Prevalence of HIV among women in Malawi: Identify the most-at-risk groups for targeted and cost-effective interventions Introduction In 2000, the

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1 1 Prevalence of HIV among women in Malawi: Identify the most-at-risk groups for targeted and cost-effective interventions Introduction In 2000, the United Nations Millennium Summit identified the reduction of HIV prevalence as one of the eight fundamental goals for improving human development index. Though global HIV/AIDS incidence is declining, the condition remains the leading cause of death among women of reproductive age in low and middle-income countries, particularly in sub-sahara Africa (SSA). With barely two years remaining to the end-date of the MDG target, HIV/AIDS remains a long-term global challenge (United Nations, 2012). Based on the current costs of HIV/AIDS treatment (US $ 4,707 over lifetime) (International HIV / AIDS Alliance, 2010), evidence-based targeted interventions have been advocated as the cost-effective strategy to fight HIV/AIDS. Such strategy helps HIV prevention interventions optimizing coverage, reducing costs and lowering the number of new infections. With HIV prevalence of about 14 percent (PRB, 2011a), HIV/AIDS constitutes a drain on the labor force and government expenditures in Malawi. Despite growing literature in health and social sciences on factors associated with HIV/AIDS during the last two decades, it is still challenging to precisely identify the most-at-risk groups for HIV especially in countries with high prevalence of HIV such as Malawi. In countries with concentrated HIV/AIDS epidemics (Latina America, East Asia and Eastern Europe), the most-at-risk groups including commercial sex workers (CSWs), long distance truck drivers, men who have sex with men, and unmarried youth (Green, 2004; Rombo, 2009; International HIV / AIDS Alliance, 2010) account for a large proportion of the new infections, while in countries with high prevalence, these groups account for a smaller share of the new infections (International HIV / AIDS Alliance, 2010). Against this background, this study aims to assess the socioeconomic predictors of HIV infections and identify the most-a-risk groups among women for better-targeted and cost-effective interventions in Malawi. 2. Data and Methods 2.1 Study setting The Republic of Malawi is a landlocked country of over 118,000 km 2 in southeast Africa, with about 15 millions people (PRB, 2011). The country is divided into three regions including Southern, Central and Northern regions. Malawi is among the world's least-developed countries with a GNI PPP per Capita of $780 (PRB, 2011). Ninety-one percent of Malawians live below 2 dollars (US) per day. The country experiences low life expectancy (54 years) and a high infant mortality (84 deaths per 1,000 live births). 2.2 Data sources This study relies on data from the 2004 and 2010 Malawi Health and Demographic Surveys (MDHS). The sample includes 8,596 women aged years. This is a subsample of one-third of women from households who were interviewed and

2 2 consented to HIV tests during the 2004 and 2010 MDHS. The principal mode of HIV transmission in Malawi is heterosexual contact; therefore, the analyses focus on women who ever had sexual intercourse (with men??). Table A1 in appendix shows the socioeconomic characteristics of the sample, whereas details of sampling approach are reported elsewhere (Malawi National Statistical Office (NSO); ICF Macro, 2011; Malawi National Statistical Office (NSO); ORC Macro, 2005). 2.3 Statistical analyses Statistical analyses were performed using Pearson Chi-square and Chi-square Automatic Interaction Detector (CHAID) (Kass, 1980; IBM Corporation, 2011) ine SPSS version 16. Pearson Chi-square was used for bivariate analysis to assess associations between the HIV infection status (positive, negative) and the selected socioeconomic variables while Chi-square Automatic Interaction Detector (CHAID) was used to detect the most significant predictors and identify the most-at-risk groups for HIV infection among women. CHAID is a nonparametric technique, which is less affected by distributional assumptions and outliers, collinearities, heteroskedasticity, or distributional error. The dependent variable and predictor variables can be categorical, ordinal, or continue (Kass, 1980; IBM Corporation, 2011). Furthermore, this method allows prediction, segmentation, stratification, data reduction, Interaction identification, and category merging and discretizing continuous variables (Kass, 1980; IBM Corporation, 2011). However, CHAID needs large sample sizes to work effectively because it uses multiway splits. The analyses uses two types of variables: A. Dependent variable: HIV status (Negative or Positive). B. Independent variables including 12 variables categorized into two major groups: 1. Demographic and reproductive behavior variables: age, age at first sex, marital status, age at first birth, number of children ever born, experience in premarital childbearing, and relationship to the head of household. 2. Socioeconomic and contextual variables: religion, region of residence, place of residence, education, and household wealth quintiles. 3. Results 3.1 Bivariate analysis Table A2 in appendix reports results from bivariate analysis. Overall, 14 percent of studied women are HIV positive. Except the religion, all independent variables are statistically associated with HIV infection status. HIV infection prevalence was high (20 percent) among women aged years. Regarding the marital status, women who are no longer in union (widowed, divorced and separated) had significantly higher prevalence (30 percent) compared to those who have never been in a marital union (10 percent). HIV prevalence was high among the head of household. Furthermore, while 25 percent of women in urban area were HIV positive, the prevalence was less than half and their counterparts from the rural areas (12 percent). The HIV epidemic shows regional heterogeneity with a higher prevalence (20 percent) observed in the Southern region. Women with secondary education had

3 3 higher HIV prevalence compared to those who never attended school (18 percent vs 14 percent). Regarding the household wealth quintiles the prevalence of HIV infection is higher among the women from the highest quintiles. With reference to sexual and reproductive behavior, HIV prevalence was higher among women who had their first sexual intercourse before the 15 th birthday and /or who have experienced a premarital childbearing. 3.2 Findings from CHAID model Out of 12 independent variables included in the initial multivariate model, 7 were kept in the final model. A few variables including, age at first birth, female education and the relationship to the head of household were dropped by the model The variables such as age at first birth, female education and the relationship to the head of household because they did not make a significant contribution to the model fit. Overall, there are 27 nodes among which 16 terminal nodes. Parent nodes include at least 100 cases whereas child nodes account for 50 cases in minimum. The tree diagram shows that Marital status (Chi-square = 323.1, P-value<0.0001) is the best predictor of HIV infection status among women in Malawi (Figure 1). The tree is spited into 3 branches: (1) Node 1 - women in union; (2) Node 2- women formerly in union; and (3) Node 3 - never married women. Depending on the marital status, other significant predictors for women formerly in union include, wealth quintiles, which are the second best predictor of HIV infection (Chi-square=92.8, p-value <0.0001); followed by the region residence (Chisquare=12.9, P-value <0.002) and Age at first sex (Chi-square=12.9, p-value <0.002) for women formerly in union. For women in union (married or living together), Figure 3 reveals that region of residence is the best predictor (Chi-square = , p-value<0.000); followed by age at the survey (Chi-square=55.9, P-value <0.0001); and place of residence (Chisquare=86.6, P-value <0.0001). Considering women who have never been in union (Figure 4), place of residence is the second best predictor (Chi-square=20.5, p-value<0.0001), followed by Whether the woman ever gave birth (Chi-square=13.9, p-value<0.0001). The region of residence (Chi-square=15.3, p-value<0.000) is the additional significant variable for never married women living in urban areas. Interaction between the most statistically significant predictors allows dividing the study population into four major groups: very high (most-at-risk populations), high, intermediate, and low HIV prevalence (least vulnerable populations). Table 1 describes composition of each group. The first group (the most-at-risk) represents 5.7 percent of the sample. HIV prevalence in this group was 54.6 percent overall, ranging between 45.3 percent and 73 percent between subgroups. This category include three subgroups: 1) Women formerly in union, living in households within the fifth wealth quintile and who had their first sex at 25 years of age; 2) Women formerly in union and living households within the fourth and third wealth quintiles and from the Southern region; 3) Women formerly in union living in

4 4 households within the fifth wealth quintiles and who had their first sex when they were between 15 and 24 years old. The second group (high prevalence) represents 21 percent of the sample. HIV prevalence was 23.3 percent, ranging from 21 percent to 28 percent across subgroups. This group comprises 5 sub-groups: 1) Women formerly in union living in households from the first wealth quintile, 2) women who have never been married and live in urban areas of the Southern or Northern region, 3) women in union living in the Southern region and who are aged years, 4) women formerly in union who living in households from the fourth and third wealth quintiles and from the Central or Northern region, 5) and women formerly in union living in households within the lowest wealth quintile and from the Southern region. Table 1 Prevalence of HIV by groups Node Group description Population HIV % N Prevalence Group 1 22 Formerly in union-richest-had first sex from 25 years old Formerly in union-richer or middle households- Southern region Formerly in union-richest-had first sex between 15 and 24 years 190 old Total Group Group 2 7 Formerly in union-poorer households Never married, living in urban area-southern or Northern region In union living in Southern region age Formerly in union-richer or middle households-central or 137 Northern region Formerly in union-poorest households-southern region Total Group Group 3 16 In union living in Central or Northern region urban area In union living in Southern region age Never married, living in rural area and ever gave birth In union living in Southern region age 15-24/ Formerly in union-poorest households-central or Northern region Total Group Group 4 15 In union, living in Northern or Central province rural areas Never married, living in urban area in Central region Never married, living in rural area and never gave birth Total Group Total The third group (intermediate prevalence) represents about 33 percent of women the sample. HIV prevalence varies between 10 and 19 percents (13.8 percent on average) across subgroups. This category could be divided into 5 subgroups: 1) women in union, living in urban areas of the Central or Northern region, 2) women in union living in the Southern region and aged years, 3) women never married living in rural areas and who have experienced childbearing, 4) women in union living in the Southern region and aged or 45-49, and 5) women in union disruption living in household within the lowest wealth quintile and from the Central or the Northern region.

5 5 The last group (low prevalence) accounts for 40 percent of the sample and include three subgroups, 1) women in union living in the rural areas from the Northern or of the Central province, 2) women who never married living in urban areas from the Central region, and 3) nulliparous never married women. HIV prevalence was 3.7 percent, ranging between 2.3 percent and 6 percent across subgroups. 4. Discussion and Conclusion This paper aimed to describe and profile HIV prevalence among women in Malawi. The study relied on data from the Malawi 2004 and 2004 DHS using Chi-square and CHAID techniques. CHAID offers a useful alternative to traditional logistic regression and allows identifying population subgroups that share similar characteristics. Analyses suggested three keys findings that could be summarized as follow. First, consistent with previous studies (Magadi & Desta, 2011; Adair, 2007), findings from bivariate analysis and chi-square test showed high HIV prevalence among women in union dissolution, among those living in wealthy households and/or among women living in urban areas as well as region heterogeneity in HIV prevalence. Second, results from CHAID models reported that marital status is the best predictor of HIV status among women in Malawi followed by the household wealth index. Women who are no longer in union (widowed and divorced or separated) and living in less poor households have significantly higher HIV prevalence. This probably because: (1) a rich husband or a male partner may have more access to transactional sex and other risk behaviors such as polygamy which may increase women s vulnerability to HIV; (2) wealthier HIV positive widowed may have better quality of life as well as better access to treatment and survive longer. Furthermore, divorced and separated are more frequent among the most educated women with economic autonomy. Their causes (polygyny and/or infidelity) as well as consequences (multiple sexual partnerships) are also factors associated with HIV prevalence. Last, CHAID model depicted also different interactions between risk factors and profiled HIV risk groups in Malawi. For instance, whilst HIV prevalence is higher among women living in urban areas (25 percent) compared to those living in rural areas (12 percent), only 3 percent of never married women living in urban areas of the Central region are HIV positive compared to 11 percent observed among single mothers living in the rural areas. Likewise, while overall HIV prevalence is low among never married women (9 percent), CHAID results revealed a higher HIV prevalence (23 percent) among never married women who live in urban area of the Southern or Northern region compared to women in union who reside in urban areas

6 6 of the Central or Northern (18 percent) as well as to women in union dissolution who live in poorest households of the Central or Northern region (10 percent). In the light of these findings, it is noteworthy that to achieve zero new infection one of HIV eradication strategy, interventions should be targeted and prioritized according to the prevalence and demographic size of different risk groups. These interventions should reinforce integration of family planning and HIV/AIDS services through community health workers; households based campaign, reproductive health services and reproductive health courses at school. Couples (males and women in union) living in the Southern region and those living in the urban areas of the Central and the Northern should be the first targets. Indeed, this group includes 45 percent of the study population, among who the HIV prevalence is estimated at 17 percent on average. Unmarried women including never married women and those in union disruption could be considered as the second target using Abstinence, Be faithful and use condom campaign. Indeed, though women in union dissolution represent only about 13 percent of women of reproductive age in Malawi, they have the higher HIV prevalence in Malawi. Similarly, despite low HIV prevalence among never married women, findings show relatively high HIV prevalence among single mothers. Therefore, zero new infection among single women can have a significant effect in achieving the MDG 6. In conclusion, this study recommends: (1) design and implementation of targeted interventions taking into account HIV prevalence and the demographic size of different groups at risk groups; (2) reinforcement of integration of family planning and HIV/AIDS services through community health workers, households based campaign, reproductive health services and reproductive health courses at school. Bibliography Asiedu, C., Asiedu, E., & Owusu, F. (2012). The Socio-Economic Determinants of HIV/AIDS Infection Rates in Lesotho, Malawi, Swaziland and Zimbabwe. Development Policy Review, 30 (3), Bärnighausen, T., Hosegood, V., Timaeus, I., & Newell, M. (2007). The socioeconomic determinants of HIV incidence: evidence from a longitudinal, population-based study in rural South Africa.. AIDS, 21 (7), S29-S38. Beauchamp, T., & Childress, J. (1994). Principles of biomedical ethics. (Vol. 4th Edition). New York: Oxford University Press. Corno, L., & Walque, D. (2007). The Determinants of HIV Infection and Related Sexual Behaviors: Evidence from Lesotho, Policy Research Working Paper, Development Research Group The World Bank.

7 7 Durevall, D., & Lindskog, A. (2007). HIV/AIDS, Adult Mortality and Fertility: Evidence from Malawi. Working Papers in Ecomics, No 284. Göteborg: School of Business, Economics and Law, University ofgothenburg. Fox, A. (2010). The Social Determinants of HIV Serostatus in Sub-Saharan Africa: An Inverse Relationship Between Poverty and HIV? Public Health Reports, 125, Green, E. (2004). Rethinking AIDS Prevention. Learning from Successes in Developing Countries. Westport, CT: Praeger publishers. IBM Corporation. (2011). IBM SPSS Decision Trees 20. Chicago: IBM Corporation. International HIV / AIDS Alliance. (2010). The cost efficiency of HIV prevention for vulnerable and most-at-risk populations and the reality of funding. What's Preventing Prevention Campaign Briefing 2. Hove: International HIV / AIDS Alliance. Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantitaties of Categorical Data. Applied Statistics, 29 (2), Kirunga, C., & Ntozi, J. P. (1997). Socio-economic determinants of HIV serostatus: a study of Rakai District, Uganda,. Health Transition Review, 7, Magadi, M. (2011). Understanding the gender disparity in HIV infection across countries in sub-saharan Africa: evidence from the Demographic and Health Surveys. Sociology of Health & Illness, Magadi, M., & Desta, M. (2011). A multilevel analysis of the determinants and crossnational variations of HIV seropositivity in sub-saharan Africa: Evidence from the DHS. Health & Place (5), Malawi National Statistical Office (NSO); ICF Macro. (2011). Malawi Demographic and Health Survey Zomba, Malawi, and Calverton, Maryland, USA: NSO and ICF Macro. Malawi National Statistical Office (NSO); ORC Macro. (2005). Malawi Demographic and Health Survey Calverton, Maryland: NSO and ORC Macro. Mutinta, G., Gow, J., Georges, G., Kunda, K., & Ojteg, K. (2011). The Influence of Socio-Economic Determinants on HIV Prevalence in South Africa. Review of Economics & Finance, Nicolosi, A., Leite, M., & Musicco, M. (1994). The efficiency of male-to-female and female-to-male sexual tranmission of the Human Immunodeficiency Virus: a study of 730 stable couples. Epidemiology, 5 (6), Öjteg, K. (2009). Socio-Economic determinants of HIV in Zambia. A district level Analysis. Ph.Dissertation. Lund: Department of Economics at the University of Lund Philipson, T., & Posner, R. (1993). Private Choices and Public Health: The AIDS Epidemic in an Economic Perspective. Cambridge: Harvard University Press. PRB. (2011). The 2011 World Population Data Sheet. Washington, D.C.: PRB. PRB. (2011). The World's Women and Girls 2011 Data Sheet. Washington D.C.: PRB. Rombo, D. (2009). Marital risk factors and HIV infection among women: A comparison between Ghana and Kenya, Ph.D. Dissertation. Minneapolis: University of Minnesota. Shisana, O., Zungu-Dirwayi, N., Toefy, Y., Simbayi, L., Malik, S., & Zuma, K. (2004). Marital status and risk of HIV infection in South Africa. South Africa Medical Journal, 94 (7),

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9 9 Appendix Table A1 Description of the sample Socioeconomic and demographic Weight Unweight Characteristics Total Total Age Average Age at first sex < & Average Marital status Single In union Ever married Number of ever born children & Age at first birth Never give birth < 20 years old & Ever had premarital child No Yes Relationship to the head of household Head of household Spouse Daughter & Grand daughter Others Region of residence Northern Central Southern Place of residence Urban Rural Religion Catholic Protestant Other Christians Muslim Others Education None Primary Secondary & Household wealth Index Poorest Poorer Middle Richer Richest Total

10 10 Table A2 Factors associated with HIV prevalence: Descriptive analyses Socioeconomic and demographic Chi- Characteristics HIV+ N Square P-value Age Age at first sex < & Marital status Single In union Ever married Number of ever born children & Age at first birth Never give birth < 20 years old & Ever experience premarital childbearing No Yes Relationship to the head of household Head of household Spouse Daughter & Grand daughter Others Region of residence Northern Central Southern Place of residence Urban Rural Religion Catholic Protestant Other Christians Muslim Others Education None Primary Secondary & Household wealth Index Poorest Poorer Middle Richer Richest Year of survey Total

11 11 Figure 1 Marital status as the best predictor of HIV in Malawi (Tree diagram) Node 0: HIV status Category % N Negative Positive Total Marital status Chi-square P-value Node 1: In union Node 2: Ever in union Node 3: Never married Category % N Category % N Category % N Negative Negative Negative Positive Positive Positive Total Total Total Figure 2 HIV predictors among Ever married women in Malawi (Tree diagram)

12 12 Figure 3 HIV predictors among women in union in Malawi (Tree diagram) Figure 4 HIV predictors among Never married women in Malawi (Tree diagram)