Association Between Risky Sexual Behaviour and Having Stis Or HIV Among Young Persons Aged 15-24 Years in Uganda

Association between Risky Sexual Behaviour and having STIs or HIV among young persons aged 15-24 years in Uganda

Gideon Rutaremwa§1, Peninah Agaba2, Elizabeth Nansubuga2 and Olivia Nankinga2

1 United Nations Economic Commission for Africa (ECA), Social Development Policy Division, P.O.Box 3001, Addis Ababa, Ethiopia 2 Center for Population and Applied Statistics (CPAS), and Department of Population Studies, Makerere University, P.O.Box 7062, Kampala, Uganda

Author contacts GR: [email protected] PA: [email protected] EN: [email protected] ONJ: [email protected]

Abstract

Adolescent pregnancy is often discussed in literature as causes of health concern and as a social problem. Taking these accounts as a starting point, this paper uses the 2011 Uganda AIDS Indicator Survey (UAIS) data to explore the factors related to sexual behaviour and risk of STI and HIV infection among youths in Uganda. A total of 2,491 males and female young persons were selected for this study. A complementary log-log regression model was used to examine the association between women’s risky sexual behaviour and having an STI, HIV, and any STI including HIV. Female youths were more likely to contract an STI and HIV compared to their male counterparts (OR=2.1; 95% CI=1.2-3.6). The risk of contracting STIs was higher in Western Region of Uganda compared to Central region (OR=1.4; 95% CI=1.0-1.9). At the same time youths in Eastern Uganda had the least odds (OR=0.63) of contracting STIs. Furthermore, youth with multiple sex

1 partners were more likely to contract STIs including HIV compared to those who had a single partner. Finally, young persons from the top two wealth quintiles were more likely to test positive for HIV compared to those who belonged to the lowest wealth quintile. The discourse in this paper shows that the youthful age category is a serious policy intervention target that requires redress.

Keywords: Sexual behavior, sexually transmitted infections, HIV/AIDS, Women, Uganda

Background

Risky sexual behaviour are of particular concern to reproductive health practitioners and other primary care clinicians in that they can lead to serious consequences both for the individual and their sexual partners. Both STIs and HIV have potential to undermine development in many ways, including loss of productivity, supply of human capital, agricultural productivity and food security (Muthengi, 2009). Researchers, development practitioners, and medical personnel are faced with 3 challenges: First, how to understand this behaviour, second, how to identify risky sexual behaviour, and third what to do about it. Risky sexual behaviour can take several forms, including unprotected intercourse, multiple or concurrent sexual partners and unsafe sexual intercourse under the influence of substances such as alcohol (Kalichman & Simbayi, 2011; Rosenberg et al., 2015).

Among the risk factors for contracting STIs are sexual violence and mental health. Studies indicate that intimate partner violence is frequently associated with increased HIV risk in women. This observation is partly because men who abuse their wives also exhibit riskier sexual behaviour (Dude, 2011; Silverman, Decker, Saggurti, & Donta, 2008). Factors found to be associated with sexual violence have also been identified as risk factors for contracting an STI. Data on the relationship between mental health most commonly identified as depression and STIs is mixed, but most studies report a positive correlation between depression and risky sexual behaviour, an established precursor to STIs (Buffardi, Kathy, King, & Manhart, 2008; Shrier, Harris, Sternberg, & Beardslee, 2001). Similarly, research suggests a positive relationship

2 between alcohol use (Burns, 2015; Kalichman & Simbayi, 2011; Ritchwood, Ford, DeCoster, Lochman, & Sutton, 2015; Rosenberg et al., 2015; Yi et al., 2014), drug and other substance use (Lansford, Dodge, Fontaine, Bates, & Pettit, 2014; Manhart et al., 2006), multiple sexual partners (Chimoyi & Musenge, 2014; Kalichman & Simbayi, 2011) and STI risk.

Concerning age, risky sexual behaviour is known to cut across all age groups. Reports of adults having had two or more sexual partners in the previous year are also common (Ministry of Health -Uganda and ICF Macro International Calverton Maryland USA, 2012) . Why should researchers care about risky sexual behaviour among young ages? One reason why this issue is important is that risky sexual behaviour increases the likelihood of contracting an STI including HIV/AIDS. It is clear that sexual activity is common among individuals in the reproductive ages, and many of the behaviour that they engage in put women at risk for contracting STIs or HIV. This paper seeks to contribute to the concern of risky sexual behaviour and its link with STIs and HIV among young persons in Uganda.

Uganda has long been regarded as an HIV success story. This was because the Ugandan government initiated a robust response to the epidemic that was praised as a model response to the HIV epidemic bringing about a substantial fall in HIV prevalence (Kibombo, Neema, & Ahmed, 2007; Kilian et al., 1999; Kirby, 2008). HIV prevalence declined from a peak of 15% in 1990/91 (Kibombo et al., 2007; Murphy, Greene, Mihailovic, & Olupot-Olupot, 2006) to a low of 6.2% in 1999/2000 before increasing to 6.4% in 2004/05. The fall in HIV prevalence observed during the 1990s was statistically significant (Shafer et al., 2011). Factors influencing the recent trends of the epidemic are not yet clear, but there are indications that the observed changes may partly be explained by the increased risky sexual behaviour. A recent Ugandan study (Tumwesigye et al., 2012), showed that 63% of men and 59% of females had unprotected sex during their last sexual encounter. No doubt, the prevalence for HIV/AIDS in Uganda continued to increase to 6.7% from 6.4% (Ministry of Health- Uganda and ICF Macro International Calverton Maryland USA, 2012) .

3 It is against this background that this research was conducted to contribute to the current debate of why there is seemingly a reversal in the earlier gains in the fight against HIV/AIDS in Uganda. It is our belief that an improved understanding of factors associated with STIs or HIV infection among young people of Uganda will lead to improved policy frameworks and programming, ultimately reducing the cases of STIs or HIV infection in this country, which has for two decades been negatively affected by STIs including HIV/AIDS.

Methods

Data Source

Data from the 2011 Uganda AIDS Indicator Survey (AIS) were used for this study. The AIS data provide information on knowledge, attitudes and sexual behaviour related to HIV/AIDS and other indicators such as HIV testing, access to antiretroviral therapy, services for treating sexually transmitted infections, and coverage of interventions to prevent mother to child transmission of HIV. Informed consent was sought from all study respondents. The protocol for the blood specimen collection and analysis was developed jointly by all parties to the survey and was reviewed and approved by the Science and Ethics Committee of the Uganda Virus Research Institute (UVRI), ICF Macro’s Institutional Review Board and a review committee at the Centers for Disease Control and Prevention (CDC) in Atlanta. It was also cleared by the Ethics Committee of the Uganda National Council of Science and Technology (Ministry of Health- Uganda and ICF Macro International Calverton Maryland USA, 2012). Furthermore, permission was sought from ICF Macro International to use the AIS data set.

Explanatory variables The independent variables considered in the analysis included respondent’s: age, residence, marital status, wealth status, region, educational level attainment, age at first sexual intercourse, consistent condom use with the last three sexual partners, alcohol

4 intake by either the respondent or partner before sexual intercourse and number of life time partners. The association between these predictor variables and STI or HIV infection was the primary relationship of interest. Dependent variable The dependent variable is dichotomous, that is, whether the respondent had an STI or HIV infection or not. Women were asked if they had ever suffered from any STI in addition to experiencing any genital discharge or genital sores in the year preceding the survey or during the last 12 months. In addition, these women were tested for HIV/AIDS. Sampling This study is based on data from a wei ght ed sample of 2,491 young persons, both male and female, aged between 15-24 years who had ever had sex in the 4 weeks preceding the AIS Survey and were tested for HIV/AIDS during the Uganda AIDS Indicator Survey (AIS) of 2011. The 2011 AIS was a nationally representative population based sample, designed to obtain national and sub-national estimates of the prevalence of HIV and syphilis infection. Information was collected on both the covariates and dependent variable used in the current study. The sample for the Uganda AIS of 2011 is deemed adequate to allow for analysis, comparisons and is also useful in identification of important factors associated with sexual behavior in the era of HIV/AIDS in Uganda. Statistical analyses Analysis in this study was done in two stages, first the descriptive analysis to describe the characteristics of youth and their sexual behaviour. Secondly, to examine the association between risky sexual behaviour and having STIs or HIV, three models were fitted including STI (Model 1), HIV (Model 2) and any STI including HIV (Model 3). The likelihood of having an STI or HIV was fitted using a complementary log-log regression reporting odds ratios based on the 95% confidence interval (Table 3). The significance level of the predictor variables was set at p<0.05 for the regression model coefficients. Typically this model is used when the positive (or negative) outcome is rare as is the case with the current data, and is an alternative to logit and probit analysis. Formally, the equation fitted to the data may be expressed as follows:

log{-log(1-pi )} =a +åXb (1)

5 Where pi is the probability of an adolescent being pregnant or ever had a child in the 5 years preceding the survey; and Xβ refers to regression estimates for the set of explanatory variables included in the models. The complementary log-log model is derived from the assumption that the error distribution (or distribution of the latent variable) follows a standard extreme value distribution (Powers & Xie, 2000). For individual level data, the parameters have a similar interpretation to those from the logistic regression model.

For purposes of accounting for the complex sample design including clustering and design effect used in the AIS, we weighted the data using the HIV sample weight provided for in the data set. To declare survey design for dataset, svyset command was run. We also tested for multi-collinearity of the variables using the Variance inflation factor (VIF) and none of the variables in the model had a threshold of 10. The goodness of fit for the model was tested using the link test.

Results

Descriptive results Results in Table 1 show that more than two-thirds of the respondents were females (69%), had primary education (66%), 80% resided in rural areas, were currently in union (78.1%) and 29.8% were from the Eastern region. Majority (87.8%) of the youths did not use a condom at their last sexual encounter and 15% had sex under the influence of alcohol. The HIV/AIDS prevalence among the youths was 5.3% and 17.4% of the youths had an STI during the past year as shown in Table 1.

At bivariate level, having an STI was higher among females (74.5%), those with primary education (70.1%), rural residents (74.8%), currently in union (79.6%) and respondents from Western region (27.2%). Except alcohol intake before last sexual encounter and condom use at last sexual encounter, all other demographic and risky sexual behaviors were significantly associated (p < 0.05) with having an STI among youths as shown in Table 2. More than three quarters (77.4%) of the youths who were HIV positive were females. Notably, the HIV sero-status distribution of respondents across the socio-

6 demographic and risky sexual behavior is similar to that observed among respondents with an STI as shown in Table 2. Furthermore, the distribution between HIV status and condom use at last sexual encounter, alcohol intake before last sexual encounter, age at first union and residence were not significantly associated (p > 0.05).

Regression model results Results in Table 3 show that females are at a greater risk of having an STI or HIV/AIDS. The odds of females having an STI during the past survey year are higher by 1.7 times compared to males. Additionally, females are twice more likely to have HIV/AIDS compared to males as shown in Model 2 (Table 3). Model 3 also shows that females are still at a higher risk of having either an STI or HIV/AIDS.

Youths with primary education were twice as likely to have an STI as compared to uneducated youths. Similarly, these youths were also twice as likely to have HIV/AIDS as compared to their uneducated counterparts as shown in Table 3 (Model 3).

The odds of having an STI among youths from Eastern and Northern regions are lower by 37% and 51% respectively compared to youths from the Central region. However, the odds of youths from Western having an STI are higher by 41% compared to their counterparts from the Central region as shown in Table 3. A similar pattern is observed with regards to having HIV/AIDS across the different regions. Youths from the Eastern and Northern region have lower odds of being infected with HIV/AIDS compared to youths from the Central region. In addition, youths from Western Uganda have an increased risk of having HIV/AIDS (OR 1.4) compared to youths from Central Uganda.

Notably, marital status had no relationship with having an STI during the past survey year. However, the odds of formerly married and married youths having HIV/AIDS were higher by 6.7 times and 4.7 times respectively compared to never married youths. Similarly, the odds of having either an STI or HIV/AIDS are still higher among formerly married youths and married youths compared to never married youths as shown in Table 3 (Model 3). Furthermore, the number of lifetime sexual partners is significantly associated with having and STI, HIV and either an STI or HIV/AIDS. Table 3 shows that youth who had 4 or

7 more lifetime partners had increased odds of having an STI (OR 2.6); and having HIV/AIDS (OR 4.5); and either an STI or HIV/AIDS (OR 2.8) in comparison. A similar trend is observed for youths who had 2–3 lifetime sexual partners being at a higher risk of having an STI, HIV, or either HIV or an STI in comparison to having one lifetime sexual partner. Notably having more lifetime partners has the greatest effect on having HIV/AIDS compared to having an STI.

Youths belonging to richer (OR 2.7) and richest categories (OR=2.9) households had higher odds of having an STI compared to youths belonging to the poorest households. There was no significant effect of wealth status on having an STI or HIV/AIDS among the respondents.

Discussion

This manuscript examined the risk factors that were most associated with having an STI or HIV among young persons aged 15-24 years in Uganda in the 12 months preceding the UAIS exercise. As noted earlier the HIV prevalence rates in Uganda have remained high. This research suggests that a number of factors including: sex of young person, region, educational level attainment, wealth status and number of lifetime partners were significant predictors of STI or HIV infection among young persons in the country.

All regions of Uganda exhibited lower odds of STI or HIV infection compared to Western region. The findings suggest that some geographical clustering’s are often associated with higher risk of HIV prevalence (Ramjee & Wand, 2014). Such regional disparities in the risk of contacting an STI or HIV could be attributed to the differences in traditions and culture relating to sex in the different regions of Uganda including wife inheritance, female genital mutilation and polygamy (Kibombo et al., 2007).

The findings with regard to education level attainment are contrary to the hypothesis that persons with more education are better equipped to make necessary decisions that help reduce the risk of STI or HIV infection (Muthengi, 2009). The current findings

8 suggest that higher levels of education are associated with deleterious health outcomes including STIs and HIV/AIDS among young persons.

The current study showed that the richer category of young people tended to have increased risks of having an STI and HIV in the one-year period prior to the survey. The findings are consistent with other literature on this subject (Johnson & Way, 2006). Evidence suggests that wealth is associated with risky sexual behaviour, including individuals having multiple sex partners (Chimoyi & Musenge, 2014). Indeed the rich in society tend to have well developed social and sexual networks (Kuhanen, 2010), which could ultimately enable STI and HIV transmission among the young persons.

Also importantly, having more sexual partners increases the risk of STI or HIV infection. This finding is consistent with expectation and also with literature (Chimoyi & Musenge, 2014; Fenton et al., 2001; Singh, Darroch, & Bankole, 2004). The apparent reductions in HIV prevalence rates in Uganda in early 1990s has been attributed to reduction in risky sexual behaviour and specifically reduction in the number of sexual partners (Kilian et al., 1999; Kirby, 2008; Murphy et al., 2006).

Study Limitations

The current study was based on cross-sectional data and therefore it is difficult to ascertain the association between explanatory variables and STI or HIV, since they were both measured at one point in time. Besides the study did not address the important question of knowledge of disease symptoms and diagnosis related factors that may affect survey outcomes in population surveys. Furthermore, the accuracy of information on STI infection may be based on women’s recall and description of discharge. The study lacked information on cultural and social practices among the various population groups of women that influence their sexual behaviour. Despite these limitations the data used for this study are reliable and appropriate analysis procedures were used, hence the findings are deemed to be reflecting accurately on the study topic.

Conclusions Given that this research explores the factors associated with the risk of having an STI or HIV among young persons in Uganda, conclusions can be harnessed from the findings and the discussion. Sexually transmitted infections and HIV vary significantly by sex, education, and region of residence, wealth, marital status and number of sexual partners. It is therefore important to understand these differentials in order to ensure that prevention policy and programme efforts are targeted towards the groups that are at greatest risk.

The findings from this study suggest very wide regional variations in the risks of STI or HIV infection. The latter implies that any intervention programs targeting STI and HIV should focus on Western Uganda, given that the region is most at risk as indicated earlier. Further, the analyses suggest that female young persons are most at risk of contracting STIs and HIV compared to their male counterparts. The implication for this finding is that any programs aimed at prevention of such infection should target especially the female young persons.

In addition, high risk of infection was observed among young persons with more education and those in higher income households. In this regard, efforts by the Government and program implementers should target these categories of young persons for prevention and treatment. Finally, a high number of lifetime partners among young persons were significantly associated with high risk of STI or HIV infection. Public health campaigns targeting the most at risk population groups would be an effective strategy. An improved understanding of factors associated with STIs and HIV infection among young persons in Uganda will lead to improved intervention policy frameworks and programing, ultimately reducing the cases of STIs and HIV and generally improving the health of the generations to come. Abbreviations

10 UAIS: Uganda AIDS Indicator Survey; HIV: Human Immune-deficiency Virus; AIDS:

Acquire Immune-Deficiency Syndrome; OR: Odds Ratio; VIF: Variance Inflation factor;

STIs: Sexually Transmitted Infections; DHS: Demographic and Health Survey; CI:

Confidence interval; UBOS: Uganda Bureau of Statistics; UVRI: Uganda Virus Research

Institute; CDC: Centers for Disease Control and Prevention; CoBAMS: College of

Business and Management Sciences – Makerere University. ECA: United Nations

Economic Commission for Africa.

Competing interests

The authors declare that they have no competing interest.

Authors’ contributions

GR, EN, PA, and ON participated in the conceptualization of the study, acquisition of data and revision of the manuscript. All authors determined the design and performed the statistical analysis. All authors interpreted the data and drafted the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We acknowledge the support received from the institutions of affiliation, Economic

Commission for Africa and Makerere University College of Business and Management

Sciences (CoBAMS). We are also grateful to all members of staff of the Department of

Population Studies for their valuable comments. We are also grateful to the Uganda

Bureau of Statistics (UBOS) and ICF Macro International Inc. for providing and authorization of use of the dataset. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the supporting institutions.

Author details

1 United Nations Economic Commission for Africa (ECA), Social Development Policy

Division, P.O.Box 3001, Addis Ababa, Ethiopia. 2 Center for Population and Applied

Statistics (CPAS), and Department of Population Studies, Makerere University, Uganda.

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15 Table 1: Percentage distribution of respondents by selected explanatory variables Variable/category Number Percentage Sex of respondent Male 764 30.8 Female 1,716 69.2 Education attainment None 108 4.4 Primary 1,647 66.4 Secondary + 725 29.2 Rural/urban residence Urban 487 19.7 Rural 1,993 80.4 Region Central 534 21.5 Kampala 203 8.2 Eastern 738 29.8 Northern 398 16.1 Western 606 24.5 Marital Status Never in union 459 18.5 Currently in union 1938 78.1 Formerly in union 84 3.4 Age at first sex <14 years 465 18.8 15-17 years 1,186 47.8 18 years plus 829 33.4 Age at first union Not in married 459 18.5 <18 years 1,013 40.8 18 years plus 1008 40.7 Number of lifetime sex partners Only 1 772 31.1 2-3 1,133 45.7 4+ 576 23.2 Alcohol before sex No 2,168 85.5 Yes 367 14.5 Condom use at last sex None 2,176 87.8 Yes 303 12.2 Wealth Status Poorest 447 18.0 Poorer 526 21.2 Middle 479 19.3 Richer 427 17.2 Richest 601 24.2 HIV Test Result Negative 2,349 94.7 Positive 131 5.3 Had an STI during past year No 2,050 82.6 Yes 431 17.4

16 Table 2: Distribution of respondents by selected background factors and by contraction of an STI during past year and by HIV status Had STI in past year HIV test Status (n-2535) (n=2491) Variable/category No Yes p Negative Positive p Sex of respondent Male 32.5 25.5 0.01 31.5 22.6 0.04 Female 67.5 74.5 68.5 77.4 Education attainment None 4.9 2.2 4.4 0.9 Primary 65.3 70.1 0.03 66.0 74.8 0.06 Secondary + 29.8 27.7 29.7 24.4 Rural/urban residence Urban 19.0 25.3 0.00 19.6 24.4 0.21 Rural 81.0 74.8 80.4 75.7 Region Central 17.6 25.7 18.8 24.4 Kampala 10.4 13.0 0.00 10.8 12.2 Eastern 31.9 24.3 31.5 16.5 0.01 Northern 21.3 9.8 19.2 19.1 Western 18.8 27.2 19.7 27.8 Marital Status Never in union 20.2 14.2 19.7 7.0 Currently in union 76.8 79.6 0.00 77.0 83.5 0.00 Formerly in union 3.1 6.1 3.3 9.6 Age at first sex <14 years 17.6 23.3 18.4 20.9 15-17 years 49.1 46.6 0.03 48.8 45.2 0.71 18 years plus 33.2 30.2 32.8 33.9 Age at first union Not in married 20.2 14.2 19.7 7.0 <18 years 40.2 44.4 0.02 41.1 40.0 0.00 18 years plus 39.6 41.4 39.2 53.0 Number of lifetime sex partners Only 1 32.9 21.8 31.5 17.4 2-3 45.0 46.1 0.00 45.8 41.7 0.00 4+ 22.1 32.1 22.7 40.9 Alcohol before sex No 86.0 83.1 0.13 86.1 81.7 0.19 Yes 14.0 16.9 13.9 18.3 Condom use at last sex None 86.4 89.4 0.09 86.9 89.6 0.40 Yes 13.6 10.6 13.1 10.4 Wealth Status Poorest 20.3 15.7 19.8 13.9 Poorer 23.3 16.2 0.00 22.5 15.7 Middle 18.5 18.9 18.8 14.8 0.01 Richer 15.3 19.9 15.8 25.2 Richest 22.6 29.4 23.2 30.4

17 Table 3: Regression model predicting the log-odds of having an STI or testing HIV positive among youths in Uganda (2011) Model 1 Model 2 Model 3 Variable/category Odds Odds Odds Ratio 95% CI Ratio 95% CI Ratio 95% CI Sex of respondent Male 1.0 - 1.0 - 1.0 - Female 1.68* [1.3-2.3] 2.11* [1.2-3.6] 1.70* [1.3-2.2] Education attainment None 1.0 - - - 1.0 - Primary 2.35* [1.2-4.8] - - 2.80* [1.4-5.6] Secondary + 2.02 [0.9-4.3] - - 2.51* [1.2-5.2] Rural/urban residence Urban 1.0 - - - 1.0 - Rural 0.71 [0.5-1.1] - - 0.82 [0.5-1.2] Region Central 1.0 - 1.0 - 1.0 - Kampala 0.74 [0.4-1.2] 0.72 [0.3-1.6] 0.78 [0.5-1.2] Eastern 0.63* [0.5-0.9] 0.68 [0.3-1.5] 0.63* [0.5-0.9] Northern 0.49* [0.3-0.7] 1.96 [0.9-4.4] 0.68* [0.5-1.0] Western 1.41* [1.0-1.9] 1.73 [0.9-3.2] 1.41* [1.1-1.9] Marital Status Never in union 1.0 - 1.0 - 1.0 - Currently in union 1.37 [1.0-1.9] 4.66* [2.1-10.2] 1.66* [1.2-2.3] Formerly in union 1.68 [1.0-2.9] 6.70* [2.5-18] 2.20* [1.3-3.6] Age at first sex <14 years 1.0 - - - 1.0 - 15-17 years 0.74* [0.6-1.0] - - 0.74* [0.6-1.0] 18 years plus 0.83 [0.6-1.1] - - 0.81 [0.6-1.1] Number of lifetime sex partners Only 1 1.0 - 1.0 - 1.0 - 2-3 1.41* [1.1-1.9] 2.05* [1.1-3.7] 1.52* [1.2-2.0] 4+ 2.57* [1.8-3.7] 4.47* [2.3-8.6] 2.78* [2.0-3.9] Wealth Status Poorest 1.0 - 1.0 - 1.0 - Poorer 0.82 [0.6-1.2] 1.36 [0.6-2.9] 0.87 [0.6-1.2] Middle 0.99 [0.7-1.4] 1.12 [0.5-2.5] 1.02 [0.7-1.4] Richer 1.09 [0.7-1.6] 2.71* [1.2-6.0] 1.24 [0.9-1.8] Richest 1.02 [0.6-1.6] 2.90* [1.2-7.1] 1.20 [0.8-1.9] Model Constant 0.06* [0.0-0.1] 0.002* [0.0-0.01] 0.04* [0.0-0.1] Note: * Represents significant results at p<0.05; n=2491 for all the 3 models; Model 1= STI model; Model 2= HIV Model; Model 3= Combined model (either STI or HIV). All the three models were tested for goodness of fit using the link test and all passed the test.