 1 year of follow-up is an improvement on cross sectional methods that have been used
previously. Other studies have used similar follow-up period. A study in America used 2 data collection
waves at baseline and after 12 months to assess factors influencing change in HRQOL (Jia et al., 2005).
Use of SPACE programme also requires that data be in a particular format. Some variables are mandatory
while others are optional. The dataset has to be in the format as shown in table 3.3 with mandatory variables
being shown with asterisks
Table 3.3: Variables used in SPACE Programme
Variable
ID*
Age*
Covariates and their coding
HSQ*
Strata*
PSU*
Weight*
Explanation
Personal identifier
Age at interview (baseline)
e.g. Sex: 1=men, 2=women and others
categorical and mutually exclusive health measure
(for this study 1=Low, 2=Moderate, 3 = Good, 4
Very Good and 5=dead)
indicator of strata in the sample
indicator of Primary Sampling Units (PSUs) in the
sample
cross-sectional weight for the current observation
55
The bootstrap samples are generated only from the first-stage sampling (i.e., at the PSU level).
For this study, personal identifiers were given to patients at baseline while the two regions were taken as the
strata with Nyanza = 1 and Central = 2. Health facilities were taken as PSU’s with Ahero = 1, Katito = 2,
Nyakach = 3, Ruiru = 4, Thika = 5 and Nyahururu = 6. The sample data were weighted with two auxiliary
variables; sex and age to make it representative of the population of people living with HIV in each of the
facilities with respect to sex and age. The weighting was done at facility level. The population of people
living with HIV in each facility was obtained from the ever enrolled patients in the facility at the time of
baseline data collection. As an example, table 3.4 represents a hypothetical data for facility A for males:
Table 3.4: Demonstration of process of obtaining weights for study participants
15-24 years
25-34 years
35-44 years
45-54 years
55 years and
above
Population
15%
25%
25%
25 %
10%
Sample
5%
40%
30%
20%
5%
The weight for a male patient aged 15-24 years in this facility was calculated by dividing population
proportion with sample proportion thus:
15/5 = 3.
This gives males 15-24 years a weight of more than 1 since they are underrepresented in the sample.
Weights for the other categories were calculated in a similar manner.
Another statistical software used in this study was SPSS version 21 to undertake logistic regression analysis
for assessing factors associated with HRQOL outcomes.
56
CHAPTER FOUR
CHARACTERISTICS OF STUDY PARTICIPANTS AND FACTORS ASSOCIATED WITH
HEALTH RELATED QUALITY OF LIVE MEASURES
4.1
Introduction
This chapter describes the characteristics of the study participants, the results of assessment of health related
quality of life for various health domains both at baseline and at one year follow-up and the associated
factors and how these measures influence 1 year patient outcomes. The chapter is aimed at providing
answers to the research objectives about health related quality of life of HIV/AIDs patients and to lays the
foundation for chapter five.
The chapter is divided into 4 sections. Section 4.2 describes the socio demographic characteristics of the
study participants at both the baseline and at one year follow-up. Section 4.3 examines health related
quality of life for the participants for all the 11 dimensions assessed and the 2 summary measures; physical
and mental. This assessment is done both at baseline and at one year follow-up and any changes in these
measures are noted. This is then followed by a comparison of these measures of health related quality of life
between Nyanza and Central while controlling for other factors such as sex, marital status, age among
others. This analysis addresses the first question of the study as stated in chapter one i.e. “How does health
related quality of life (HRQOL) of adult HIV/AIDS patients newly started on HIV care in Central region
compare with that of similar patients in Nyanza region?” These health measures are also used for further
analysis as described in section 4.4.
Section 4.4 is a brief assessment of study participants’ treatment outcomes at the end of one year follow-up.
The outcomes assessed were retention in the programme, dead, lost to follow-up or being transferred to
other facilities to continue receiving care. This analysis is undertaken for the two regions. The section also
seeks to establish if there is any relationship between baseline measures of health related quality of life
(section 4.3) and patient status at one year follow-up.
57
Section 4.5 provides a brief summary of the key findings highlighted in the chapter. In this section, the
researcher provides a succinct answer to the question how HRQOL of adult HIV/AIDS patients in Nyanza
compare with that of patients in Central and provides a transition to chapter five which uses most of the
results of this chapter to answer the other questions the study sought to address.
4.2.
Basic characteristics of study participants
Before data analysis, all cases were weighted using the weights obtained by applying the procedure
described in chapter three. The purpose of weighting the cases was to ensure the sample was representative
of the population of people living with HIV in the sampled facilities with respect to sex and age.
4.2.1. Demographic and socio-economic characteristics of study participants
Demographic and socio-economic characteristics were age, sex, marital status, religion, education,
residence, distance from home, ownership of household items, occupation, income and number of
dependants. Table 4.1 summarizes results for some of these characteristics. The table shows results per
strata and also at aggregate level. A total of 393 participants (weighted cases) were recruited into the study:
194 (49.4 percent) were in Nyanza while 199 (50.6 percent) were in Central region. Overall, the study
recruited 156 (39.7 percent) males and 237 (60.3 percent) females. Nyanza had lower proportion of male
participants (34.0 percent) compared to Central which had 45.2 percent. Figure 4.1 shows distribution of
participants into different age groups. About 40 percent of participants were in age group 25-34 years
followed by those in age group 35-44 years. The least number of participants were in 55 years and above
age group.
58
Figure 4.1: Distribution of participants by different age groups, n=393
Source: Analysis of study data
4.2.1.1 Marital status
At the time of data collection, 225 (58.1 percent) of participants were married for the combined sample,
compared to 117 (61.6 percent) in Nyanza and 108 (54.8 percent) in Central. Only 63 (16.3 percent) were
never married at aggregate level compared to 25 (13.2 percent) in Nyanza and 38 (19.3 percent) in Central.
The rest of the participants were divorced, separated or widowed.
4.2.1.2 Religion
A total 130 (67.4 percent) participants in Nyanza were protestants compared to 127 (63.8percent) in Central.
Catholics also formed a substantial proportion of study participants; 54 (28.0 percent) in Nyanza and 64
(32.2 percent) in Central. The rest of the participants belonged to other religions including Muslim,
traditionalists and others.
4.2.1.3 Education
Nyanza had slightly higher proportion of participants who had primary as their higher level of education
compared to Central, 112 (58.3 percent) and 100 (50.5percent) in Nyanza and Central respectively. The
59
proportion with post primary education was higher in Central compared to Nyanza, 95 (48.0percent) and 68
(35.4 percent) in Central and Nyanza respectively.
4.2.1.4 Other background characteristics
Nyanza region had higher proportion of study participants who resided in rural areas, 174 (89.7 percent)
compared to Central region 57 (28.8 percent) indicated they resided in rural areas.
Distance from
participants’ home to health facility ranged from less than 2 km to more than 10 km. In Central region the
proportion of participants who covered more than 10 km to reach the health facility was 29.1 percent
compared to 27.5 percent in Nyanza region. Ownership of at least one household item was almost universal
with 187 (96.9 percent) in Nyanza region and 178 (89.4 percent) in Central region reporting owning a
household item. Owning a business was the most common occupation in Nyanza region (94 (48.7 percent)
while informal employment was the most common occupation in Central region (74 (37.4 percent). Students
formed the minority group with both regions having less than 5 percent of study participants being students.
Proportion of study participants with an income above ten thousand Kenya shillings was higher in Central
region (20.6 percent) compared to Nyanza region (6.2 percent). Having 3-5 dependants was the most
common in Nyanza region, 85 (48.6 percent) while in Central, majority of study participants reported that
they had 1-2 dependants, 76 (39.6 percent). Table 4.2 summarizes these results.
Table 4.1: Distribution of study participants by various background characteristics n=393
Nyanza
Central
Total
Weighted
Weighted
Weighted
Characteristic
cases(n)
%
Cases(n)
%
cases(n)
Sex
Male
66
34.0
90
45.2
156
Female
128
66.0
109
54.8
237
Age Group
15-24
21.1
11.1
41
22
63
25-34
39.2
37.2
76
74
150
35-44
21.6
31.7
42
63
105
45-54
11.9
13.1
23
26
49
55+
6.2
7.0
12
14
26
60
%
39.7
60.3
16.0
38.2
26.7
12.5
6.6
Table 4.1 continued
Nyanza
Characteristic
Marital Status
Never Married
Previously Married
Currently Married
Weighted cases(n)
Central
Weighted
Cases(n)
%
%
Total
Weighted
cases(n)
%
25
48
117
13.2
25.3
61.6
38
51
108
19.3
25.9
54.8
63
99
225
16.3
25.6
58.1
130
54
9
67.4
28.0
4.7
127
64
8
63.8
32.2
4.0
257
118
17
65.6
30.1
4.3
12
112
68
6.3
58.3
35.4
3
100
95
1.5
50.5
48.0
15
212
163
3.8
54.4
41.8
Religion
Protestant
Catholic
Others
Education
No Education
Primary
Post Primary
4.2.2. Health related characteristics of study participants
The health related characteristics that were studied included number of days between HIV diagnosis and
enrolment into HIV care, whether or not a participant had a treatment supporter, and the relationship of the
participant with treatment supporter. Others were WHO stage of the disease, CD4 count, TB status,
pregnancy status for women, if or not the patient was put on cotrimoxazole preventive therapy (CTX), if a
patient had an opportunistic infection (OI) and BMI. Some categories of these variables that had few
responses were combined. Table 4.3 summarizes these results.
61
Table 4.2: Other background characteristics of study participants by region
Nyanza
Central
Total
Weighted
Weighted
Weighted
Characteristic
cases(n)
%
Cases(n)
%
cases(n)
%
Residence
174 89.7
57
28.8
231
58.9
Rural
20 10.3
141
71.2
161
41.1
Urban
Distance from home to
hospital
48 24.9
31
15.6
79
20.2
=<2 km
56 29.0
79
39.7
135
34.4
3-5 KM
36 18.7
31
15.6
67
17.1
6-10 KM
53 27.5
58
29.1
111
28.3
>10 KM
Own a household Item
187 96.9
178
89.4
365
93.1
Yes
6 3.1
21
10.6
27
6.9
No
Occupation
54 28.0
49
24.7
103
26.3
Not Employed
9
8
17
Student
4.7
4.2
4.3
Informal
31
74
105
Employment
16.1
37.4
26.9
5 2.6
25
30
Formal Employment
12.6
7.7
94 48.7
42
136
Business
21.1
34.8
Income
102 52.3
120
222
0-10000
60.3
56.5
12 6.2
41
53
Above 10000
20.6
13.5
118
Not indicated
80 41.2
38
19.1
30.0
Dependants
51
0
11 6.3
40
20.8
13.9
119
1-2
43 24.6
76
39.6
32.4
156
3-5
85 48.6
71
37.0
42.5
41
>5
36 20.6
5
2.6
11.2
Nyanza region had higher proportion of participants enrolled into care within the same day of diagnosis,
137 (70.6 percent) compared to Central region where only 74 (37.2 percent) were enrolled on the same day
of diagnosis. Nyanza region also had higher proportion of participants with a treatment supporter, 188 (97.4
percent) compared to 186 (93.5 percent) in Central region. Most of the treatment supporters were spouses
with Central region having a higher proportion compared to Nyanza region, 90 (48.4 percent) and 89 (47.1
percent) respectively.
62
WHO stage indicates how advanced the AIDS disease is in an individual while CD4 count provides a
measure of how strong the immune system is with low CD4 indicating weak immune system and high
values indicating strong immune system. Low WHO stages (1 and 2) indicate that the HIV/AIDS disease is
not at advanced stage while high stages (3 and 4) show that the disease is at advanced stage. Majority of
study participants were at stage in Nyanza region, 72 (69.2 percent) while in Central region, majority were
in stage 2 (45.2 percent). Central region reported a higher proportion of participants who had low baseline
CD4 count (500 and lower) compared to Nyanza region, 130 (89.0 percent) and 63 (77.7 percent)
respectively. At enrolment into HIV care, participants were expected to be screened for TB. The results for
this variable were abstracted from patient records in the patient file. The results showed that only 8.8
percent of participants in Nyanza region were either on TB treatment or were presumed to have TB
compared to 20.1 percent in Central region. Pregnancy was more prevalent in Nyanza region, 23.7 percent
than 10.9 percent in Central region.
While CTX uptake at enrolment is expected to be universal, there was a marked variation in the uptake at
regional level among the study participants. Only 72.2 percent of study participants in Central region had
CTX at baseline compared to 94.3 percent in Nyanza region. Opportunistic infections (OI) at enrolment
were more common in Central region where 26.3 percent of the participants indicated they had an OI
compared to Nyanza region where 21.6 percent of the participants had an OI. This result was consistent with
the WHO stage where in Central region, majority of participants were in stage 2 while in Nyanza region,
majority were in stage 1. BMI was measured by abstracting height and weight of participants from the
medical records and using the universal formula to calculate the values. The values were then categorized
into 4 universal categories and results showed that for majority of study participants in Nyanza region, 64.8
percent, their BMI was in the normal category (18.5 – 24.9) as compared to Central region where only 48.7
percent were in this category.
63
Table 4.3: Health related characteristics of study participants
Nyanza
Weighted
Characteristic
cases(n)
%
Days to enrolment
Same day
137 70.6
Within a week
38 19.6
Within a month
19 9.8
Treatment Support
Yes
188 97.4
No
5 2.6
Relationship with Treatment Supporter
Parent
26 13.8
Child
20 10.6
Spouse
89 47.1
Sibling
17 9.0
Others
37 19.6
WHO Stage
Stage 1
72 69.2
Stage 2
28 26.9
Stage 3 or 4
5 3.9
CD4 Count
0-250
30 37.0
251-500
33 40.7
Above 500
18 22.2
TB Status
No TB Signs
159 82.4
On TB Treatment/Presumptive TB
17 8.8
Not Screened
17 8.8
Pregnancy Status (among women only)
Not pregnant
94 69.6
Pregnant
32 23.7
Don’t Know
9 6.7
On CTX
Yes
182 94.3
No
11 5.7
Presence of Opportunistic Infection
Yes
42 21.6
No
152 78.4
BMI
<18.5
28 15.9
18.5 - 24.9
114 64.8
25.0 - 29.9
25 14.2
30 and above
9 5.1
64
Central
Weighted
Cases(n) %
Total
Weighted
cases(n)
%
74 37.2
76 38.2
49 24.6
211 53.7
114 29.0
68 17.3
186 93.5
13 6.5
374 95.4
18 4.6
29
12
90
25
30
15.6
6.5
48.4
13.4
16.1
55
32
179
42
67
14.7
8.5
47.7
11.2
17.9
21 33.9
28 45.2
13 21.0
93 56.0
56 33.7
17 10.2
66 45.2
64 43.8
16 11.0
96 42.3
97 42.7
34 15.0
141 70.9
40 20.1
18 9.0
300 76.5
57 14.5
35 8.9
93 84.5
12 10.9
5 4.5
187 76.4
44 18.0
14 5.7
143 72.2
55 27.8
325 83.1
66 16.9
52 26.3
146 73.7
94 24.0
288 76.0
55 29.4
91 48.7
30 16.0
11 5.9
83 22.9
205 56.5
55 15.2
20 5.5
4.3
Health related quality of life (HRQOL) and associated factors
4.3.1. Overview
As described in chapter 3, health related quality of life (HRQOL) of the study participants was assessed
using 11 dimensions of health - all measured on a scale of 0-100 with 0 indicating the worst health status in
that dimension and the score of 100 indicating perfect health. From these 11 dimensions, 2 summary
measures were obtained, physical health summary measure (PHS) and mental health summary measure
(MHS). Both of these summary measures were also measured on a 0-100 scale with the scores being
interpreted in the same way as scores for individual dimensions except that 0 represented death. In this
section, the average scores for each of the 11 dimensions and the 2 summary measures both at baseline and
at one year follow-up are provided and these are compared between the two study regions. Further an
assessment of the effect of region on the HRQOL scores while controlling for other factors was done.
4.3.2. Average HRQOL scores
Table 4.4 gives a summary of mean scores for the different dimensions of health related quality of life both
at baseline and at one year follow-up and the associated change for each of the regions. Nyanza region had
lower average scores in all health dimensions at baseline compared to Central region except in health
transition where it scored higher. At one year follow-up, Nyanza region had better scores in distress, quality
of life and health transition domains compared to Central region. Both regions reported a positive change in
all health domains between baseline and I year follow-up with Nyanza recording the highest absolute
change in distress, role functioning and quality of life domains. Central region had higher scores of both
mental and physical health summary measures compared to Nyanza region both at baseline and at one year
follow-up.
65
Table 4.4: Baseline and one year follow-up average measures of different dimensions of health related
quality of life (out of a possible 100)
Health Dimension
Nyanza
Central
Total
General health
perception
Physical health
perception
Role functioning
Social
functioning
Cognitive
functioning
Pain
Mental health
Vitality
Distress
Quality of life
Health transition
Physical Health
Summary
Mental Health
Summary
4.3.3
Baseline
One
Year
Followup
Baseline
One
Year
Followup
Baseline
One
Year
Followup
Change
Change
Change
36.2
38.1
1.9
46.2
55.1
8.9
41.2
46.3
5.1
53.8
66.4
12.6
77.7
93.1
15.4
66.0
79.4
13.4
69.9
93.8
23.9
75.9
93.2
17.3
72.9
93.5
20.6
57.6
75.6
18.0
74.8
93.3
18.5
66.4
84.1
17.8
59.6
79.0
19.4
70.6
84.4
13.8
65.1
81.6
16.5
57.6
56.5
51.5
55.4
57.5
69.8
64.4
66.1
55.5
79.9
77.6
79.1
6.8
9.6
4.0
24.5
20.1
9.3
66.8
59.8
51.7
58.4
58.5
65.3
83.2
70.7
67.3
76.4
70.5
72.5
16.4
10.9
15.6
18.0
12.0
7.1
62.3
58.2
51.6
56.9
58.0
67.6
73.5
68.3
61.2
78.2
74.2
75.9
11.2
10.1
9.6
21.3
16.2
8.3
41.1
45.8
4.7
48.5
55.2
6.8
44.8
50.4
5.5
40.9
49.3
8.3
42.5
50.1
7.6
41.7
49.7
7.9
Factors associated with baseline and one year follow-up HRQOL scores
Multiple linear regression analysis was undertaken to assess the effect of the various variables in the
HRQOL scores for the different health dimensions. Categorical variables were converted into interval
variables as described in section 3.7. This section describes the factors associated with the baseline and one
year follow-up scores for each of the 11 dimensions of health and the two health summary measures. At one
year follow-up, the assessed factors were measured at baseline with an aim of answering the question
whether factors measured at the start of chronic care for patients newly started on HIV care could be used as
predictors of follow-up scores of the HRQOL measures of different health domains.
66
4.3.3.1 General health perception
Table 4.5 summarizes factors associated with both baseline and one year follow-up score for general health
perception. A participant in Central region had a higher score for this health domain compared to one in
Nyanza region both at baseline and at one year follow-up. Having or being presumed to have TB as
compared to having no signs for TB reduced one’s score in the domain at baseline but no association was
seen at one year follow-up. As would be expected, higher CD4 count as compared to CD4 of less than 250
and being on CTX as compared to not being on CTX were associated with higher scores of the health
domain. While possession of household item had no significant role in determining baseline score of general
health perception, at one year follow-up, participants who possessed no household item were likely to have
lower scores compared to those who possessed at least 1 household item. This result demonstrates the
important role environmental factors play in improving one’s quality of life.
Table 4.5: Factors associated with both baseline and one year follow-up measure of general health
perception
Baseline
one year follow-up
Factor
Coefficient p-value
Coefficient
p-value
Central Region (Ref Nyanza Region)
3-5 km from home (Ref <2km)
Presumptive TB (Ref No TB signs)
Not Screened for TB (Ref No TB signs)
On TB Treatment (Ref No TB signs)
Other Supporter (Ref Parent)
Other Religion (Ref Protestant)
Obese (Ref Underweight)
No house hold item (Ref Has house hold item)
251-500 CD4 (Ref 0-250 CD4 count)
45-54 years (Ref 15-24 years)
Currently Married (Ref Never married)
On CTX (Ref Not on CTX)
Above 500 CD4 (Ref 0-250 CD4 Count)
ns = not shown since factor not significant
11.653
7.674
-14.586
-14.466
-19.062
6.4
ns
ns
ns
ns
ns
ns
ns
ns
67
<0.001
<0.001
<0.001
<0.001
0.001
0.018
>0.05
>0.05
>0.05
>0.05
>0.05
>0.05
>0.05
>0.05
15.539
ns
ns
ns
ns
ns
10.931
9.253
-6.267
6.021
7.171
3.998
5.723
6.294
<0.001
>0.05
>0.05
>0.05
>0.05
>0.05
0.01
0.017
0.047
0.003
0.006
0.017
0.016
0.033
4.3.3.2
Physical health perception
There were significant regional differences in the physical health perception score both at baseline and at
one year follow-up. at baseline, a participant in Central region could be expected to have 22 points higher
than one from Nyanza region holding all other factors constant and this difference increased at one year
follow-up where the same participant could be expected be have 30 points higher than one from Nyanza
region. Other factors were WHO stage where as expected, participants in higher WHO stages were expected
to have lower scores compared to those in stage 1, TB status with those on TB treatment having lower score
compared to those with no TB signs and CTX where those on CTX expected to have higher scores than
those not on CTX. Possession of household item manifested itself as a factor significantly associated with
physical health perception with those possessing no household item expected to have 17 points lower than
those possessing a household item at one year follow-up. Table 4.6 summarizes these results.
Table 4.6: Factors associated with baseline and one year follow-up measure of physical health perception
one year follow-up
Baseline
Factor
Coefficient
P-Value Coefficient
P-Value
29.786
<0.001
22.352 <0.001
Central Region (Ref Nyanza Region)
WHO Stage 2 (Ref WHO Stage 1)
WHO Stage 3 (Ref WH Stage 1)
-9.849
0.009
ns
Presumptive TB (Ref No TB signs)
Not Screened for TB (Ref No TB signs)
On TB Treatment (Ref No TB signs)
On CTX (Ref No CTX)
Informal Employment (Ref Not employed)
No house hold item (Ref Has house hold item)
Catholic (Ref Protestant)
ns=not shown since factor not significant
68
>0.05
ns
-18.921
>0.05
0.011
-17.921
<0.001
-10.72
0.005
-17.2
<0.001
ns
>0.05
-18.081
0.012
ns
>0.05
11.403
0.003
ns
>0.05
6.986
0.026
ns
ns
>0.05
>0.05
ns
-16.824
-6.228
>0.05
<0.001
0.018
4.3.3.3 Role functioning
There were no observed regional differences in the score for role functioning both at baseline and one year
follow-up. Having no opportunistic infection compared to having an infection was associated with higher
score at baseline but had no role in determining the score at one year follow-up. Possession of no household
item significantly led to a lower score at one year follow-up compared to possessing at least 1 item. Being in
WHO stage 3 compared to being in stage 1 led to a significant lower score. These results are summarized in
table 4.7.
Table 4.7: Factors associated with baseline and one year follow-up measure of role functioning
Baseline
one year follow-up
Variable
Coefficient
P-Value
Coefficient
P-Value
>5 Dependants (Ref No Dependants)
27.679
-18.026
<0.001
0.005
ns
ns
>0.05
>0.05
Not Screened for TB (Ref No TB signs)
-19.778
0.004
ns
>0.05
WHO Stage 2 (Ref WHO Stage 1)
14.19
0.01
ns
>0.05
WHO Stage 3 (Ref WHO stage 1)
ns
>0.05
-18.973
0.014
45-54 years age Group (Ref 15-24 age Group)
-13.798
0.019
ns
0.05
No house hold item (Ref Has house hold item)
ns
>0.05
-18.519
<0.001
Sibling Supporter (Ref Parent)
Ns=Not shown since factor not significant
ns
>0.05
7.725
0.05
No Opportunistic Infection (Ref Presence of OI)
4.3.3.4 Social functioning
As illustrated in table 4.8, a participant in Central region was expected to have a significant higher score
than one in Nyanza region both at baseline and at one year follow-up. Having no opportunistic infections as
compared to having an infection was significantly associated with a higher score at baseline but there were
no differences observed at one year follow-up. Being in WHO stage 3 and 4 as compared to being in stage 1
was significantly associated with lower scores at one year follow-up and at baseline respectively. At
baseline, a participant aged between 45 and 54 years compared to one aged between 15 and 24 years had a
significant lower score but the effect of age on the score was not observed at one year follow-up.
69
4.3.3.5 Cognitive functioning
Participants in Central region had a significant higher score in the cognitive functioning domain compared
to those in Nyanza region both at baseline and at one year follow-up. As is expected, having TB and being
in higher WHO stage were associated with lower score while having higher CD4 count as compared to
lower CD4 count was significantly associated with higher scores. Being in possession of no household item
significantly led to lower scores at one year follow-up compared to being in possession of least 1 household
item. Having no opportunistic infection as compared to having an infection was associated with higher
scores at baseline but there was no association observed at one year follow-up. The results are summarized
in table 4.9.
Table 4.8: Factors associated with baseline and one year follow-up measure of social functioning
Factor
No Opportunistic Infection (Ref Presence
of OI)
Central Region (Ref Nyanza Region)
No house hold item (Ref Owns Household
Item)
WHO Stage 3 (Ref WHO Stage 1)
WHO Stage 4 (Ref WHO stage 1)
45-54 years age group (Ref 15-24 age
group)
Baseline
Coefficient
23.419
one year follow-up
P-value Coefficient P-value
<0.001
ns
>0.05
20.601
<0.001
20.476
<0.001
-19.795
0.001
-19.908
<0.001
ns
>0.05
-15.051
0.026
-37.383
0.003
ns
>0.05
-12.012
0.009
ns
>0.05
ns
>0.05
-7.645
0.026
Presumptive TB (Ref No TB signs)
ns=Not shown since factor not significant
70
Table 4.9: Factors associated with baseline and one year follow-up measure of cognitive functioning
Baseline
one year follow-up
Factor
Coefficient
P-value Coefficient
P-value
Central Region (Ref Nyanza Region)
9.892
0.001
6.698
0.006
-20.482
<0.001
ns
>0.05
-9.093
0.005
ns
>0.05
-16.204
0.001
ns
>0.05
17.056
0.001
ns
>0.05
-18.278
0.031
ns
>0.05
1-2 Dependants (Ref no Dependants)
-6.029
0.047
ns
>0.05
No Opportunistic Infection (Ref Presence of OI)
22.768
<0.001
ns
>0.05
ns
ns
ns
>0.05
>0.05
>0.05
6.901
7.839
-19.945
0.01
0.014
<0.001
Not Screened for TB (Ref No TB signs)
Enrolment within a week (Ref Same day enrolment)
6-9 Dependants (Ref No Dependants)
Formal Employment (Ref Not Employed)
WHO Stage 3 (Ref WHO Stage 1)
251-500 CD4 (Ref 0-250 CD4 count)
More than 10 KM from home (Ref <10 km)
No house hold item (Ref Has house hold item)
ns= not shown since factor not significant
4.3.3.6 Pain
A higher score in this domain like with all other domains discussed here signifies better performance or less
pain while lower scores signify poor performance or severe pain. A participant in Central region had a
higher score both at baseline and at one year follow-up as compared to one in Nyanza region. The score
improved from baseline to one year follow-up. As would be expected, having TB or being a presumptive
TB case as compared to having no signs for TB significantly led to lower scores both at baseline and at one
year follow-up. The same pattern was observed among patients in different WHO stages. Those on higher
stages as compared to those on stage 1 had significant lower scores at one year follow-up. Putting patients
on CTX was associated with significant higher scores as compared to being not on CTX. At one year
follow-up, being in possession of a household item led to significant higher scores at one year follow-up as
compared to having no house hold item. Table 4.10 summarizes the results
71
Table 4.10: Factors associated with baseline and one year follow-up measure of pain
Baseline
one year follow-up
Factor
Coefficient P-value
Coefficient P-value
Central Region (Ref Nyanza Region)
10.795
<0.001
23.695
<0.001
No Opportunistic Infection (Ref Presence of OI)
15.178
<0.001
ns
>0.05
9.256
0.001
ns
>0.05
Presumptive TB (Ref No TB signs)
-25.142
<0.001
-9.364
0.015
Not Screened for TB (Ref No TB signs )
-21.389
<0.001
ns
>0.05
On TB Treatment (Ref No TB signs)
-29.853
<0.001
ns
>0.05
11.621
<0.001
ns
>0.05
-10.069
0.017
ns
>0.05
8.792
ns
ns
ns
ns
ns
ns
0.021
>0.05
>0.05
>0.05
>0.05
>0.05
>0.05
10.581
10.591
-21.736
-23.517
-6.172
16.41
-15.851
0.004
0.002
0.002
0.029
0.041
0.005
<0.001
3-5 km from Home (Ref <2 km from home)
Other Supporter (Ref Parent)
6-9 dependants (Ref No dependant)
45-54 years age group (Ref 15-24 age group)
On CTX (Ref Not on CTX)
WHO Stage 3 (Ref WHO stage 1)
WHO Stage 4 (Ref WHO stage 1)
Urban (Ref Rural)
Other religion (Ref Protestant)
No house hold item (Ref Has house hold item)
ns=not shown since factor not significant
4.3.3.7 Mental health
There were no observed regional differences in this score. The most significant factor associated with
mental health perception at baseline was opportunistic infection. Having no opportunistic infection
compared to having one significantly led to higher scores. Participants on CTX compared to those not on
CTX had significantly higher scores both at baseline and at one year follow-up. Other factors as seen in
table 4.11 were TB status, employment and number of dependants that one had. Having 1-2 dependants as
compared to not having one was associated with higher scores at one year follow-up while those on formal
employment compared to those not employed had higher scores both at baseline and at one year follow-up.
Possession of a house item was significantly associated with a higher score at one year follow-up compared
to having no household item.
72
Table 4.11: Factors associated with baseline and one year follow-up measure of mental health
Baseline
one year follow-up
Factor
Coefficient P-value
Coefficient P-value
8.338
<0.001
ns
>0.05
No Opportunistic Infection (Ref Presence of OI)
On CTX (Ref Not on CTX)
On TB Treatment (Ref No TB signs)
Formal Employment (Not Employed)
Informal Employment (Ref Not employed)
1-2 Dependants (Ref No dependants)
6-9 Dependants (Ref No Dependants)
WHO Stage 3 (WHO stage 1)
WHO Stage 4 (Ref WHO stage 1)
Child Supporter (Ref Parent)
No house hold item (Ref Has house hold item)
More than 10 KM from home (Ref <2km)
Enrolment within a week (Ref Same day enrollment)
10.165
<0.001
9.974
<0.001
-13.04
0.002
12.251
0.02
8.195
0.008
9.505
0.021
ns
>0.05
5.89
0.007
ns
>0.05
5.377
0.009
-6.516
0.012
ns
>0.05
ns
>0.05
-18.482
0.005
-14.121
0.03
ns
>0.05
-6.077
0.035
ns
>0.05
ns
ns
ns
>0.05
>0.05
>0.05
-21.841
8.449
-6.027
<0.001
0.002
0.005
ns=not shown since factor not significant
4.3.3.8 Vitality
Significant differences in vitality score was observed between regions, occupation, TB status, timing of
enrolment among other factors. At regional level, a participant from Central region as compared to one in
Nyanza had a significant higher score at one year follow-up. Being employed as compared to being not
employed was associated with a higher score. At one year follow-up, having a dependant as compared to
having none was associated with higher scores. Having signs for TB as opposed to having none was
associated with lower scores. At one year follow-up, participants into HIV/AIDS care more than 1 day after
diagnosis had lower scores compared to those enrolled on the same day of diagnosis.
73
Table 4.12: Factors associated with baseline and one year follow-up measure of vitality
Baseline
one year follow-up
Factor
Coefficient P-value
Coefficient P-value
ns
>0.05
10.048
<0.001
Central Region (Ref Nyanza Region)
11.484
0.002
ns
>0.05
Formal Employment (Ref Not Employed)
ns
>0.05
7.225
0.001
Informal Employment (Ref Not employed)
10.964
<0.001
17.817
<0.001
No Opportunistic Infection (Ref Presence of OI)
10.419
<0.001
-18.228
<0.001
On CTX (Ref Not on CTX)
-5.965
0.003
11.009
<0.001
1-2 Dependants (Ref No Dependants)
ns
>0.05
7.711
0.003
3-5 dependants (Ref No dependants)
ns
>0.05
8.457
0.021
6-9 dependants (Ref No dependants)
-9.967
0.003
-7.98
0.014
Not Screened for TB (Ref No TB Signs)
-6.81
0.042
ns
>0.05
Presumptive TB (Ref No Tb signs)
4.521
0.02
ns
>0.05
3-5 km from home (Ref <2 KM)
ns
>0.05
6.234
0.016
More than 10 KM from home (Ref <2km)
9.473
0.002
-23.21
<0.001
Overweight BMI (Ref Underweight)
4.986
0.015
13.484
0.008
Normal BMI (Ref Underweight)
4.171
0.025
ns
>0.05
Spouse Supporter (Ref Parent)
ns
>0.05
-6.665
0.003
Enrolment within a week (Ref Same day enrolment)
ns
>0.05
-6.265
0.023
Enrolment within a month (Ref Same day enrolment)
ns=not shown since factor not significant
4.3.3.9 Distress
TB status, opportunistic infection and number of dependants were some of the significant factors associated
with distress. There were no observed differences in the score by region. Being a presumptive TB case as
compared to having no TB signs was significantly associated with lower scores of distress while having no
infection was associated with higher scores as compared to having an infection. A participant with 6-9
dependants had worse scores in this dimension compared to one with no dependants, being on CTX led to
higher scores as compared to being not on CTX and having no household item as compared to having at
least 1 item was associated with lower scores at one year follow-up. The results are summarized in table
4.13.
74
Table 4.13: Factors associated with baseline and one year follow-up measure of distress
Baseline
one year follow-up
Factor
Coefficient
P-value
Coefficient P-value
18.611
<0.001
ns
>0.05
Formal Employment (Ref Not Employed)
8.553
0.009
ns
>0.05
15.202
<0.001
ns
>0.05
Not Screened for TB (Ref No TB signs)
-15.487
0.001
ns
>0.05
Presumptive TB (No TB signs)
-15.937
<0.001
ns
>0.05
9.028
0.001
ns
>0.05
-8.522
0.003
-5.211
0.042
-18.02
0.022
-22.031
0.003
-23.818
0.022
ns
>0.05
3-5 dependants (Ref No Dependants)
-5.265
0.05
ns
>0.05
6-9 dependants (Ref No Dependants)
-13.416
0.002
ns
>0.05
7.235
0.006
ns
>0.05
ns
>0.05
-15.36
0.001
No Opportunistic Infection (Ref Presence of OI)
On CTX (Ref Not on CTX)
More than 10 KM from home (Ref <2 km)
Enrolment within a week (Ref Same day
enrolment)
WHO Stage 3 (Ref WHO Stage 1)
WHO Stage 4 (Ref WHO stage 1)
Spouse Supporter (Ref Parent)
No house hold item (Ref Has household item)
ns=not shown since factor not significant
4.3.3.10
Quality of life
As with many other health domains, opportunistic infection was significantly associated with quality of life.
A participant with no infection had a higher score of quality of life compared to one with an infection.
Residence also played a significant role in determining scores in quality of life with those in urban areas
having a higher score compared to those in rural areas. CD4 count, age, WHO stage, treatment supporter
and treatment supporter were also among other factors significantly associated with quality of life as seen in
table 4.14. At baseline, participants who reported that they did not have a treatment supporter had
significantly lower scores compared to those who said they had a supporter. Possessing no household item
as compared to possessing at least 1 item was associated with significant lower score at one year follow-up.
There were no observed differences by region.
75
Table 4.14: Factors associated with baseline and one year follow-up measure of quality of life
Baseline
one year follow-up
Factor
Coefficient P-value
Coefficient P-value
No Opportunistic Infection (Ref Presence of OI)
Above 500 CD4 (Ref 0-250 CD4 Count)
251-500 CD4 (Ref 0-250 CD4 Count)
Catholic (Ref Protestant)
Urban (Ref Rural)
35-44 years age Group (Ref 15-24 years)
No Treatment supporter (Ref Has treatment supporter)
11.417
16.002
7.617
-7.663
7.919
-7.226
-14.925
0.001
0.001
0.019
0.011
0.005
0.021
0.025
8.182
ns
7.2
ns
ns
ns
ns
0.003
>0.05
0.009
>0.05
>0.05
>0.05
>0.05
WHO Stage 3 (Ref WHO Stage 1)
Enrolment within a week (Ref Same day enrolment)
6-9 dependants (Ref No dependants)
More than 10 KM from home (Ref <2km)
No house hold item (Ref Has household item)
-19.114
0.026
ns
>0.05
ns
ns
ns
ns
>0.05
>0.05
>0.05
>0.05
-7.499
9.794
7.236
-14.181
0.004
0.011
0.029
0.002
ns=not shown since factor not significant
4.3.3.11
Health transition
Different from other discussed health domains, a participant from Central region had a significant lower
score in this domain at one year follow-up compared to one from Nyanza region. TB status and
opportunistic infection were among other significant factors associated with health transition with the
direction of their effect being as would be expected. Having no opportunistic infection compared to having
one was associated with a higher score, while being a presumptive TB case as compared to having no TB
signs was associated with a lower score. Another important factor was treatment supporter where having no
treatment supporter was associated with a lower score as compared to a participant with a treatment
supporter. Table 4.15 gives other factors significantly associated with health transition domain score both at
baseline and at one year follow-up.
76
Table 4.15: Factors associated with baseline and one year follow-up measure of health transition
Baseline
one year follow-up
Factor
Coefficient P-value
Coefficient P-value
Central Region (Ref Nyanza Region)
No Opportunistic Infection (Ref Presence of OI)
WHO Stage 2 (WHO Stage 1)
WHO Stage 3 (Ref WHO Stage 1)
Presumptive TB (Ref No TB signs)
No Treatment supporter (Ref Has treatment supporter)
Other Supporter (Ref Parent)
251-500 CD4 (Ref 0-250 CD4 Count)
No house hold item (Ref Has household item)
ns
10.828
10.249
-24.407
-9.753
-13.327
7.582
ns
ns
>0.05
0.001
0.007
0.003
0.031
0.035
0.031
>0.05
>0.05
-5.399
ns
ns
-14.712
-9.42
ns
ns
6.393
-12.768
0.013
>0.05
>0.05
0.023
0.004
>0.05
>0.05
0.01
0.001
ns=not shown since factor not significant
4.3.3.12
Physical health summary
Factors associated with physical health summary measure were region, opportunistic infection, TB status,
WHO stage, CTX uptake and possession of a household item. Being from Central region as compared to
being from Nyanza, having no opportunistic infection as compared to having one, and being on CTX as
compared to being not on CTX were associated with higher scores in this health summary measure while
being a presumptive TB case as compared to having no TB signs, being in higher WHO stage as compared
to being in WHO stage 1 and possessing no household item compared to possessing at least 1 household
item were associated with lower scores in this summary measure both at baseline and at one year follow-up.
Table 4.16 gives a summary of these factors and their associated significance.
4.3.3.13
Mental health summary
There were no observed differences by region in this health summary measure. The observed differences
were by TB status, opportunistic infection, CTX uptake, occupation, WHO stage, number of dependants,
religion and possession of household items. Having no opportunistic infection as compared to having an
infection, being on CTX as compared to being not on CTX, being in formal employment compared to being
77
not employed and being a catholic compared to being a protestant were associated with higher scores in this
measure.
Table 4.16: Factors associated with baseline and one year follow-up measure of physical health summary
Factor
Central Region (Ref Nyanza Region)
No Opportunistic Infection (Ref Presence of OI)
Not Screened for TB (Ref No TB signs)
Presumptive TB (Ref No TB signs)
On TB Treatment (Ref No TB signs)
WHO Stage 3 (Ref WHO stage 1)
WHO Stage 4 (WHO stage 1)
No house hold item (Ref Has house hold item)
On CTX (Ref Not on CTX)
Baseline
one year follow-up
Coefficient P-value
Coefficient P-value
8.727
6.992
-8.786
-9.872
-12.259
ns
-10.757
-4.618
3.061
<0.001
<0.001
<0.001
<0.001
<0.001
>0.05
0.01
0.025
0.037
10.394
ns
ns
-5.453
ns
-11.792
-12.147
-9.74
3.981
<0.001
>0.05
>0.05
0.006
>0.05
0.001
0.031
<0.001
0.025
ns=not shown since factor not significant
Having signs of TB or being on TB treatment as compared to having no TB signs, being in higher WHO
stage as compared to being in stage 1, having 6-9 dependants as compared to having no dependants and
possessing no household item compared to being in possession of at least 1 item were all associated with
significant lower scores in this summary measure. Table 4.17 summarizes these factors associated with the
mental health summary measure.
78
Table 4.17: Factors associated with baseline and one year follow-up measure of mental health summary
Baseline
one year follow-up
Factor
Coefficient P-value Coefficient P-value
6.289
<0.001
ns
>0.05
No Opportunistic Infection (Ref Presence of OI)
6.66
<0.001
6.1
0.001
Not Screened for TB (Ref No TB signs)
-7.937
<0.001
ns
>0.05
Presumptive TB (Ref No TB signs)
-4.154
0.015
-4.163
0.049
On TB Treatment (Ref No TB signs)
-6.173
0.022
ns
>0.05
5.314
0.004
ns
>0.05
-6.054
0.051
-11.435
0.004
-8.499
0.026
ns
>0.05
3.258
0.008
ns
>0.05
ns
>0.05
2.776
0.044
6-9 dependants (Ref No dependants)
-3.592
0.018
ns
>0.05
Child Supporter (Ref Parent)
-3.844
0.023
ns
>0.05
No house hold item (Ref Has household item)
ns
>0.05
-10.801
<0.001
Student (Ref Unemployed)
Catholic (Ref Protestant)
ns
>0.05
-7.434
0.017
ns
>0.05
2.898
0.037
4.4.
One year follow-up outcomes of study participants and their association with
HRQOL measures
baseline
On CTX (Ref Not on CTX)
Formal Employment (Ref Not employed)
WHO Stage 3 (Ref WHO stage 1)
WHO Stage 4 (Ref WHO stage 1)
Enrolled within a month (Ref Same day enrolment)
1-2 Dependants (Ref No Dependants)
ns=not shown since factor not significant
Retention outcomes of study participant at one year follow-up was assessed. There were four possible
outcomes: the participant was alive and still receiving care in the facility where there were initially enrolled,
dead, lost to follow-up (LTFU) or transferred out (TO). These outcomes are analyzed by first finding out if
there is any association between them and the region (Nyanza and Central). Further analysis is done using
multinomial logistic regression to assess factors associated with the outcomes. Baseline values of HRQOL
for the two health summary measures obtained in the previous section are also used in a predictor model to
79
determine their role in determining one year follow-up statuses of the study participants. The baseline scores
for the other 11 dimensions of health are not used as factors in the predictor model since the two summary
measures are functions of the 11 dimensions and the effect of the 11 dimensions could be represented by the
two summary measures.
4.4.1
Status of study population at the end of one year follow-up
As can be seen in table 4.18, a total of 348 (87.9 percent) of the study participants were still active in the
HIV care at the end of one year. Nyanza region had higher proportion of retained participants at 95.3
percent compared to Central region which had only 81.0 percent. Death rate was 3.8 percent for the entire
study population. Nyanza region had lower death rate at 3.1 percent compared to Central region which had
4.4 percent death rate. The observed regional differences were statistically significant.
Table 4.18: One year status of study population
Region
Nyanza
Count
%
Central
Count
%
Total
Count
%
Sources: Analysis of study data
Alive
182
95.3
166
81.0
348
87.9
Status at one year follow-up
Dead
LTFU
6
0
3.1
0.0
9
12
4.4
5.9
15
12
3.8
3.0
TO
3
1.6
18
8.8
21
5.3
4.4.2. Baseline factors associated with the status of the study population at the end of one year
follow-up
Multinomial logistic regression analysis was done to establish what baseline factors were associated with
one year follow-up status of the study population. The factors assessed were MHS and PHS scores
categorized into low, medium, good and very good, sex, age, region, opportunistic infection, possession of
household item, and TB status. These factors were selected due to their contribution in baseline and followup HRQOL scores as described in the previous section. In the analysis, being alive at follow-up was used as
the reference category.
80
4.4.2.1.
Dead at one year follow-up with reference to being alive and still active in the
programme
Table 4.19: Baseline factors associated with being dead at one year follow-up relative to being alive
95% Confidence
Interval for Exp(B)
Odds
Lower
Upper
B
p-value
Ratio
Bound
Bound
Factor
MHS (Ref Very Good MHS)
Low MHS
.693
.528
2.000
.232
17.223
Medium MHS
.168
.887
1.183
.117
11.982
Good MHS
.812
.466
2.253
.253
20.032
PHS (Ref Very Good PHS)
Low PHS
.243
.856
1.275
.091
17.775
Medium PHS
.413
.751
1.511
.119
19.273
Good PHS
-.332
.800
.718
.055
9.369
Opportunistic Infection (Ref No OI)
Has OI
-.487
.555
.615
.122
3.095
TB Status (Ref Not Screened for TB)
No TB signs
Presumptive TB
On TB treatment
Possession of a household item (Ref No
household item)*
Possesses household item
Region (Ref Nyanza)
Central
Sex (Ref Female)
Male
Age group (Ref 55 years and above)
15-24
25-34
35-44
45-54
-1.302
.621
.260
.139
.487
.838
.272
1.860
1.297
.049
.323
.108
1.523
10.718
15.648
-1.807
.014
.164
.039
.694
.050
.947
1.052
.238
4.651
.558
.379
1.747
.505
6.046
-1.226
-.343
-.304
-1.966
.441
.767
.790
.247
.294
.709
.738
.140
.013
.073
.079
.005
6.644
6.872
6.905
3.894
81
As shown in table 4.19, region, baseline MHS, PHS, TB status, sex and age group were not found to be
predictors of death relative to being alive at one year follow-up status of study population. Only possession
of a household item was found to be a significant predictor of death relative to being alive at one year
follow-up where those who possessed at least 1 household item had lower odds of being dead relative to
being alive , OR = 0.164, p=0.014. Results show that the odds of study participants from both Nyanza and
Central regions of being dead relative to being alive at one year follow-up were equal. These results are
summarized in table 4.19.
4.4.2.2.
Lost to follow-up (LTFU) at one year follow-up with reference to being alive and still
active in the programme
Only MHS score at baseline and region were found to be statistically associated with being lost to follow-up
at 1 year follow relative to being alive. A patient at low MHS category compared to one at very good MHS
category was less likely to be lost to follow-up relative to being alive at one year follow-up (OR=0.079, p =
0.018) while a patient at good MHS category compared to one at very good MHS category was more likely
to be lost to follow-up at one year follow-up relative to being alive (OR=16.319, p=0.048). A participant in
Central region compared to one in Nyanza was less likely to be lost to follow-up relative to being alive at
one year follow-up, (OR = 0.031, p=0.000). Being a male or female, having an opportunistic infection or not
or having a household item or not did not affect the likelihood of being lost to follow-up relative to being
alive.
82
Table 4.20: Baseline factors associated with being lost to follow-up at one year follow-up relative to being
alive
Variable
B
p-value
Odds
95% Confidence Interval for
Ratio
Exp(B)
Lower
Upper Bound
Bound
MHS (Ref Very Good MHS)*
Low MHS
-2.533
.018
.079
.010
.644
Medium MHS
2.093
.199
8.108
.332
197.743
Good MHS
2.792
.048
16.319
1.026
259.504
PHS (Ref Very Good PHS)
Low PHS
-.458
.810
.632
.015
26.510
Medium PHS
1.026
.505
2.789
.137
56.786
Good PHS
-.800
.479
.449
.049
4.130
Opportunistic Infection (Ref No OI)
Has OI
3.061
.123
21.339
.438
1040.447
TB Status (Ref Not Screened for TB)
No TB signs
-2.680
.158
.069
.002
2.831
Presumptive TB
-5.169
.091
.006
1.429E-05
2.266
On TB treatment
-2.381
.074
.092
.007
1.263
Possession of a household item (Ref No
household item)
Possesses household item
2.138
.131
8.486
.529
136.081
Region (Ref Nyanza)*
Central
-3.466
.000
.031
.010
.095
Sex (Ref Female)
Male
Age group (Ref 55 years and above)
15-24
25-34
35-44
45-54
1.858
.071
6.411
.852
48.227
-.856
-.563
.372
-1.515
.422
.752
.834
.302
.425
.569
1.451
.220
.053
.017
.044
.012
3.434
18.616
47.646
3.895
4.4.2.3.
Transferred out at one year follow-up with reference to being alive and still active in
the programme
Only age group was a factor associated with being transferred out at one year follow-up relative to being
alive. A patient aged 15-25 compared to one aged 55 years and above was more likely to be transferred out
relative to being alive at one year follow-up. This was the case with all other age groups compared to those
83
aged 55 years and above. Being from Central or Nyanza regions did not affect the odds of one being
transferred out relative to being retained in the programme.
Table 4.21: Baseline factors associated with being transferred out at one year follow-up relative to being
Alive
Variable
95% Confidence Interval for
Exp(B)
POdds
B
value
Ratio
Lower Bound Upper Bound
MHS (Ref Very Good MHS)
Low MHS -1.338
.182
.262
.037
1.874
Medium MHS
2.296
.082
9.933
.747
132.122
Good MHS
2.272
.054
9.697
.962
97.771
PHS (Ref Very Good PHS)
Low PHS
Medium PHS
Good PHS
Opportunistic Infection (Ref No
OI)
Has OI
TB Status (Ref Not Screened for
TB)
No TB signs
Presumptive TB
On TB treatment
Possession of a household item
(Ref No household item)
Possesses household item
Region (Ref Nyanza)
Central
Sex (Ref Female)
Male
-.975
-.577
-.113
.544
.502
.905
.377
.561
.893
.016
.104
.139
8.779
3.029
5.716
1.196
.327
3.308
.303
36.108
1.377
-.536
-.228
.150
.653
.879
3.962
.585
.796
.608
.056
.042
25.811
6.074
15.030
.842
.439
2.321
.275
19.561
-.987
.339
.373
.049
2.821
1.150
.194
3.157
.557
17.903
.000 83688793.100
9585733.487
.000 12230416.040
1231509.322
.000 29050943.851
3999937.212
0.000 224140792.125 224140792.125
730649782.854
121463210.937
210992646.610
224140792.125
Age group (Ref 55 years and
above)*
15-24
25-34
35-44
45-54
18.243
16.319
17.185
19.228
84
4.5.
Conclusion
This chapter focused on three elements of the analysis - characteristics of study participants, both baseline
and one year follow-up measures of HRQOL for all health domains and the two health summary measures
and the associated factors and one year follow-up status of the study participants and the associated baseline
factors.
A total of 393 weighted cases were enrolled into the study and as was expected, there were more females
than males enrolled into the study. Majority of the participants were aged between 25-34 years followed by
35-44 years. The association between the factors studied and the region was assessed and some of the
factors that had a statistically significant association with region were education, residence, distance of
health facility from home, ownership of a household item, number of dependants and TB status.
The results of the assessment of the average HRQOL scores for each of the 11 health domains and the 2
health summary measures and the associated factors showed that the Central region was doing better at
baseline than Nyanza in all health domains except in health transition. The results showed a positive change
in all the domains and the 2 health summary measures at end of one year follow-up. This is a positive
finding given that HIV chronic care is among other things expected to improve the quality of life of people
living with HIV. The baseline HRQOL measures were mostly associated with opportunistic infection, TB
status, number of dependants and region. At one year follow-up, most of the factors that were associated
with baseline HRQOL scores were either no longer significant or their contribution was reversed. As an
example, opportunistic infection which was associated with many baseline HRQOL scores was only
significant in a few of these domains while number of dependants had its effect reversed. Region was less
significant in most of the one year follow-up HRQOL scores as opposed to baseline HRQOL scores.
Possession of household items which was mostly not significantly associated with baseline HRQOL scores
became a significant factor in all HRQOL scores for all health domains and the 2 health summary measures
85
at one year follow-up. For region, being in Central as compared to being in Nyanza was associated with
better HRQOL scores both at baseline and at one year follow-up except for health transition at one year
follow-up where it was associated with lower score in this domain.
The status of participants at one year follow-up was assessed. A participant was alive, LTFU, dead or TO at
1 year. Majority of the participants were alive at the end of 1 year. There was statistically significant
association between these outcomes and the region. Multinomial logistic regression analysis showed that
possession of household item was a significant factor in explaining death relative to being alive, MHS at
baseline and region were significant factors in explaining LTFU in reference to being alive and age was a
significant factor in explaining TO relative to being alive.
86
CHAPTER FIVE
HEALTH ADJUSTED LIFE EXPECTANCY AND ASSOCIATED FACTORS
5.1
Introduction
This chapter provides results of HALE measured using both cross sectional (Sullivan) and longitudinal
(MSLT) approaches and factors associated with these measures. In this chapter, four objectives of the study
(the first objective was addressed in chapter 4) have been addressed. The four objectives addressed in this
chapter are:
(i) To compare transition probabilities from one health state to another across different time periods for
adult HIV/AIDS patients in Nyanza and Central regions;
(ii) To compare health adjusted life expectancy among adult HIV/AIDS patients in Nyanza and Central
Kenya regions;
(iii) To determine factors associated with health adjusted life expectancy for adult HIV/AIDS patients
in Nyanza and Central Kenya regions;
(iv) To compare HALE estimates obtained by both Sullivan and MSLT approaches.
The chapter is divided into 4 sections. The first section assesses HALE using published Sullivan method.
Sullivan method calculates HALE using cross sectional data and for this study, data collected at baseline is
used. Section two provides results for transition probabilities from one health state to another using baseline
and one year follow-up data. These probabilities are calculated using SPACE programme as described in
chapter three. The transition probabilities are calculated for both physical and mental health summary
measures. Section three provides results of HALE calculated using MSLT approach. Transition probabilities
calculated in section two are used as data in this section and SPACE programme is used to calculate HALE
for both physical and mental health summary measures. The last section in this chapter provides a
conclusion of the results obtained in the different sections. The key results obtained are highlighted and their
implications discussed.
87
5.2.
Calculation of HALE using Sullivan approach
As discussed in chapters three and four, HRQOL measures for the 11 dimensions of health obtained through
MOS-HIV tool were used to calculate the two health summary measures; physical health summary measure
(PHS) and mental health summary measure (MHS). For each individual participant, their PHS and MHS
scores were obtained and these scores ranged between 0 – 100, with a zero score indicating death while 100
score indicated that an individual was in perfect health. At baseline, no patient had a score of 0 in any of the
two health summary measures since they were all alive at this point in time but this status was expected to
change at one year follow-up. The PHS and MHS results presented in chapter four were continuous scores
with mean for each measure being used to describe the results. For the purpose of calculating HALE, the
scores were put into four categories for each of the health summary measure; low, medium, good and very
good category for example low MHS, medium MHS, good MHS and very good MHS. The same was done
for PHS. Cut off point for categorization was based on quartile values for each health summary measure at
baseline with all patients falling within the first quartile being put in low category, those in second quartile
put in medium category, those in third quartile put in good category and those in fourth quartile put in very
good category.
With these categories, the proportion of patients in each category in each age group was calculated e.g. for
patients aged 15-24, the proportion in low MHS category, medium MHS category, good MHS category and
very good MHS category. These proportions were then applied to existing life table to calculate the number
of remaining years of an individual that could be expected to be spent in a particular health status for a given
measure e.g. if from the life table the life expectancy of a 15-24 individual was 52 years and 25 percent of
these individuals were in low MHS category, then these individual could expect to live about 13 of their
remaining years in low (poor) mental health. The actual calculation uses lx column, the number of person
years lived in this state of health. As already mentioned, the 2012 WHO life tables for Kenya for the general
88
population were used (Overall, male and female life tables). The use of these life tables assumed that
mortality patterns among people living with HIV was similar to that of the general population in Kenya.
HALE was calculated for different sub populations including the entire study population, males, females,
Central, Nyanza, those who possessed household items, those who did not possess house hold items, study
participants with opportunistic infections and those with no opportunistic infections. Calculation of HALE
for these sub populations was based on the fact that these factors were found to be associated with health
summary measures as discussed in chapter 4. It is also conventional to get gender differentials of life
expectancy. Calculated HALE for different sub populations was then assessed for any statistically
significant differences e.g. between male and female, Central and Nyanza and having an opportunistic
infection or not.
5.2.1. Proportion of patients in different categories of health summary measures at baseline
5.2.1.1 PHS
Table 5.1 shows the proportion of study participants in different statuses of physical health summary by
different factors. Overall, 18% of participants aged 15-24 years were in low physical health category, 22
percent were in moderate category, 32 percent were in good category and 28 percent were in very good
category. Conversely, for participants aged 55 years and above, 32 percent of them were in low physical
health category, another 32 percent were in moderate category, 23 percent were in good category and 14
percent were in very good category. When broken down by gender, 31 percent of males aged 15-24 years
were in low physical health category, 23 percent in moderate category, 38 percent in good category and 8
percent in very good category compared to 16 percent, 23 percent, 29 percent and 32 percent respectively of
females in the same age category. Majority of participants aged 55 years and above who had opportunistic
infection belonged to low category, 50 percent compared to only 17 percent of participants in the same age
89
category who had no opportunistic infection. The results showed a clear trend in proportion of participants
in various health statuses across different factors with majority of younger participants being in better health
statuses and majority of older participants being in worse physical health statuses.
5.2.1.2 MHS
Majority of younger study participants were in better statuses of mental health while older participants were
in worse statuses of mental health. Overall, 14 percent of participants in age group 15-24 were in low status
compared to 32 percent of participants aged 55 years and above, and 18 percent in the same age group were
in moderate status compared to 36 percent among those aged 55 years and above. Another 32 percent of
those aged 15-24 years were in the good status compared to only 9 percent while 36 percent were in the very
good status compared to 23 percent among those aged 55 years and above. Table 5.2 provides a summary of
these results.
Table 5.1: Proportion of participants in various statuses of physical health across different factors
Sub Population
Age
PHS
Low
moderate
Good
Very Good
15-24
25-34
35-44
45-54
55+
0.18
0.27
0.26
0.35
0.32
0.22
0.29
0.22
0.19
0.32
0.32
0.22
0.27
0.16
0.23
0.28
0.22
0.25
0.30
0.14
15-24
25-34
35-44
45-54
55+
0.31
0.24
0.36
0.36
0.42
0.23
0.43
0.14
0.09
0.17
0.38
0.10
0.24
0.23
0.25
0.08
0.22
0.26
0.32
0.17
15-24
25-34
35-44
45-54
55+
0.16
0.28
0.19
0.33
0.20
0.23
0.22
0.28
0.33
0.50
0.29
0.29
0.30
0.07
0.20
0.32
0.20
0.24
0.27
0.10
All
Male
Female
90
Sub Population
Age
PHS
Low
moderate
Good
Very Good
15-24
25-34
35-44
45-54
55+
0.26
0.29
0.30
0.50
0.18
0.26
0.42
0.48
0.40
0.55
0.33
0.25
0.18
0.10
0.27
0.15
0.05
0.03
0.00
0.00
15-24
25-34
35-44
45-54
55+
0.07
0.24
0.24
0.30
0.45
0.17
0.15
0.08
0.11
0.09
0.30
0.19
0.32
0.19
0.18
0.47
0.42
0.37
0.41
0.27
15-24
25-34
35-44
45-54
55+
0.50
0.64
0.68
0.38
1.00
0.00
0.14
0.11
0.00
0.00
0.50
0.21
0.21
0.25
0.00
0.00
0.00
0.00
0.38
0.00
15-24
0.17
25-34
0.22
35-44
0.16
45-54
0.34
55+
0.17
Possesses household item
15-24
0.20
25-34
0.24
35-44
0.22
45-54
0.34
55+
0.33
Does not possess household item
15-24
0.33
25-34
0.57
35-44
0.45
45-54
0.67
55+
0.00
0.24
0.31
0.25
0.24
0.39
0.31
0.22
0.29
0.14
0.28
0.29
0.24
0.31
0.28
0.17
0.24
0.29
0.25
0.17
0.29
0.32
0.24
0.26
0.20
0.24
0.24
0.23
0.27
0.29
0.14
0.00
0.14
0.18
0.00
1.00
0.00
0.14
0.27
0.00
0.00
0.67
0.14
0.09
0.33
0.00
Nyanza
Central
Has OI
Has no OI
91
Table 5.2: Proportion of participants in various statuses of mental health across different factors
MHS
Sub Pop
Age
Low
Moderate
Very
Good
Good
All
15-24
25-34
35-44
45-54
55+
0.14
0.28
0.26
0.38
0.32
0.18
0.24
0.34
0.14
0.36
0.32
0.29
0.20
0.19
0.09
0.36
0.19
0.20
0.30
0.23
15-24
25-34
35-44
45-54
55+
0.23
0.29
0.19
0.36
0.42
0.15
0.24
0.43
0.05
0.33
0.38
0.29
0.21
0.14
0.08
0.23
0.18
0.17
0.45
0.17
15-24
25-34
35-44
45-54
55+
0.13
0.28
0.31
0.40
0.20
0.19
0.24
0.28
0.27
0.40
0.31
0.29
0.19
0.27
0.10
0.37
0.19
0.22
0.07
0.30
15-24
25-34
35-44
45-54
55+
0.17
0.27
0.27
0.50
0.18
0.15
0.29
0.45
0.20
0.55
0.33
0.31
0.12
0.20
0.09
0.35
0.13
0.15
0.10
0.18
15-24
25-34
35-44
45-54
55+
0.10
0.29
0.25
0.33
0.45
0.23
0.18
0.29
0.11
0.18
0.30
0.26
0.24
0.19
0.09
0.37
0.27
0.22
0.37
0.27
15-24
25-34
35-44
45-54
55+
0.50
0.43
0.47
0.38
1.00
0.00
0.21
0.42
0.13
0.00
0.50
0.29
0.05
0.38
0.00
0.00
0.07
0.05
0.13
0.00
Male
Female
Nyanza
Central
Has OI
92
MHS
Sub Pop
Age
Low
Moderate
Very
Good
Good
Has no OI
15-24
25-34
35-44
45-54
55+
Possesses household item
15-24
25-34
35-44
45-54
55+
Does not possess household
item
15-24
25-34
35-44
45-54
55+
0.13
0.26
0.21
0.38
0.17
0.19
0.24
0.32
0.14
0.44
0.31
0.29
0.23
0.14
0.11
0.38
0.21
0.23
0.34
0.28
0.16
0.27
0.24
0.37
0.33
0.19
0.24
0.35
0.14
0.33
0.33
0.28
0.22
0.23
0.10
0.32
0.21
0.19
0.26
0.24
0.33
0.29
0.55
0.33
0.00
0.00
0.29
0.09
0.33
1.00
0.00
0.14
0.09
0.00
0.00
0.67
0.29
0.27
0.33
0.00
5.2.2. Health adjusted life expectancy for different groups of study participants
5.2.2.1 Physical health
Table 5.3 provides results of HALE for various groups of study participants across all the 4 physical health
statuses. Overall, while a participants aged 15-24 could expect to live on average 52.3 more years (based on
2012 WHO life table for Kenya), 14.6 (95% CI 10.9, 18.2) of these years would be spent in the low physical
health status, 13.4 (95% CI 9.9, 17.0) in the moderate status, 12.7 (95% CI 9.3 16.0) in the good status and
11.5 (95% CI 8.6, 14.5) in the very good physical health status. Similarly, a participant aged 55 years and
above could expect to live another 21.3 years out of which 6.8 (95% CI 2.6, 10.9) years would be spent in
the low physical health status, 6.8 (95% CI 2.8, 10.9) years in the moderate health status, 4.8 (95% CI1.1,
8.6) years in the good health status and 2.9 (95% CI 0.0, 6.0) years in the very good status. Statistical
comparison of HALE across different groups is described in the next section.
93
Table 5.3: Life expectancy adjusted for physical health for various categories of study participants
Unadjusted
LE
Age
Group
All
15-24
25-34
35-44
45-54
55+
Male
15-24
25-34
35-44
45-54
55+
Female
15-24
25-34
35-44
45-54
55+
Central
15-24
25-34
35-44
45-54
55+
Nyanza
15-24
25-34
35-44
45-54
55+
Has OI
15-24
25-34
35-44
45-54
55+
Low Physical Health
HALE
95% CI
Moderate Physical
Health
HALE
95% CI
Good Physical Health
HALE
95% CI
Very Good Physical
Health
HALE
95% CI
52.3
44.0
36.3
29.0
21.3
14.6
13.2
11.3
9.5
6.8
10.
9.69
7.5
5.5
2.6
18.2
16.9
15.1
13.6
10.9
13.4
11.6
9.4
7.7
6.8
9.9
8.0
5.7
3.8
2.6
17.0
15.2
13.1
11.6
10.9
12.7
10.0
8.2
6.2
4.8
9.3
6.7
4.8
2.6
1.1
16.0
13.3
11.6
9.8
8.6
11.5
9.1
7.5
5.5
2.9
8.6
6.2
4.5
2.4
0.0
14.
12.5
10.0
8.65
6.0
51.1
42.9
35.2
27.9
20.4
17.7
15.2
13.6
11.1
8.5
12.
10.2
8.42
5.6
2.8
23.1
20.3
18.8
16.6
14.2
11.3
8.8
5.4
3.9
3.4
15.6
12.6
9.3
7.9
7.7
12.4
9.2
8.6
6.7
5.1
7.5
4.9
4.1
1.9
0.1
17.2
13.6
13.2
11.4
10.1
10.2
9.8
8.1
6.1
3.4
6.0
5.8
4.0
1.8
0.0
14.
13.4
12.9
10.2
7.74
53.5
45.1
37.4
30.0
22.2
12.0
10.8
8.5
7.3
4.4
7.1
5.8
3.3
1.7
0.0
17.0
15.8
13.8
12.9
9.9
17.5
15.9
14.5
12.8
11.1
6.9
5.0
1.6
0.10.9
11.
9.97
8.2
6.1
4.2
23.4
21.9
20.8
19.5
18.0
12.6
10.0
7.5
5.4
4.4
7.7
5.1
2.4
0.0
0.0
17.4
14.8
12.5
10.7
9.9
11.1
8.2
6.6
4.6
2.2
7.0
4.1
2.4
0.2
0.0
15.
12.2
10.2
9.08
6.3
52.3
44.0
36.3
29.0
21.3
14.8
14.6
13.0
11.7
9.7
9.5
9.3
7.5
5.7
3.4
20.0
20.0
18.6
17.6
16.0
6.2
4.7
3.5
2.8
1.9
2.8
1.5
0.2
0.0
0.0
9.6
7.9
6.8
6.3
5.6
12.0
9.3
7.7
5.3
3.9
7.6
5.0
3.3
0.7
0.0
16.5
13.6
12.1
9.9
8.7
19.4
15.4
12.0
9.2
5.8
14.
10.3
6.94
3.8
0.2
52.3
44.0
36.3
29.0
21.3
14.9
12.8
10.6
8.3
3.9
9.9
7.8
5.4
3.0
0.0
19.8
17.7
15.7
13.6
8.7
21.5
19.6
16.7
13.2
11.6
15.
13.9
10.9
7.08
5.4
27.2
25.3
22.7
19.5
17.9
13.1
10.5
8.5
7.0
5.8
8.1
5.4
3.2
1.5
0.2
18.1
15.5
13.8
12.6
11.4
2.2
0.8
0.3
0.0
0.0
1.0
0.1
0.0
0.0
0.0
24.
20.5
17.3
14.1
11.6
4
3.5
1.5
0.9
0.0
0.0
52.3
44.0
36.3
29.0
21.3
28.6
26.2
22.7
18.6
15.2
21.
19.7
16.8
11.0
8.17
35.6
32.7
29.3
25.6
22.4
7.4
5.4
3.3
1.4
0.0
3.8
2.9
1.1
0.4
0.0
11.0
7.9
5.5
3.1
0.0
13.7
10.8
8.7
7.6
6.1
7.0
4.6
2.3
0.8
0.0
20.4
17.0
15.1
14.4
13.2
2.6
1.5
1.6
1.4
0.0
0.0
0.0
0.0
0.0
0.0
5.1
3.2
3.3
3.1
0.0
5.8 12.7
4.2 11.1
2.6 9.7
1.6 9.1
0.0 6.5
16.2
14.4
12.2
10.7
9.9
11.
9.86
7.4
5.5
4.6
20.7
19.0
17.0
15.8
15.3
12.2
9.6
7.9
5.5
4.3
8.4
5.8
3.9
1.3
0.0
16.1
13.4
11.8
9.7
8.6
14.6
12.4
10.1
7.5
4.3
10.
8.47
5.9
3.1
0.0
18.
16.6
14.3
11.2
8.68
10.
9.16
7.2
5.6
2.8
13.2
11.3
9.0
7.2
6.1
9.6
7.7
5.3
3.2
2.0
16.8
14.9
12.7
11.1
10.2
13.1
10.3
8.5
6.5
5.1
9.6
6.9
4.9
2.8
1.2
16.6
13.8
12.1
10.3
9.0
11.5
9.5
7.7
5.5
3.0
8.4
6.5
4.6
2.3
0.1
14.
12.6
10.5
8.78
6.2
No OI
15-24
52.3
25-34
44.0
35-44
36.3
45-54
29.0
55+
21.3
Has Household Item
15-24
52.3
25-34
44.0
35-44
36.3
45-54
29.0
55+
21.3
9.2
7.7
6.2
5.4
2.8
14.4
12.9
11.1
9.7
7.1
18.2
16.7
15.1
13.9
11.4
94
Unadjusted
LE
Age
Group
Has
no household
item
15-24
52.3
25-34
44.0
35-44
36.3
45-54
29.0
55+
21.3
Low Physical Health
HALE
18.0
15.2
10.2
6.4
0.0
95% CI
9.9
8.9
4.8
1.3
0.0
Moderate Physical
Health
HALE
95% CI
26.0
21.5
15.7
11.5
0.0
19.2
19.9
19.6
19.4
21.3
16.
16.1
17.6
19.4
21.4
3
Good Physical Health
HALE
22.4
23.2
21.8
19.4
21.3
3.7
3.9
2.6
0.0
0.0
95% CI
0.4
0.4
0.1
0.0
0.0
7.1
7.3
5.1
0.0
0.0
Very Good Physical
Health
HALE
95% CI
11.4
5.0
3.8
3.2
0.0
4.0
0.0
0.0
0.0
0.0
18.
10.7
8.83
8.3
0.0
5.2.2.2 Mental health
The summary of life expectancy for study participants adjusted for mental health is shown in table 5.4. The
results are for various categories of study participants including female, males, those in Central versus those
in Nyanza among others. For participants from Central aged 15-24 years, 15.9 (95% CI 10.6, 21.2) years of
their remaining 52.3 years would be spent in the low mental health status compared to 13.5(95% CI 8.7,
18.3) of the remaining 52.3 years for their counterparts from Nyanza. As with PHS, comparison of HALE
for mental health across different groups is described in the next section.
Table 5.4: Life Expectancy adjusted for mental health for various categories of study participants
Unadjusted
LE
HALE
Age Group
All
15-24
25-34
35-44
45-54
55+
Male
15-24
25-34
35-44
45-54
55+
Female
15-24
25-34
35-44
45-54
55+
Central
15-24
25-34
35-44
45-54
55+
Low Mental Health
52.3
44.0
36.3
29.0
21.3
14.6
13.5
11.5
9.7
6.8
51.1
42.9
35.2
27.9
20.4
15.6
14.0
11.9
11.0
8.5
53.5
45.1
37.4
30.0
22.2
13.6
12.5
10.6
7.9
4.4
52.3
44.0
36.3
29.0
21.3
15.9
15.4
13.7
12.0
9.7
95% CI
11
9.
.0
7.
8
5.
7
2.
6
6
10
9.
.3
6.
0
5.
8
2.
5
8
8.
7.
6
5.
4
2.
2
0.
3
0
10
10
.6
8.
.1
6.
1
3.
1
4
Moderate Mental
Health
HALE
95% CI
18.
17.
3
15.
1
13.
4
10.
8
9
20.
19.
8
17.
0
16.
1
14.
4
2
18.
17.
6
15.
6
13.
9
9.9
5
14.1
12.7
11.0
8.6
7.7
10.
9.1
4
7.1
4.5
3.5
12.7
11.7
9.9
7.0
6.8
7.9
7.1
5.1
1.9
1.4
15.7
14.3
12.6
10.7
8.9
9.9
8.4
6.4
4.2
2.1
21.
20.
2
19.
8
18.
3
16.
0
0
10.6
8.5
6.9
4.6
3.9
6.3
4.3
2.5
0.0
0.0
95
17.
16.
7
14.
4
12.
8
12.
6
0
17.
16.
5
14.
4
12.
7
12.
0
3
21.
20.
5
18.
2
17.
8
15.
3
6
15.
12.
0
11.
7
9.2
2
8.7
Good Mental Health
HALE
10.7
7.8
5.5
3.8
1.9
11.2
7.3
5.2
3.2
1.7
11.2
8.5
6.0
4.6
2.2
10.8
7.6
5.8
3.9
1.9
95% CI
8.
5.
1
2.
3
1.
9
0.
1
0
7.
4.
2
2.
1
0.
0
0.
0
0
7.
4.
1
1.
5
0.
9
0.
2
0
7.
4.
0
2.
2
0.
3
0.
3
0
Very Good Mental
Health
HALE
95% CI
13.3
10.3
8.1
6.4
4.5
12.8
9.9
8.3
6.9
4.8
9.5
6.6
4.9
3.3
1.1
16.
13.
2
11.
2
10.
8
8.6
6
15.2
10.5
8.4
6.4
4.9
11.6
9.8
8.1
6.8
3.4
16.
13.
0
12.
9
11.
2
7.7
1
15.2
12.5
10.2
9.0
6.3
13.0
9.8
8.2
6.8
6.6
7.2
5.8
4.0
2.5
0.9
7.7
4.4
2.6
0.8
0.4
14.5
11.1
9.2
7.5
5.6
15.0
12.4
10.0
8.5
5.8
10.
7.5
0
4.9
3.1
0.2
18.
15.
3
13.
1
12.
8
12.
7
9
20.
17.
0
15.
3
13.
1
11.
9
4
Unadjusted
LE
HALE
Age Group
Nyanza
15-24
25-34
35-44
45-54
55+
Has OI
15-24
25-34
35-44
45-54
55+
NO OI
15-24
25-34
35-44
45-54
55+
Has household
item
15-24
25-34
35-44
45-54
55+
Has no
household item
15-24
25-34
35-44
45-54
55+
Low Mental Health
52.3
44.0
36.3
29.0
21.3
13.5
11.9
9.8
7.9
3.9
52.3
44.0
36.3
29.0
21.3
26.6
24.1
21.7
18.6
15.2
52.3
44.0
36.3
29.0
21.3
95% CI
Moderate Mental
Health
HALE
95% CI
8.
7.
7
4.
1
2.
8
0.
6
0
19
17
.6
15
.7
11
.0
8.
.7
1
18.
16.
3
14.
7
13.
8
8.7
1
18.5
17.7
16.0
13.2
11.6
12.
12.
9
10.
0
7.0
0
5.4
33.
30.
5
28.
6
25.
3
22.
6
4
10.7
10.0
7.2
4.1
3.0
5.4
4.8
2.0
0.0
0.0
9.8
8.7
6.8
5.4
2.8
6.
5.
4
3.
2
1.
2
0.
6
0
13.
12.
3
10.
2
9.1
4
6.5
15.6
14.2
12.8
10.7
9.9
52.3
44.0
36.3
29.0
21.3
14.5
13.4
11.5
10.0
7.1
10
9.
.8
7.
6
5.
5
2.
8
8
18.
17.
3
15.
2
14.
5
11.
2
4
52.3
44.0
36.3
29.0
21.3
13.4
10.5
8.2
3.2
0.0
5.
4.
5
2.
4
0.
7
0.
0
0
21.
16.
3
13.
6
8.3
7
0.0
Good Mental Health
HALE
24.
23.
1
21.
4
19.
9
17.
4
9
16.
15.
1
12.
1
9.5
4
8.6
10.5
7.7
5.0
3.5
1.9
11.
9.6
0
7.9
5.5
4.6
20.
18.
1
17.
8
15.
6
15.
8
3
13.8
12.3
10.6
7.8
7.1
10.
8.6
1
6.7
3.8
2.8
22.4
23.2
21.7
22.6
21.3
16.
17.
9
16.
5
17.
7
21.
5
3
95% CI
Very Good Mental
Health
HALE
95% CI
6.
4.
6
1.
0
20.
0.
04
6.
2.
6
0.
1
0.
0
0.
0
0
14.3
11.5
8.8
7.5
5.6
9.8
6.6
5.6
4.4
3.9
5.5
2.4
1.1
0.0
0.0
14.
10.
2
10.
9
9.1
1
8.7
18.1
12.1
10.2
10.3
8.6
2.6
2.7
2.3
1.4
0.0
0.7
0.7
0.3
0.0
0.0
4.6
4.7
4.2
3.1
0.0
10.6
8.0
5.6
3.3
1.4
7.
5.
7
2.
2
0.
7
0.
4
0
13.5
10.8
8.4
6.2
4.1
16.3
13.0
11.2
9.7
7.1
11.
8.6
8
6.5
4.7
2.0
20.
17.
8
15.
5
14.
8
12.
6
2
17.
16.
4
14.
1
11.
4
11.
9
4
11.3
8.3
5.9
4.0
2.0
8.
5.
5
3.
6
1.
2
0.
3
0
14.0
10.9
8.6
6.8
4.7
12.7
9.9
8.4
7.1
5.1
9.2
6.5
4.8
3.3
1.2
16.
13.
2
12.
4
10.
0
9.0
9
27.
29.
9
26.
0
27.
7
21.
7
3
2.1
2.2
0.9
0.0
0.0
0.
0.
0
0.
0
0.
0
0.
0
0
5.0
5.2
2.5
0.0
0.0
14.3
8.0
5.6
3.2
0.0
6.5
2.0
0.2
0.0
0.0
22.
14.
1
10.
0
8.3
9
0.0
12.3
7.1
5.1
4.8
3.0
5.2.3. Comparison of Sullivan HALE estimates for different sub populations of study participants
Statistical analysis was done to compare HALE estimates for various categories of study participants. Test
statistic Z for the difference in HALE across the factors was obtained through Sullivan methods and p value
for 2 tailed test obtained from standard normal table. HALE was compared between male and female,
Central and Nyanza, Participants with opportunistic infection and those with no opportunistic infection and
participants with household items and those with none. Tables 5.5 and 5.6 summarize results for physical
and mental health respectively. For physical health, HALE was statistically significantly different across
regions, having or not having an OI and possessing or not possessing household item, (p <0.05). Sex was
96
not associated with LE adjusted for physical health. For mental health, having or not having OI and
possessing or not possessing household item were found to be statistically significantly associated with
HALE, (p < 0.05). Region and sex were not associated with HALE for this health summary measure.
Table 5.5: Comparison of LE adjusted for physical health across different categories of participants
Low Physical Health
Age
Moderate Physical Health
HALE
Good Physical Health
HALE
HALE
p value
Male
Female
15
17.7
12
25
15.2
35
45
55+
Very Good Physical Health
HALE
p value
Male
Female
>0.05
11.3
17.5
10.8
>0.3
8.8
13.6
8.5
>0.3
11.1
7.3
>0.3
8.5
4.4
>0.3
Central
Nyanza
15
14.8
14.9
25
14.6
35
45
55+
p value
Male
Female
>0.05
12.4
12.6
15.9
>0.05
9.2
5.4
14.5
>0.05
3.9
12.8
>0.05
3.4
11.1
>0.05
Central
Nyanza
>0.3
6.2
21.5
12.8
>0.3
4.7
13
10.6
>0.3
11.7
8.3
>0.3
9.7
3.9
>0.05
Has OI
No OI
15
28.6
9.2
25
26.2
7.7
35
22.7
45
18.6
p value
Male
Female
>0.3
10.2
11.1
>0.3
10
>0.3
9.8
8.2
>0.3
8.6
7.5
>0.3
8.1
6.6
>0.3
6.7
5.4
>0.3
6.1
4.6
>0.3
5.1
4.4
>0.3
3.4
2.2
>0.3
Central
Nyanza
Central
Nyanza
<0.001
12
13.1
>0.3
19.4
2.2
<0.001
19.6
<0.002
9.3
10.5
>0.3
15.4
0.8
<0.001
3.5
16.7
<0.01
7.7
8.5
>0.3
12
0.3
<0.001
2.8
13.2
<0.05
5.3
7
>0.3
9.2
0
<0.001
1.9
11.6
<0.05
3.9
5.8
>0.3
5.8
0
<0.05
Has OI
No OI
Has OI
No OI
Has OI
No OI
<0.001
7.4
16.2
<0.05
13.7
12.2
>0.3
2.6
14.6
<0.001
<0.001
5.4
14.4
<0.01
10.8
9.6
>0.3
1.5
12.4
<0.001
6.2
<0.002
3.3
12.2
<0.01
8.7
7.9
>0.3
1.6
10.1
<0.01
5.4
<0.01
1.4
10.7
<0.01
7.6
5.5
>0.3
1.4
7.5
<0.05
55+
15.2
2.8
<0.05
0
9.9
<0.001
6.1
4.3
>0.3
0
4.3
<0.05
15
Has HH
item
14.4
Has no
HH item
18
>0.3
Has HH
item
13.2
Has no
HH item
19.2
>0.05
Has HH
item
13.1
Has no
HH item
3.7
<0.01
Has HH
item
11.5
Has no
HH item
11.4
>0.3
25
12.9
15.2
>0.3
11.3
19.9
<0.01
10.3
3.9
>0.05
9.5
5
>0.3
35
11.1
10.2
>0.3
9
19.6
<0.001
8.5
2.6
>0.05
7.7
3.8
>0.3
45
9.7
6.4
>0.3
7.2
19.4
<0.001
6.5
0
<0.001
5.5
3.2
>0.3
55+
7.1
0
<0.002
6.1
21.3
<0.001
5.1
0
<0.01
3
0
>0.05
HH=Household item, OI=Opportunistic Infection
97
Table 5.6: Comparison of LE adjusted for mental health across different categories of participants
Low Mental Health
Age
HALE
Male
Female
Moderate Mental Health
p value
HALE
Male
Female
Good Mental Health
p value
HALE
Male
Female
Very Good Mental Health
p value
HALE
Male
Female
p value
15
15.6
13.6
>0.3
12.7
15.7
>0.3
11.2
11.2
>0.3
11.6
13
>0.3
25
14
12.5
>0.3
11.7
14.3
>0.3
7.3
8.5
>0.3
9.8
9.8
>0.3
35
11.9
10.6
>0.3
9.9
12.6
>0.3
5.2
6
>0.3
8.1
8.2
>0.3
45
11
7.9
>0.3
7
10.7
>0.3
3.2
4.6
>0.3
6.8
6.8
>0.3
55+
8.5
4.4
>0.3
6.8
8.9
>0.3
1.7
2.2
>0.3
3.4
6.6
>0.3
Central
Nyanza
Central
Nyanza
Central
Nyanza
Central
Nyanza
15
15.9
13.5
>0.3
10.6
18.5
>0.05
10.8
10.5
>0.3
15
9.8
>0.05
25
15.4
11.9
>0.3
8.5
17.7
>0.05
7.6
7.7
>0.3
12.4
6.6
>0.05
35
13.7
9.8
>0.3
6.9
16
>0.05
5.8
5
>0.3
10
5.6
>0.3
45
12
7.9
>0.3
4.6
13.2
>0.05
3.9
3.5
>0.3
8.5
4.4
>0.3
55+
9.7
3.9
>0.05
3.9
11.6
>0.05
1.9
1.9
>0.3
5.8
3.9
>0.3
Has OI
No OI
Has OI
No OI
Has OI
No OI
Has OI
No OI
15
26.6
9.8
<0.002
10.7
15.6
>0.3
12.3
10.6
>0.3
2.6
16.3
<0.001
25
24.1
8.7
<0.002
10
14.2
>0.3
7.1
8
>0.3
2.7
13
<0.002
35
21.7
6.8
<0.01
7.2
12.8
>0.3
5.1
5.6
>0.3
2.3
11.2
<0.01
45
18.6
5.4
<0.01
4.1
10.7
>0.3
4.8
3.3
>0.3
1.4
9.7
<0.01
55+
15.2
<0.05
3
>0.3
3
>0.3
0
<0.01
15
14.5
2.8
Has no
HH
Item
13.4
>0.3
13.8
9.9
Has no
HH
Item
22.4
25
13.4
10.5
>0.3
12.3
35
45
11.5
8.2
>0.3
10
3.2
>0.05
55+
7.1
0
<0.002
Has HH
Item
5.3.
>0.05
11.3
1.4
Has no
HH
Item
2.1
23.2
<0.05
8.3
10.6
21.7
<0.01
7.8
22.6
<0.002
7.1
21.3
<0.001
Has HH
Item
<0.002
12.7
7.1
Has no
HH
Item
14.3
2.2
<0.05
9.9
8
>0.3
5.9
0.9
<0.05
8.4
5.6
>0.3
4
0
<0.01
7.1
3.2
>0.3
2
0
>0.05
5.1
0
Has HH
Item
Has HH
Item
>0.3
<0.01
Estimation of MSLT functions using SPACE programme
The foregoing section has discussed the results obtained from application of Sullivan method. As discussed
earlier, Sullivan method uses cross-sectional data and in this case, measures obtained at baseline were used
in the estimation. Sullivan method assumes that the current health states remain the same throughout the
study period i.e. if a participant was in poor health state at baseline, the method assumes that the person
remains in that state. It gives no room for recovery if in poor state nor does it allow deterioration of health.
In reality however, persons can move from one health state to another including death. Another key
98
characteristic of the Sullivan method is that it only partitions a given life table into years lived in different
states of health. In the current study, the WHO 2012 life table for the general population was used. The life
table used may or may not be true for people living with HIV.
In this section, results obtained using a different method are discussed. A Multistate Life Table approach is
used which not only allows a person to transition from one health state to another but also calculates health
expectancies based purely on existing data without relying on an existing life table. Life table functions
calculated using this approach are a better representation of the reality and the results are more accurate than
those obtained through Sullivan approach.
Data were collected in two waves one year apart and only participants who were interviewed at both data
collection points or those who were recorded to have died during the follow-up period were included in the
analysis. Table 5.7 gives the distribution of participants who were interviewed in wave two or reported dead
and who were included in the analysis. MSLT functions were estimated using SPACE programme. As
already discussed earlier, the programme is a set of many SAS programmes with different capabilities and
specifically, this study used MSLT_SIMxCOV_S and MSLT_SIMxCOV_S programmes which calculate
MSLT functions including transition probabilities from one health state to another and health adjusted life
expectancies both overall and for different sub groups of the target population. For ease of computation and
interpretation of results, patients were re-categorized into 3 health states; poor, which included those
originally put in poor and moderate health states, good which included those initially in good and very good
categories and dead. Death was coded 3 and no patient was in this state at baseline. Only two covariates
were considered – sex and region due to analysis complexities and the fact that most of the other factors
were found no be associated with HRQOL estimates and hence were not expected to be associated with
MSLT estimates of HALE.
99
As can be seen in the table 5.7, attrition of study participants was at 5.5 percent, which could be considered
as being low. The inclusion of 20 percent non-response rate in the calculated sample helped offset this
attrition rate thus ensuring the results were not biased.
Table 5.7: Number of participants interviewed in each of the two data collection waves
Age Group
15-24
25-34
35-44
45-54
55+
Total
5.3.1
Wave 1
49
84
38
11
11
193
Nyanza
Wave 2
47
82
38
10
11
188
Wave 1
Central
Wave 2
29
66
62
27
11
195
28
63
53
25
10
179
Wave 1
78
150
100
38
22
388
All
Wave 2
75
145
91
35
21
367
Transition probabilities to different physical health summary (PHS) statuses
Using SPACE programme, transition rates from one health state to another were calculated. This was an
important step since the obtained transition probabilities were used as data input in the estimation of MSLT
functions discussed in the following sub section. Table 5.8 shows the proportion of study participants in
different PHS health states at both baseline and one year follow-up. The table clearly demonstrates a shift of
health states from baseline to one year follow-up. Across all age groups, there was a positive transition from
poor to good health states except for a few participants who died during the follow-up. As an example,
whereas at baseline 52.6 percent of participants (all ages) were in poor PHS status, only 23.4 percent were
in this state at one year follow-up. The proportion in good PHS state increased from 47.2 percent at baseline
to 72.8 percent at one year follow-up with a death rate of 3.8 percent. Similar trends were observed across
all the age groups.
100
Table 5.8: Proportion of participants in different states of PHS at both baseline and at one year follow-up
Wave 1
Wave 2
Good
Good
PHS
PHS
Age Group
Poor PHS State
State
Poor PHS State
State
Dead
44.0
56.0
30.7
68.0
1.3
15-24
54.1
45.9
26.2
70.3
3.4
25-34
52.2
47.8
22.0
71.4
6.6
35-44
57.1
42.9
8.6
88.6
2.9
45-54
66.7
33.3
9.5
85.7
4.8
55+
52.6
47.4
23.4
72.8
3.8
Total
In the next step, probabilities of transitioning from one health state to the other for participants in different
age categories and with different characteristics are shown. As mentioned above, the characteristics
considered here are sex and region.
5.3.1.1 Transition to poor PHS status
Table 5.9 shows probabilities of transition from various baseline PHS health statuses to poor PHS status at
one year follow-up. The table shows probabilities for patients of different ages. As can be seen from the first
row, a 15 year old male patient in Nyanza had a probability of 0.24 of remaining in the poor health status at
follow-up compared to a probability of 0.00 of transitioning from good PHS status to poor status at one year
follow-up. A similar patient in Central region had a probability of 0.06 and 0.00 of transitioning from poor
and good health status respectively to poor health status at one year follow-up. It is also clear from the table
that women had higher probabilities of transitioning from good to poor health status at one year follow-up.
As an example, a 15 year old female in Nyanza had a probability of 0.42 of transitioning from good to poor
health status at one year follow-up compared to a probability of 0.05 for a similar patient in Central region.
101
Table 5.9: Transition probabilities to poor PHS health status at one year follow-up
Sex
Region
Nyanza
Male
Central
Nyanza
Female
Central
Baseline PHS Status
Poor
Good
Poor
Good
Poor
Good
Poor
Good
15
0.24
0.00
0.06
0.00
0.14
0.42
0.04
0.05
25
0.18
0.00
0.04
0.00
0.32
0.42
0.05
0.25
35
0.40
0.00
0.05
0.00
0.16
0.42
0.02
0.05
Age
45
0.10
0.00
0.02
0.00
0.11
0.42
0.03
0.09
55
0.14
0.00
0.01
0.00
0.04
0.42
0.01
0.02
65
0.05
0.00
0.01
0.00
0.03
0.41
0.01
0.00
75
0.03
0.00
0.01
0.00
0.02
0.41
0.00
0.00
85
0.02
0.00
0.00
0.00
0.02
0.41
0.00
0.00
5.3.1.2 Transition to good PHS status
Table 5.10 gives probabilities of transitioning from different baseline health statuses to good PHS status for
patients with various characteristics. For males in good health status at baseline, the probability of
remaining in that status was almost 1 in both regions across all ages. The probabilities of transitioning from
poor health to good health status were also high for males across all ages and regions. As an example, a 15
year old male participant in Nyanza had a probability of 0.7 of transitioning from poor to good health status
at the end of one year follow-up compared to 0.71 for a similar patient in Central region. Females also had
high probabilities of transitioning from poor health status to good health status at one year follow-up. A 15
year old female in Nyanza in poor health status at baseline had a probability of 0.84 of transitioning to good
health status at one year follow-up compared to 0.90 probability for a similar participant in Central region.
The probability of remaining at good health status was markedly lower for females compared to male. A 15
year old female in Nyanza in good health status at baseline had a probability of 0.58 of remaining in that
status while a similar participant in Central had a probability of 0.95 compared to a probability of 1.00 for
their male counterparts in both regions.
102
Table 5.10: Transition probabilities to good PHS health status at one year follow-up
Age
Sex
Region Baseline PHS Status
15
25
35
45
55
0.70
0.74
0.76
0.78
0.78
Poor
Nyanza
1.00
1.00
1.00
1.00
1.00
Good
Male
0.71
0.69
0.66
0.62
0.57
Poor
Central
1.00
1.00
1.00
1.00
1.00
Good
0.84
0.87
0.89
0.91
0.91
Poor
Nyanza
0.58
0.58
0.58
0.58
0.58
Good
Female
0.90
0.89
0.88
0.86
0.84
Poor
Central
0.95
0.95
0.93
0.82
0.40
Good
65
0.76
1.00
0.52
1.00
0.92
0.59
0.82
0.08
75
0.74
1.00
0.48
1.00
0.91
0.59
0.79
0.01
85
0.71
1.00
0.43
1.00
0.90
0.59
0.75
0.00
Analysis of transition trends across different ages among participants initially in poor health status to good
health status show that the probabilities generally decreased as one aged as shown in figure 5.1.
Figure 5.1: Trends in probabilities of transitioning from poor PHS status at baseline to good PHS status
at one year follow-up across different ages
5.3.1.3 Transition to death
Transition probabilities from other health statuses to death were generally low. Males in both regions who
were in good PHS status at baseline had 0 probability of transitioning to death at one year follow-up. This
was the same case with females in Nyanza region. Females in Central region however had a different
experience from their counterparts in Nyanza. Whereas females in Central aged 15 and 25 years who were
in good health status had a 0 probability of transitioning to death at follow-up, this probability increased
steadily to almost 1 for females aged 85 years. For participants who were in poor PHS status at baseline,
103
their probabilities of transitioning to death at one year follow-up were also low but increased steadily as one
aged. Figure 5.2 illustrates this steady increase. Males in Central who were in poor health at baseline
generally had higher probabilities of transitioning to death compared to other categories of participants.
Table 47 shows these probabilities for the different categories of participants.
Table 5.11: Transition probabilities to death PHS health status at one year follow-up from different
statuses at baseline
Sex
Region
Nyanza
Male
Central
Nyanza
Female
Central
Baseline PHS Status
Poor
Good
Poor
Good
Poor
Good
Poor
Good
15
0.06
0.00
0.23
0.00
0.02
0.00
0.07
0.00
25
0.08
0.00
0.27
0.00
0.02
0.00
0.08
0.00
35
0.10
0.00
0.32
0.00
0.03
0.00
0.10
0.02
Age
45
0.13
0.00
0.37
0.00
0.04
0.00
0.12
0.14
55
0.15
0.00
0.42
0.00
0.04
0.00
0.15
0.58
65
0.19
0.00
0.47
0.00
0.05
0.00
0.18
0.92
75
0.22
0.00
0.52
0.00
0.07
0.00
0.21
0.99
85
0.26
0.00
0.57
0.00
0.08
0.00
0.24
1.00
The trends in transition probabilities across different ages depicted in the figure 6 show that participants in
Central who were initially in poor PHS health status had a higher probability of transitioning to death at one
year follow-up compared to those in Nyanza.
Figure 5.2: Trends in probabilities of transitioning from poor PHS status at baseline to death at one year
follow-up across different ages
104
5.3.2
Transition probabilities to different mental health summary (MHS) statuses
There was a positive shift of health statuses from what was recorded at baseline to statuses recorded at one
year follow-up. Across all ages, the proportion in poor MHS health status reduced during the one year
follow-up while the proportion of those in good MHS status increased. Overall, the proportion of those in
poor MHS health status reduced from 51.5 percent at baseline to 14.4 percent at one year follow-up, a 72.9
percent reduction while those in good MHS health status increased from 48.5 percent to 81.7 percent, a 68.5
percent increase. Since the same participants were analyzed for PHS and MHS, the proportion of those who
died across the different ages was the same for both summary health measures. Table 5.12 shows these
proportions in both waves of data collection.
Table 5.12: Proportion of participants in different states of MHS at both baseline and at one year followup
Age Group
15-24
25-34
35-44
45-54
55+
Total
Wave 1
Poor
34.7
51.4
60.9
54.3
66.7
51.5
Good
65.3
48.6
39.1
45.7
33.3
48.5
Poor
17.3
17.2
11.0
8.6
9.5
14.4
Wave 2
Good
81.3
79.3
82.4
88.6
85.7
81.7
Dead
1.3
3.4
6.6
2.9
4.8
3.8
5.3.2.1 Transition to poor MHS status
Probability of remaining in poor MHS health status at one year follow-up was relatively higher among
younger participants as compared to older participant. A 15 year old male participant in Nyanza had a
probability of 0.66 of remaining in poor MHS health status as compared to a similar 85 year old participant
who had a probability of 0.20 of remaining in the poor MHS health status. It was not common for males to
transition from good MHS health status at baseline to poor status at follow-up but the case was not the same
for females. Older females in good MHS health status at baseline in both regions had higher probabilities of
transitioning to poor MHS health status at one year follow-up compared to younger females. A 15 year old
female in Central for example had 0.03 probability of transitioning from good to poor health status
105
compared to 0.10 probability for a female participant aged 85 years. Females in Nyanza who were in good
initial health status had relatively lower probabilities of transitioning to poor health status at follow-up as
compared to their Central counterparts. Table 5.13 shows these transition probabilities while figure 5.3
shows the trends across ages in probabilities of remaining in poor health status at follow-up.
Table 5.13: Transition probabilities to poor MHS health status at one year follow-up from different
statuses at baseline
Sex
Region
Nyanza
Male
Central
Nyanza
Female
Central
Baseline MHS Status
Poor
Good
Poor
Good
Poor
Good
Poor
Good
15
0.66
0.00
0.22
0.00
0.16
0.01
0.03
0.03
25
0.59
0.00
0.17
0.00
0.12
0.02
0.03
0.04
35
0.52
0.00
0.14
0.00
0.10
0.02
0.02
0.05
Age
45
55
0.44
0.37
0.00
0.00
0.11
0.08
0.00
0.00
0.07
0.06
0.02
0.03
0.01
0.01
0.06
0.07
65
0.31
0.00
0.06
0.00
0.04
0.03
0.01
0.08
75
0.25
0.00
0.05
0.00
0.03
0.04
0.01
0.09
85
0.20
0.00
0.04
0.00
0.02
0.04
0.00
0.10
As can be seen in figure 5.3, males in Nyanza had the highest probability of remaining in poor MHS health
status followed by males in Central. Females in Central had the lowest probabilities of remaining in poor
health status at follow-up.
Figure 5.3: Trends in probabilities of remaining in poor MHS status at one year follow-up across
different ages
106
5.3.2.2 Transition to good MHS status
Probabilities of transitioning from poor to good MHS health status increased as one aged, a picture that was
the reverse of transitioning to poor PHS status where the probabilities decreased as one aged. These
probabilities and trends are shown in table 5.14 and figure 5.4 respectively. Probabilities of remaining in
good MHS health status were relatively high compared to those of transitioning from poor to good but these
probabilities decreased as one aged.
Table 5.14: Transition probabilities to good MHS health status at one year follow-up from different
statuses at baseline
Sex
Region
Nyanza
Male
Central
Nyanza
Female
Central
Baseline MHS Status
Poor
Good
Poor
Good
Poor
Good
Poor
Good
15
0.31
0.99
0.54
0.99
0.83
0.96
0.89
0.95
25
0.38
0.98
0.58
0.99
0.86
0.95
0.90
0.94
35
0.44
0.98
0.60
0.98
0.89
0.93
0.91
0.92
Age
45
55
0.51
0.58
0.96
0.94
0.63
0.65
0.98
0.96
0.91
0.93
0.90
0.86
0.91
0.92
0.89
0.86
65
0.64
0.92
0.67
0.94
0.94
0.80
0.92
0.81
75
0.69
0.88
0.68
0.92
0.95
0.73
0.92
0.75
85
0.74
0.82
0.69
0.88
0.96
0.64
0.93
0.68
In figure 5.4, the probabilities of transitioning from poor health status to good MHS status increased as one
aged and were relatively higher among females as compared to males.
Figure 5.4: Trends in probabilities of transitioning from poor MHS status at baseline to good MHS
health status at one year follow-up across different ages
107
5.3.2.3 Transition to death
Transition probabilities to death are shown in table 5.15. Males in Central who were in poor MHS status at
baseline had the highest probabilities of transitioning to death across all ages. These probabilities increased
slightly as one aged as figure 5.5 shows. Across many categories of participants, the younger participants
(45 and below) in poor MHS health status had higher probabilities of transitioning to death as compared to
their counterparts in good MHS health status. This trend was reversed among older participants. As an
example, a 15 year older male participant in Nyanza who was in poor MHS health status at baseline had
0.03 probability of transitioning to death as compared to 0.01 for a similar participant in good initial MHS
health status. Conversely, an 85 year old male participant in Nyanza in poor initial MHS health status had
0.06 probability of transitioning to death as compared to a similar participant in good MHS health status
who had 0.18 probability of transitioning to death.
Table 5.15: Transition probabilities to death at one year follow-up from different MHS statuses at
baseline
Age
Sex
Region Baseline MHS Status
15
25
35
45
55
65
75
0.03
0.03
0.04
0.04
0.05
0.05
0.06
Poor
Nyanza
0.01
0.02
0.02
0.04
0.06
0.08
0.12
Good
Male
0.24
0.25
0.26
0.26
0.27
0.27
0.27
Poor
Central
0.01
0.01
0.02
0.02
0.04
0.06
0.08
Good
0.01
0.01
0.02
0.02
0.02
0.02
0.02
Poor
Nyanza
0.02
0.04
0.05
0.08
0.12
0.17
0.23
Good
Female
0.08
0.07
0.07
0.07
0.07
0.07
0.07
Poor
Central
0.02
0.02
0.03
0.05
0.08
0.11
0.16
Good
85
0.06
0.18
0.27
0.12
0.01
0.32
0.07
0.22
As shown in the figure 5.5, males in central had the highest probabilities of transitioning from poor health
status to death across all ages followed by their female counterparts. Participants from Nyanza generally had
lower probabilities of transitioning from poor MHS health status at baseline to death across all ages as
compared to their counterparts in Nyanza region.
108
Figure 5.5: Trends in probabilities of transitioning from poor MHS health status at baseline to death at
one year follow-up across different ages.
5.4.
Estimation of health adjusted life expectancy (HALE) using SPACE programme
Using transition probabilities as data input, Health adjusted life expectancy (HALE) was estimated for
different categories of participants including those in various health statuses at baseline using SPACE
programme. The following section presents these estimates and compares them across the different sub
categories of participants including sex, region and baseline health status.
5.4.1
Estimation of HALE adjusted for various PHS statuses
5.4.1.1 HALE among all study participants regardless of baseline PHS health status
Table 5.16 gives LE among all study participants adjusted for various statuses of PHS. On average, a study
participant could expect to live another 32.1 (SE 8.8) years out of which 6.0 (SE 4.7) would be spent in poor
PHS health status and 26.2 (SE 11.9) would be in good PHS health status. A participant in Nyanza could
expect to live another 27.6 (SE 13.9) years out of which 12.1 (SE 12.1) years would be in poor PHS health
status and 15.4 (SE 17.7) years in good PHS health status. In comparison, a participant in Central could
expect to live on average another 35.9 (SE 8.4) years out of which 0.9 (SE 0.7) and 35.0 (SE 8.9) years
would be in poor and good PHS health statuses respectively. A 15-24 year old participant had an average
LE of 39.3 (SE 9.1) years 9.3 (SE 3.0) of which would be in poor PHS health status and 30.0 (SE 10.1)
years would be in good PHS health status. A similar participant in Nyanza would live 16.9 (SE 4.5) years of
109
their 34.9 (SE 18.8) remaining years in poor PHS health status compared to 1.3 (SE 0.1) of remaining 44.0
(SE 6.9) years lived in the same health status by a similar participant in Central. Table 5.16 gives the
different HALE for different participants regardless of their baseline PHS health status
Table 5.16: Life expectancy in various statuses of PHS among all study participants regardless of their
baseline PHS status
All
32.1
15
25
Central
Std Error
Value
Good LE
Std Error
Value
Value
Poor LE
Std Error
Total LE
Std Error
Good LE
Std Error
Value
Poor LE
Std Error
Value
Value
Total LE
Std Error
Good LE
Std Error
Value
Std Error
Value
Age
Nyanza
Poor LE
Value
All
Total LE
8.8
6.0
4.7
26.2
11.9
27.6
13.9
12.1
8.6
15.4
17.7
35.9
8.4
0.9
0.7
35.0
8.9
39.3
9.1
9.3
3.0
30.0
10.1
34.9
18.8
16.9
4.5
18.0
18.1
44.0
6.9
1.3
0.1
42.7
7.0
35.6
16.8
6.8
3.5
28.8
15.1
30.8
23.2
14.0
7.9
16.8
19.2
39.5
8.6
1.1
0.2
38.3
8.7
35
31.4
15.7
5.8
3.1
25.6
15.0
26.8
20.5
11.4
6.2
15.3
18.2
35.2
10.1
1.0
0.3
34.2
10.3
45
28.1
18.9
5.3
6.4
22.8
14.4
23.9
22.6
10.2
11.5
13.7
14.9
32.0
17.4
0.8
1.3
31.2
16.2
55+
26.2
20.1
2.7
1.3
23.6
18.8
19.3
25.4
6.7
2.6
12.6
26.0
30.3
19.3
0.3
1.1
30.0
17.8
5.4.1.2 HALE among male study participants regardless of baseline PHS status
Table 5.17 gives HALE adjusted for various PHS health statuses for male study participants regardless of
their baseline PHS health status. A male participant could expect to live another 32.2 (SE13.2) years out of
which 2.8 (SE 3.0) would be in poor PHS health status and 29.4 (SE 15.3) would be in good PHS health
status. Similarly, a male participant in Nyanza could expect to live another 22.0 (SE 17.1) years, 7.4 (SE
7.5) years of which would be in poor PHS health status and 14.7 (SE 22.9) years would be in good PHS
health status. For male participants in Central, their LE was 37.8 (SE 12.2) years out of which 0.4 (SE 0.6)
years would be in poor PHS health status and 37.4 (SE 12.3) years would be in good PHS health status). At
55 years, a male participant in Nyanza had a total LE of 18.6 (SE 28.0) years distributed thus; 5.7 (SE 3.1)
years in poor PHS health status and 12.8 (SE 29.8) years in good PHS health status. A similar male
participant in Central had 31.0 (SE 19.8) years, 0.3 (SE 1.1) years and 30.7 (SE 18.3) years total LE, poor
LE and good LE respectively.
110
Table 5.17 Life expectancy in various statuses of PHS among male study participants regardless of their
baseline PHS status
32.2
57.9
41.2
33.8
30.6
26.8
13.2
19.8
24.4
17.2
20.7
21.0
2.8
0.4
4.0
4.1
2.8
2.1
3.0
0.2
2.7
2.8
3.6
1.0
29.4
57.6
37.3
29.8
27.7
24.7
15.3
20.0
25.1
19.9
19.5
20.0
22.0
17.1
7.4
7.5
14.7
22.9
28.1
24.5
21.3
18.6
25.1
18.8
19.7
28.0
9.9
8.5
7.2
5.7
7.5
5.9
8.7
3.1
18.2
16.0
14.1
12.8
27.6
24.3
18.9
29.8
12.2
19.8
18.0
17.7
5.7
19.8
0.4
0.4
0.5
0.5
0.5
0.3
0.6
0.2
0.2
0.2
1.5
1.1
37.4
57.6
48.4
40.6
35.2
30.7
Std Error
Value
Good LE
Std Error
Std Error
Value
37.8
57.9
48.9
41.1
35.6
31.0
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
15
25
35
45
55+
Value
All
Poor LE
Total LE
12.3
20.0
18.2
17.9
6.1
18.3
5.4.1.3 HALE among female study participants regardless of baseline PHS status
Table 5.18 gives HALE adjusted for various PHS health statuses for female study participants regardless of
their baseline PHS health status. A female participant could expect to live another 32.1 (SE6.4) years 8.5
(SE 3.4) would be in poor PHS health status and 23.5 (SE 5.5) would be good PHS health status. Similarly,
a female participant in Nyanza could expect to live another 30.6 (SE 12.9) years 14.7 (SE 5.3) years of
which would be in poor PHS health status and 15.9 (SE 12.1) years would be in good PHS health status. For
female participants in Central, their LE was 33.7 (SE 10.9) years out of which 1.4 (SE 0.7) years would be
in poor PHS health status and 32.3 (SE 10.4) years would be in good PHS health status. At 55 years, a
female participant in Nyanza had a total LE of 22.4 (SE 17.8) years distributed thus; 10.5 (SE 4.4) years in
poor PHS health status and 11.9 (SE 13.3) years in good PHS health status. A similar female participant in
Central had 21.4 (SE 10.4) years life expectancy out of which 1.2 (SE 0.4) years would be in poor PHS
status and 20.3 (SE 10.1) years in good PHS status.
111
Table 5.18: Life expectancy in various statuses of PHS among female study participants regardless of
their baseline PHS status
32.1
37.4
33.5
29.2
25.4
22.0
6.4
7.8
6.0
11.3
19.9
13.3
8.5
10.2
8.0
7.3
8.0
6.9
3.4
3.2
2.2
3.9
9.6
4.4
23.5
27.3
25.5
21.9
17.4
15.1
5.5
8.6
6.4
8.5
10.5
8.9
12.9
18.8
17.0
20.0
14.2
17.8
14.7
16.9
15.3
13.9
12.1
10.5
5.3
4.5
4.4
8.1
10.0
4.4
15.9
18.0
16.4
14.8
13.4
11.9
12.1
18.1
15.5
13.8
7.2
13.3
10.9
6.1
6.9
7.7
13.1
10.4
1.4
1.5
1.4
1.5
1.4
1.2
0.7
0.1
0.2
0.3
1.2
0.4
32.3
39.3
33.6
28.2
24.0
20.3
Std Error
Value
Good LE
Std Error
Std Error
Value
33.7
40.7
35.0
29.7
25.3
21.4
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
30.6
34.9
31.7
28.7
25.5
22.4
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
15
25
35
45
55+
Value
All
Poor LE
Total LE
10.4
6.1
6.9
7.6
11.8
10.1
5.4.1.4 HALE among all study participants in poor baseline PHS status
Table 5.19 gives HALE adjusted for various PHS health statuses for all study participants in poor baseline
PHS health status. A study participant in poor baseline PHS health status could expect to live another 28.3
(SE 15.6) years, 7.5 (SE 3.7) of which would be in poor PHS health status and 20.7 (SE 14.0) would be in
good PHS health status. Similarly, a study participant in Nyanza could expect to live another 26.2 (SE 19.7)
years, 11.4 (SE 6.4) years of which would be in poor PHS health status and 14.8 (SE 17.2) years would be
in good PHS health status. For study participants in Central, their LE was 31.7 (SE 12.8) years out of which
1.3 (SE 0.4) years would be in poor PHS health status and 30.4 (SE 12.6) years would be in good PHS
health status. At 55 years, a participant in Nyanza had a total LE of 20.6 (SE 20.9) years distributed thus;
8.5 (SE 5.6) years in poor PHS health status and 12.1 (SE 15.7) years in good PHS health status. A similar
participant in Central had 24.6 (SE 1.3) years, 1.0 (SE 0.2) years and 23.6 (SE 1.5) years total LE, poor PHS
status and good PHS status respectively.
112
Table 5.19: Life expectancy in various statuses of PHS among all study participants in poor baseline PHS
health status
28.3
32.6
29.0
25.8
21.7
15.6
18.0
15.1
14.9
17.7
7.5
9.5
7.4
6.5
6.5
3.7
4.6
4.1
2.9
3.5
20.7
23.1
21.6
19.3
15.2
14.0
16.2
14.1
13.1
14.3
26.2
30.4
26.8
23.8
20.6
11.4
13.5
11.6
10.2
8.5
6.4
7.5
6.6
5.3
5.6
14.8
16.9
15.2
13.5
12.1
17.2
19.9
18.8
15.4
15.7
12.8
10.6
7.8
12.0
1.3
1.3
1.4
1.4
1.2
1.0
0.4
0.2
0.2
0.3
0.2
30.4
35.6
30.9
27.7
23.6
Std Error
Value
Good LE
Std Error
Std Error
Value
31.7
37.0
32.3
28.9
24.6
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
19.7
23.0
20.5
17.9
20.9
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Std Error
Value
Std Error
Value
Age
All
25
35
45
55+
Good LE
Value
All
Poor LE
Total LE
12.6
10.7
7.9
11.9
1.5
5.4.1.5 HALE among male study participants in poor baseline PHS status
Table 5.20 gives HALE adjusted for various PHS health statuses for male study participants in poor baseline
PHS health status. A male participant could expect to live another 28.2 (SE 19.1) years 5.4 (SE 3.3) would
be in poor PHS health status and 22.8 (SE 19.9) would be in good PHS health status. Similarly, a male
participant in Nyanza could expect to live another 23.6 (SE 20.7) years 8.5 (SE 6.5) years of which would
be in poor PHS health status and 15.1 (SE 22.6) years would be in good PHS health status. For male
participants in Central, their LE was 35.0 (SE 15.2) years out of which 0.9 (SE 0.2) years would be in poor
PHS health status and 34.2 (SE 15.3) years would be in good PHS health status. At 55 years, a male
participant in Nyanza had a total LE of 18.9 (SE 21.6) years distributed thus; 6.7 (SE 3.5) years in poor PHS
health status and 12.2 (SE 22.5) years in good PHS health status. A similar male participant in Central had
26.2 (SE 8.4) years total LE, 0.8 (SE 0.1) years of which would be in poor PHS status and 25.4 (SE 8.5)
years in good PHS status.
113
Table 5.20: Life expectancy in various statuses of PHS among male study participants in poor baseline
PHS status
28.2
32.9
29.4
26.4
21.2
19.1
22.1
16.2
17.5
19.9
5.4
7.1
5.7
4.4
4.9
3.3
4.7
3.3
2.4
2.5
22.8
25.9
23.7
21.9
16.2
19.9
23.5
19.3
18.0
20.2
20.7
24.4
18.4
18.6
21.6
8.5
10.3
8.8
7.6
6.7
6.5
7.8
6.2
5.5
3.5
15.1
17.9
15.6
13.8
12.2
22.6
26.8
24.1
20.2
22.5
35.0
42.1
37.0
31.9
26.2
0.9
0.9
0.9
0.8
0.8
0.2
0.2
0.2
0.2
0.1
34.2
41.3
36.1
31.1
25.4
Std Error
Value
Good LE
Std Error
Std Error
15.2
18.5
15.7
13.5
8.4
Value
Central
Poor LE
Total LE
Std Error
Value
Std Error
Value
Std Error
Value
23.6
28.1
24.4
21.4
18.9
Good LE
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
25
35
45
55+
Value
All
Poor LE
Total LE
15.3
18.7
15.8
13.6
8.5
5.4.1.6 HALE among female study participants in poor baseline PHS status
Table 5.21 gives HALE adjusted for various PHS health statuses for female study participants in poor
baseline PHS health status. Overall, a female participant could expect to live another 28.3 (SE 13.1) years,
9.3 (SE 5.0) of which would be in poor PHS health status and 19.0 (SE 9.2) would be in good PHS health
status. Similarly, a female participant in Nyanza could expect to live another 28.2 (SE 19.1) years 13.7 (SE
7.9) years of which would be in poor PHS health status and 14.5 (SE 13.0) years would be in good PHS
health status. For female participants in Central, their LE was 28.5 (SE 9.8) years out of which 1.7 (SE 0.5)
years would be in poor PHS health status and 26.7 (SE 9.4) years would be in good PHS health status. At 55
years, a female participant in Nyanza had a total LE of 22.7 (SE 16.7) years distributed thus; 10.8 (SE 4.9)
years in poor PHS health status and 11.9 (SE 11.8) years in good PHS health status. A similar female
participant in Central had a total life expectancy of 21.3 (SE 12.4) years, 1.6 (SE 0.5) years of which would
be in poor PHS status and 19.7 (SE 8.5) in good PHS status.
114
Table 5.21: Life expectancy in various statuses of PHS among female study participants in poor baseline
PHS status
28.3
32.4
28.8
25.3
22.4
13.1
9.2
12.0
12.7
9.9
9.3
11.1
8.8
8.7
8.8
5.0
2.4
5.3
4.7
4.3
19.0
21.2
20.0
16.6
13.6
9.2
9.0
8.5
8.5
5.6
19.1
16.6
19.9
13.1
16.7
13.7
15.6
14.0
12.3
10.8
7.9
4.6
8.1
3.9
4.9
14.5
16.3
14.8
13.4
11.9
13.0
15.2
13.7
10.7
11.8
9.8
7.5
7.7
9.2
12.4
1.7
1.8
1.7
1.6
1.6
0.5
0.2
0.3
0.6
0.5
26.7
31.6
27.1
22.9
19.7
Std Error
Value
Good LE
Std Error
Std Error
Value
28.5
33.4
28.8
24.5
21.3
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
28.2
31.9
28.8
25.7
22.7
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
25
35
45
55+
Value
All
Poor LE
Total LE
9.4
7.5
7.6
8.9
12.0
5.4.1.7 HALE among all study participants in good baseline PHS status
A participant in good baseline PHS health status could expect to live another 33.8 (SE 7.9) years out of
which 5.3 (SE 5.2) would be in poor PHS health status and 28.5 (SE 10.7) would be in good PHS health
status. Similarly, a participant in Nyanza could expect to live another 28.6 (SE 15.0) years 12.7 (SE 10.4)
years of which would be in poor PHS health status and 15.9 (SE 17.8) years would be in good PHS health
status. For participants in Central, their LE was 37.0 (SE 7.3) years out of which 0.8 (SE 0.7) years would
be in poor PHS health status and 36.2 (SE 7.8) years would be in good PHS health status. At 55 years, a
participant in Nyanza had a total LE of 18.9 (SE 28.3) years distributed thus; 6.1 (SE 2.3) years in poor PHS
health status and 12.8 (SE 29.2) years in good PHS health status. A similar participant in Central had a total
life expectancy of 30.6 (SE 20.9) years, 0.3 (SE 1.1) years of which would be in poor PHS status and 30.3
(SE 19.3) years in good PHS status.
115
Table 5.22: Life expectancy in various statuses of PHS among all study participants in good baseline PHS
status
33.8
39.3
37.0
35.2
29.9
26.8
7.9
9.1
8.5
11.6
19.9
21.8
5.3
9.3
5.6
3.1
4.4
2.2
5.2
3.0
2.6
0.9
10.7
1.1
28.5
30.0
31.4
32.1
25.5
24.7
10.7
10.1
10.1
12.2
9.1
20.4
15.0
18.8
18.1
17.2
27.6
28.3
12.7
16.9
14.5
10.7
10.2
6.1
10.4
4.5
3.9
2.1
14.4
2.3
17.8
18.1
17.9
15.8
12.9
29.2
37.0
44.0
40.0
37.8
33.6
30.6
7.3
6.9
8.2
11.8
13.8
20.9
0.8
1.3
1.1
0.8
0.6
0.3
0.7
0.1
0.2
0.3
2.2
1.1
36.2
42.7
39.0
37.0
33.1
30.3
Std Error
Value
Good LE
Std Error
Value
Std Error
Value
Value
15.9
18.0
16.7
16.1
13.8
12.8
Central
Poor LE
Total LE
Std Error
Good LE
Std Error
Std Error
Value
28.6
34.9
31.2
26.8
24.1
18.9
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
15
25
35
45
55+
Value
All
Poor LE
Total LE
7.8
7.0
8.3
12.1
10.9
19.3
5.4.1.8 HALE among male study participants in good baseline PHS status
A male participant in good PHS status at baseline could expect to live another 34.0 (SE 11.9) years 1.7 (SE
2.7) years of which would be in poor PHS health status and 32.3 (SE 12.4) would be in good PHS health
status. Similarly, a male participant in Nyanza could expect to live another 20.2 (SE 18.1) years 6.1 (SE 9.5)
years of which would be in poor PHS health status and 14.1 (SE 22.2) years would be in good PHS health
status. For male participants in Central, their LE was 38.4 (SE 11.1) years out of which 0.3 (SE 0.9) years
would be in poor PHS health status and 38.2 (SE 10.9) years would be in good PHS health status. At 55
years, a male participant in Nyanza had a total LE of 18.5 (SE 29.1) years distributed thus; 5.5 (SE 2.8)
years in poor PHS health status and 12.9 (SE 30.5) years in good PHS health status. A similar male
participant in Central had a total life expectancy of 31.2 (SE 20.6) years, 0.3 (SE 1.1) years of which would
be in poor PHS status and 30.9 (SE 19.0) years good PHS status.
116
Table 5.23: Life expectancy in various statuses of PHS among male study participants in good baseline
PHS status
34.0
57.9
48.0
40.0
33.7
27.3
11.9
19.8
19.1
17.9
13.2
21.4
1.7
0.4
1.4
1.8
1.7
1.9
2.7
0.2
1.7
1.9
5.0
1.1
32.3
57.6
46.5
38.2
32.0
25.4
12.4
20.0
20.6
19.7
9.5
20.1
20.2
18.1
6.1
9.5
14.1
22.2
28.0
24.9
21.2
18.5
25.8
21.5
24.3
29.1
8.5
7.5
6.4
5.5
7.6
5.4
10.3
2.8
19.5
17.4
14.8
12.9
33.2
26.6
13.3
30.5
11.1
19.8
18.6
17.4
11.7
20.6
0.3
0.4
0.3
0.3
0.3
0.3
0.9
0.2
0.2
0.2
2.4
1.1
38.2
57.6
50.7
43.7
37.0
30.9
Std Error
Value
Good LE
Std Error
Std Error
Value
38.4
57.9
51.1
44.0
37.3
31.2
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Age
All
15
25
35
45
55+
Value
Total LE
Value
All
Poor LE
10.9
20.0
18.8
17.6
9.7
19.0
5.4.1.9 HALE among female study participants in good baseline PHS status
A female participant who at baseline was in good PHS status could expect to live another 33.6 (SE 6.9)
years, 8.2 (SE 3.8) years of which would be in poor PHS health status and 25.4 (SE 5.9) would be in good
PHS health status. Similarly, a female participant in Nyanza could expect to live another 31.8 (SE 14.4)
years 15.3 (SE 6.3) years of which would be in poor PHS health status and 16.6 (SE 13.4) years would be in
good PHS health status. For female participants in Central, their LE was 35.3 (SE 11.6) years out of which
1.4 (SE 0.7) years would be in poor PHS health status and 33.9 (SE 11.0) years would be in good PHS
health status. At 55 years, a female participant in Nyanza had a total LE of 25.3 (SE 29.3) years distributed
thus; 11.9 (SE 16.1) years in poor PHS health status and 13.4 (SE 13.2) years in good PHS health status. A
similar female participant in Central had a total life expectancy of 25.9 (SE 16.1) years, 1.2 (SE 2.0) years
of which would be in poor PHS and 24.7 (SE 13.6) years good PHS status.
117
Table 5.24: Life expectancy in various statuses of PHS among female study participants in good baseline
PHS status
3.8
3.2
2.6
1.2
16.6
6.0
25.4
27.3
27.0
25.5
18.1
16.1
5.9
8.6
5.9
5.4
15.5
18.3
31.8
34.9
31.5
28.4
25.3
22.1
14.4
18.8
17.3
15.4
29.3
26.0
15.3
16.9
15.1
13.4
11.9
10.3
6.3
4.5
4.4
4.0
16.1
5.0
16.6
18.0
16.5
14.9
13.4
11.8
13.4
18.1
15.7
13.3
13.2
20.7
35.3
40.7
35.4
30.6
25.9
21.5
11.6
6.1
6.9
7.9
16.1
10.1
1.4
1.5
1.4
1.3
1.2
1.1
0.7
0.1
0.2
0.3
2.0
0.4
33.9
39.3
34.0
29.3
24.7
20.4
Std Error
Value
Good LE
Std Error
Estimation of HALE adjusted for various MHS statuses
8.2
10.2
6.9
4.5
7.4
5.7
Value
5.4.2
6.9
7.8
4.9
5.8
32.8
24.6
Std Error
33.6
37.4
33.8
30.0
25.6
21.8
Value
Value
Age
All
15
25
35
45
55+
Central
Poor LE
Total LE
Std Error
Good LE
Std Error
Std Error
Value
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Value
All
Poor LE
Total LE
11.0
6.1
6.8
7.8
13.6
9.6
5.4.2.1 HALE among all study participants regardless of their baseline MHS status
Table 5.25 gives HALE adjusted for various MHS health statuses for all study participants regardless of
their baseline MHS health status. A participant could expect to live another 30.9 (SE 15.6) years 2.5 (SE
1.9) years of which would be in poor MHS health status and 28.2 (SE 15.7) would be in good MHS health
status. Similarly, a participant in Nyanza could expect to live another 22.1 (SE 6.0) years 4.8 (SE 4.3) years
of which would be in poor MHS health status and 17.2 (SE 5.2) years would be in good MHS health status.
For participants in Central, their LE was 37.7 (SE 24.8) years out of which 0.7 (SE 0.3) years would be in
poor MHS health status and 36.7 (SE 24.9) years would be in good MHS health status. At 55 years, a
participant in Nyanza had a total LE of 13.4 (SE 8.4) years distributed thus; 5.2 (SE 2.7) years in poor MHS
health status and 8.2 (SE 8.2) years in good MHS health status. A 55 years old participant in Central had a
total life expectancy of 23.2 (SE 20.4) years, of which 1.0 (SE 0.2) years and 22.2 (SE 19.9) years would be
in poor and good PHS status respectively.
118
Table 5.25: Life expectancy in various statuses of MHS among all study participants regardless of their
baseline MHS health status
30.9
31.1
27.7
25.4
22.0
20.1
15.6
10.6
20.3
27.7
34.3
19.1
2.5
4.2
3.2
2.0
1.5
1.7
1.9
3.8
2.6
1.9
1.4
0.3
28.2
26.9
24.5
23.4
20.6
18.4
15.7
9.0
19.4
27.4
33.5
18.6
6.0
7.1
11.8
13.7
7.3
8.4
4.8
6.2
5.4
4.0
2.9
5.2
4.3
5.3
4.8
4.0
3.9
2.7
17.2
20.4
17.9
15.7
13.1
8.2
5.2
4.1
9.5
12.8
6.3
8.2
24.8
31.2
29.6
41.7
42.2
20.4
0.7
0.6
0.8
0.6
0.4
1.0
0.3
0.5
0.5
0.3
0.4
0.2
36.7
38.3
31.7
29.1
26.3
22.2
Std Error
Value
Good LE
Std Error
Std Error
Value
37.7
38.9
32.5
29.6
26.7
23.2
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
22.1
26.6
23.2
19.7
16.0
13.4
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
15
25
35
45
55+
Value
All
Poor LE
Total LE
24.9
31.7
30.0
41.7
41.9
19.9
5.4.2.2 HALE among male study participants regardless of their baseline MHS status
A male participant could expect to live another 38.5 (SE 16.0) years 2.0 (SE 1.4) years of which would be in
poor MHS health status and 36.1 (SE 16.3) would be in good MHS health status. Similarly, a male
participant in Nyanza could expect to live another 24.6 (SE 2.7) years 5.3 (SE 4.8) years of which would be
in poor MHS health status and 19.3 (SE 4.7) years would be in good MHS health status. For male
participants in Central, their LE was 44.1 (SE 22.3) years out of which 0.7 (SE 0.2) years would be in poor
MHS health status and 42.9 (SE 22.3) years would be in good MHS health status. At 55 years, a male
participant in Nyanza had a total LE of 20.6 (SE 4.2) years distributed thus; 4.9 (SE 2.7) years in poor MHS
health status and 15.5 (SE 3.9) years in good MHS health status. A similar participant in Central had a total
LE of 24.1 (SE 20.4) years distributed thus; 1.0 (SE 0.2) years in poor MHS health status and 23.1 (SE 19.8)
years in good MHS health status.
119
Table 5.26: Life expectancy in various statuses of MHS among male study participants regardless of their
baseline MHS status
38.5
45.6
34.3
31.3
26.9
20.2
16.0
23.1
10.5
28.6
36.9
18.7
2.0
3.1
3.9
2.3
1.4
1.5
1.4
0.4
3.7
2.2
1.7
0.3
36.1
42.5
30.4
29.0
25.5
18.7
16.3
23.3
12.1
28.5
36.2
18.1
2.7
13.7
4.6
10.6
3.3
4.2
5.3
8.3
6.9
5.2
3.6
4.9
4.8
0.0
6.4
4.9
5.1
2.7
19.3
25.8
21.6
19.4
16.6
13.7
4.7
13.7
6.2
10.0
4.5
3.9
22.3
26.5
27.4
43.9
42.0
20.4
0.7
0.6
0.8
0.6
0.3
1.0
0.2
0.4
0.4
0.2
0.4
0.2
42.9
50.6
39.7
34.8
30.1
23.1
Std Error
Value
Good LE
Std Error
Std Error
Value
44.1
51.2
40.4
35.4
30.4
24.1
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
24.6
34.1
28.5
24.5
20.2
18.6
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
15
25
35
45
55+
Value
All
Poor LE
Total LE
22.3
26.8
27.7
43.8
41.6
19.8
5.4.2.3 HALE among female study participants regardless of their baseline MHS status
A female participant could expect to live another 23.9 (SE 15.5) years 2.9 (SE 2.5) years of which would be
in poor MHS health status and 20.9 (SE 14.9) would be in good MHS health status. Similarly, a female
participant in Nyanza could expect to live another 20.9 (SE 8.2) years 4.6 (SE 4.2) years of which would be
in poor MHS health status and 16.2 (SE 7.1) years would be in good MHS health status. For female
participants in Central, their LE was 27.8(SE 29.1) years out of which 0.7 (SE 0.5) years would be in poor
MHS health status and 27.1 (SE 29.4) years would be in good MHS health status. At 55 years, a female
participant in Nyanza had a total LE of 11.9 (SE 21.4) years distributed thus; 5.8 (SE 0.5) years in poor
MHS health status and 6.1 (SE 21.1) years in good MHS health status. A 55 year old female participant in
Central had a total LE of 16.5 (SE 22.9) years of which 1.3 (SE 0.4) years would be in poor MHS health
status and 15.2 (SE 22.9) years in good MHS health status.
120
Table 5.27: Life expectancy in various statuses of MHS among female study participants regardless of
their baseline MHS status
23.9
29.1
24.6
20.1
15.2
13.8
15.5
10.5
21.4
25.6
29.4
22.6
2.9
4.3
2.8
1.8
1.5
3.9
2.5
4.0
2.3
1.8
1.4
0.4
20.9
24.8
21.8
18.3
13.7
9.9
14.9
8.9
20.4
25.5
28.5
22.5
8.2
7.4
13.2
14.9
10.9
21.4
4.6
6.1
4.6
3.2
2.3
5.8
4.2
5.3
4.1
3.5
3.2
0.5
16.2
20.0
16.2
12.9
10.2
6.1
7.1
4.5
10.9
14.6
9.6
21.1
29.1
32.6
30.8
38.5
42.6
22.9
0.7
0.6
0.8
0.6
0.4
1.3
0.5
0.6
0.6
0.5
0.5
0.4
27.1
34.8
27.9
22.9
18.4
15.2
Std Error
Value
Good LE
Std Error
Std Error
Value
27.8
35.4
28.7
23.5
18.8
16.5
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
20.9
26.1
20.8
16.1
12.5
11.9
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
15
25
35
45
55+
Value
All
Poor LE
Total LE
29.4
33.2
31.3
38.6
42.6
22.9
5.4.2.4 HALE among all study participants in poor baseline MHS status
A participant who at baseline was in poor MHS health status could expect to live another 22.9 (SE 18.0)
years 2.9 (SE 2.6) years of which would be in poor MHS health status and 20.0 (SE 17.0) would be in good
MHS health status. Similarly, a participant in Nyanza could expect to live another 19.7 (SE 10.9) years 4.5
(SE 4.2) years of which would be in poor MHS health status and 15.2 (SE 8.9) years would be in good MHS
health status. For participants in Central, their LE was 27.1 (SE 31.2) years out of which 0.9 (SE 0.3) years
would be in poor MHS health status and 26.2 (SE 31.3) years would be in good MHS health status. At 55
years, a participant in Nyanza had a total LE of 14.4 (SE 3.0) years distributed thus; 2.9 (SE 0.1) years in
poor MHS health status and 11.6 (SE 3.0) years in good MHS health status. A similar participant in Central
had a total LE of 19.5 (SE 19.0) years distributed thus; 0.8 (SE 0.2) years in poor MHS health status and
18.7 (SE 19.2) years in good MHS health status.
121
Table 5.28: Life expectancy in various statuses of MHS among all study participants in poor baseline
MHS status
22.9
26.6
23.6
18.0
15.5
18.0
18.8
20.3
12.2
6.4
2.9
3.6
2.6
2.3
2.4
2.6
3.0
2.1
2.7
0.1
20.0
23.0
21.0
15.8
13.1
17.0
17.5
19.5
10.4
6.5
10.9
11.7
5.7
9.3
3.0
4.5
5.7
4.6
3.1
2.9
4.2
5.1
4.5
3.6
0.1
8.9
9.2
4.9
6.6
3.0
27.1
30.5
26.4
22.9
19.5
31.2
30.7
29.9
23.3
19.0
0.9
1.0
0.9
0.8
0.8
0.3
0.4
0.3
0.2
0.2
26.2
29.4
25.4
22.0
18.7
Std Error
Value
Good LE
Std Error
Value
Std Error
Value
Value
15.2
17.8
15.9
12.0
11.6
Central
Poor LE
Total LE
Std Error
Good LE
Std Error
Std Error
Value
19.7
23.5
20.4
15.1
14.4
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Age
All
25
35
45
55+
Value
Total LE
Value
All
Poor LE
31.3
31.0
30.0
23.5
19.2
5.4.2.5 HALE among male study participants in poor baseline MHS status
A male participant who was initially in poor MHS health status could expect to live another 26.4 (SE 14.5)
years, 3.6 (SE 3.6) years of which would be in poor MHS health status and 22.8 (SE 13.4) would be in good
MHS health status. Similarly, a male participant in Nyanza could expect to live another 23.3 (SE 3.1) years
5.4 (SE 5.3) years of which would be in poor MHS health status and 17.9 (SE 4.7) years would be in good
MHS health status. For male participants in Central, their LE was 30.8 (SE 29.7) years out of which 1.0 (SE
0.2) years would be in poor MHS health status and 29.8 (SE 29.7) years would be in good MHS health
status. At 55 years, a male participant in Nyanza had a total LE of 15.9 (SE 1.1) years distributed thus; 3.2
(SE 0.1) years in poor MHS health status and 12.8 (SE 1.1) years in good MHS health status. A similar
male participant in Central had a total LE of 22.0 (SE 17.4) years distributed thus; 0.8 (SE 0.0) years in poor
MHS health status and 21.2 (SE 17.4) years in good MHS health status.
122
Table 5.29: Life expectancy in various statuses of MHS among male study participants in poor baseline
MHS status
26.4
31.3
27.5
23.2
17.2
14.5
6.0
17.4
9.3
3.0
3.6
4.9
3.4
2.6
2.7
3.6
4.5
2.9
3.2
0.0
22.8
26.4
24.2
20.6
14.5
13.4
7.5
16.5
8.9
3.0
3.1
4.3
3.0
3.8
1.1
5.4
7.2
5.6
4.2
3.2
5.3
6.4
5.4
4.4
0.1
4.7
6.0
4.3
4.5
1.1
30.8
36.5
30.9
26.7
22.0
29.7
29.8
29.4
22.2
17.4
1.0
1.1
1.0
0.9
0.8
0.2
0.2
0.2
0.2
0.0
29.8
35.4
29.9
25.8
21.2
Std Error
Value
Good LE
Std Error
Value
Std Error
Value
Value
17.9
21.1
18.8
15.8
12.8
Central
Poor LE
Total LE
Std Error
Good LE
Std Error
Std Error
Value
23.3
28.2
24.4
20.0
15.9
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
25
35
45
55+
Value
All
Poor LE
Total LE
29.7
29.9
29.4
22.3
17.4
5.4.2.6 HALE among female study participants in poor baseline MHS status
A female participant initially in poor MHS health status could expect to live another 20.5 (SE 20.8) years
2.4 (SE 2.0) years of which would be in poor MHS health status and 18.1 (SE 19.9) would be in good MHS
health status. Similarly, a female participant in Nyanza could expect to live another 17.0 (SE 13.0) years 3.7
(SE 3.5) years of which would be in poor MHS health status and 13.2 (SE 11.0) years would be in good
MHS health status. For female participants in Central, their LE was 24.8 (SE 29.4) years out of which 0.9
(SE 0.5) years would be in poor MHS health status and 23.9 (SE 29.8) years would be in good MHS health
status. At 55 years, a female participant in Nyanza had a total LE of 10.1 (SE 9.9) years distributed thus; 1.9
(SE 0.0) years in poor MHS health status and 8.2 (SE 9.9) years in good MHS health status. A similar
female participant in Central had a total LE of 14.8 (SE 22.2) years of 0.7 (SE 0.5) years would be in poor
MHS health status and 14.0 (SE 22.7) years in good MHS health status.
123
Table 5.30: Life expectancy in various statuses of MHS among female study participants in poor baseline
MHS status
20.5
24.4
20.3
14.6
11.3
20.8
21.6
20.2
14.3
15.3
2.4
3.0
2.0
2.0
1.6
2.0
2.2
1.4
2.5
0.2
18.1
21.4
18.3
12.6
9.8
19.9
20.5
19.9
12.1
15.5
13.0
13.3
10.2
12.2
9.9
3.7
4.9
3.5
2.5
1.9
3.5
4.2
3.6
3.2
0.0
13.2
16.0
12.9
10.1
8.2
11.0
10.9
8.6
9.3
9.9
29.4
31.2
28.0
24.6
22.2
0.9
1.0
0.9
0.8
0.7
0.5
0.5
0.4
0.4
0.5
23.9
27.3
22.3
18.1
14.0
Std Error
Value
Good LE
Std Error
Std Error
Value
24.8
28.2
23.2
18.9
14.8
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
17.0
20.8
16.4
12.7
10.1
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
25
35
45
55+
Value
All
Poor LE
Total LE
29.8
31.6
28.3
24.8
22.7
5.4.2.7 HALE among all study participants in good baseline MHS status
Being in good MHS health status at baseline guaranteed a participant a life expectancy of 34.4 (SE 15.8)
years, 2.3 (SE 1.7) years of which would be in poor MHS health status and 31.8 (SE 15.8) would be in
good MHS health status. For a similar participant in Nyanza, their life expectancy was 23.7 (SE 6.1) years,
5.1 (SE 4.4) years of which would be in poor MHS health status and 18.5 (SE 5.0) years would be in good
MHS health status. For participants in Central, their life expectancy was 41.0 (SE 24.2) years out of which
0.6 (SE 0.2) years would be in poor MHS health status and 40.0 (SE 24.3) years would be in good MHS
health status. At 55 years, a participant in Nyanza had a total LE of 13.7 (SE 10.0) years distributed thus;
3.8 (SE 2.7) years in poor MHS health status and 9.9 (SE 9.1) years in good MHS health status. A similar
participant in Central had a total LE of 24.3 (SE 20.7) years, 1.0 (SE 0.2) years of which would be spent in
in poor MHS health status and 23.3 (SE 20.2) years in good MHS health status.
124
Table 5.31: Life expectancy in various statuses of MHS among all study participants in good baseline
MHS status
34.4
31.1
29.4
26.9
24.2
21.4
15.8
10.6
15.8
35.0
44.3
21.4
2.3
4.2
2.4
1.5
1.0
1.6
1.7
3.8
2.4
1.9
0.4
0.3
31.8
26.9
27.0
25.4
23.2
19.8
15.8
9.0
15.9
34.7
43.8
20.8
6.1
7.1
6.2
17.3
4.1
10.0
5.1
6.2
4.7
3.5
2.6
3.8
4.4
5.3
4.7
3.9
0.0
2.7
5.0
4.1
4.7
16.8
4.1
9.1
41.0
38.9
35.3
31.9
27.8
24.3
24.2
31.2
27.8
51.4
43.9
20.7
0.6
0.6
0.5
0.3
0.2
1.0
0.2
0.5
0.4
0.2
0.4
0.2
40.0
38.3
34.9
31.6
27.6
23.3
Std Error
Value
Good LE
Std Error
Value
Std Error
Value
Value
18.5
20.4
17.9
15.5
14.2
9.9
Central
Poor LE
Total LE
Std Error
Good LE
Std Error
Std Error
Value
23.7
26.6
22.6
19.0
16.8
13.7
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Value
Age
All
15
25
35
45
55+
Value
All
Poor LE
Total LE
24.3
31.7
28.2
51.2
43.4
20.2
5.4.2.8 HALE among male study participants in good baseline MHS status
A male participant in good MHS health status at baseline could expect to live another 42.7 (SE 17.1) years
1.5 (SE 0.7) years of which would be in poor MHS health status and 40.8 (SE 17.1) would be in good MHS
health status. Similarly, a male participant in Nyanza could expect to live another 26.1 (SE 2.8) years 5.1
(SE 4.1) years of which would be in poor MHS health status and 20.9 (SE 5.0) years would be in good MHS
health status. For male participants in Central, their LE was 46.5 (SE 21.7) years out of which 0.7 (SE 0.1)
years would be in poor MHS health status and 45.3 (SE 21.7) years would be in good MHS health status. At
55 years, a male participant in Nyanza had a total LE of 16.3 (SE 12.9) years distributed thus; 7.2 (SE 3.1)
years in poor MHS health status and 9.1 (SE 11.3) years in good MHS health status. A similar male
participant in Central had a total LE of 24.8 (SE 20.5) years distributed thus; 1.0 (SE 0.2) years in poor
MHS health status and 23.8 (SE 20.0) years in good MHS health status.
125
Table 5.32: Life expectancy in various statuses of MHS among male study participants in good baseline
MHS status
42.7
45.6
39.3
34.1
28.1
23.6
17.1
23.1
14.4
36.9
43.8
20.6
1.5
3.1
2.3
1.5
1.1
1.3
0.7
0.4
2.5
2.1
0.4
0.3
40.8
42.5
37.0
32.6
27.1
22.8
17.1
23.3
15.7
37.0
43.2
20.1
26.1
34.1
29.4
24.7
20.2
16.3
5.1
8.3
6.2
4.5
3.2
7.2
4.1
0.0
6.3
4.8
0.0
3.1
20.9
25.8
23.1
20.2
17.1
9.1
5.0
13.7
7.4
13.0
1.0
11.3
21.7
26.5
24.3
49.5
43.3
20.5
0.7
0.6
0.5
0.3
0.2
1.0
0.1
0.4
0.3
0.2
0.4
0.2
45.3
50.6
43.7
37.3
31.0
23.8
Std Error
Value
Good LE
Std Error
Std Error
Value
46.5
51.2
44.1
37.6
31.2
24.8
Value
Central
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
2.8
13.7
5.6
12.8
1.0
12.9
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Std Error
Value
Std Error
Value
Age
All
15
25
35
45
55+
Good LE
Value
All
Poor LE
Total LE
21.7
26.8
24.6
49.2
42.8
20.0
5.4.2.9 HALE among female study participants in good baseline MHS status
Table 5.33 gives HALE adjusted for various MHS health statuses for female study participants in good
baseline MHS health status. A female participant in good MHS health status at baseline could expect to live
another 25.6 (SE 15.2) years 3.2 (SE 2.7) years of which would be in poor MHS health status and 22.4 (SE
14.4) would be in good MHS health status. Similarly, a female participant in Nyanza could expect to live
another 22.8 (SE 7.7) years 5.1 (SE 4.4) years of which would be in poor MHS health status and 17.8 (SE
6.4) years would be in good MHS health status. For female participants in Central, their LE was 29.6 (SE
30.2) years out of which 0.5 (SE 0.5) years would be in poor MHS health status and 29.0 (SE 30.5) years
would be in good MHS health status. At 55 years, a female participant in Nyanza had a total LE of 11.7 (SE
13.8) years distributed thus; 2.7 (SE 0.6) years in poor MHS health status and 9.0 (SE 8.2) years in good
MHS health status.
A similar female participant in Central had a total LE of 15.3 (SE 24.8) years
distributed thus; 1.5 (SE 0.4) years in poor MHS health status and 13.8 (SE 24.7) years in good MHS health
status.
126
Table 5.33: Life expectancy in various statuses of MHS among female study participants in good baseline
MHS status
25.6
29.1
24.9
19.9
15.8
13.6
15.2
10.5
16.7
32.7
46.2
17.4
3.2
4.3
2.5
1.6
0.9
5.0
2.7
4.0
2.5
2.0
0.3
0.5
22.4
24.8
22.4
18.3
14.9
8.6
14.4
8.9
16.3
32.5
46.0
17.1
7.7
7.4
8.7
19.3
11.1
13.8
5.1
6.1
4.3
2.9
1.9
2.7
4.4
5.3
4.4
3.6
0.0
0.6
17.8
20.0
16.4
13.0
10.2
9.0
6.4
4.5
6.7
19.2
11.1
8.2
29.6
35.4
29.5
23.9
18.7
15.3
0.5
0.6
0.5
0.3
0.2
1.5
0.5
0.6
0.5
0.5
0.4
0.4
29.0
34.8
29.1
23.5
18.5
13.8
Std Error
Value
Good LE
Std Error
Std Error
30.2
32.6
30.2
54.3
44.5
24.8
Value
Central
Poor LE
Total LE
Std Error
Value
Std Error
Value
Std Error
Value
22.8
26.1
20.7
15.9
12.1
11.7
Good LE
Value
Nyanza
Poor LE
Total LE
Std Error
Value
Good LE
Std Error
Std Error
Age
All
15
25
35
45
55+
Value
Total LE
Value
All
Poor LE
30.5
33.2
30.7
54.2
44.6
24.7
5.5 Conclusion
The results discussed in this chapter indicate that participants from Central region had lower probabilities of
remaining in poor health status after one year follow-up compared to those from Nyanza. A participant from
Nyanza region had a higher probability to transition from good health status at baseline to poor health status
in both PHS and MHS at one year follow-up compared to one from Central region. Compared to females,
males had higher probabilities to transition from poor health status to good health status. Participants
initially in poor health status had a higher probability of transitioning to death at one year follow-up
compared to those initially in good health status.
Compared to multistate life table approach, Sullivan method estimates for number of years spent in poor
health states were higher. This was due to the fact that this approach does not give room for ones status to
change over the study period while the results clearly showed that majority of study participants who were
initially in poor health state transitioned to good health state at the end of one year follow-up compared to
only a few who transitioned from good to poor state.
127
There were statistically significant differences in health adjusted life expectancy among participants in
Central and Nyanza regions. A participant in Central region had higher HALE estimates for years spent in
good PHS health state compared to one from Nyanza while participants with no opportunistic infections
compared to those with opportunistic infections and those with a household item compared to those with no
household item had higher estimates of HALE spent in good PHS status. There were no statistically
significant differences in HALE estimates between males and females in both health summary measures.
128
CHAPTER SIX
SUMMARY, CONCLUSION AND RECOMMENDATIONS
6.1
Introduction
The study set out to address four objectives namely: assess health related quality of life of adult HIV/AIDS
patients in different waves of data collection in Nyanza and Central Kenya; compare transition probabilities
from one health state to another across different time periods for adult HIV/AIDS patients in Nyanza and
Central regions of Kenya; compare health adjusted life expectancy among adult HIV/AIDS patients in
Nyanza and Central Kenya regions; and determine factors associated with health adjusted life expectancy
for adult HIV/AIDS patients in Nyanza and Central Kenya regions. The study was conducted between April
2014 and January 2016 with a total of 388 participants recruited into the study. Data were collected in two
waves at baseline and after one year of follow-up among adults living with HIV/AIDS aged 15 years and
more. The data collected included sociodemographic information and HRQOL as measured using a
standardized tool called MOS-HIV which provides information on 11 dimensions of health related quality
of life. Information collected using MOS-HIV tool gave rise to two health summary measures – physical
health summary (PHS) measure and mental health summary (MHS) measure. The scores of these two
measures ranged between 0-100 with higher scores reflecting better health in the specific summary measure.
The measures were then categorized into different thresholds and using different approaches, the number of
years spent in each threshold was obtained. These years lived in different thresholds of health were referred
to as health adjusted life expectancy and they were compared across different sub groups of study
participants.
6.2
Summary of results
Participants displayed varying characteristics. In general, majority of the participants started HIV chronic
care within 1 week of being diagnosed with HIV though this varied across the two regions. Having a
treatment supporter was almost universal, a key component that influences many aspects of HRQOL as
129
discussed later in this section. Having or not having a treatment supporter did not differ by region. Another
characteristic of the study participant is that majority of them were being enrolled with low CD4, with about
85 percent joining with less than 500 CD4 count. Co-infection rate with TB was relatively high at 20
percent while presence of an opportunistic infection including TB was also relatively high, (24 percent).
HRQOL measures for various domains of health as well as for the two health summary measures increased
significantly from baseline to one year follow-up. The score for distress increased by 21.3 points, role
functioning by 20.6 points, social functioning by 17.8 points and cognitive functioning by 16.2 points. There
was also a 5.5 point increase in PHS measure and 7.9 point increase in MHS measure. This increase in
HRQOL measures is in line with the goal of the national ART programme which among other things aims at
improving the quality of life of people living with HIV (NACC 2014). While there was an increase in the
two health summary measures across the two regions, Nyanza recoded lower increase of 4.7 points in PHS
compared to Central but had a higher increase in MHS. The scores for Nyanza however remained 50 point
mark even at one year follow-up while those of Central surpassed the 50 point mark at one year follow-up.
There were statistically significant differences by region in general health perception, physical health
perception, social functioning, cognitive functioning, pain and PHS scores at baseline. At one year followup, region differences were observed in general health perception social functioning, cognitive functioning,
pain, vitality, health transitioning and PHS scores. The increase in number of health domains associated
with region at one year follow-up could not be explained by the study and it may be an interesting area for
further research. Other than regional differences, there were observed differences in HRQOL scores by TB
status, having a treatment supporter, having an opportunistic infection and possessing a household item.
Differences by possession of a household item in HRQOL scores was only observed at follow-up, a clear
indication of the role of environmental factors in influencing HRQOL.
130
In assessing HIV/AIDS programmatic outcomes at one year follow-up, possessing a household item was
found to be a significant factor influencing a participant being dead relative to being alive with those
possessing household items having lower odds of being dead relatively to be alive. Mental health summary
measure was an important factor in determining being lost to follow-up relative to being alive at one year
follow-up whereas age was found to be a factor associated with being transferred out at one year follow-up
relative to being alive. Younger participants had higher odds of transferring out relative to being in the
initial facility compared to those aged 55 years and above. These results clearly show the dynamics of a
population where younger people are more mobile than the older people probably in search of employment
and where tracking mechanisms do not exist for establishing if the transferred HIV/AIDS patients are still
actively receiving care, there is need for strategies to retain young people into the programme for better
outcomes. There were no any observed differences in one year follow-up programmatic outcomes in
HIV/AIDS patients by region and baseline PHS/MHS statuses.
Estimation of HALE using Sullivan approach showed that bigger proportion of the remaining life would be
spent in poor (low and moderate) health status as compared to proportion spent in good (good and very
good) health status for both PHS and MHS. As an example, a participant aged 55 years and above could
expect to live 13.6 (66 percent) years of their remaining live in poor PHS status compared to 7.7 (33
percent) of remaining years lived in good PHS status. As discussed further in this section, the use of
Sullivan method overestimated years lived in poor health which is a function of poor HRQOL scores
recorded at baseline. This over estimation can be attributed to that fact that Sullivan method uses cross
sectional data that assumes that the assessed HRQOL status would remain the same over a given follow-up
period thus giving no room for improving of the status despite interventions meant to improve the same.
Factors that were associated with Sullivan estimates of HALE were region, opportunistic infection and
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possession of household item for PHS and opportunistic infection and possession of household item for
MHS.
As mentioned earlier, HALE was also estimated using MSLT approach. To use this approach, it was
necessary to have PHS and MHS measures in different thresholds (statuses) at both baseline and at one year
follow-up and calculate transition probabilities from one health state to another. Among participants across
all age groups, the proportion in poor health status in both summary measures reduced significantly at
follow-up while those in good health status increased significantly. The increase in proportion of those in
good PHS health status was highest among those aged 55 years, above 150 percent increase and lowest
among those in 15-24 age group, about 20.0 percent. In general, the proportion of participants in good PHS
health status increased by 53 percent. Transition to death was also estimated with about 3.8 percent of
participants reported dead at the end of one year follow-up. The probability of transitioning from good to
poor PHS health status was 0 for males in both regions but was relatively high among females especially in
Nyanza. The probability of transitioning to good PHS health status was high among males and though high
among females, their probabilities were lower than those for males. All males in initial good PHS health
status remained in this status at one year follow-up but this was not the case for females. Transition
probabilities from good PHS health status to poor PHS health status were high among younger women.
Probability of transitioning to death was 0 among participants in Nyanza in initial good PHS health status.
In Central, probability of transitioning to death was relatively high among those in initial poor PHS health
status and it increased with age.
The proportion of study participants who were in good MHS health status increase from 48 percent to 81
percent at one year follow-up and it was highest among those aged 55 years and above and lowest among
those aged 15-24 years. Probability of transitioning to poor MHS health status was zero among male
participants initially in good MHS health status. The probability in remaining in poor MHS health status
132
reduced with age, a significant finding since it would be expected that younger people would benefit more
in terms of improved quality of life from ART programmes. A significant proportion of females transitioned
from good MHS health status to poor at follow-up and this was found to be higher in Nyanza compared to
Central. The probability of transitioning from poor MHS health status to good MHS health status increased
with age and it was high among women relative to males and in Central relative to Nyanza. This was also a
significant finding which was in agreement with an earlier finding where younger participants did not seem
to benefit as much as older participants in improving their health related quality of life. Probability of
transitioning to death was higher among those in poor MHS health status at baseline and increased with age
as expected. The probabilities were higher among participants in Nyanza compared to those in Central. In
general, participants from Central region had higher probabilities of transitioning to good health status from
poor health status compared to those in Nyanza region.
Using the above discussed probabilities of transitioning from one health status to another, HALE was
estimated using MSLT approach. In general, life expectancy adjusted for various MHS statuses was lower
than that adjusted for various PHS statuses. HALE was higher among male than female across all health
status and regions, was higher among those initially in good health statuses than those in poor health
statuses and higher among those in Central region than in Nyanza region. The findings of the study are in
line with those of another similar study done in Malawi, (Payne, 2013). With mental disorders expected to
be higher among patients experiencing higher index of stigmatization, the study results are consistent with
measured stigma levels in the two regions. In a study conducted by National AIDS Control Council
(NACC), stigma levels in counties within Nyanza region are significantly lower than those in counties in
former Central region; 35 percent against 38.9 percent (NACC, 2014). From this, it would be expected that
patients I Nyanza would have better mental health than those in Central, a fact confirmed by the results of
the study.
133
The findings of this study clearly confirm one of the main tenets of care for HIV/AIDS patients; putting
HIV/AIDS patients on care is meant to “deconstruct the idea of death arising from the diagnosis of
HIV/AIDS and construct one of a better prospects for life”, (Barreto et al., 2015). With HIV/AIDS therapy
expected to improve longevity of patients, quality of life measure becomes an important outcome to assess
(Basararaj et al., 2010). The results recorded both at baseline and at one year follow-up where patients
recorded very low baseline HRQOL scores and had high probabilities of transitioning to better health status
validate this precept. Infection with HIV/AIDS leads to both physical and psychological problems for the
infected persons (Tang et al., 2015). While appreciating that one of the objectives of putting HIV/AIDS
patients into chronic care is to improve their wellbeing, there is agreement that this wellbeing is determined
by a series of comprehensive elements including social, psychological and physical wellbeing (Tang et al.,
2015). By using MOS-HIV tool, the study measured most of these elements and the associated factors and
the results show that putting HIV/AIDS patients on chronic care indeed leads to improved wellbeing as
measured by HRQOL. Being in care for 12 months was associated with high PHS and MHS scores, and
these results are similar to those obtained in a study conducted in Bukina Faso among similar study
population (Jaquet et al., 2013).
The study has showed regional differences in most of the measures assessed. The observed regional
differences could not be explained by the study but possible explanation could be in the underlying
differences in unmeasured site characteristics or even differences in health seeking behavior or other
practices among the participants from the two regions. Models of care that exist in different facilities and
regions could be possible explanation to these differences. The observed differences by region make a case
for the national TB programme to seek standardizing care provided to HIV/AIDS patients across all regions
and facilities.
134
Most demographic characteristics did not yield significant associations with measures assessed in this study,
a finding that is in agreement with other studies (Haseli et al., 2014; Mekuria et al., 2015). Despite this,
female participants were found to have worse performance than males. It has been shown that economic
status, marital status and social support are possible factors associated with health related quality of life
(Sun et al., 2013; Haseli et al., 2014) and females are likely to be at worse economic status than males since
they depend on men for financial support, are less educated, widowed, may suffer more stigma than their
male counterparts and lack disclosure (Mutabazi-Mwesigire eta al., 2015; Vigreshwaran et al., 2015). It has
also been argued that men may have better results since the society expects them to adapt to situations better
(Mutabazi-Mwesigire et al., 2015). This may explain why female performed worse than males in this study.
As also explained earlier, there might be genetic factors, differences in immune system response, hormones,
disease pattern or even underreporting of health problems by men (Oksuzyan et al., 2008). Women are also
more likely to report poorer health than men (Luy & Minagawa, 2014).
Consistent with results from other studies, the results show that HIV/AIDS patients in higher WHO stages
have poor PHS and MHS scores. Higher WHO stages and OIs are associated with symptoms of AIDS which
in turn are associated with poor PHS score - as the AIDS disease progresses, one’s physical status declines
(Mutabazi-Mwesigire et al., 2015). Coinfection with TB, a function of HIV disease progress, was also found
to be associated with poor PHS scores. This was consistent with results obtained in a study conducted in
Ethiopia (Deribew et al., 2009). Studies have also shown association between CD4 count and HRQOL
(Bokiono et al., 2015). In this study, patients with low CD4 count had lower HRQOL scores compared to
those with higher CD4, a result consistent in other reported studies (Bokiono et al., 2015; MutabaziMwesigire et al., 2015).
While the findings show that possession of a household item played a role in influencing transition
probabilities and the actual scores of both physical and mental health, this needs to be interpreted with
135
caution. The findings may not necessary reflect actual improvement of perceived quality of life but rather a
reflection of the role played by the said items to ease coping with challenges brought about by ill health.
6.3
Conclusion
The study has shown that people living with HIV start HIV chronic care in worse health statuses but this
gets better as they continue receiving care in the programme. This however varies across different
subpopulations including region, gender among others. Transition to different health statuses during followup also varies across different subpopulations of study participants with younger participants having lower
probabilities of transitioning from poor to good health status, higher probabilities of remaining in poor
health status and higher probabilities of transitioning from good to poor health status. The study has also
shown that HALE is higher among males, among participants in Central, and among those in initial good
health status. Lastly, going by the study findings, the researcher contend that MSLT approach in estimating
HALE gives more reliable estimates as opposed to using Sullivan method for people living with HIV whose
health related quality of live improves greatly when they are in HIV chronic care programme. This
contention however only holds when Sullivan method is used with data collected at the start of chronic care.
The estimates obtained using Sullivan method if data is collected at any other point other than at the start of
chronic care may be different from what is obtained here and may be comparable to estimates obtained
through MSLT approach.
6.4
Implications of the study
This study has methodological and programmatic implications. It also has its contribution to existing
knowledge in the field of health demography. The study has shown that apart from helping improve clinical
outcomes of people living with HIV, enrolling people into HIV care programmes not only helps improve
their health related quality of life but also increases the proportion of their lives spent in better health
136
statuses.
Secondly the study has shown that the amount of life spent in a specific status of health is a
functional of initial health status which on the other hand is associated with such factors as region, sex
opportunistic infections, having a treatment supporter, possession of a household item and ones TB status
among others. While some of these factors such as having a treatment supporter are amenable through
programming, some require further research to understand them better such as sex while others need a shift
in policy for instance the kind of social support that vulnerable people living with HIV receive given the
important role played by possession of a household item.
6.4.1
Methodological implications
As mentioned above, two approaches were applied in estimation of HALE; Sullivan and MSLT using
SPACE programme. Sullivan method uses cross sectional information while MSLT uses information
collected at different points in time. The main assumption of Sullivan approach is that the target population
is not expected to transition to other health states and is appropriate for conditions that are not expected to
change often such as permanent disabilities or chronic conditions in which conditions can only get worse.
However for conditions with high transitions to different states including conditions where ones status can
greatly improve in time, Sullivan approach is not appropriate. This has been clearly demonstrated by this
study. Using Sullivan approach proportion of life spent in worse health statuses was over estimated while
that spent in better health statuses underestimated. This is because Sullivan approach relied on baseline
health statuses which have been shown to be worse at enrolment as compared to one year follow-up. The
method ignored the fact that by enrolling into chronic HIV care programmes, the health status of people
living with HIV was expected to improve, a fact that MSLT approach acknowledges and fully exploits. The
estimates of HALE through MSLT approach are the reverse of those estimated by Sullivan method. Amount
of life spent in better health statuses was higher than that spent in worse health statuses while the reverse
was true for estimates obtained through Sullivan approach. As an example, a female participant aged 55
137
years and above could expect to life 15.1 years in good PHS health status and 6.9 years in poor PHS health
status as estimated by MSLT while Sullivan estimate showed that the same participant could expect to live
6.6 years in good PHS health status and 15.5 years in poor PHS health status and this was the case across all
subpopulations of study participants. It is the assertion of this study therefore that MSLT estimates are more
reliable estimates for people living with HIV who are already enrolled in chronic care programmes since
their health is expected to improve as they continue receiving care.
6.4.2
Programmatic implications
As discussed above, the findings of this study have implications for programming. First, there is need to
better understand why younger people have worse outcomes than the older people, why women have worse
HALE compared to men and what can be done to ensure people living with HIV get treatment supporters,
have less OIs and enroll into chronic HIV programmes. The most critical implication of the study on
programming is that putting people on HIV/AIDS care has positive outcomes including helping them
improve on their health related quality of life and more importantly their HALE. Therefore more efforts
should be made to strengthen and expand HIV/AIDS care and treatment programmes so that all people
living HIV/AIDS can receive reap the benefits.
6.5
Recommendations
Arising from the above results, the following recommendations were made:
(i)
In estimating HALE for people living HIV or other chronic conditions with favorable transitions to
better health statuses, MSLT approach should be considered since it gives more reliable estimates compared
to Sullivan approach. MSLT takes into consideration transitioning to different health statuses while Sullivan
method assumes individuals remain in their current status. This however only holds when Sullivan method
utilizes data for HRQOL measures assessed at the start of chronic care. The present study did not compare
138
Sullivan estimates obtained from HRQOL measures assessed at any other point during follow-up of
HIV/AIDS patients;
(ii)
Secondly, the results of the study have shown that possession of household items plays an important
role in determination of HRQOL. With existence of many social programmes targeting people living with
HIV, there is need for a policy shift in ensuring targeted support especially in ensuring people living with
HIV are assisted to possess household items which greatly improve the environment they live in thus
translating into better HRQOL and HALE;
(iii)
Having a treatment supporter is of great importance which shows that one has disclosed their HIV
status to their significant others thus ensuring they receive supporter from them. Having a treatment
supporter should therefore be encouraged as a strategy to improve HRQOL and HALE;
(iv)
With the results consistently showing that presence of an opportunistic infection is related to poor
HRQOL measures, treatment of the same among people living with HIV/AIDS is an important contribution
to improving HRQOL and HALE and should therefore be strengthened among HIV programmes;
(v)
The results have shown that younger people have poorer outcomes than older people. In this regard,
HIV programmes need to come up with strategies targeting younger people so as to improve their outcomes
including transitioning to better health statuses once they start HIV care and thus increasing years spent in
better health statuses;
(vi)
There is need for the national HIV programme in Kenya to do an assessment of different models of
care in different regions of Kenya and come up with a policy that would ensure there is standardization in
delivery of services that would probably ensure that benefits of HIV chronic care programmes in improving
HRQOL and HALE are uniform across the regions;
(vii)
Further research is needed in Kenya to establish why males living with HIV/AIDS have better
HRQOL and HALE than females as well as establish mechanisms through which possession of a household
item interacts with HRQOL.
139
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Appendices
Appendix 1: Demographic questionnaire
Form P1:
Demographic and HIV/AIDS status questionnaire
Profile and Background Information
Q1 Patient’s study unique number
Q2
Patient's sex male=1, female=2
Q3
Date of birth
(ddmmyy)
unknown day=15, unknown month =06,
unknown year 888888
Age of the patient (years)?
Q4
Q5
Marital status of
the patient?
Never married = 1
Married monogamous = 2
Married polygamous = 3
Cohabiting = 4
Separated/divorced = 5
Widowed = 6
Q6 Religion of the
Protestant = 1
patient?
Catholic = 2
Muslim = 3
Traditionalist = 4
Atheist =5
Other (specify) = 6
Q7 The highest level of None=1
education the
Primary=2
patient has
Secondary=3
achieved?
Tertiary=4
University=5
Q8 Patient’s residence Rural = 1
Urban = 2
Q9 The main material
Earth, mud, sand=1
used to make the
Cement=2
floor of the
Linoleum=3
patient’s house?
Parquet/polished wood=4
Tile=5
Carpet=6
Stone=7
Q10 The main material
Thatched/straw=1
used to make the
Mud and poles=2
walls of the
Un-burnt bricks=3
patient’s house?
Burnt bricks with mud=4
148
Cement blocks=5
Stone=6
Wood timber=7
Burnt bricks with
cement=8
Q11 The main material
Thatched=1
used to make the
Wood/planks=2
roof of the patient’s Corrugated iron sheets=3
house?
Asbestos=4
Tiles=5
Tin=6
Cement/concrete=7
Q12 The type of toilet
Private flush=1
used by the patient Private VIP latrine =2
at home?
Private traditional pit
(covered)=3
Private traditional pit
(uncovered)=4
Public/shared=5
Bush/field/other=6
Q13 Approximately how far from this facility does
the patient live? (kilometers)
If not known, don't know=888
Q14 The main
source of
safe
drinking
water for
the
patient’s
house?
Bottled=0
Piped inside house=1
Piped outside house (yard, public
tap)=2
Protected well=3
Borehole=4
Spring/rain water=5
Unsafe unprotected well=6
River/stream/pond=7
Tanker truck=8
Q15 Does
Q15.1Car
anyone in
Q15.2 Bicycle
the
Q15.3 Refrigerator
patient’s
Q15.4 Television
household
Q15.5 Mobile phone
own a …?
Q15.6 Radio
yes = 1, no = 2
Q16 What is the Unemployed=1
patient’s
Student = 2
occupation? Skilled laborer = 3
Unskilled laborer = 4
Professional employed = 5
149
km
Professional consultant = 6
Own Business =7
0-10,000 = 1
10,001 – 38,000 = 2
38,001 – 100,000 = 3
Above 100,000 = 4
Q17 What is the
patient’s
net income
per month
in KSh?
Q18 How many dependants does the patient have?
(family members who are dependent on the
patient including children)
HIV/AIDS Status Information
Q19 Date diagnosed HIV positive (ddmmyy)
Q20 Date of enrolment into HIV care
Q21 Date of current visit
(ddmmyy)
(ddmmyy)
Q22 Was the patient Yes=1
scheduled to
No =2
come to the
If this is the initial visit, Not
clinic today?
Applicable = 3
Q23 If no to Q22, date scheduled to come (ddmmyy)
Q24 Does the patient have a treatment supporter? Yes=1,
No =2
If no skip to Q26
Q25 If yes to Q25, what Mother=1
is the relationship Father=2
of the treatment
Son = 3
supporter with the Daughter=4
patient?
Wife = 5
Husband = 6
Aunt = 7
Uncle =8
Grandmother = 9
Grandfather = 10
Friend = 11
Other(specify) =12
Q26 Why did the
Normal = 1
patient visit the
CD4 = 2
clinic today?
Adherence counseling = 3
Sick = 4
New = 5
Refill = 6
Others (specify) = 7
Q27 Date started on ARV
(ddmmyy)
not on ARV =777777
150
Others__________________________
Others__________________________
Q28 Regimen started (Use standard codes)
Not on ARV = 777777
Q29 ARV baseline CD4 count
Not on ARV = 777777
Q30 ARV baseline WHO stage
Not on ARV = 777777
Q31 Date started on current regimen
(ddmmyy)
not on ARV =777777
Q32 Current regimen (Use standard codes)
not on ARV =777777
Q33 Patient ARV
Satisfactory = 1
adherence level
Not satisfactory = 2
Assessment not done = 3
Not on ARV = 4
Q34 Current WHO clinical stage (1-4) 8=don't know
Q35 Most recent CD4 count (for initial visit use baseline
CD4)
Q36 Date of most recent CD4 count
(ddmmyy)
(for initial visit use baseline CD4 date)
Q37 What is the
No signs = 1
patient’s current TB TB Suspect = 2
status?
On treatment = 3
Screening not done= 4
Q38 What is the
Not Pregnant = 1
patient’s pregnancy Pregnant = 2
status?
Don’t Know = 3
N/A (for males)= 4
Q39 Is the patient on
Yes=1
CTX/Dapsone?
No = 2
Q40 What is the
Satisfactory = 1
patient’s
Not satisfactory = 2
CTX/Dapsone
Assessment not done = 3
adherence level
Not on CTX/Dapsone=4
Q41 Does the patient have any new OI during this visit
Yes=1, No = 2
Q42 If yes, mention all new OI’s during this visit
(use standard codes for new OI’s)
Q43 What is the patient’s current height (cm)
Q44 What is the patient’s current weight (kg)
151
Appendix 2: MOS-HIV questionnaire
152
153
154
155
156
157
Appendix 3: Consent form for the HALE study
Hello.
My name is _________________________and I am a research assistant for a study aimed at assessing
various health issues that may be affecting you. The purpose of the study is to partly help the researcher
obtain a PhD degree and partly to help in understanding what interventions can have better impact in
improving quality of life for HIV patients. This study aims at collecting information on your health status
as reported by you as well as other personal information. The survey does not involve any clinical or
laboratory assessment of your health status beyond what you will regularly receive from the health care
workers as you continue with your visits to this health facility. There are no any health risks involved by
participating in this study other than you giving personal and private information.
No information you provide during this study will be divulged to a third party or used for any other purpose
other than for the study purpose. You will be interviewed three times in the cause of this study; now, after
12 months and after 24 months from today. Each interview will take approximately 20 minutes. Two
questionnaires will be filled during each interview.
Two years after the end of the study, you will be requested to provide more information as a follow-up. This
information will be important to help improve HIV treatment programs in the country.
Your participation in this study is voluntary. In the course of the interview, you are free to decline
answering any question you are not comfortable with or you can request to stop the whole interview at any
time. However, I hope that you will participate in this study since your views are important. If a question
asked reminds you of any discomforting moment, please feel free to share and we will help you get the
necessary counseling support that you may need. I wish to guarantee you that no unauthorized person will
have access to any of your medical information that you may have provided to a health care worker. Such
information will be accessed by authorized personnel in this facility only
There will be no direct material or financial benefit to you for participating in the study but the information
you provide will help in improving our understanding of the effects of the services you and other patients
receive in the facility and eventually improve their quality for the good of all. Your participation will also
help the researcher advance in his academic studies and the knowledge gained will be of benefit to the
whole society. You will not incur any expense in the course of the study.
At this time, do you want to ask me anything about the study?
Are you willing to participate in the study? Yes__________ No__________
Please confirm your willingness to participate in the study by signing here:
Name_________________ _Signature of the study participant______________
Signature of research assistant:______________ Date:__________________
158
Appendix 4: Adapted SPACE code
MSLT_SIMxCOV_S
%MACRO MSLT(DATA=S.Sample,S=0,VAR=HSQ1,NS=3,COV=SEX
REGION,NC=2,STRATA=STRATA,PSU=PSU,WGT=WEIGHT,LOI=1,BEG=15,END=100,SIMSIZE=10
0000);
%DO MA=1 %TO andNC;
%LET COVandMA=%SCAN(andCOV,andMA,' ');
PROC SORT DATA=andDATA;
BY andandCOVandMA;
RUN;
DATA TEMPandMA(KEEP=ID andandCOVandMA);
SET andDATA;
BY andandCOVandMA;
IF FIRST.andandCOVandMA;
RUN;
DATA _NULL_;
SET TEMPandMA END=FINAL;
N+1;
IF FINAL THEN CALL SYMPUT("LCandMA", N); *** LC: LEVEL OF
COVARIATES 1,2,... ***;
RUN;
%END;
PROC SORT DATA=andDATA;
BY ID AGE;
RUN;
DATA SEM;
SET andDATA;
BY ID AGE;
IF FIRST.ID THEN DO;
PREVAGE=.;
PREVST=.;
PREVWGT=.;
END;
OUTPUT;
PREVAGE=AGE;
PREVST=andVAR;
PREVWGT=andWGT;
RETAIN PREVAGE PREVST PREVWGT;
RUN;
159
DATA SEM1(DROP=MIDAGE AGE andVAR andWGT J PREVAGE PREVST PREVWGT
RENAME=(NAGE=AGE HS=andVAR NWGT=andWGT)) ;
SET SEM;
BY ID;
IF FIRST.ID THEN DO;
HS=andVAR;
NAGE=AGE;
NWGT=andWGT;
OUTPUT;
END;
ELSE DO;
IF andVAR NE andNS THEN DO;
*IF MOD(GAP,2)=1 THEN MIDAGE=PREVAGE+FLOOR(GAP/2);
*ELSE
MIDAGE=PREVAGE+CEIL(GAP*RANUNI(ROUND(DATETIME())))-1;
MIDAGE=PREVAGE+CEIL((AGE-PREVAGE)*RANUNI(ROUND(DATETIME())))-1;
DO J=PREVAGE+1 TO AGE;
IF J<=MIDAGE THEN DO;
HS=PREVST;
NWGT=PREVWGT+ROUND((andWGT-PREVWGT)/(AGEPREVAGE));
END;
ELSE DO;
HS=andVAR;
NWGT=andWGT;
END;
NAGE=J;
PREVWGT=NWGT;
OUTPUT;
END;
END;
ELSE DO;
IF PREVAGE+1<AGE THEN DO;
DO J=PREVAGE+1 TO AGE;
IF J NE AGE THEN DO;
HS=PREVST;
NWGT=PREVWGT+ROUND((andWGTPREVWGT)/(AGE-PREVAGE));
END;
ELSE DO;
HS=andVAR;
NWGT=andWGT;
END;
NAGE=J;
PREVWGT=NWGT;
OUTPUT;
END;
160
END;
ELSE IF PREVAGE+1=AGE THEN DO;
HS=andVAR;
NAGE=AGE;
NWGT=andWGT;
OUTPUT;
END;
END;
END;
RUN;
DATA PRSNYR(RENAME=(andVAR=ENDST));
SET SEM1;
BY ID;
AGE=LAG(AGE);
BEGST=LAG(andVAR);
andWGT=LAG(andWGT);
IF FIRST.ID THEN DO;
AGE=0;
BEGST=0;
DELETE;
END;
RUN;
DATA SEM2;
SET SEM1;
BY ID;
IF FIRST.ID;
RUN;
PROC FREQ DATA=SEM2 NOPRINT;
TABLE AGE*andVAR/OUT=HSPREV OUTPCT;
WEIGHT andWGT;
WHERE 15<=AGE<=55;
RUN;
DATA HSPREV2;
SET HSPREV;
PCT=PCT_ROW/100;
KEEP AGE andVAR PCT;
RUN;
PROC SORT;
BY AGE andVAR;
RUN;
DATA HP;
161
DO AGE=15 TO 55;
DO andVAR=1 TO andNS-1;
PCT=0;
OUTPUT;
END;
END;
RUN;
PROC SORT;
BY AGE andVAR;
RUN;
DATA HSPREV3;
UPDATE HP HSPREV2;
BY AGE andVAR;
RUN;
DATA _NULL_;
IF andNC>1 THEN CALL SYMPUT("CP",TRANSLATE("andCOV",'*',' '));
RUN;
DATA COVPREV;
SET SEM2;
BY ID;
IF 15<=AGE<=24 THEN AGE=15;
ELSE IF 25<=AGE<=34 THEN AGE=25;
ELSE IF 35<=AGE<=44 THEN AGE=35;
ELSE IF 45<=AGE<=54 THEN AGE=45;
ELSE IF AGE>=55 THEN AGE=55;
RUN;
PROC SORT;
BY AGE andVAR;
RUN;
PROC FREQ DATA=COVPREV NOPRINT;
BY AGE andVAR;
%IF andNC>1 %THEN %DO;
TABLE andCP / OUT=PREVEST OUTPCT;
%END;
%ELSE %DO;
TABLE andCOV / OUT=PREVEST OUTPCT;
%END;
WEIGHT andWGT;
RUN;
DATA PREVEST2;
SET PREVEST;
162
PCT=PERCENT/100;
KEEP AGE andVAR andCOV PCT;
RUN;
%MACRO MODEL_logit;
DATA SEST;
%DO MA=1 %TO andNC;
DO andandCOVandMA=1 TO andandLCandMA;
%END;
DO BEGST=1 TO andNS-1;
DO AGE=andBEG TO andEND;
CONTROL=1;
OUTPUT;
END;
END;
%DO MA=1 %TO andNC;
END;
%END;
RUN;
DATA MODEL;
SET SEST PRSNYR;
IF CONTROL=. THEN CONTROL=0;
KEEP ID andCOV AGE BEGST ENDST andWGT CONTROL;
RUN;
PROC SORT;
BY BEGST;
RUN;
PROC LOGISTIC DATA=MODEL DESCENDING NOPRINT;
BY BEGST;
CLASS andCOV;
MODEL ENDST=AGE andCOV / L=GLOGIT;
WEIGHT andWGT;
OUTPUT OUT=PROBS PREDPROBS=I;
RUN;
DATA TRANPR;
SET PROBS(WHERE=(CONTROL=1));
%DO U=1 %TO andNS;
PandU=IP_andU;
%END;
KEEP andCOV AGE BEGST P1-PandNS;
RUN;
PROC SORT;
BY andCOV AGE BEGST;
163
RUN;
%MEND;
%MODEL_logit;
DATA BSLE;
STATE=.;
RUN;
DATA PLY4;
COL1=.;
RUN;
%DO A=15 %TO 55 %BY 10;
DATA PLY3;
COL1=.;
RUN;
DATA TRANPR2;
SET TRANPR(WHERE=(AGE>=andA)) END=FINAL;
N+1;
IF FINAL THEN CALL SYMPUT('NT', N);
RUN;
%DO B=1 %TO andNS-1;
DATA PREVEST3;
SET PREVEST2(WHERE=(AGE=andA and andVAR=andB)) END=FINAL;
N+1;
IF FINAL THEN CALL SYMPUT('NR', N);
RUN;
PROC IML;
USE HSPREV3;
READ ALL VAR {PCT} INTO HSPREV WHERE (AGE=andA);
TIMES=ROUND(andSIMSIZE*HSPREV[andB,]);
PLY=J(TIMES,100,0);
*** RECORD HEALTH AT EACH AGE FOR
EACH PERSON ***;
*** PREVALENCE OF COVARIATES BY AGE and HEALTH STATES ***;
USE PREVEST3;
READ ALL VAR {andCOV PCT} INTO PCNT; *** andNR ROWS AND
andNC+1 COLUMNS ***;
*** IF USE %MODEL_LOGIT TO GET THE TRANSITION
PROBABILITIES ***;
USE TRANPR2;
READ ALL VAR {andCOV %DO U=1 %TO andNS; PandU %END;} INTO
SRVP; *** andNT ROWS AND andNS COLUMNS ***;
164
*** SIMULATION ***;
DO T=1 TO TIMES;
PLY[T,1]=100000000*andA+10000000*andB+T;
PLY[T,2]=andA;
***S: RANDONLY SELECTED COVARIATE SET ***;
S=PCNT[RANTBL(ROUND(DATETIME())%DO W=1 %TO andNR; ,PCNT[andW,andNC+1]
%END;),1:andNC];
PLY[T,3:3+andNC-1]=S;
*** FIND THE CORRESPONDING TRANSITION MATRIX FOR
THIS PERSON ***;
%DO J=1 %TO andNT;
IF SRVP[andJ,1:andNC]=S THEN SRVP2=SRVP2//SRVP[andJ,andNC+1:NCOL(SRVP)];
%END;
IHS=andB;
PLY[T,3+andNC]=IHS;
DO K=1 TO NROW(SRVP2)/(andNS-1);
%DO U=1 %TO andNS;
PandU=SRVP2[(andNS-1)*(K-1)+IHS,andU];
%END;
HS=RANTBL(ROUND(DATETIME())%DO Z=1 %TO andNS;,PandZ %END;);
IF 3+andNC+K<=100 THEN PLY[T,3+andNC+K]=HS;
IHS=HS;
IF HS=andNS | (andNS-1)*K=NROW(SRVP2) THEN DO;
PLY[T,3+andNC+K]=andNS;
GOTO ENDA;
END;
END;
ENDA:
FREE SRVP2;
END;
CREATE PLY2 FROM PLY;
APPEND FROM PLY;
CLOSE PLY2;
QUIT;
DATA PLY3;
SET PLY3 PLY2;
IF COL1 NE .;
RUN;
165
%END;
DATA PLY4;
SET PLY4 PLY3;
IF COL1 NE .;
RUN;
%END;
DATA PHSB(KEEP=ID andCOV IAGE AGE andVAR);
SET PLY4;
LENGTH andCOV AGE andVAR 3;
ARRAY COL{100};
ID=COL1;
IAGE=COL2;
%DO MA=1 %TO andNC;
%LET COVandMA=%SCAN(andCOV,andMA,' ');
andandCOVandMA=COL%EVAL(2+andMA);
%END;
DO I=andNC+3 TO 100;
AGE=IAGE+(I-andNC-3);
andVAR=COL{I};
IF andVAR NE 0 THEN OUTPUT;
END;
RUN;
PROC SORT DATA=PHSB OUT=COMAREC;
BY IAGE ID AGE;
RUN;
DATA MSPandVAR.2;
SET COMAREC;
BY IAGE ID;
RENAME andVAR=ENDST;
BEGST=LAG(andVAR);
BAGE=LAG(AGE);
IF FIRST.ID THEN DO;
BEGST=0;
BAGE=0;
DELETE;
END;
RUN;
DATA PHSC(KEEP=ID IAGE BH DAGE);
SET MSPandVAR.2;
BY IAGE ID;
RETAIN BH;
IF FIRST.ID THEN BH=BEGST;
166
IF LAST.ID THEN DO;
DAGE=AGE;
OUTPUT;
END;
RUN;
DATA MSPandVAR.3;
MERGE MSPandVAR.2(RENAME=(AGE=EAGE)) PHSC;
BY IAGE ID;
IF FIRST.ID THEN DO;
YRLVED=0;
YRINAH=0;
YRINAD=0;
END;
IF BEGST=1 THEN DO;
IF ENDST IN (1,andNS) THEN AH=1;
ELSE IF ENDST=2 THEN DO;
AH=0.5;
AD=0.5;
END;
END;
ELSE IF BEGST=2 THEN DO;
IF ENDST IN (2,andNS) THEN AD=1;
ELSE IF ENDST=1 THEN DO;
AH=0.5;
AD=0.5;
END;
END;
YRLVED+1;
YRINAH+AH;
YRINAD+AD;
IF LAST.ID THEN OUTPUT;
RUN;
PROC MEANS DATA=MSPandVAR.3 MEAN P25 P50 P75 NOPRINT;
CLASS IAGE andCOV BH;
VAR YRLVED YRINAH YRINAD;
OUTPUT OUT=MEDLE MEAN=TLE ALE DLE
P25=TLY25 ALY25 DLY25
P50=TLY50 ALY50 DLY50
P75=TLY75 ALY75 DLY75;
RUN;
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DATA MEDLE2;
SET MEDLE;
IF BH=. THEN BH=0;
RENAME BH=STATE;
FORMAT TLE ALE DLE 5.1;
DROP _TYPE_;
RUN;
DATA S.BSLEandS;
SET BSLE MEDLE2;
IF STATE NE .;
RUN;
PROC SORT;
BY andCOV IAGE STATE;
RUN;
%MEND;
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MSLT_SIMxCOV_M
PROC PRINTTO LOG="F:\SAS\Practice\S\MSLT_SIMxCOV_M.LOG";
LIBNAME S "F:\SAS\Practice\S";
%INCLUDE "F:\SAS\Practice\S\MSLT_SIMxCOV_S.SAS";
*** POINT ESTIMATES USING THE FULL SAMPLE ***;
%MSLT(DATA=S.SAMPLE,S=0,VAR=HSQ1,NS=3,COV=SEX
REGION,NC=2,STRATA=STRATA,PSU=PSU,WGT=WEIGHT,LOI=1,BEG=15,END=100,SIMSIZE=10
0000);
DATA S.MSLT_SIMxCOV_LE;
BS=0;
SET S.BSLE0;
RUN;
%MACRO BOOTSTRP(BSIZE=,VAR=,NS=,STRATA=,PSU=,WGT=);
*** THE BOOTSTRAP PART ***;
PROC SORT DATA=S.SAMPLE OUT=SAMPLE3;
BY andSTRATA andPSU;
RUN;
DATA BS1;
SET S.SAMPLE(KEEP=ID andSTRATA andPSU);
BY ID;
IF FIRST.ID;
RUN;
PROC SORT NODUPKEY OUT=BS2;
BY andSTRATA andPSU;
RUN;
DATA BS3(DROP=andPSU);
SET BS2(DROP=ID);
BY andSTRATA andPSU;
IF FIRST.andSTRATA THEN NU=0;
NU+1;
IF LAST.andSTRATA THEN OUTPUT;
RUN;
%DO BS=1 %TO andBSIZE;
PROC IML;
USE BS2;
READ ALL VAR {andPSU} INTO X;
CLOSE BS2;
169
USE BS3;
READ ALL VAR {andSTRATA NU} INTO Y;
CLOSE BS3;
CUMS=J(1,1,0);
DO I=1 TO NROW(Y);
CUMS=CUMS+Y[I,2];
RS=X[CUMS-Y[I,2]+1:CUMS];
IF Y[I,2]=1 THEN DO;
NEWSU[,1]=Y[I,1];
NEWSU[,2]=RS;
*** SINGLE PSU IS SELECTED
WITH CERTAINTY ***;
NEWSU[,3]=Y[I,2];
END;
ELSE DO;
NEWSU=J(Y[I,2]-1,3,0);
NEWSU[,1]=Y[I,1];
NEWSU[,3]=Y[I,2];
*** # OF PSU IN EACH STRATUM
***;
DO J=1 TO Y[I,2]-1;
NEWSU[J,2]=RS[ROUND(RANUNI(ROUND(DATETIME()))*Y[I,2]+0.5)];
END;
END;
BSU=BSU//NEWSU;
END;
VAR={"andSTRATA" "andPSU" "PS"};
CREATE NBSU FROM BSU [COLNAME=VAR];
APPEND FROM BSU;
CLOSE NBSU;
QUIT;
PROC SORT DATA=NBSU;
BY andSTRATA andPSU;
RUN;
DATA NBSU2;
SET NBSU;
BY andSTRATA andPSU;
IF FIRST.andPSU THEN NU=0;
NU+1;
IF LAST.andPSU THEN OUTPUT;
RUN;
DATA BSMPL;
MERGE NBSU2(IN=SU2) SAMPLE3;
170
BY andSTRATA andPSU;
IF SU2;
IF PS>1 THEN andWGT=andWGT*NU*(PS/(PS-1));
RUN;
%MSLT(DATA=BSMPL,S=0,VAR=HSQ1,NS=3,COV=SEX
REGION,NC=2,STRATA=STRATA,PSU=PSU,WGT=WEIGHT,LOI=1,BEG=15,END=100,SIMSIZE=10
0000);
DATA LE;
BS=andBS;
SET S.BSLE0;
RUN;
DATA S.MSLT_SIMxCOV_LE;
SET S.MSLT_SIMxCOV_LE LE;
IF BS NE . and BS<=250;
RUN;
%END;
%MEND;
%BOOTSTRP(BSIZE=5,VAR=HSQ1,NS=3,STRATA=STRATA,PSU=PSU,WGT=WEIGHT);
PROC FORMAT;
VALUE SEX 0='ALL'
1='MALE'
2='FEMALE';
VALUE
REGION 0='ALL'
1='NYANZA'
2='CENTRAL';
VALUE STATE 0='ALL'
1='POOR'
2='GOOD';
VALUE IAGE 0='ALL';
RUN;
*** PCT LE and STD ERR ***;
DATA MSLT_SIMxCOV_LE;
SET S.MSLT_SIMxCOV_LE;
IF IAGE=. THEN IAGE=0;
IF SEX=. THEN SEX=0;
IF RACE=. THEN REGION =0;
RUN;
PROC MEANS STD NOPRINT;
171
VAR TLE ALE DLE TLY25 ALY25 DLY25 TLY50 ALY50 DLY50 TLY75 ALY75 DLY75;
CLASS IAGE SEX REGION STATE;
WHERE BS>0;
OUTPUT OUT=TLEMEAN STD=TLE_STD ALE_STD DLE_STD TLY25_STD ALY25_STD
DLY25_STD
TLY50_STD ALY50_STD DLY50_STD TLY75_STD ALY75_STD DLY75_STD;
RUN;
DATA BOOT_TLE;
SET TLEMEAN(WHERE=(_TYPE_=15));
IF STATE=. THEN DELETE;
DROP _TYPE_ _FREQ_;
RUN;
PROC SORT;
BY IAGE SEX REGION;
RUN;
DATA PT_LE;
SET MSLT_SIMxCOV_LE(WHERE=(BS=0));
RUN;
PROC SORT;
BY IAGE SEX REGION;
RUN;
DATA S.MSLT_SIMxCOV_LESTD;
MERGE PT_LE BOOT_TLE;
BY IAGE SEX REGION;
DROP BS;
FORMAT IAGE IAGE. SEX SEX. REGION RACE. STATE STATE.
TLE ALE DLE TLY25 ALY25 DLY25 TLY50 ALY50 DLY50 TLY75 ALY75 DLY75 6.2
TLE_STD ALE_STD DLE_STD TLY25_STD ALY25_STD DLY25_STD
TLY50_STD ALY50_STD DLY50_STD TLY75_STD ALY75_STD DLY75_STD 5.2;
RUN;
172