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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”. 7 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 REFERENCES Andersson, N., Ho-Foster, A., Matthis, J., Marokoane, N., Mashiane, V., Mhatre, S., et al. (2008).National cross sectional study of views on sexual violence and risk of HIV infection and AIDS among South African school pupils. British Medical Journal, 329, 952–954. Becker, Gary (1976). The Economic Approach to Human Behavior, University of Chicago Press, Chicago. Bloom et al (2001), Health, wealth, AIDS and poverty, Background paper at the HIV/AIDS and Development in the Asia Pacific Ministerial Conference in October 2001 Cerwonka, E.R., Isbell, T.R., Hansen, C.E. (2000).Psychosocial factors as predictors of unsafe sexual practices among young adults. AIDS Education Prevention, 12(2), 141-153. Crosby RA, Meyerson B, Yarber WL. Frequency and predictors of condom use and reasons for not using condoms among low-income women. Journal of Sex Education and Therapy. 1999; 24:73–70. Doyle, A.M., Mavedzenge, S.N., Plummer, M.L., Ross D.A. (2012). The sexual behavior of adolescents in sub-Saharan Africa: patterns and trends from national surveys. Tropical Medicine and International Health, 17, (7):796-807. Fylkesnes et al (2001). Declining HIV prevalence and risk behaviours in Zambia: evidence from surveillance and population-based surveys, AIDS, Vol. 15, No. 7 37 Gillespie, S., Kadiyala, S., Greener, R.(2007). Is poverty or wealth driving HIV transmission? AID,21:s5–s16 Gregson, S., Garnett, G.P., Nyamukapa, C.A et al. (2006). HIV decline associated with behavior change in eastern Zimbabwe. Science; 311:664. Gregson S, Nyamukapa CA Garnett GP, Manson PR, Zhuwau T, Carael M, et al. (2002). Sexual mixing patterns and sex- differential in teenage exposure to HIV infections in rural Zimbabwe. Lancet 2002; 359: 1986-1903. Hale, J.L.; Householder, B.J. & Greene, K.L. (2002). The theory of reasoned action. In J.P. Dillard & M. Pfau (Eds.), The persuasion handbook: Developments in theory and practice (pp. 259– 286). Thousand Oaks, CA: Sage. Johnson, B.T., Scott-Sheldon, L.A., Huedo-Medina, T.B., Carey, M.P. (2011). Interventions to reduce sexual risk for human immunodeficiency virus in adolescents: a meta-analysis of trials, 1985-2008. Archive of Pediatric and Adolescent Medicine, 165(1), 77-84. Joint United Nations Programme on HIV/AIDS (UNAIDS) 2013. Global Epidemic Report. Joint United Nations Programme on HIV/AIDS (UNAIDS) 2014. The Gap Report. Kalichman SC, Simbayi LC, Kaufman M, Cain D, Jooste S (2007) Alcohol Use and Sexual Risks for HIV/AIDS in Sub-Saharan Africa: Systematic Review of Empirical Findings. Prev Sci 8: 141–151. PMID: 17265194. 38 Karen, G; Rimer, B; Viswanath, K. (2008).Health behavior and health education: theory, research, and practice. (4th ed.). San Francisco, CA: Jossey-Bass. pp. 45–51. Kazianga, H (2005). HIV/AIDS Prevalence and the Demand for Safe Sexual Behavior: Evidence from West Africa. Columbia University. Kayiki, S.P., Forste, R. (2011). HIV/AIDS related knowledge and perceived risk associated with condom use among adolescents in Uganda. African Journal of Reproductive Health, 15(1), 57-63. Kenya National Bureau of Statistics (KNBS) and ICF Macro. 2010. Kenya Demographic and Health Survey 2008-09. Calverton, Maryland: KNBS and ICF Macro. Kiene, S.M., Subramanian, S.V. (2013). Event-level association between alcohol use and unprotected sex during last sex: evidence from population-based surveys in subSaharan Africa. BMC Public Health, 213, 583. Kirby, D.B., Laris, B.A., Rolleri, L.A. (2007). Sex and HIV education programs: their impact on sexual behaviors of young people throughout the world. Journal of Adolescent Health, 40(3), 206-217. Leclerc-Madlala, S. (2008). Age-disparate and intergenerational sex in Southern Africa: the dynamics of hyper vulnerability. AIDS, 22(Suppl 4), S17–S2. Luke, N. (2005).Confronting the sugar daddy stereotype: age and economic asymmetries and risky sexual behavior in urban Kenya. International family planning perspective, 31, 6-14. Leclerc-Madlala, S. (2008). Age-disparate and intergenerational sex in southern Africa: the dynamics of hyper-vulnerability, AIDS,22, Suppl 4:S17-25. 39 Marteau TM, Hollands, GJ, Fletcher PC (2012). Changing human behavior to prevent disease: the importance of targeting automatic processes. Science, 2012, 337:1492–1495. Ministry of Education, Science and Technology (2014). Ministerial commitment on comprehensive sexuality education and sexual and reproductive health services for adolescents and young people in Eastern and Southern Africa. Moore A M, Awusabo-Asare K, Madise N, John-Langba J, Kumi-Kyereme A. Coerced first sex among adolescent girls in sub-Saharan Africa: prevalence and context. Afr J Reprod Health. 2007; 11[3]:62-82) Mwenda JM, Arimi MM, Kyama MC, Langat DK (2003) Effects of Khat (Catha Edulis) Consumption on Reproductive Functions: A Review. East African Medical Journal 80: 318– 323. PMID: 12953742 National AIDS Control Council 2013: Kenya AIDS Epidemic Report. O’Donnell BL, O’Donnell CR, Stueve A: Early sexual initiation and subsequent sex-related risks among urban minority youth: the reach for health study. Fam Plann Perspect 2001, 33(6):268-275. Papo JK et al (2011). Exploring the condom gap: is supply or demand the limiting factor – condom access and use in an urban and a rural setting in Kilifi district, Kenya. AIDS, 2011, 25:247– 255. Pettifor AE, van der Straten A, Dunbar MS, Shiboski SC, Padian NS: Early age of first sex: a risk factor for HIV infection among women in Zimbabwe. AIDS 2004, 18(10):14351442. 40 Philipson, Tomas and Posner, Richard (1993), Private Choices and Public Health: The AIDS Epidemic in an Economic Perspective, Harvard University Press. Rao, V., I. Gupta, M. Lokshin, and S. Jana (2003): Sex Workers and the Cost of Safe Sex: The Compensating Differential for Condom Use in Calcutta. Journal of Development Economics, 72(2), 585–603. Santelli JS, Kaiser J, Hirsch L, Radosh A, Simkin L, Middlestadt S (2004). Initiation of Sexual Intercourse among Middle School Adolescents: The Influence of Psychosocial Factors. Journal of Adolescent Health 34: 200–208. PMID: 14967343 Shelton JD, Cassell MM, Adetunji J. Is poverty or wealth at the root of HIV? Lancet 2005; 366:1057–1058. Snelling et al (2007). HIV/AIDS knowledge, women’s education, epidemic severity and protective sexual behavior in low- and middle-income countries, Journal of Biosocial Science, 39 (3), p 421-442. UNAIDS 2013 HIV Estimates United Nations Children’s Fund: Africa’s Orphaned and Vulnerable Generations: Children affected by AIDS. 2006. Weiser SD, Leiter K, et al. Food insufficiency is associated with HIV-risk sexual behaviour among women in Botswana and Swaziland. PLoS Med 2007; 4:1589–1598. Wooldridge, J.M. (2002) Econometric Analysis of Cross Section and Panel Data, Cambridge, MA: MIT Press. 41 World Health Organization 2005; Multi-country study on women’s health and domestic violence against women. Geneva: (http://www.who.int/gender/violence/who_multicountry_study/summary_report/en/, accessed 7 July 2014). 42