Download Social economic determinants of unsafe sex among young women

SOCIAL ECONOMIC DETERMINANTS OF UNSAFE SEX AMONG
YOUNG WOMEN AGE 15-24 IN KENYA
BY
PATRICK MURIITHI KABURI
A RESEARCH PROJECT SUBMITTED TO THE UNIVERSITY OF NAIROBI,
SCHOOL OF ECONOMICS IN PARTIAL FULFILMENT OF THE
REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER OF
SCIENCE IN HEALTH ECONOMICS AND POLICY
NOVEMBER 2015
DECLARATION
I hereby declare that the research Project is my original work has never been presented either in
whole or in part to any other examining body for the award of certificates, diploma or degree.
SIGNATURE ……………………………..
DATE ………………………………
Patrick Muriithi Kaburi
X53/65589/2013
The research project has been submitted for examination with my approval as the university
supervisor
SIGNATURE: ………………………………
DATE………………………………
Dr. Phyllis Machio
University of Nairobi
i
DEDICATION
I dedicate this work to my son Allen Wamugunda and my daughter Amarah Wairimu
ii
ACKNOWLEDGMENT
I would like to express my special thanks to my supervisor Dr. Phyllis Machio for her invaluable
guidance during this project. Secondly I would also like to thank my classmates and friends who
helped me a lot in critiquing my work and giving me useful comments. Last but not least, I want
to thank my immediate family members: Carol, Allen and Amarah for their support and
understanding during the time of my project.
iii
ABSTRACT
Sub-Saharan Africa has been severally affected by the HIV pandemic. Kenya has a high HIV
burden with an estimated 100,000 new infections per year. In set ups with high HIV prevalence,
unprotected sex puts young unmarried women at risk of HIV infection. Few studies have been
done to determine the determinants of demand for condoms use among this population in Kenya.
This study aimed to determine the social economic determinants of condom use at last sex among
young unmarried women age 15-24 year in Kenya. This study utilized Kenya Demographic and
Health Survey of 2008/9 which is a national representative survey. The analyzed sample included
young unmarried women age 15-24 years, who were sexually active in the past 12 months
preceding the survey. A logit regression model was used to identify social demographic and
economic factors independently associated with condom use at last sex. Our analysis identified
612 young women who met the inclusion criteria. Overall, 38 percent of young women used a
condom at last sex. Factors independently associated with condom use at last sex included condom
use at first sex, secondary and higher education level and knowledge of HIV prevention methods.
HIV prevention programs for the young women should therefore aim to target our young girls
before sex debut to increase their risk perception of acquiring HIV infection and therefore
empower them to use protection during their first sexual intercourse with a partner of unknown
HIV status. Our education system should aim to provide education to girls to the highest level of
education.
iv
TABLE OF CONTENT
DECLARATION ........................................................................................................................................... i
CHAPTER ONE: INTRODUCTION ........................................................................................................... 1
1.1
Background ................................................................................................................................... 1
1.1.1
Global and Country HIV Burden .......................................................................................... 1
1.1.2
The Economic Burden of HIV .............................................................................................. 2
1.1.3
HIV/AIDS and Unsafe Sex ................................................................................................... 2
1.1.4
Unsafe sex practices among young women .......................................................................... 3
1.2
Problem Statement ........................................................................................................................ 4
1.3
Study Justification ......................................................................................................................... 4
1.4
Objective of the study ................................................................................................................... 5
1.4.1
1.5
Specific Objectives ............................................................................................................... 5
Research questions .................................................................................................................... 5
CHAPTER TWO: LITERATURE REVIEW ............................................................................................... 7
2.0 Introduction ......................................................................................................................................... 7
2.1
Theoretical Literature.................................................................................................................... 7
2.1.1
The Economics of Human Behavior ......................................................................................... 7
2.1.2
Economic Model of Risky Sexual Behaviour ........................................................................... 9
2.2
Empirical literature ..................................................................................................................... 11
2.3
Summary of literature review ..................................................................................................... 14
2.4
Conceptual framework ................................................................................................................ 15
CHAPTER THREE: METHODOLOGY ................................................................................................... 16
3.0
Introduction ................................................................................................................................. 16
3.1
Analytical Framework................................................................................................................. 16
3.2
The Econometric Model.............................................................................................................. 17
3.3
Specification of the Model .......................................................................................................... 19
3.4
Variables Definition and Measurement ...................................................................................... 21
3.5
Data Source ................................................................................................................................. 23
CHAPTER FOURFINDINGS………..………………………………………………………………..….28
CHAPTER FIVE: DISCUSSION CONCLUSION AND RECOMMENDATION………………………35
REFERENCES .......................................................................................................................................... 37
v
LIST OF TABLES
Table 4.1 Descriptive Statistics of Study Respondents…………………………………………..…..27
Table 4.2 Exposure to Mass Media
………………………………………………………….…29
Table 4.3 Sexual behaviours Knowledge of HIV prevention and attitudes towards condoms…….…31
Table 4.4 Bivariate analysis of condom use at last sex with independent variables……………….....33
Table 4.5 Multivariate analysis………………………………………………………………………..37
vi
LIST OF ABREVIATION
AIDS
Acquired Immune Deficiency Syndrome
HIV
Human Immunodeficiency virus
UNAIDS
Joint United Nations Programme on HIV/AIDS
NACC
National AIDS Control Council
NASCOP
National AIDS and STI Control Program
KDHS
Kenya Demographic and Health Survey
KNBS
Kenya National Bureau of Statistics
KAIS
Kenya AIDS Indicator Survey
CSW
Commercial Sex Workers
IDU
Injecting Drug Users
MSM
Men having Sex with Men
vii
CHAPTER ONE: INTRODUCTION
1.1
Background
1.1.1 Global and Country HIV Burden
Since the first Acquired Immune Deficiency Syndrome (AIDS) case was reported in 1981, the
Human Immunodeficiency Virus (HIV) pandemic has grown to become one of the greatest public
health scourges in the world. The United Nation Joint Program on AIDS (UNAIDS) estimates that
globally, by the end of 2013, 35 million people were living with HIV and that approximately 2.1
million people globally were newly infected with HIV while an estimated 1.5 million died from
AIDS related causes (UNAIDS, 2014).
Sub-Saharan Africa has been severally affected by the HIV pandemic in the world and it is home
to 70 percent of People living with HIV in the world (UNAIDS 2014). Kenya has the fourth biggest
HIV epidemic in the world after, South Africa, India and Nigeria (UNAIDS 2014).The National
AIDS Control Council (NACC) estimated that by the end of 2013, approximately 1.6 million
Kenyans were living with HIV. NACC further estimates that there were 100,000 new infections
in Kenya in 2013. With the number of new infections still higher than estimated deaths, the
absolute number of people living with HIV is projected to rise in the coming years (NACC, 2014).
Young women 15–24 years old in sub-Saharan Africa are twice as likely as young men to be living
with HIV (UNAIDS 2014). In Kenya, HIV prevalence is significantly higher among women age
15-24 years at 2.7 percent compared to young men in the same age group at 1.7 percent (NACC,
2014). It is estimated that 20 percent of all new HIV infections in Kenya in 2013 occurred among
young women aged 15-24 years (NACC, 2014).
1
1.1.2
The Economic Burden of HIV
The high HIV prevalence in sub-Saharan Africa has compounded the high poverty rates in the
continent. The high morbidity rate associated with HIV and AIDS and increasing access to
emerging HIV services such HIV testing and antiretroviral therapy, has overburdened already
constrained health system. For example, in Kenya, over 800,000 people were accessing
antiretroviral in public health facilities by the end of 2014 (NACC 2014). HIV/AIDS is the leading
cause of morbidity and death among adults in Kenya. In 2013, the number of orphans in Kenya
were estimated at 2.4 million half of which was due to HIV/AIDS.
The national AIDS spending assessment by NACC (2014) showed that Kenya spent over $ 800
Million on HIV/AIDS programs. This excludes the indirect costs related to seeking health care –
a cost mostly met from the individual’s out-of pocket. Moreover, the high morbidity associated
with HIV has reduced productivity of the increasing number of people living with HIV/AIDS. The
cost to finance HIV/AIDS programs will continue to rise if the high new infection rate is not tamed.
1.1.3
HIV/AIDS and Unsafe Sex
The HIV epidemic in sub-Saharan Africa is described as generalized epidemic where the most
HIV transmission is through unprotected heterosexual route among general population. This is
different from the HIV epidemic in United States of America, Eastern Europe and Asia where the
epidemic is concentrated among Men who have Sex with Men, Injecting drug users and
Commercial sex workers.
2
According to Modes of Transmission Study, over 95 percent of new HIV infection in Kenya are
sexually transmitted (NACC, 2009). Condom use is a critical element of combination prevention
and one of the most efficient technologies available to reduce the sexual transmission of HIV.
Despite the near universal awareness of AIDS in Kenya, unprotected sex is still common among
the general population in Kenya where only one in four Kenyans reported that they used a condom
the first time they had sex (KNBS 2010). Although this has almost doubled from 2003, this level
is still unacceptably low in an environment with high HIV prevalence which therefore makes
young people vulnerable to HIV acquisition (UNAIDS, 2012.
1.1.4
Unsafe sex practices among young women
In the context of such high HIV prevalence in the general population, HIV programs aimed at
promoting safer sex practices are the cornerstone of HIV programs. Condom use is one of the most
effective methods to prevent HIV and it is a cornerstone of most HIV prevention programs in subSaharan Africa. Yet, a significant proportion of young women don’t use condoms consistently and
still engage in high risk sex predisposing them to HIV infections (UNAIDS, 2014).
UNAIDS (2014), highlighted that in Sub-Saharan Africa, young women acquire HIV five to seven
years earlier than men. In Kenya, 11 percent of young women aged 15-24 years reported that they
had had sex by the age of 15 years (KAIS, 2012. By the age of 18 years, 59 percent of the young
women had had sex. Yet only a quarter of the young girls report use of condom the first time they
have sex. Moreover, among the young women that reported having sex in the previous 12 months
preceding the survey, only one out of three used a condom during a high risk sex (KAIS, 2012).
3
Modelling studies done in South Africa shows that shows that the increases in condom use, which
occurred at the same time as the distribution of male condoms significantly increased and played
a primary role in declines in national HIV incidence that occurred during 2000–2008(UNAIDS,
2014).
1.2
Problem Statement
In an environment of high HIV prevalence in the general population as occasioned in East and
Southern Africa, unsafe sex puts young women at considerable risk of HIV infections. Young
women continue to have high incidence of HIV in Kenya with close to 20 percent of the annual
new infections estimated to occur among young women 15-24 years (NACC, 2014).Although
decline in HIV prevalence and incidence among this age group is being reported in Kenya, the
number of new HIV infections are still unacceptably high. Despite an almost universal HIV and
AIDS awareness in the general population, young women in Kenya continue to practice unsafe
sex oblivious of the dangers of HIV acquisition (UNAIDS, 2014).
Behavior change among unmarried youth is a cornerstone intervention in HIV programs in
countries experiencing generalized HIV epidemic like Kenya. Yet, proven effective behavior
change interventions remain elusive. This could partly be due to lack of adequate understanding
of social and economic factors that are highly correlated with unsafe sex among young women
(Doyle, et al; 2012.
1.3
Study Justification
Kenya has adopted the UNAIDS call of getting to zero new infections (NACC, 2014). In order to
get new infections towards zero, the HIV prevention agenda needs to incorporate evidence based
4
behavior change interventions as part of the HIV programs. These new interventions need to be
based on the evidence based determinants of high risk sex.
Determination of factors related to high risk sex is therefore important step in informing behavior
change HIV programs. Unfortunately, very little documentation of social economic factors
independently related to high risk sexual behavior among young women exists in Kenya. This
study therefore aims to determine independent social economic correlates of unsafe sex among
young women using national representative population based data.
1.4
Objective of the study
The main objective of this study is to identify social and economic determinants that are correlated
with unprotected sex among young women aged 15-24.
1.4.1
Specific Objectives
1. To determine the HIV knowledge, attitude and sexual practices of young women
aged 15-24 years
2. To determine demographic and socioeconomic factors associated with high risk sex
among young women aged 15-24 years
3. To provide policy recommendations for reducing unsafe sex among the young
women age 15-24 years in Kenya.
1.5
Research questions
This study therefore aims to answer the following research questions.
5
i.
What is the HIV knowledge, attitude and sexual practices of young women aged
15-24 years in Kenya?
ii.
What are the social economic and demographic factors that are associated with
unsafe sex among young women aged 15-24 years?
6
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction
This chapter reviews a number of past studies both theoretical and empirical that have been
conducted touching on the topical issue of demand for safe sex.
2.1
Theoretical Literature
In this section, we highlight the applicable theories and models that aim to explain the rationale
behind choices of certain behaviors.
2.1.1
The Economics of Human Behavior
Gerry Becker, (1976) was among the first pioneers of economic approach to human behavior. He
argued that economic approach can provide a unified framework for understanding all human
behavior where human behavior can be seen as rational and utility maximizing. In his approach,
he defined the following four assumptions. First, human behavior is interlinked in market systems
in which choices of individuals are shaped by costs and benefits in the context of stable
preferences. Second, resources are scarce and desirable, and they are allocated by market
influences, for example price shifts. Third, there is competition among sellers of goods or services.
Fourth, individuals want to maximize their outcomes, meaning their utility (Becker, 1976).
In his book sex and reason, Posner (1992) argued that even during sexual pleasure, human beings
behave rationally, choosing sexual strategies that optimally fit their desires and goals. This is in
contrast to earlier Aristotle’s theory that “pleasures” especially sexual ones “impede wise
thinking”.
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In their book “The private choices and public health the AIDS epidemic in economic perspective”,
Philipson and Posner (1993) emphasized the role of voluntary, rational choice to engage or refrain
in risky sexual activities in the spread and control of AIDS epidemic. According to Philipson and
Posner (1993), the decision to have sex is a rational one, based on the full expected costs and
benefits to demanders and suppliers. In their argument, they pointed that there is an ex ante utility
to engaging in unprotected sex (sex without a condom). Set against these benefits are the expected
costs of unprotected sex – costs which rise with the stock, rate, and probability of infections
(Philipson and Posner, 1993).
In their analysis of risk and rationality in adolescent decision making, Reyna and Farley (2006)
argued that adolescent’s goals are more likely to maximize immediate pleasure. They further argue
that although perceived risks and especially benefits predict behavioral intentions and risk-taking
behavior, behavioral willingness is an even better predictor of susceptibility to risk taking – and
has unique exploratory power – because adolescent are willing to do risker things than they either
intend or expect to do (Reyna& Farley, 2006).
People engaging in behaviours that are known to pose HIV related risks may choose to weigh the
potential perceived risks associated with risk involvement (e.g., becoming HIV-infected,
contracting hepatitis) against the potential perceived risks associated with electing not to engage
in a particular behaviour (Elifson et al, 2010).
8
2.1.2 Economic Model of Risky Sexual Behaviour
According to Philipson &Posner (1993), the decision to engage in unsafe sex can be modelled as
a problem of making a rational choice under uncertainty. The expected utility (EU) of risky sex
for m and for f is equivalent to the benefits (B) minus the expected costs (C) of risky sex. The two
utility functions can therefore be defined as follows:
EUm = B – C(Ptf(1 – Pm)Pf)
(1.1)
EUf = B – C(Ptm(1 – Pf) Pm)
(1.2)
Where:
EU = expected utility
B = benefit of unsafe sex
m = male
m = female
C = cost of becoming infected with HIV
Pti = probability of transmission; i = m, f
Pi = probability that m or f is already infected; i = m, f
Accordingly a risky sexual exchange will only take place if expected utilities for both sexes are
positive, that is if (EUm, EUf)> 0, and the trade is mutually beneficial. There are few exceptions
when the expected utility of either gender is negative but a risky sexual trade still takes place, for
example in the cases of rape. There is also the possibility for either m or f to compensate the other
partner to engage in unsafe sex; an example being a sex customer paying a prostitute more for an
unsafe sexual activity (Philipson & Posner, 1993).
9
Assuming that safe sex entails use of condom, the benefit (B) of unsafe sex equals the disutility of
using a condom. The benefit is assumed to be the same for both m and f, but they might of course
have different utilities. Men and women do not necessarily experience the same disutility of using
a condom during sexual intercourse. C is the cost of becoming infected with the HIV virus. This
includes both pecuniary costs such as medical expenses and loss of earnings and non-pecuniary
costs such as the disutility of a painful and incapacitating disease and a premature death as well as
possible social stigma (common in developing countries) and exclusion from sexual activity. Both
m and f are assumed to be non-altruistic to each other, meaning that the cost to one’s sexual partner
if he or she becomes infected is not a cost to oneself. Altruism can both reduce and increase the
cost of risky sex. C may also be interpreted as the discounted cost of HIV/AIDS given the span of
time between infection and serious illness.
According to this theory, the safe to unsafe sex ratio will be positively associated with factors that
increases the prevalence of HIV/AIDS; not only sexual preference but also socio-economic
attributes such as location, gender and age. Behavior change programs seek to promote safer
individual behavior as well as changes in social norms that generate healthier patterns of sexual
behavior. Behavior change is complex; it involves knowledge, motivations and choices, which are
influenced by sociocultural norms, as well as risk assessment in relation to immediate benefits and
future consequences. It involves both rational decision making and impulsive and automatic
behavior (Marteau & Hollands, 2012). HIV behavior change programs have largely been measured
against the outcomes of reduction in the number of young people initiating sexual intercourse early
10
and the number of sexual partners and increase in the correct and consistent use of condoms among
people who are sexually active.
2.2
Empirical literature
Increasing condom use requires both adequate supply and adequate demand. A recent study in
Kenya estimated that, although condom use was low in the study population, so was the unmet
need for condoms, highlighting the importance of building demand for condoms in the context of
HIV prevention (Papo et al, 2011). The demand for condoms to protect against HIV infection may
also be affected by other prevention programs, such as perceptions that risks are lower because of
interventions such as male circumcision or post-exposure prophylaxis or that partners receiving
antiretroviral therapy will be less infectious, and similarly, the consequences of HIV infection may
be seen as less devastating in the era of effective therapy thus decreasing the need for protection.
A study done in Uganda showed that correct knowledge of condom use, as well as positive
attitudes towards the use of condoms are associated with the likelihood that adolescents used
condoms (Kayiki & Forste, 2013). A meta-analysis study found that HIV education was effective
in reducing HIV associated stigma, and preventing HIV transmission by increasing age of sex
debut. For the sexually active young people, sex and HIV education was associated with increase
in condom use, HIV testing, and a reduction in the number of sexual partners (Johnson et al; 2011).
A review of pattern and trends of sexual behavior among adolescents in sub-Saharan Africa
concluded that young men and women are at high risk of HIV /STI infections and unwanted
pregnancies due to multiple sexual partnerships and low level of condom use (Doyle et al., 2012).
11
Females who reported being single and those with higher education were more likely to report
multiple sexual partnerships. Additionally, urban youth and those with higher education were more
likely to report use of condom.
Few studies have reported on a relationship between age and condom use (Cerwonka, Hansen, &
Isbell, 2000; Crosby, Meyerson, & Yarber, 1999). Married persons or those living with a partner
tend to use condoms less than their peers whose marital status is something other than married
(Booth, 1995; Crosby, Meyerson, & Yarber, 1999). Living in a rural area (versus an urban area)
has also been associated with less condom use (Crosby, Meyerson, & Yarber, 1999).Systematic
reviews conducted before 2000 showed that increased years of schooling was associated with an
increased risk of HIV infection among men and women from both rural and urban communities in
Africa (Hargreaves 2002). However in recent times, this trend is shifting and some studies have
shown a HIV infections protective effect of education especially among young girls who have
attained secondary education (Gillespie 2007). In Botswana and Swaziland, more educated young
girls were less likely to report inconsistent condom use and also to report having intergeneration
sexual relationships (Weiser 2007).Bloom et al. (2001) found that the wealthiest women are twice
as likely to practice safe sex and twice as likely to know how to prevent HIV infection and almost
four times more likely to know where to get tested. Snelling et al (2006) and Fylnesnes et al. (2001)
found that education is strongly associated with condom use.
Finance-related considerations also appear to play a role in women’s use of sexual protection, with
research showing that being financially dependent or interdependent upon one’s sexual partner
relates to less condom use (Crosby, Meyerson, & Yarber, 1999; Sherman & Latkin, 2001). A
12
number of studies have shown that food insecurity makes young women more vulnerable to high
risk sexual behaviors (Weiser 2007) and that food insecurity is a risk factor for HIV infections
(Rollins 2007).Rollins (2007) suggested that HIV programs should consider ways of improving
access to basic needs among vulnerable groups, as failure to do so may limit the effectiveness of
other prevention effort.
Disproportionately high HIV infection rates among young women aged 15-24 years have been
attributed to their greater involvement in relationships with older-aged partners (Gregson et al.
2002). A review of DHS in select sub-Saharan Africa counties showed that in most countries 2-6
percent of young girls who had had sex in the past one year reported having a sexual partner who
was 10 or more year’s older (Doyle et.al 2007). Whereas early studies emphasized economic
concerns in the context of poverty as driving girls to accept or seek the attentions of older employed
men, close-grained studies reveal a complex interplay of meanings and motives that prompt both
men and women across socioeconomic strata to engage in intergenerational sex (Leclerc-Madlala
2007). Studies have revealed that age-disparate relationships are meaningful and perceived as
beneficial at a number of levels, including social, physical, psychological, as well as economic and
symbolic. This increases their vulnerability to HIV because with that age difference, their ability
to negotiate condom use is diminished.
Luke (2008) found that lucrative economic gifts make young women to less likely have protected
sex. This type of relationship is more common among less economically empowered and school
going girls. Orphans are a sub-group of adolescents that has been found to be most vulnerable by
engaging in high risk behaviors that predispose them to HIV infections (UN 2006). A crosssectional study conducted to identify predictors of high risk sex among orphan in Rwanda found
13
that orphans had an early sex debut, had low perception of HIV risk and reported very low condom
use at risk sex (Ntaganira 2012).
Problems related to condom use may be exacerbated when youths consume substances that reduce
their inhibition and affect their self-control such as ‘khat’ leaves (Mwenda et. al 2003) alcohol,
marijuana and others (Kalichman et. al 2007, Santelli et.al 2004).These substances can affect
decision making before and during sex that impacts on condom use and exposes them to risky
sexual acts. Studies in Ethiopia show that the odds of engaging in risky sexual behavior was 3.4
times higher among alcohol consumers and 2.3 times among ‘khat’ chewers compared to nonconsumers of these substances.
The effect of alcohol has been linked with un-protected sex. A study conducted in eight countries
in sub- Saharan Africa showed that being drunk at last sex was associated with failure to use a
condom for both men and women (Kiene and Subramanian 2013). From the findings, young
women engaging with drunken male partners were therefore more likely to engage in unsafe sex
and therefore increasing the chance of HIV transmission. The study recommended implementation
of HIV prevention interventions that considered the role of alcohol use in precipitating unprotected
sex.
2.3
Summary of literature review
From the reviewed literature above, numerous studies have been conducted in line with the cited
theoretical models. The studies approach has also not been systematic across different settings and
for this reason, there is no single conclusive socioeconomic determinants of unsafe sex among
14
young women that can be generalized to entire sub-Saharan Africa. Due to the differences in the
local settings, it is therefore important to understand the determinant of unsafe sex in the Kenya
context
2.4
Conceptual framework
Independent Variables
•
Dependent Variable
Knowledge of HIV
Prevention
Motivation
•
Individual risk
perception of acquiring
HIV
Safer sexual Practices
Behavioral skills
•
Age at first sex, condom
use at first sex,
knowledge of HIV status,
number of sex partners
in the lifetime.
•
Use of condom in
the last sexual
Contact
Modifying variables
•
Social demographic
characteristics ( Age,
wealth index,
residence, marital
status
Source; Author
15
CHAPTER THREE: METHODOLOGY
3.0
Introduction
This section describes the study approach to answer the study objectives and research questions.
The section includes the analytic framework, the econometric model, the data sources and
definition of study variables.
3.1
Analytical Framework
In this study, I utilize the utility functions as defined by Philipson & Posner (1993).The expected
utility (EU) of risky sex for m and for f is equivalent to the benefits (B) minus the expected costs
(C) of risky sex. The two utility functions are defined as follows:
EUm = B – C(Ptf(1 – Pm)Pf)
(3.1)
EUf = B – C(Ptm(1 – Pf)Pm)
(3.2)
Where
EU = expected utility
B = benefit of unsafe sex
C = cost of becoming infected with HIV
m = Male
f = Female
Pti = probability of transmission; i = m, f
Pi = probability that m or f is already infected; i = m, f
The two utility functions, portrayed by equations 3.1 and 3.2, create the joint demand for unsafe
sex for all possible infection probabilities of m and f.
16
3.2
The Econometric Model
Following Wooldridge estimation procedure (2002), It is assumed that there is unobservable
variable y* which is the propensity of engaging in protected sex. This variable is linearly related
to independent variables as follows.
Y* = X i β + ε i
(1)
Where;
y*is the unobserved latent variable from - ∞to∞
Xi
is the vector of explanation variables– the social economic factors that are
associated with demand for safer sex
β is the vector of parameters to be estimated
Ɛ is the error term
The latent variable is related to the binary variable as follows;
1 if y *i > 0
yi={
0 if y *i ≤ 0
(2)
Where yi = 1 if an individual used a condom during the last sexual intercourse and yi =0 if
otherwise.
The binary models can be written as
Pr(Y = 1) | X ) = Pr(Y * > 0) | X )
(3)
Replacing the latent variable using equation (1) we have
17
Pr( y = 1) | X ) = Pr( X i β + ε > 0) | X )
Subtracting both sides of the inequality by Xβ
Pr(Y = 1) | X ) = Pr(ε > − Xβ | X )
Since the cumulative density function (CDF) expresses the probability of a variable being less that
some value, we must change the sign of inequality.
We can therefore write the probit and logit models as
Pr(Y = 1) | X ) = Pr(ε ≤ Xβ | X )
(4)
This is simply the CDF or error distribution evaluated at Xβ
Thus we write this as
Pr(Y = 1) | X ) = Λ ( Xβ )
To specify the likelihood function we define p as the probability of observing whatever value of y
we observe for a given observation.
Pr( y i | X i ) if y i = 1 is observed
As Pi = {
1 − Pr( y i | X i ) if y i = 0 is observed
The likelihood function is thus given as
N
L( β | Y , X ) = Π Pi
i =1
(5)
Using equation 2 we can write the likelihood as
18
L( β | Y , X ) = Π Pr(Yi | X i ) Π [1 − Pr(Yi | X i )]
Y =1
Y =0
Using equation (3) this becomes
L( β | Y , X ) = Π F ( X i β ) Π [1 − Λ ( X i | β )]
Y =1
Y =0
Since we maximize the log likelihood function we obtain this by taking logs to get
Ln( β | Y , X ) = ∑ ln Λ ( X i β ) + ∑ ln[1 − Λ ( X i β )]
Y =1
Y =0
The formula for the average effect of a binary variable on an explanatory variable XK is given by
Pr(y=1| Xk =1) - Pr(y=1| Xk =0) =
3.3
ʌ (Xβ| Xk =1) - ʌ (Xβ| Xk =0)
Specification of the Model
The model to be estimated can be specified as. Safe sex is the dependent variable while the
variables presented on the right hand side of the equation are the independent variables. Our aim
was to evaluate which independent variables correlate with our outcome of interest – Safe sex.
SS =β 0+ β1Age+ β2Educ+ β3Wealth+ β4Residence + β5+ β6Province+ β7Knowl+ β8 knowledge of HIV
+ β9csource+ β10Risk+ β11 Attit + β12 Asex+ β13 Conduse + β14 HIVtest + β15 Agediff
Where
Ss
Safe sex at last sex
Age
Age of the respondent
19
Educ
Respondents level of education
Wealth
Respondent wealth Index
Residence
Respondents area of residence
Knowl
Knowledge about HIV Prevention methods
Csource
Know condom source
Risk
Respondent Risk Perception
Attit
Attitude towards Youth condom Education
Agex
Age at first Sex
Condouse
condom use at first sex
HIVtest
Ever had HIV test and Counselling
Agediff
Age difference between respondent and sex partner
Partners
Number of sex partners in the last 12 months
μ
Error term
20
3.4
Variables Definition and Measurement
Variables
Measurement Description
Dependent Variable
Safer sex practice
Reported use of a condom in the last sexual act
Binary measure
“1” if a condom was used “0 “ if a condom not used
Independent Variables
Social Demographic
Age
Completed years since birth
(Continuous)
Education
Measured as dummy variable. Four dummy variables were
created:
No education = 1 if a respondent has no formal education, 0
otherwise
Primary education level= 1if a respondent has completed
primary education , 0 otherwise
Secondary education level = 1if a respondent has completed
secondary education , 0 otherwise
Tertiary education level =1if a respondent has completed tertiary
education, 0 otherwise.
Household Wealth Index
Household wealth index Continuous - ranges from 1 to 5
Residence
Measured using dummy variables
Place of Residence
Urban = 1 if urban, 0 otherwise
Rural =1 if rural, 0 otherwise
Information
21
Knowledge
of
HIV Measured through dummy for the following three methods.
prevention methods
1)Knowledge if HIV prevention can be prevented through
consistent Condoms use, (2)Being faithful to one uninfected
partner, and (3)abstaining from sex.
No knowledge of any HIV prevention method =1 otherwise 0
Knowledge of only one HIV prevention methods = 1
otherwise“0”
Knowledge of two HIV prevention methods only = 1 otherwise
0
Knowledge of all three HIV prevention methods = 1 if knows
three methods otherwise 0
Knowledge of source of Knowledge of a place a person can get condoms = 1 if they know
condom
0 otherwise
Motivation
Individual Risk perception Respondent’s perception of chances of getting AIDS.
of acquiring HIV
”0” No risk at all
“1” Small
“2” Moderate
“3” great
Attitudes towards condom Agrees that youth be taught about condoms in school = 1
education for youth
otherwise 0
Behavior skills
Age at first sex
Age of first sex (Continuous)
Condom use at first sex
Used condom at first sex = 1 if condom was used otherwise 0
Previous HIV testing
Ever had HIV test and Counseling = 1 if ever tested for HIV
otherwise 0
Age difference between continuous
respondent and sex partner
Number of sex partners in
“Continuous”
life time
22
3.5
Data Source
Kenya Demographic and Health Survey (KDHS) of 2008/9 (Kenya National Bureau of Statistics,
2010) was used in this study. This was a household – based survey and covered a sample of
households in the entire country. Using the National Sample Survey and Evaluation Program
(NASSEP IV) Framework to select a total of 400 clusters both urban and rural, a representative
sample of 10,000 households were drawn from all former provinces of Kenya.
The analytic sample consisted of unmarried women respondents age 15 -24 years who reported
being sexually active in the last 12 months.
23
CHAPTER FOUR: FINDINGS
4.0 Introduction
This chapter presents the study findings and it outlined as follows. Section one presents the
descriptive statistics; Section two presents the findings of association of dependent variable with
our proposed independent variables while section three presents the logit regression model results.
4.1 Social demographic characteristics of survey respondent
This study selected a sample of women respondents who were aged 15-24 years, had ever had sex
and were sexually active in the past 12 months. A weighted number of 612 survey respondents
met this criterion and were used in our subsequent analysis.
The study respondents age ranged from 15-24 years with a mean of 19.7(2.60). More than half
(55%) of the respondents were in 20-24 years age category. Over two thirds (67.4%) were
residence of urban areas. Former Rift Valley followed by Nyanza Province had the highest number
of study respondents with 171 (28%) and 138 (22.6%) respectively.
The study respondent’s number of education years ranged from 0 to 10 years with a mean of 5.2
years of education years. Majority (53.6%) had primary level as the highest level of education.
This was followed by secondary education 203 (33.2%), tertiary education (9.9%) while (3.4%)
had no education at all.
Majority of respondents households heads were Females (54.9%). A third (33.2%) of the
respondents belonged to households in the highest wealth quintile. This was followed by middle
and fourth quintile at 18.2% each.
24
Table 4.1 Descriptive Statistics, Social Demographic Variables
Variable
Mean +sd (%)
Age in completed years (N= 612)
Mean 19.7 ± 2.66
Age category (N= 612)
15-19
274 (45%)
20-24
336 (54%)
Place of residence (N= 612)
Rural
200 (32.6%)
urban
412 (67.4%)
Education in completed years(N=
612)
Mean 5.2 ± 2.47
Range 0-10
Religion (N= 612)
Roman catholic
154 (25.2%)
Protestant/other Christian
429 (70.2%)
Muslim
20 (3.2%)
No religion
9 (1.4%)
Highest Education level(N= 612)
No education
21 (3.4%)
Primary
327 (53.6%)
Secondary
203 (33.2%)
Higher
60 (9.9%)
Age of household head (N= 612)
Mean 46.1 ±14.9 Range 16-84
Sex of household head(N= 612)
Male
336 (54.9%)
Female
276 (45.1%)
25
Wealth index (N= 612)
Lowest
83 (13.6%)
Second
103 (16.9%)
Middle
111 (18.2%)
Fourth
111 (18.2%)
Highest
203 (33.2%)
4.1.3 Sexual behaviour
The age at first sexual intercourse ranged from 8-24 years with a mean age of 19.9 ± 2.61. The
number of life time sexual partners ranged from 1- 50 years with a mean of 1.9 ± 1.89 partners.
Majority of the respondents (57.9%) reported ever being tested for HIV. Only 37.4% of the survey
respondents reported use of condom at last sex.
4.1.4 Knowledge of HIV prevention methods
Respondents were asked to mention three HIV prevention methods. These methods were
abstaining from sexual intercourse, using condoms every time they have sexual intercourse and by
having one sex partner who is not infected and has no other partners.
The responses on knowledge were categorised by the number of methods known by the
respondents. As shown in table 3, majority of survey respondents (74.4%) knew all the three HIV
prevention methods, 21.3% knew two methods only 3.5% knew only one method. Less than one
percent knew no HIV prevention method at all.
26
4.1.5 Attitudes towards condom and Risk perception
The survey sought to find out the attitudes of survey respondents towards condoms. Respondents
were asked if condom education should be taught in schools. Majority of respondents (71%) were
in affirmative that condom education should be taught in schools.
When the respondents were asked about their perception of risk of acquiring HIV, most of them
(40.5%) rated themselves to be at a small risk of acquiring HIV. This was followed 37.2% who
rated themselves at moderate risk of acquiring HIV. Only 14.7% and 7.2% rated themselves at
great risk and no risk respectively.
27
Table 4.3 Sexual behaviours Knowledge of HIV prevention and attitudes towards condoms
Behaviour Variable
Descriptive statistics
Age at first sex intercourse
Mean: 19.98 ± 2.61
Range: 8-24
Used condom at first sex
Yes
386 (63.8%)
No
219 (36.3%)
Ever tested for HIV
Yes
351 (57.9%)
No
256 (42.1%)
Total lifetime partners
Mean: 1.94 ± 1.89
Range: 1-50
Age difference with last sexual partner
3.4 ±3.1 Range -6 -28
Knowledge of HIV prevention methods
None
5 (0.8%)
One method only
21 (3.5%)
Two method only
130 (21.3%)
All three methods
451 (74.3%)
Risk perception of acquiring AIDS
Small
246 (40.6%)
Moderate
226 (37.3%)
Great
89 (14.8%)
No risk at all
44 (7.2%)
28
4.3 Bivariate analysis
When the association of dependent variables with the independent variables was tested, the results
are as shown in Table 4.4.
Education, frequency of reading newspaper, frequency of listening to a radio, wealth index and
were significantly associated with use of condom at last sex. Those with higher education, had
high frequency of reading newspaper and listening to a radio, of higher wealth index were more
likely to use condoms. Respondent’s age, type of residence, region, sex of household head, age of
household head had no association with use of condom at last sex.
For past sexual behaviour, the mean age of respondents who used condom at last sex was
significantly higher than those who did not (P= 0.0485). The mean difference in age between the
last sexual partner and the respondent was higher among those didn’t use condom at last sex
(p=0.022). Those who used condom at first sex were also more likely to have used condom at last
sex (P= 0.0000). There was no association between use of condom at last sex with history of ever
tested, favourable attitudes towards sex education in schools, total number of lifetime sex partners,
ever receiving a gift for sex and individual perception of risk of acquiring HIV.
Table 4.4 Bivariate analysis of condom use at last sex with independent variables
Social Demographic Variable
Used condom at last sex
Yes
Age in completed years
No
Mean 19.69 ±0.25
Mean 19.75 ±0.30
15-19
40.5%
59.5%
20-24
34.8%
65.2%
Rural
82 (40.9%)
118 (59.4%)
urban
147 (35.7%)
265 (67.4%)
P Value
P = 0.869
Age category
P = 0.316
Place of residence
P= 0.4253
29
Social Demographic Variable
Used condom at last sex
Yes
No
P Value
Region
Nairobi
43 (58%)
31(42%)
Central
15 (30%)
34(70%)
Coast
11 (23.4%)
35(76.6%)
Eastern
23 (30.2%)
52 (69.8%)
Nyanza
55 (40.0%)
83 (60.0%)
Rift valley
59 (34.6%)
112 (65.4%)
Western
23 (39.4%)
36 (60.6%)
North Eastern
0 (0%)
0 (0%)
Mean 5.49 ±0.142
Mean 4.70 ±0.22
Roman catholic
48 (31.0%)
106 (69.0%)
Protestant/other Christian
176 (41.1%)
253 (58.9%)
Muslim
4 (20.4%)
16 (79.6%)
No religion
9 (100%)
0 (0.00%)
No education
2 (3.4%)
19 (3.4%)
Primary
97 (29.7%)
230 (70.3%)
Secondary
93 (45.9%)
110 (54.1%)
Higher
36 (60.4%)
24 (39.6%)
Not at all
17 (32.7%)
36 (67.3%)
Less than once a week
9 (14.1%)
52 (85.9%)
At least once a week
31 (28.3%)
79 (71.3%)
Almost every day
172 (44.2%)
Education in completed years
P = 0.0640
P=0.0015**
Religion
P = 0.0283*
Highest Education level
P = 0.0001***
Frequency of listening to radio
P= 0.0043**
217 (55.8%)
30
Social Demographic Variable
Used condom at last sex
P Value
Yes
No
Male
124 (36.9%)
211 (63.1%)
Female
105 (37.9%)
171 (62.1%)
Mean 46.2 ± 1.22
45.9 ± 1.58
Lowest
14 (16.4%)
69 (83.6%)
Second
43 (41.8%)
60 (58.2%)
39 (35.6%)
72 (64.4%)
45 (41.0%)
66 (59.0%)
87 (42.7%)
116 (57.3%)
Mean: 17.1± 0. 03
Mean 16.39 ± 0.20
P=0.0485*
Yes
158 (72.2%)
61 (27.8%)
P = 0.0000***
No
70 (18.1%)
316 (81.9%)
Yes
32 (42.6%)
43 (57.4%)
No
197 (36.6%)
340 (63.4%)
Yes
126 (35.9%)
225 (64.1%)
No
103 (40.2%)
153(59.8%)
Total lifetime partners
Mean: 1.85 ± 0.14
Mean: 2.0 ± 0.14
P = 0.1880
Age difference with last sexual partner
Mean: 3.69 ± 3.4
Mean: 3.03± 2.5
P= 0.022**
None
0 (0%)
5 (100%)
One method only
5 (23.5%)
16 (76.5%)
Sex of household head
Age of household head
P = 0.845
0.835
Wealth index
Middle
Fourth
Highest
Age at first sex intercourse
P = 0.0244*
Used condom at first sex
Received gifts for sex
P = 0.5619
Ever tested for HIV
P = 0.4719
Knowledge of HIV prevention methods
P = 0.0026**
31
Social Demographic Variable
Used condom at last sex
Yes
No
Two method only
29 (22.6%)
100 (77.4%)
All three methods
194 (43.1%)
257 (56.9%)
Yes
164 (39.0%)
257(61.0%)
No
63 (36.7%)
109 (63.3%)
Small
95(38.5%)
151 (61.5%)
Moderate
83 (36.8%)
143 (63.2%)
Great
32 (35.4%)
58 (64.6%)
No risk at all
19 (43.5%)
25 (56.5%)
P Value
Condoms education taught in school
P = 0.7330
Risk perception of acquiring AIDS
P = 0.9202
32
Logistic regression analysis
This section presents the results of analysis, the logit regression model.
Table 4.5 Logistic Regression of use of condom at last sex with independent variables
Variables
Coefficient
Marginal effect
dy/dx
Age at first sex
-0.006
0.2
Used condom at first sex
2.27 ±( 0.2)***
0.362***
Education
1.2
1.5*
1.8*
0.222
0.232*
0.284**
Knowledge of HIV prevention
One Method only
Two method only
All three methods
14.0***
14.9***
15.3***
2.2***
2.3***
2.5 ***
Knowledge of condom source
0.8**
0.13**
Age difference with last sexual partner
-.59**
-0.093**
Frequency of listening to radio
Less than once a week
At least once a week
Almost every day
-1.3 *
-0.9
-0.7
-0.22*
-.20
-.12
n/a
Frequency of reading Newspaper
Less than once a week
At least once a week
Almost every day
Wealth Index
0.27
0.25
0.51
-.032*
0.02
0.03
0.14
0.019*
Primary
Secondary
Higher Education
*P<0.1 ** p<0.05 **p<0.001
As shown in table 4.5, use of condom at first sexual intercourse was significantly associated with
condom use at last sex. Additionally, individuals with knowledge on HIV prevention were more
likely to use condom at last sex all factors kept constant. Increase in education level, was also was
statistically associated with condom use at last sex
To interpret the magnitude we compute marginal effects.
All factors kept constant, use of condom at first sex increased the probability of condom use at last
sex by 36 percent. Knowledge of one HIV prevention method, two HIV prevention methods and
33
three prevention methods increased the probability of condom use at last sex by 2.2, 2.3 and 2.5
times respectively. Additionally, having secondary and higher education increased the probability
of using condom at last sex by 23 percent and 28 percent respectively. An increase in one year
difference between the respondent and their last sexual partner reduced the probability of using
condom at last sex by 9 percent. Having knowledge of a source of condom increased the
probability of use of condom at last sex by 13 percent.
Discussion
This study has used a sample of representative population from Demographic and Health survey.
While to a big extent, its very representative of sexually active unmarried women in Kenya, the
respondents in this study were more from urban areas than rural areas which is a deviation of the
total population. This study used a sample of respondents who were unmarried and therefore need
not evaluate the association of condom with marital status. We deliberately excluded married
women from the sample because previous studies pointed married persons or those living with a
partner tend to use condoms less than their peers whose marital status is something other than
married (Booth, 1995; Crosby, Meyerson, & Yarber, 1999)
Only 37 percent of respondents Condom use at last sex. This is low proportion considering that
the HIV prevalence in Kenya is generalised and that casual sex is the biggest contributor of new
HIV infections in Kenya. This means that young women at are at high risk of acquiring HIV,
having unwanted pregnancies and also acquiring sexually transmitted diseases. Despite the fact
that the survey respondents had had sex in the past 12 months without condom, the survey
respondents had very low risk perception of acquiring HIV where majority of respondents (8 out
10 young women) rated their risk as either low or moderate.
A big age difference between young girls and their sexual partner was seen to be associated with
a likelihood of unprotected sex in this study. This could be a pointer that the power to demand safe
sex is diminished when the sexual partner is much older. Programs should therefore aim to impart
sex negotiation skills for the young girls to ensure that they negotiate for safe sex. This is unlike a
study in Uganda showed that correct knowledge of condom use, as well as positive attitudes
towards the use of condoms are associated with the likelihood that adolescents used condoms
34
(Kayiki & Forste, 2013). This could be partly due to high acceptance levels of condom use in this
study population and hence less variability of this indicator.
In this study having secondary and higher level of education increased the likelihood of condom
use at last sex. This finding was similar to a study in Botswana and Swaziland, where more
educated young girls were less likely to report inconsistent condom use. Snelling et al. (2006) and
Fylnesnes et al. (2001) also found that education is strongly associated with condom use.
Though exposure to newspaper, radio and Television were associated with condom use at bivariate
analysis, this association was not significant after multivariate analysis. This could be due to a
strong association between these variables and education. Not many studies have assessed the
association between media exposure and protected sex.
Having high knowledge of HIV prevention methods (abstinence, condom use and having only one
uninfected partner who has no other sexual partners) increased the likelihood of using condom at
last sex. Use of condom at first sex was the strongest statistically significant predictor of use of
condom at last sex. Our findings suggest that whether a young women used a condom at first sex,
determines her future demand for condom during sex. This findings suggests that there is a very
good reason to provide sexuality education among young girls before they engage in sex.
Wealth index was significantly associated with condom use at last sex. This in agreement with a
study by bloom et al. ((2001) found that the wealthiest women are twice as likely to practice safe
sex.
Our analysis suffered from a number of limitations. One, this is a cross-sectional survey data and
therefore cannot prove causation. Second, this study was based on self-reported data on
respondent’s sexual behaviour and hence could be prone to report bias. Third, we used condom
use at last sex as our proxy indicator for practicing safer sex. While respondents may have used
condom at last sex, this does not necessary mean that the respondents used sex consistently in
every act they have had in the past. Fourth, we limited our variables for exposure to HIV/AIDS
education to newspaper, radio and TV only. This excluded exposure to HIV/AIDS education
through community outreach activities by community based organisations that are a common
feature of HIV interventions in Kenya.
35
6.2
Conclusion and policy recommendations
This study showed that un-protected sex among young women is prevalent in Kenya putting young
women at high risk of acquiring HIV. Condom use at first sex, high HIV prevention knowledge,
higher level of education are significant factors associated with condom use at last sex. Our HIV
prevention programs for the youth should therefore aim to target our young girls before sex debut
to increase their risk perception of acquiring HIV infection and therefore empower them to use
protection during their first sexual intercourse with a partner of unknown HIV status. The ministry
of education should consider enhancing sexuality education to young adolescent girls to increase
the likely hood of condom use at first sex. Additionally, the education system should be geared
towards retaining young girls in school as long as possible.
36
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