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Elizabeth Mary Vaughan
2010
The Capstone Committee for Elizabeth Mary Vaughan Certifies that this is the
approved version of the following Capstone:
An Assessment of Kilocalories and Protein in the Diets of
HIV-infected Adults in Kenya
Committee:
_______________________________
Cardenas, Victor J, M.D.
_______________________________
Bunce, Harvey, Ph.D.
_______________________________
Arcari, Christine, Ph.D.
____________________________
Dean, Graduate School
An Assessment of Kilocalories and Protein in the Diets of
HIV-infected Adults in Kenya
by
Elizabeth Mary Vaughan, DO, RD
Capstone
Presented to the Faculty of the Graduate School of
The University of Texas Medical Branch
in Partial Fulfillment
of the Requirements
for the Degree of
Master of Public Health
The University of Texas Medical Branch
August 2010
Dedication
This is dedicated to my loving family—Mom and Dad for prayers and perseverance, Deb
for always being there for me, and my brothers, David and Daniel, for their ongoing
support and many edits.
Couldn’t have done it without you.
Acknowledgements
There are several people whom were key to the completion of this capstone. I am grateful for
my committee—Victor Cardenas, MD, Harvey Bunce, PhD, and Christine Arcari, PhD—for
their dedication and commitment to complete my Master’s of Public Health. Robert Johnson,
MD, MPH, MBA brought me to UTMB and inspired to me to pursue global health. Next, Philip
Keiser, MD provided the opportunity to work in Kijabe, Kenya as an extension of his current
research. Helen Kimemia, RN and Stephen Kinyanjui, RD were key assets for cultural and
nutritional guidance while in Kenya. Also, thanks to Marinel Ammenheuser, PhD for tedious
document editing along with Maurice Bennink, PhD and Chris Messenger, RD for persistent and
continual nutritional assistance. Finally, I’m grateful to Mayo Clinic, in particular Nell
Robinson, MS, RD, who provided excellent training in nutrition during my work as a dietitian.
It is rare that milestones in life are reached alone; this was certainly no exception.
v
An Assessment of Kilocalories and Protein in the Diets of
HIV-infected Adults in Kenya
Publication No._____________
Elizabeth Mary Vaughan, D.O, M.P.H., R.D.
The University of Texas Medical Branch, 2010
Supervisor: Victor J. Cardenas, Jr.
INTRODUCTION: The detrimental effects of HIV are well known throughout the world. This
public health burden is particularly evident in Kenya, Africa, where 6.7% of adults are infected
with HIV. One problem of those living with HIV in this resource-poor country is the concurrent
existence of malnutrition. Previous investigators undertook many studies to bridge the link
between HIV and malnutrition. However, they were unable to gather individual diet information
to allow accurate estimation of kilocalorie and protein needs. These deficits hinder accurate
dietary interventions for these patients.
METHODS: Over the period of April 2009 to August 2009, we designed and implemented a
dietary instrument (3-day recall survey) to assess the kilocalorie and protein consumption for
HIV-infected adults in Kijabe, Kenya. We used this data to compare subject consumption to
predicted nutrient needs. We used Harris-Benedict (HB) and Mifflin formulas for kilocalorie
need predictions and World Health Organization (WHO) equation for protein need predictions.
We then characterized the relationship between dietary intake, BMI and CD4 levels.
RESULTS: A total of 201 patients were surveyed, 122 (60.7% [27% males, 73% females]) met
inclusion criteria. There was no statistical difference between HB and Mifflin equations for
kilocalories. Males averaged 68.8% (SD 23.3) of estimated kilocalorie requirements (HB) and
100.5% (SD 45.3) of protein. Women averaged 74.4% (SD 24.4) of kilocalorie needs (HB) and
100.5% (SD 42.5) of protein. Differences between genders were not statistically significant
(p=0.247 [kilocalorie], p=0.936 [protein]). There was a significant correlation between protein
intake and CD4 for males (r=0.7035, p=0.0004) but not for females (r=-0.1911, p=0.1546).
There was no statistical significance found between kilocalories (male r=0.104, p=0.654; female
r=0.0420, p=0.765) and CD4. No relationship was found between nutrient intake and BMI (male
p=0.690; female p=0.477).
CONCLUSIONS: A 3-day recall dietary assessment appears to be an adequate method to obtain
dietary information for adult HIV patients in East Africa. Further, we conclude that current
predictive formulas for protein underestimate needs in the male HIV population.
vi
Table of Contents
List of Tables ......................................................................................................... ix
List of Figures ..........................................................................................................x
Chapter 1: Introduction ............................................................................................1
Chapter 2: Background and Significance ................................................................3
2.1 Malnutrition in Kenya ..............................................................................3
2.2 HIV in Kenya ............................................................................................5
2.3 Malnutrition and HIV ..............................................................................7
2.4 Nutrition Assessment ...............................................................................8
2.4a Dietary instruments .......................................................................8
2.4b Nutrient values ............................................................................11
2.4c Nutrient-predictive equations .......................................................11
2.4d Dietary components and clinical markers of HIV infection ........12
Chapter 3: Methodology ........................................................................................14
3.1 Location ..................................................................................................14
3.2 Subjects ..................................................................................................14
3.3 Materials and Methods ............................................................................15
3.3a Pilot study of the data-collection instrument ..............................15
3.3b Determination of dietary components of the Kenyan diet ...........15
3.4 Data Collection ......................................................................................15
3.4a 3-day recall surveys .....................................................................15
3.4b Medical record review ................................................................16
3.5 Predictive Equations ..............................................................................16
3.6 Statistical Analysis ..................................................................................17
Chapter 4: Results ..................................................................................................18
Chapter 5: Discussion ............................................................................................26
vii
Appendix 1 ...........................................................................................................32
Literature Cited ......................................................................................................33
Vita
.....................................................................................................................38
viii
List of Tables
Table 2-1:
World Health Organization HIV Staging ...........................................6
Table 2-2:
Criteria for initiation of antiretroviral therapy in Kenya ....................7
Table 3-1:
Predictive equations for kilocalories, protein, and IBW for
HIV patients in Africa ......................................................................17
Table 4-1:
Demographics and clinical characteristics of HIV-infected
persons in Kijabe, Kenya by gender .................................................19
Table 4-2:
Kilocalorie and protein content of common Kenyan foods
as identified by study ........................................................................20
Table 4-3:
Kenyan recipes with kilocalorie and protein content as
identified by the study.......................................................................21
ix
List of Figures
Figure 2-1: Global prevalence of HIV in adults 2007 ...........................................4
Figure 4-1: Flow-chart showing results of the survey selection of
HIV-positive patients in Kijabe, Kenya ............................................18
Figure 4-2: Comparison of HB vs. Mifflin equations for calculating average
percent of kilocalories requirements met in all subjects: ................22
Figure 4-3: Percent needs of kilocalorie and protein requirements met
versus BMI in infected male patients ..............................................23
Figure 4-4: Percent of needed kilocalorie and protein requirements met
versus BMI in infected female patients ...........................................23
Figure 4-5: Percent of needed kilocalorie requirements met
versus current CD4 counts for 21 male subjects ...............................24
Figure 4-6: Percent of needed kilocalorie requirements met
versus current CD4 counts for 57 female subjects............................24
Figure 4-7: Percent of protein requirements met versus
current CD4 counts for 21 male subjects ..........................................25
Figure 4-8: Percent of protein requirements met versus
current CD4 counts for 57 female subjects .......................................25
x
Chapter 1: Introduction
Malnutrition and infection with human immunodeficiency virus (HIV) are two large
burdens to public health worldwide. Of the 6.6 billion people in the world, 33 million are
estimated to be infected with the HIV virus (Population Reference Bureau, 2007; UNAIDS,
2008). One billion people in the world are estimated to be suffering from malnutrition (Food and
Agriculture Organization of the United Nations, 2009). In sub-Saharan Africa, these two
conditions are especially devastating.
Although the 650 million people in sub-Saharan Africa
comprise less than 10% of the world’s population, this region contains about 26 million people
infected with the HIV virus, representing approximately 79% of the worldwide burden of HIV
(UNAIDS & WHO, 2005). Additionally, of the one billion people estimated to be suffering
from malnutrition, 265 million live in sub-Saharan Africa, which is more than 40% of its
population (Food and Agriculture Organization of the United Nations, 2009). It is evident that
these two public health problems are magnified in this region of the world, but what is the impact
when the problems are combined?
There is no argument that malnutrition is associated with poor outcome in HIV patients.
The impact of malnutrition on HIV is intuitive but we have yet to find the crucial links where the
two connect. Previous studies examining the impact of nutrition on HIV infection summarized
by Fawzi et al. (2005) did not rigorously determine the baseline diet or individual nutrition needs
of the patient. Specifically, it is not clear which dietary component and measure of HIV outcome
is appropriate to employ in understanding the link between malnutrition and HIV outcome.
Effective interventional studies cannot be designed in the absence of this information.
Thus, in order to obtain objective measurements and ascertain the effects of malnutrition
on HIV patients in sub-Saharan Africa, dietary intake habits need to be determined in this region
of the world; then the links between nutrition and HIV outcome may be better understood.
Therefore, the specific aims of this study are to:
(1) Design a dietary intake instrument, obtain a 3-day recall dietary survey in HIV patients, and
access the kilocalorie and protein values of commonly consumed foods in Kijabe, Kenya.
1
(2) Determine appropriate kilocalorie- and protein-predictive equations to estimate nutrition
needs for persons in sub-Saharan Africa.
(3) Characterize the relationship between kilocalorie and protein intake with BMI and with CD4
count in HIV patients in Kijabe, Kenya.
2
Chapter 2: Background and Significance
Malnutrition and infection with HIV are two public health burdens causing substantial
morbidity and mortality worldwide. Both weaken the immune system, decrease energy levels,
and impair functional output.
Combined, their effects are magnified (Scrimshaw &
SanGiovanni, 1997; WHO, 2003). In 2009, an estimated 15% of the people in the world suffered
from malnutrition with over one-quarter of these from sub-Saharan Africa (Food and Agriculture
Organization of the United Nations, 2009). While only about 0.5% of the world’s population is
infected with HIV, more than three-fourths are from sub-Saharan Africa (UNAIDS & WHO,
2005; Population Reference Bureau, 2007; UNAIDS 2008). These two conditions combined
result in an increase in opportunistic infections, gastrointestinal tract complications, and weight
loss that, in the end, result in furthering of the disease process (Wilcox, 1996; Scrimshaw &
SanGiovanni, 1997; WHO, 2003).
Malnutrition and HIV are often complicated by poverty. Low-income areas have few financial
resources, leading to political and social limitations. These limitations result few resources to
obtain manpower for labor, to provide access to education, and to give healthcare opportunities
(Haacker, 2002). Figure 2-1 provides the 2007 estimates of global prevalence of HIV (UNAIDS,
2008), demonstrating the disproportionate frequency of this condition in Africa. In poverty
stricken countries, persons infected with HIV are at high risk for poor treatment options, lack of
medical-information tracking, and hindered delivery of care.
2.1 MALNUTRITION IN KENYA
Malnutrition is a major public health problem in Kenya, Africa, and throughout
the world. Every 3.6 seconds someone in the world dies from malnutrition (United Nations [UN]
Secretary General & UN Development Group, 2006). Malnutrition is defined as deficiencies,
imbalances, or an excess of nutrients. Any of these may lead to many long-term health
problems.
The two most common forms of malnutrition are inadequate kilocalorie intake
(marasmus) and inadequate protein intake (kwashiorkor) (Sizer & Whitney, 2003).
3
Figure 2-1: Global prevalence of HIV in adults 2007
Open Source: UNAIDS, 2008
Nutrients are divided into macronutrients and micronutrients.
Macronutrients are
carbohydrates, fats, and protein. Micronutrients are vitamins and minerals. Both groups are
required for human survival. Combined, macronutrients are the components of kilocalories-carbohydrate (4 kilocalories/g), fat (9 kilocalories/g), and protein (4 kilocalories/g).
The
building blocks of protein are amino acids—twelve of which are essential to consume since the
body cannot produce them (Sizer & Whitney, 2003). This study focuses specifically on the
macronutrient protein and the total daily kilocalories from carbohydrates, fats, and protein.
In studying nutrition in individuals an initial step is the determination of clinical
guidelines that define malnutrition. In many low-income countries, such as Kenya, methods of
assessment for nutrition status include anthropometric measures (the fatfold test and waist
circumference), mid-arm circumference, and functional outcomes (i.e. step test, hand grip).
Body mass index (BMI) was commonly used to determine healthy body weight, but its use has
declined since it fails to recognize fat distribution and excess fat locations (Sizer & Whitney,
2003).
These are simple, inexpensive methods that do not require a clinical setting for
4
assessment. By using such methods, Kenya has opportunities to assist in country food security
and set up early warning systems to decrease the prevalence of malnutrition (USAID, 2006).
2.2 HIV IN KENYA
According to the Kenya Demographic and Health Survey, 6.7% of Kenyan adults are
living with HIV (Central Bureau of Statistics et al., 2004). In Kenya, the distribution of HIV
prevalence differs by gender, age, location, tribe, and education. Nearly 9% of women have HIV
while only 4.6% of men are known to be infected. Prevalence in young women (25-29 years) is
higher at 13% compared to 4% of older women (45-49 years). Target populations for screening
are predominately younger females due to the ease of accessibility, such as in labor and delivery.
Yet, this disproportionately provides less opportunity to screen men and older women, which
may account in part for the reported differences in the prevalence by gender and age of HIV
infection (Central Bureau of Statistics et al., 2004).
Urban areas have a higher prevalence than rural areas (10% vs. 6%, respectively). The
country’s highest prevalence rates are in Nyanza Province and Nairobi (15.1% and 9.9%
respectively). The country’s lowest prevalence is in the North Eastern Province (1%) (Central
Bureau of Statistics et al., 2004). Another area where prevalence varies is across various tribes.
Highest prevalence is in the Luo tribe (17.5% men, 25.8% women). The Taita/Taveta tribes
were also high at 9.7%. Polygamous men were more likely to be infected than monogamous
men (11.6% vs. 6.9%) (Central Bureau of Statistics et al., 2004). Male circumcision reduced
infection. Countrywide, 83% of men are circumcised in Kenya. Three percent of circumcised
men compared to 12.5% of uncircumcised men are HIV-positive (Central Bureau of Statistics et
al., 2004).
HIV mortality rates in Kenya are among the highest in the world. The total number of
deaths due to HIV in Kenya was estimated to be 150,000 in 2003, which ranks 4 th behind South
Africa, India, and Nigeria from 2001 to 2007 according to Central Intelligence Agency data
(Central Intelligence Agency, 2010). However, Kenya anticipates a decline due to antiretroviral
therapy. Over the last ten years, highly active antiretroviral therapy (HAART) gained much
attention and support within the country.
Although there are still needs in therapy
administration, there have been great improvements and nationwide awareness of HIV and the
importance of HAART (Republic of Kenya Ministry of Health, 2005).
5
In Kenya, the standard of care is combination drug therapy. The commonly used drugs
include antiretroviral drugs—stavudine, lamivudine, and nevirapine (or, sometimes, efavirenz.).
For women of childbearing age, nevirapine is preferred over efavirenz due to the teratogenic
effects of efavirenz (Republic of Kenya Ministry of Health, 2005). In Kenya, therapy initiation
is based on WHO Staging (WHO, 2005) (Table 2-1) combined with medical and social criteria in
Kenya (Table 2-2) (Republic of Kenya Ministry of Health, 2005).
Table 2-1: World Health Organization HIV Staging
Source WHO, 2005
Stage 1:
Asymptomatic, persistent generalized lymphadenopathy (PGL)
Stage 2:
Moderated unexplained weight loss (<10% body weight)
Recurrent respiratory tract infections
Herpes Zoster
Angular chelitis
Recurrent oral ulcerations
Papular pruritic eruptions
Seborrhoeic dermatitis
Fungal nail infections of fingers
Stage 3:
Conditions where a presumptive diagnosis can be made on the basis of clinical signs or simple investigations:
severe weight loss (>10%, oral candidiasis, unexplained diarrhea (>1 month), unexplained persistent fever,
oral hairy leukoplakia, pulmonary TB in the last 2 years, severe presumed bacterial infections, acute
necrotizing ulcerative stomatitis, gingivitis or periodontitis
OR
Conditions where confirmatory diagnostic testing is necessary:
unexplained anemia, neutropenia, thrombocytopenia
Stage 4:
Conditions where a presumptive diagnosis can be made on the basis of signs or simple investigations:
HIV wasting syndrome, pneumocystis pneumonia, recurrent severe or radiological bacterial pneumonia,
chronic herpes simplex, esophageal candidiasis, extrapulmonary TB, Kaposi’s sarcoma, central nervous system
(CNS) toxoplasmosis)
OR
Conditions where confirmatory diagnostic testing is necessary:
extrapulmonary, cryptococcosis including meningitis, disseminated non-tuberculosis mycobacterium infection,
progressive multifocal leukoencephalopathy (PML), candida of trachea, bronchi or lungs, cryptosporidiosis,
isosporiasis, visceral herpes simplex infection, cytomegalovirus, disseminated mycosis, recurrent non-typhoidal
salmonella septicemia, lymphoma (cerebral or B cell non-Hodgkin), invasive cervical carcinoma, visceral
leishmaniasis, HIV encephalopathy
6
Table 2-2: Criteria for initiation of antiretroviral therapy in Kenya
Source Republic of Kenya Ministry of Health, 2005
Medical criteria (must meet one requirement)

WHO clinical stage 1 and 2, with CD4 count <200/mm3

WHO stage 3 with CD4 count <350/mm3

WHO stage 4 regardless of CD4 cell count

Total lymphocyte count <1200/ mm3
Social criteria (must meet all requirements)

Demonstrate understanding of the importance of adherence and monthly attendance to the clinic

Able to afford medications

Able to identify a partner (family, friend, etc.) to help with support and compliance

Willing to disclose contact information and be contacted if appointments are missed

Willing to commit to safe sex practices
2.3 MALNUTRITION AND HIV
The malnourished individual, from onset, is immunocompromised and thus more
susceptible to infections, leading to increased morbidity and mortality. Malnutrition among
people living with HIV/AIDS is characterized by as weight loss, wasting, increase of
opportunistic infections, earlier onset of need for antiretroviral therapy, decrease in quality of
life, and increased mortality (Wilcox, 1996; Scrimshaw & SanGiovanni, 1997; WHO, 2003).
Impaired socio-economic conditions lead to increased prevalence of HIV-infected persons
suffering from poverty as well as malnutrition. Combined, these accelerate their disease process.
Malnutrition, HIV, and poverty are abundant in Africa (Haacker, 2002).
The natural course of HIV infection is affected by nutrition, as demonstrated by several
studies. In a study of 77 HIV patients, investigators found that poor nutritional status (measured
by albumin and weight loss) resulted in increased mortality independent of CD4 counts (Guenter
et al., 1993). In a recent study of 467 weight-stable HIV-infected men (CD4 count <200
cells/mm3) protein intake was found to be positively associated with an increase in body cell
mass (p<0.001). No statistically significant difference between the sources of dietary protein
was found. However, protein intake was not differentiated into animal verses non-animal
7
sources. Since essential amino acid composition varies between protein sources, unrecognized
differences may have affected outcome (Williams et al., 2003). Castetbon et al. (1997) studied
the importance of energy intake and HIV. In the low-resource country of Cote d’Ivoire, Africa,
HIV-infected patients (n=100) underwent nutrition assessments using anthropometric measures
(weight, baseline weight, arm circumference (AC), triceps skinfold (TS), muscular
circumference (MC)). They found that symptomatic patients resulted in ACs and MCs
significantly lower than those in asymptomatic patients. Energy intake was also lower than
WHO recommendations. However, the authors concluded that further studies are needed in the
African setting to link nutrition and HIV (Castetbon et al. 1997).
Fawzi et al. (2004) noted the importance of nutrient adequacy for persons with HIV
infection. Some studies have focused on individual nutrient supplementation. In a study of 1075
HIV-positive pregnant women (12 to 27 weeks’ gestation) in Tanzania, multivitamin (MVI)
supplementation was found to decrease risks for low birth weight (<2500g) by 44% (p=0.003)
and preterm birth (<34 weeks gestation) by 39%. CD4 and CD8 levels were also increased with
MVI use but not with vitamin A supplementation alone (Fawzi et al., 1998). In another study,
with 40 HIV-infected patients taking stavudine and/or didanosine as part of their HAART
regimen, received micronutrient supplements twice daily for 3 months. In the micronutrient
group, CD4 count increased (average 24%) compared to 0% in the placebo group (p=0.01)
(Kaiser et al., 2006).
It is evident that malnutrition has a direct impact on the clinical prognosis of HIV
patients.
In resource-poor areas, these effects are even more profound (Haacker, 2002).
Although it is evident that nutrition plays a role in HIV outcome, there are gaps in the literature
for nutritional recommendations for HIV patients. Past studies were limited by sample size,
methodology of data collection, and cultural barriers in low-income countries. These limitations
have resulted in many unanswered questions and a need for an accurate nutrition assessment in
these regions.
2.4 NUTRITION ASSESSMENT
2.4a Dietary instruments
Dietary assessments can be gathered through a variety of methods, including a 24-hour or
3-day recall, a food frequency questionnaire (FFQ), food logs, and journals (National and
8
Scottish Research Studies, 2003). In addition, some experts add anthropometric data for a more
complete nutritional assessment (Sizer & Whitney, 2003).
A common method of dietary assessment is a diet recall, typically for 24-hours or 3-days.
There are several advantages and disadvantages to these recall methods (National and Scottish
Research Studies, 2003). Diet recalls are labor intensive and require a trained interviewer to ask
questions. Responses rely on an accurate memory of the individual and the ability to estimate
his/her portion sizes. These barriers can be overcome by training for the interviewer, prompting
the subject with the time of food consumption, and props for visualization of portion sizes.
Other disadvantages of diet recalls include differential reporting of “good/bad” foods and single
observations over a limited period of time. Overall, these methods are suitable for large-scale
surveys, multiple collection modes (telephone, email, individual person or group settings), and
limited time available with subjects (National and Scottish Research Studies, 2003). Several
studies have been successful with these methods. In a study in rural Kenya, investigators
performed a cross-sectional assessment of 24-hr recalls by mothers and their children. They
concluded that recall methods provided an acceptable alternative to previously used and laborintensive weighing techniques (Gewa et al., 2008). In another Kenyan study, 24-hour and 3-day
recall methods were compared. Both produced reliable data, but the 24-hour recall was much
less time consuming than the 3-day recall (Kigutha, 1997).
The food journal is another method to record dietary history. Subjects record all food and
beverage intake, usually over a 7-day period. They are taught how to measure with cups and/or
spoons or given a weighing instrument to measure foods (National and Scottish Research
Studies, 2003). Problems with obtaining food records include failure to follow-up, poor literacy,
distant patient geographic locations, and low patient educational levels. Also, subjects may
change their dietary habits while keeping food journals because of their perceptions of “good”
and “bad” foods (National and Scottish Research Studies, 2003).
Another method—inventory surveys—help decrease these biases seen in food journals.
This type of survey was used in the China Health and Nutrition Survey where 4,400 households
(~19,000 individuals) were inventoried by research staff. All processed foods, including edible
oils and salts left from the last meal, were weighed and recorded before the next meal
preparation. This detailed technique allowed them to draw associations between increased
9
height and weight and obesity and to develop hypotheses to decrease malnutrition in China with
follow-up interventional studies (Du, 1997).
FFQs are another method of gathering diet information (National and Scottish Research
Studies, 2003). In this method, subjects recall food type consumed, together with quantity and
frequency, over a set time period, and fill out a questionnaire. Serving sizes are quantified as
small, medium, or large. The time required by the researcher to obtain FFQ data, compared to
24 hour or 3-day surveys, is much less. The only time needed is for questionnaire collection, and
thus, this method is commonly used in epidemiological studies. This method, however, may fail
to estimate portion sizes correctly and may fail to include culture-specific foods (National and
Scottish Research Studies, 2003).
Above all, in choosing a nutrition-assessment tool, the method chosen greatly depends on
the reason for obtaining the diet history, the population of interest, and the balance of quantity
verses quality of information (National and Scottish Research Studies, 2003). In the Kenyan
studies by Kigutha (1997) and Gewa et al. (2008), investigators tried to balance these
components by attempting to find efficient yet reliable methods to obtain diet. They compared
previously validated weighing methods for determining subject diet intake to diet recalls.
Researchers visited households to understand cultural traditions and beliefs, learned foods typical
to the region, and provided scales for families to weigh foods. Interviewers obtained diet recalls
from patients and compared them with weighed-food records. They found that, although not
flawless, recalls provided a reliable measure and an acceptable alternative to weighing foods in
this population.
We designed this research project with the above study methodologies in mind. We
learned from methods used by other investigators how to obtain nutrient values for culture- and
region-specific foods. We also paralleled their methodology by working in homes and foodproduction areas to understand food preparation in this culture. Further, based on their findings
we chose a recall survey to assess nutrition for its validity and reliability in this setting instead of
food-weighing measures. Unlike Kigutha (1997) and Gewa et al. (2008), however, we included
adults of all ages, and targeted HIV infected subjects. We also compared actual kilocalorie and
protein intake with predicted needs.
10
2.4b Nutrient values
In order to determine nutrient content of foods, databases that contain nutrient
information translated into food codes are often used. There are several governmental and
commercial databases available. Inaccuracies of databases occur in several situations, including
multiple researchers entering data, logging of inappropriate food codes, over-generalizing
individual foods, and failure of making adjustments for culture-specific foods (Nelson &
Williams, 2007).
Because of the risk of inaccuracy in databases, some researchers hand-
calculate data from each patient, but this is time-consuming and limits the number of subjects.
2.4c Nutrient-predictive equations
The gold standard for estimation of basal metabolic rate (BMR) is indirect calorimetry—
an estimation of energy expenditure by measuring respiratory gases, oxygen consumption, and
carbon dioxide release—it is expensive and not available in most resource-poor countries.
Nutrient-predictive equations estimate nutrient needs and determine if a subject’s actual intake is
sufficient (Frankenfield et al., 2003; Walker & Heuberger, 2009). Because of the ease of use,
these equations are utilized in many clinical settings (hospitals or clinics, and in fieldwork).
The most common nutrient need estimated using predictive-equation is kilocalories. The
first step in this calculation is the estimation of BMR. In a review of seven equations (American
College of Chest Physicians, Harris-Benedict [HB], Ireton-Jones [1992 and 1997], Penn State
[1998 and 2003], and Swinamer) Walker and Heuberger (2009) tested their prediction accuracy.
They found prediction equations were rarely within 10% of actual BMR.
However, they
concluded that, in the absence of indirect calorimetry, a predictive equation is an acceptable
alternative. Mifflin et al. (1990) stated that HB equations overestimated the measure of resting
energy expenditure (REE), a term often used interchangeably with BMR, by 5% and that fat-free
mass was the best single predictor of REE. Another study comparing HB with other equations,
adjusted body weight in obese individuals using HB and Mifflin equations and found Mifflin
provided an accurate estimate of BMR in normal weight (BMI <30 kg/m2) and obese (BMI >30
kg/m2) patients while HB overestimated these BMRs (Frankenfield et al., 2003). Yet, the study
by Mifflin et al. (1990) focused on Caucasians in the United States and lacked testing in nonCaucasians, underweight, subjects (BMI<18.5 kg/m2)—a common condition in resource-poor
11
countries. Nevertheless, HB currently is used by most experts for both HIV and non-HIV
patients.
Once BMR is estimated, it is multiplied by two variables to determine daily kilocalorie
needs. One variable, an activity factor (AF), accounts for the activity expenditure of subjects
(e.g. 1.0 for sedentary individuals and 2.0 for highly-competitive athletes) (Sizer & Whitney,
2003).
The other variable consists of guidelines set by WHO for increased caloric
recommendations for persons with HIV. For persons in HIV disease stages 1 and 2, it is
recommended to increase predicted needs by 10%. For stages 3 and 4, WHO recommends a 20
to 30% increase (WHO, 2003).
WHO guidelines are also used for protein-predictive equations (WHO, 2003). WHO
recommends that protein calories should be 12 to 15% of total kilocalories or 0.85 g/kg.
Currently, there is no difference between protein recommendations for healthy and HIV-infected
persons, but a need was noted for future studies to provide more appropriate estimations. (WHO,
2003). The American Dietetic Association (ADA) formula has higher values (1.0g/kg) but also
does not adjust for the presence of HIV (Powers, 2003).
Ideal body weight (IBW) equations are sometimes used in place of actual weight for
predicative equations.
This is seen in low-resource countries where many subjects are
underweight, and using their actual weight would underestimate needs. Equations for IBW are
abundant.
More than 200 articles have listed formulas for IBW since 1974 when Devine
published his article describing what is now a widely accepted equation for IBW. Pai and
Paloucek (2000) compared three equations commonly used to estimate IBW: Devine (1974),
Robinson et al. (1983), and Miller et al. (1983). Despite different equations, and a different
rationale behind each equation, all resulted in similar results for subjects 60-72 inches in height.
For subjects less than 60 inches or over 72 inches, there were often slight deviations. They
concluded that any of the three would be appropriate for heights 60-72 inches.
2.4d Dietary components and clinical markers of HIV infection
There is no standard measurement for nutrition outcome. BMI is one measurement that
has been suggested since it has been useful as an independent predictor of disease progression.
A study of 1657 HIV patients (age >14) in Gambia, recruited within 3 months of HIV diagnosis,
revealed that the mortality hazard ratio of those with a BMI less than 18 kg/m2 versus those > 18
12
kg/m2 was 3.4 (95% CI, 3.0-3.9) and the median survival time of those with BMI < 16 kg/m2
was 0.8 years compared with 8.9 years for those with BMI > 22 kg/m2 (van der Sande et al.,
2004). BMI has also been used as an independent predictor of survival in non-HIV chronic
disease states. In a study of adults (age 21 to 89) with severe chronic obstructive pulmonary
disease (COPD), mortality increased for those with low BMI (< 18 kg/m2) compared to those
with normal weight, 1.64 in men (95% CI: 1.2 to 2.23) and 1.42 in women (95% CI: 1.07 to 1.89
(Landbo et al., 1999).
However, though studies showed BMI to be a strong independent predictor of survival
for HIV-infected persons and non-HIV-infected persons with a chronic disease, it has not been
shown it to be a strong indicator of nutrition outcome because of variations in muscle mass,
acute disease state, and age (Sizer & Whitney, 2003). Further, reverse causality, where the
disease process itself is the primary determinant of BMI, is a major impediment to using BMI as
a reliable marker for nutritional intake in chronic progressive disease states (Lawler et al., 2006).
For these reasons, there are no studies to support BMI as an outcome for nutrition status.
HIV outcome has commonly been predicted by using measurements of CD4 and viral
load trends. These measures assist in determining response to highly active retroviral therapy
(HAART), appropriate interventional points for opportunistic infection prophylaxis, and
mortality risks (WHO, 2003; Republic of Kenya Ministry of Health 2005; Kaiser et al., 2006).
In several studies CD4 and/or viral load has been used as the outcome measurement. One
interventional trial showed a modest CD4 increase of 31 +/- 84 cells/mm3 following whey
protein supplementation. In this study, participant baseline energy and protein intakes exceeded
requirements (Sattler et al., 2008).
13
Chapter 3: Methodology
3.1 LOCATION
This study was performed in the vicinity of Kijabe, Kenya—a town located an hour west
of the capital city of Nairobi. Kijabe Hospital is a heavily used medical center for Kijabe, its
surrounding regions, and as far as Somalia. The hospital was established in 1915 to provide
training for African medical professionals and medical services for East Africans. Presently, the
establishment consists of Kijabe Hospital and outpatient clinic and 3 other satellite clinics—
Marida, Githunguri, Navasha—ranging from 5 to 42 kilometers from Kijabe. HIV medical care
services include HIV testing and diagnosis, initiation and distribution of HAART, monthly
support groups, and hospitalization as needed.
The University of Texas Medical Branch (UTMB) is affiliated with Kijabe Hospital to
provide medical assistance and training for local medical personnel. Kijabe Hospital provides
unique pathology and learning experiences for UTMB students and residents.
3.2 SUBJECTS
Subjects were recruited from the 4 clinic locations (Kijabe, Marida, Navasha, and
Githunguri) for a cross-sectional study in April and August 2009. Enrollment into the study
occurred during clinic follow-up appointments or monthly support group sessions. Subjects
were eligible if they were (1) aged > 15 years, (2) were diagnosed with HIV and, (3) on HAART
therapy for ≥ 6 months. Criteria for exclusion were (1) pregnancy, (2) incomplete medical
records, and (3) non-compliance on HAART therapy.
The protocol for this study was approved by the University of Texas Medical Branch,
Institutional Review Board for human subjects research prior to the start of the project. A wavier
of the need for a signed consent form was allowed, with assent (verbal permission) used instead.
The waiver was approved for the following reasons: the research involves no more than minimal
risk to the subjects, the waiver did not adversely affect the rights and welfare of the subjects, the
subjects were provided with additional pertinent information after participation, if applicable,
and the research could not practicably be carried out without the waiver. Hospital chart numbers
identified subjects.
14
3.3 MATERIALS and METHODS
3.3a Pilot study of the data-collection instrument
The purpose of the pilot study was to field test a 3-day food recall survey (Appendix 1)
on a small group of subjects and to identify the typical foods consumed by the target population.
For the pilot study, patients were enrolled only at the Kijabe Hospital clinic to avoid challenges
associated with obtaining data at distant clinic sites.
3.3b Determination of dietary components of the Kenyan diet
PubMed, MEDLINE, and Internet searches were performed to determine the ingredients
in the prepared foods and recipes identified in the pilot study. In order to verify the information
obtained by internet searches, observation in commercial and household kitchens was initiated. I
worked alongside the head cook in the Kijabe hospital cafeteria during food preparation and
recorded portion sizes, ingredients, additives (fats and sugars), and cooking methods (i.e. bake
vs. fry). I also visited several households and worked with the Registered Dietitian at Kijabe
Hospital to verify the recipes and ingredient list identified in the pilot study.
To compare regional variation of foods, I visited several surrounding areas of Kenya and
Tanzania.
Areas observed included: Masi Mara (bush tribe), Nairobi (urban), Kilimanjaro
(rural), Lake Victoria (coastal), and Zanzibar (an island with Muslim predominance).
Information was obtained through direct interviews with local people.
3.4 DATA COLLECTION
3.4a 3-day recall surveys
Surveys were collected over a five-week period in August-September 2009 at all 4 clinic
sites. The site of fieldwork shifted daily among clinic sites. Geographical and infrastructure
constraints allowed only one site to be visited each day. Initially, individual subjects were
recruited and interviewed in the various clinics however this resulted in gathering only 1 to 2
surveys per day. Therefore, group recruitment during monthly meetings of HIV support groups
was initiated in order to increase enrollment. Individual surveys occurred at the clinics while
support group surveys occurred in churches, vacated buildings, or open fields. Travel to support
groups required 5.5 (2–9) hours of transit time using multiple (2–8) different matatus (public
buses) for each roundtrip. Seven support groups participated in the survey.
15
Subjects who met inclusion criteria were recruited to participate at their 6-month clinic
follow-up appointment or during their monthly support group meeting. After granting verbal
assent, surveys were distributed to the participants. The surveys requested 3-day written recall of
food and beverage intake and a statement of HAART compliance. Subjects were prompted for
accuracy of serving sizes and food types by displaying measuring utensils and food samples
brought to each site from the Kijabe cafeteria. Subjects were permitted to hold or measure food,
as needed. Participants who spoke Swahili only were provided a translator to write surveys in
English. Subjects who only spoke their tribal language required two translators—one from the
tribal language to Swahili and another to English. Those who were not literate were given
assistance in survey completion.
To assist with data collection, native Kenyan volunteers were taught interview skills and
trained to take dietary histories (such as use of measuring utensils and portion-size references).
Before leaving the site, I reviewed all surveys and met again with subjects for clarification as
needed.
3.4b Medical record review
After gathering the surveys, I traveled to the area clinics to review medical records (paper
charts). Data abstracted from medical records included (1) age, (2) gender, (3) height and weight
history since HAART initiation, (4) date of HIV diagnosis and HAART initiation, (5) WHO
stage, (6) CD4 counts since HAART initiation, and (7) pregnancy history. Information was
entered into an Excel spreadsheet. Variation (i.e. language barriers, diet-recall discrepancies,
HAART compliance) were addressed by repeated interviews, ongoing training for interviewers,
and re-visiting medical records.
3.5 PREDICTIVE EQUATIONS
After surveys were collected, food items were translated into kilocalories and protein,
based on United States Department of Agriculture data (United States Department of Agriculture
[USDA], 2009). The 3-day averages of kilocalories and protein intake were calculated for each
subject. For kilocalories, HB and Mifflin formulas were applied to calculate BMR with the
activity factor (AF) set at 1.3 for a moderate activity level—housework, cooking, childcare,
etc… The result was multiplied by the WHO guideline increase for HIV stage, resulting in the
16
total daily predicted kilocalories. Since all subjects were stage 3 or 4, 25% was used for all
equations. For daily predicted protein requirements, the WHO equation of 0.85g/kg was utilized.
All calculations used ideal body weight (IBW). The Robinson formula was used for IBW
calculations (Pai & Paloucek, 2000). Table 3-1 provides a summary of each equation.
Table 3-1: Predictive equations for kilocalories, protein, and IBW for HIV patients in Africa
HB equation for kilocalories
Men: ((66 + (13.7 x weight (kg)) + (5 x height (cm)) - (6.8 x age))*AF)*WHO)
Women: ((655 + (9.6 x weight (kg)) + (1.8 x height (cm)) - (4.7 x age)*AF)*WHO)
Mifflin equation for kilocalories
Men: ((((10*weight (kg)) + (6.25*(height (cm)) - (5*age) + 5)*AF)*WHO)
Woman: ((((10*weight (kg)) + (6.25*(height (cm)) - (5*age) -161)*AF)*WHO)
WHO equation for protein
Men and women: 0.85 g protein/kg IBW
Robinson formula for IBW
Men: IBW (kg) = 52 kg + 1.9 kg for each inch over 5 feet
Women: IBW (kg) = 49 kg + 1.7 kg for each inch over 5 feet
3.6 STATISTICAL ANALYSIS
All data obtained from the 3-day dietary recall, the medical chart review, and nutrientestimation equations (HB, Mifflin, WHO) was entered into an Excel spreadsheet and analyzed
using MedCalc (version 11.3.1.0). All analyses were stratified by gender. Age (years) and CD4
count (cells/mm3) were treated as continuous variables; BMI (kg/m2) was calculated from height
and weight and treated as both a continuous variable and a categorical variable (underweight
<18.5 kg/m2, normal weight 18.5-24.9 kg/m2, and overweight >24.9 kg/m2). Location
(Githunguri, Kijabe, Merida, and Navasha) was analyzed as a categorical variable. Student’s ttest and one way ANOVA were used for between and among group comparisons. Associations
were estimated with Pearson’s product-moment correlation coefficient.
17
Chapter 4: Results
A flow chart of the study participants is shown in Figure 4-1. A total of 19 surveys were
completed during the pilot and 182 during full implementation for a total of 201 surveys. Of
these 201 assessments, 79 were excluded from analysis due to: incomplete charts (n = 29),
HAART <6 months (n = 30), and pregnancy (n = 20). For the final analysis, 122 (60.7%)
participants were included.
_______________________________________________________________________
Total surveys
19: Pilot
182: Full
n = 201
Incomplete charts, n = 29 (14.4%)
n= 201 (100%)
Completed
Charts
n = 172 (85.6%)
HAART <6 mo, n = 30 (14.9%)
HAART > 6mo
n = 142 (70.7%)
Pregnant, n = 20 (10.0%)
Not Pregnant
n = 122 (60.7%)
Study patients, n = 122 (60.7%)
Figure 4-1: Flow-chart showing results of the survey selection of HIV-positive patients in Kijabe, Kenya
Table 4-1 describes the demographics and clinical characteristics of the study population.
Males were slightly older, 42.4 years (range 33.0-57.0 years), compared to females, 39.4 years
(range19.0-61.0 years). For BMI, 27.3% of males were underweight, 60.6% of males were
normal body weight, and 12.1% of males were overweight. The average CD4 count for males
18
was 297.6 cells/mm3 (34.0-927.0). Males were diagnosed with HIV, on average, 29.0 months
prior to study enrollment (range 10-94 months).
Of the 122 patients studied, 73% were females. The plurality of female patients was
enrolled at the Navasha Clinic (42.7%). The BMI of the female subjects differed from that of
males: 5.6% of females were underweight, 65.2% were normal body weight, and 29.2%, were
overweight (p<0.001). The average CD4 count was 351.1 cells/mm3 (13.0-1010.0), and on
average, they were diagnosed with HIV 33.0 months (range 6.0-127.0 months) prior to the study.
For both genders, most started HAART within one year after HIV diagnosis. The median time
since initiation of HAART to the time of the survey was 24.0 months for males (range 7.0-80.0
months) and 23.0 months for females (range 6.0-97.0 months).
Table 4-1: Demographics and clinical characteristics of HIV-infected persons in Kijabe, Kenya by gender
_______________________________________________________________________
Males
Total population (row %)
Females
p-value
n
(%)
n
(%)
33
(27.1)
89
(73.0)
Location
<0.0001
0.62
Githunguri
1
(3.0)
3
(3.4)
Kijabe
7
(21.2)
20
(22.5)
Merida
14
(42.4)
28
(31.5)
Navasha
11
(33.3)
38
(42.7)
42.4
(33.0 – 57.0)
39.4
(19.0 – 61.0)
Age, years (mean, range)
2
BMI kg/m
0.05
<0.001
<18.5
9
(27.3)
5
(5.6)
18.5 – 24.9
20
(60.6)
58
(65.2)
4
(12.1)
26
(29.2)
CD4 count, cells/mm (mean, range)
297.6
(34.0-927.0)
351.1
(13.0-1010.0)
0.19
Time since diagnosis, months (range)
29.0
(10.0 – 94.0)
33.0
(6.0 – 127.0)
<0.005
> 24.9
3
Time since initiation of HAART,
months (range)
<0.005
24.0
(7.0- 80.0)
19
23.0
(6.0 – 97.0)
Tables 4-2 and 4-3 describe the 29 individual food items and 8 recipes identified from the
surveys. Kilocalorie and protein content values in Table 4-2 are derived from USDA standards
(USDA, 2009). Travels to nearby regions (Masi Mara, Nairobi, Kilimanjaro, Lake Victoria, and
Zanzibar) revealed no major differences in diet. No changes were made to the original food list.
Table 4-2: Kilocalorie and protein content of common Kenyan foods as identified by study
Food
Amount
Kilocalories (kcal)
Protein (g)
Avocado
Banana
Beef (ground)
Beef Stew
Biscuit (cookie)
Butter
Beans (kidney)
Bread/toast
½ medium
4 inches
3 oz
1 cup
1 piece
1T
½ cup
1 piece
160
60
200
160
15
100
100
80
3
0
24
10
0
0
7
2
Carrots (raw)
Chai tea
Chicken
Chicken soup
Eggs
Githeri(corn/beans)
Goat meat
Green grams (mung)
1 medium
1 cup (4:1 water: milk)
3 oz
1 cup
1 egg
1 cup
3 oz
1/2 cup
25
40
120
170
70
170
120
105
1
2
21
12
7
8
23
7
Mandazi
Mandazi
Milk (whole)
Mukimo
Nuts (ground)
Omena
Orange
Orange juice
1 piece
1 small solid
1 cup
1 cup
¼ cup
1/2 cup
1 medium
1 cup
400
130
150
220
200
35
60
110
12
2
8
10
5
9
1
0
Pancake
Pear
Potato (boiled)
Queen Cake
Rice
Spinach (cooked)
Sugar
Sukuma Wiki
1 piece
1 medium
½ cup
2 sm. pieces
1 cup
½ cup
1 tsp
1 cup
100
100
70
250
240
40
15
200
3
1
1
4
4
2
0
9
Tilapia
Ugali
Ugali
Uji (porrige)
Yam/kunde
Yam
3 oz
1 cup
I portion
1 cup
1 cup
1 medium (5”x2”)
70
180
270
110
200
115
16
3
5
3
4
2
20
Table 4-3: Kenyan recipes with kilocalorie and protein content as identified by the study
Chapatti (1 medium piece), 180 kilocalories, 3 g protein
1 cups white flour and 1 cup whole-wheat flour
1/2 tsp salt
1 T oil
2 T water to make dough
1 T soft butter
pan fry oil
Chicken Soup (1 cup), 160 kilocalories, 6 g protein
3 cups water
2 tomatoes
1 onion
1 small chicken
Githeri (1 cup), 170 kilocalories, 8 g protein
2 cups corn
2 cups cooked beans
water to cover
salt and pepper to taste
Mandazi (1 medium piece), 400 kilocalories, 8 g protein
3 tsp baking powder
1 tsp cream of tartar
2 cups flour
1/2 cup sugar
2 eggs cracked
Mukimo (1 cup), 220 kilocalories, 12 g protein
2 cups corn
2 cups beans
2 cups potatoes
2 cups kale/pumpkin leaves
water, salt, pepper to taste
Sukuma Wiki (1 cup), 200 kilocalories, 4 g protein
fat for frying
1 onion
2 tomatoes (medium)
2 T flour
2 pounds of sukuma
Ugali (1 cup), 180 kilocalories, 3 g protein
1 cup water
1 cup cornmeal
1 tsp salt
Ugi/Sorghum (thin) porridge (1 cup), 110 kilocalories, 3 g protein
¼ cup millet flour
½ cup corn flour
¼ cup sorghum flour
4 cups water
21
Over 10,000 individual food items were compiled from the surveys. The means of the 3day kilocalorie averages for all participants were calculated. Males averaged a kilocalorie intake
of 1712.0 (SD 577.8) while females were slightly lower at 1548.6 (SD 483.9), p = 0.20.
Predicted kilocalorie needs were derived using both HB and Mifflin equations and
compared to the 3-day average intake. The average subject met over 68% of estimated caloric
needs with both HB and Mifflin equations. There was no statistically significant difference in
predicted requirements achieved between results obtained with the two equations across all
subjects or by gender (Figure 4-2). Therefore, the Harris Benedict equation was used in all
subsequent analyses for kilocalorie predictions. Males averaged 68.8% (SD 23.3) of estimated
kilocalorie requirements with the HB equation while women averaged 74.4% (SD 24.4). This
difference did not reach statistical significance, p = 0.247.
Figure 4-2: Comparison of HB vs. Mifflin equations for calculating average percent of kilocalories requirements met
in all subjects: All subjects p = 0.186; males p=0.923; females p=0.125.
The predicted protein requirements in the subjects were calculated using the WHO
equation and compared to intake. In males, the average intake was 56.5 grams (SD 25.6) or
100.51% of predicted intake (SD 45.3). In females, average protein intake was 46.2 grams (SD
18.2) or 100.46% of predicted intake (SD 42.5). There was no statistically significant difference
between genders, p = 0.936.
22
The percentage of kilocalorie and protein requirements met compared with BMI for both
genders are demonstrated in Figures 4-3 and 4-4. BMI was subdivided into underweight (<18.5),
normal (18.5-24.9), and overweight (>24.9) categories.
Figure 4-3: Percent needs of kilocalorie and protein requirements met versus BMI in infected male patients.
ANOVA: Kcal F-ratio 0.498, p=0.613; Protein F-ratio 0.375, p=0.690.
Figure 4-4: Percent of needed kilocalorie and protein requirements met versus BMI in infected female patients.
ANOVA: Kcal F-ratio 0.443, p=0.643; Protein F-ratio 0.747, p=0.477
23
The CD4 lymphocyte count and macronutrient intake was examined. There was no
correlation between kilocalorie intake and CD4 in either males or females (Figures 4-5 and
4-6).
Figure 4-5: Percent of needed kilocalorie requirements met versus current CD4 counts for 21 male subjects. Pearson
correlation coefficient r=0.104, p=0.654
Figure 4-6: Percent of needed kilocalorie requirements met versus current CD4 counts for 57 female subjects.
Pearson correlation coefficient r=0.0420, p=0.765.
24
Conversely, there was a significant correlation with protein intake and CD4 for males
(Figure 4-7). For females, there was not a statistically significant relationship between protein
intake and CD4 counts (Figure 4-8).
Figure 4-7: Percent of protein requirements needs met versus current CD4 counts for 21 male subjects. Pearson
correlation coefficient r = 0.7035, p = .0004.
Figure 4-8: Percent of protein requirements met versus current CD4 counts for 57 female subjects. Pearson
correlation coefficient r = -0.1911, p = 0.1546.
25
Chapter 5: Discussion
Analysis of recent reviews of dietary assessment methods revealed that different types of
instruments had been used in previous studies (e.g. 24-hr and 3-day recall, FFQs, food diaries)
(National and Scottish Research Studies, 2003). For this study, a 3-day recall dietary survey was
chosen, since this was a method similar to previous successful African studies (Kigutha, 1997;
Gewa et al., 2008).
Distant sites and limited resources made food-weighing techniques
impractical, and a variety of different tribal languages and significant illiteracy rates made food
journals a poor choice. Since the Kenyan population had limited variation in their diets, a 24-hr
recall study might have yielded results similar to a 3-day recall, and a shorter survey would have
decreased time spent collecting data, allowing more subjects to participate. However, economic
factors can play a role in temporal diet variation. At the beginning of the month when subjects
receive income, diets contain more variation and animal-based foods, but at the end of the
month, low funds can limit food variety.
Sukumu wiki (“stretch the week”), a low-cost
vegetable, received its name, as it is the only food available when funds are low. Also, since this
culture is, primarily, an agricultural-working population, there can be significant seasonal
variation in dietary intake—both quantitatively and qualitatively, making a one-time recall study
less representative of diet. Future studies may benefit from several 24-hour recall surveys at
various times of the month or year for better overall diet representation. However, this strategy
would need preparation for how to re-contact subjects in remote locations and would be
logistically challenging. Thus, a 3-day recall was chosen to help mitigate monthly variation in
this cross-sectional study.
The actual gathering of information from the 3-day recall proved the most challenging
aspect of the study. The 4 clinic sites were separated by 42 kilometers. While this allowed
access to a broader spectrum of patients, geographical challenges made the study difficult to
conduct. Transportation to and from these areas required 2 to 8 matatus (buses). Traveling time
and survey collection at one site consumed the day. Nighttime travel was not permissible for
safety reasons. In the beginning of the study, subjects were interviewed individually, but lack of
infrastructure, limited manpower, and scheduling difficulties resulted in gathering only 1 to 2
surveys per day. Therefore group surveys were initiated during monthly HIV support group
meetings in order to increase enrollment. These groups were predominately female, since they
26
met during the day, while many of the males worked. Females, even after excluding 10% due to
pregnancy, were the majority of our participants.
At each site, several native Kenyans assisted with data collection, but for standardization,
I was the only one teaching diet-interview methodology. Surveys (Appendix 1), food samples,
and measuring utensils brought daily to sites provided visual prompts for interviewers and
participants. There were at least 3 advantages to having natives assist with surveys. One, they
provided translational skills. The national languages of Kenya are English and Swahili, but
English is spoken only by those with a formal education, which the majority of patients did not
have. Most subjects spoke Swahili or a tribal language, requiring 1-2 translators. Two, Kenyan
natives assisted with data collection accuracy. In resource-poor areas, a desire for secondary
gain is a problem. When natives obtain data, there is less likelihood that the subjects will
deliberately underestimate intake with hopes of financial gain from a non-native researcher.
Three, native interviewers assisted in overcoming cultural barriers. When participants gave an
unclear history, natives were better equipped to understand common problems or
misconceptions.
Upon completion of data collection, during a group lecture, each survey was checked to
clarify portion sizes and confirm that all Swahili or tribal words were translated. Completing
these tasks on site enabled re-contacting the subjects if needed. Although HIV patients are
required to attend a monthly support group, they do not have to go to the same site each time,
which made re-contacting subjects difficult.
When surveys were complete, HIV patients were taught skills to improve their nutrition,
using participant-survey information. This encouraged participation for subsequent studies at
other sites and provided an educational service.
When collection of survey data was completed, health information was gathered on all subjects
through medical record review. Due to the adverse events associated with initiation of HAART
therapy (i.e. nausea, vomiting, GI upset), it was important for patients to have at least 6 months
on medications to provide substantial time to adjust and avoid transient dietary changes. Lack of
computerized or centralized charting system resulted in inconsistent medical record availability
and resulted in exclusion of numerous surveys.
Determining the kilocalories and protein content of commonly consumed local foods was
critical to this study. In addition to the identification of food items in the pilot study, observing
27
and participating in food preparation in the hospital cafeteria during full study implementation
was essential to confirm the nutritional values derived from Internet searches.
Further,
participating in cooking, built relationships with Kijabe Hospital cafeteria workers who readily
contributed food examples and utensils to be used at outlying clinics. I believe that the crosscultural relationships that were developed contributed greatly to the success of our fieldwork.
It was necessary to account for potential regional diversity in eating habits. The Kijabe
Medical Center drew a population from Kijabe, Kenya to Somalia. This led to a heterogeneous
patient population from multiple regions. By personally interviewing the local populous in a
radius around Kijabe of several hundred miles (bush, rural, coast, urban), I confirmed that there
was little variation in the foods consumed. Therefore, there was no need to adjust nutrient values
by geographical residence.
There has been much discussion regarding BMR equations (Mifflin et al., 1990;
Frankenfield et al., 2003). We compared two equations for applicability to our population.
Unlike the widely-used HB equation, the Mifflin equation has never been used with nonCaucasian or non-American populations. Muscle and fat-free mass appear to be its limiting
factor, since individuals with more lean-body mass have less accurate predictions. Therefore,
Mifflin may be less accurate in populations with more lean-body mass, such as Africans, but
there is no conclusive data (Mifflin et al., 1990).
After calculating the BMR, we accounted for additional nutritional needs based on the
subject activity level (AF) as well as the state of their disease, according to WHO
recommendations (WHO, 2003). AFs can range from 1 to 2, depending on the specific activities
of the patient. There is substantial subjectivity in determining which adjustment value to use. I
observed several daily routines of subjects and based on clinical judgment, found subjects to be
moderately active. This correlates with an AF of 1.3. Also, our patients were exclusively Stage 3
and 4 of HIV infection, and WHO recommends a kilocalorie increase of 20 to 30% for these
stages. Thus, we elected to use an average value of 25% for all our subjects.
The average kilocalorie estimations for men and women, using the HB and Mifflin
calculations, were not significantly different statistically, so we concluded that either equation
may be used in this population. Since HB is more widely used and Mifflin may be less accurate
in populations with lower BMI, we elected to use this equation in the subsequent analyses.
28
WHO guidelines are widely used to estimate protein needs. The guidelines recommend
no increase in protein requirements above that of the average healthy adult (0.85 g/kg or 12 to
15% of total kilocalories) (WHO, 2003).
There are, however, some concerns with these
guidelines. If 12 to 15% of total kilocalories are used, the protein requirements may be falsely
elevated due to increased caloric recommendations.
Also, not all experts agree that HIV
patients, especially those in stage 3 and 4, have protein needs similar to healthy adults. As
Coyne-Meyers and Trombley (2004) noted in their review, general protein recommendations are
1.0 to 1.4 g/kg for maintenance and 1.5 to 2.0 g/kg for anabolism in HIV patients. We elected to
use the WHO recommendations as our standard to allow comparison with other studies.
Upon determining appropriate nutrition equations for the population, we compared their
actual intake with predicted intake.
On average, patients met over 68% of recommended
kilocalorie intake (HB and Mifflin equations) and over 100% of their protein intake (WHO
equation). Given that their diet was high in fat, it was not surprising that patients met the
majority of their recommended kilocalories. Although fat is not nutrient-dense, it is caloriedense and has a low cost/calorie value.
For example, one hundred calories of butter (1
tablespoon) costs 6 cents compared with one hundred calories of meat (3 oz), which costs $1.13.
In contrast to kilocalories, it was surprising that the majority of protein needs seemed to
be met. Kenya is a resource-poor area where the cost of protein-rich foods is assumed to limit
consumption. Yet, though they eat fewer animal products than individuals on typical Western
diets, Kenyans still eat several non-animal foods containing protein (i.e. rice, legumes, sukuma
wiki).
Unfortunately, non-animal proteins do not mimic animal proteins in their amino acid
content. To form complete proteins from non-animal foods, similar to those from animal
products, planning and education are needed to combine complementary amino acids in foods
such as peanut butter and whole-wheat bread or beans and rice (Sizer & Whitney, 2003).
Our findings showed most patients met their protein needs; however, there are two
additional factors to consider. One, since there is no predictive equation available to determine
the total protein requirements in the HIV-infected population, and values must be extrapolated
from non-HIV patients, it is possible this study is underestimating the patients’ true
requirements. Two, the current analysis does not address significant amino acid deficiencies,
which could impair protein metabolism, resulting in reduced humoral and cellular immunity.
Further, amino acid deficiencies could lead to impaired barrier function in highly metabolically
29
active mucosal surfaces (respiratory, gastrointestinal, genitourinary) resulting in increased
infections, without significantly affecting CD4 counts. .
The last aim of this study examined a potential link between nutrition intake and a marker
of HIV outcome, CD4 levels. Multiple studies have shown underweight status in patients (BMI
<18.5 kg/m2) to be a strong independent factor predicting survival in HIV and also in non-HIV
infected persons with certain chronic diseases (Landbo et al., 1999; van der Sande et al., 2004).
Conversely, there are no studies that show BMI as a sole predictor of nutrition outcome.
Difficulty obtaining nutrient information for foods common in other cultures, as well as lack of
consistent methodology during fieldwork may have limited previous work.
Although
difficulties were mostly overcome in this study, a correlation between BMI and nutrient intake
was still not detected. Some investigators have suggested that BMI is more reflective of the
disease state rather than macronutrient intake, a condition known as reverse causality. BMI is
significantly impacted by several factors in addition to nutrition, especially in acute disease
processes, including pseudo weight gain from ascites, peripheral edema, or variations of lean
body mass (Sizer & Whitney, 2003). Instead of a sole predictor of nutrition outcome, BMI may
have more use as a component of nutritional assessment. The lack of correlation between both
kilocalorie or protein intake and BMI would support this view.
A positive correlation was observed between CD4 levels and protein intake in males but
not in females, and there was no correlation between CD4 levels and kilocalories in either
gender. This occurred despite the fact that subjects achieved a higher percentage of protein
requirements than kilocalories. WHO does not account for activity or disease modification in
their recommendations for protein intake. Because males may be more active and they often
perform heavy labor in this ambulatory HIV-infected population, their protein requirements may
have been grossly underestimated. Therefore, our findings suggest that the current predictive
equations for protein, requirements could underestimate protein needs in this setting and with
this disease state. The higher proportion of underweight males (BMI <18.5 kg/m2) compared to
females in this sample population suggests that cultural differences related to issues such as work
may be more responsible for the results of this study than gender difference per se, but this
remains speculative based on limited data.
In addition, it remains unclear if the protein
consumption of the study subjects was lacking in certain essential amino acids, as discussed
above.
Interventional strategies may need to include quantitative and qualitative protein
30
supplementation with hopes of a beneficial response in the patient’s co-morbidities (Lawler,
2006). Interventions must take into account availability of food resources, cultural norms, and
economic factors.
In this research project, there were several limitations in study design. First, this was a
cross-sectional study. Data was obtained on a one-time basis and subjects could not be recontacted to gather missing information. Additionally, there was potential recall bias. Although
this was overcome to some extent by the use of food examples and measuring utensils, it was
still necessary for the subjects to remember consumption.
The need to obtain primary data in the field led to other limitations. Medical records
were paper charts located in four clinics separated by considerable distance, thus requiring hours
of transit. If more information was needed from a medical record, long transit time and poor
retrieval systems often resulted in the exclusion of that survey.
limitations because of difficulties estimating nutrient needs.
In addition, there were other
The gold standard for energy
expenditure is indirect calorimetry (Frankenfield et al., 2003; Walker & Heuberger, 2009).
However, as in most low-resource areas, this methodology was not available in Kijabe.
Lack of sufficient numbers of skilled assistants limited enrollment in this study. All chart
abstracting had to be done by the author to ensure accuracy of transcription. Gathering data from
201 charts under difficult conditions greatly limited contact time available to gather additional
surveys. Furthermore, all diet calculations were performed manually for each food for each
participant. A nutritional data program requires less time, but it would have limited the ability to
include several foods consumed often in Kenya, since most programs are U.S. based. Finally,
the lack of adjustment for activity and disease state in current prediction formulas for protein
requirements remains problematic.
Considering the findings in the study, several new questions were identified.
One
particular question is the role of type of proteins in HIV outcome. Since it is known that a diet
rich in essential amino acids is important (Sizer & Whitney, 2003), merely meeting protein needs
while consuming a diet lacking essential amino acids may further expedite the disease process.
Overall, to address this and other questions, longitudinal studies are needed in resource-poor
countries, such as Kenya.
31
APPENDIX 1: 3-DAY RECALL SURVEY TOOL
Nutrition Survey
Clinic
Gender
age
Height
hosp#
study#
Sick/healthy
Weight now
Weight 6 mo ago
Weight at HAART initiation
Weight 6 mo after
Date of HIV dx
date Rx started
compliance
CD4 now
VL now
CD4 6 mo ago
VL 6 mo ago
CD4 at start
CD4 6 mo after
Time
Day 1
Day 2
Breakfast
Lunch
Dinner
Snack
32
Day 3
Enroll#
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Vita
Elizabeth M. Vaughan was born in North Carolina and studied Nutrition and Spanish at
The Ohio State University followed by medical school at Michigan State University.
Exposure to travel and medicine from an early age was attributed to her father’s, Lt. Col.
H. Gwynn Vaughan, career in the military and mother’s, Mary C. Vaughan, career as a nurse.
Her first trip abroad was at age 2 to Korea for her father’s work. She was initially exposed to a
resource-poor area during a faith-based trip to Ecuador at age 16. Fascinated by the need of
public health in low-income areas, she traveled on almost a yearly basis to other countries to
work as a teacher, translator, and, ultimately, a physician. Her trip to Kenya marked her 18th
country.
After completing her undergraduate degree, she completed an internship in dietetics at
the Mayo Clinic and became a Registered Dietitian. She worked for 4 years as a dietitian from
clinical work in inner cities to corporate wellness for executives. Yet, her experiences at Mayo
never left her. Inspired by the physicians’ broad ability to help their patients, she decided to
return to schooling for medical school.
During medical school she participated in medical trips abroad to the Dominican
Republic, Haiti, and Nicaragua. At graduation, she was elected as the medical student of the
year for commitment to help the underserved. Upon completion of medical school, she began
residency and a Master’s of Public Health at the University of Texas Medical Branch. There,
one of her leadership activities was establishing a monthly Global Health Seminar, a forum to
disseminate awareness of public health opportunities in the United States and abroad. She also
planned community events including Operation Christmas Child, donating gifts to children in
resource-poor countries. In February 2010 she was awarded the Thayer Award for Excellence in
Teaching by the Osler Student Scholars.
Elizabeth Vaughan is now completing her residency in Internal Medicine and plans to
pursue a career in global health.
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