When context matters: Multilevel determinants of self-reported sexually transmitted infections symptoms among men engaged in transactional sex in 26 Sub-Saharan African countries

When context matters: Multilevel determinants of self-reported sexually transmitted infections symptoms among men engaged in transactional sex in 26 Sub-Saharan African countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When context matters: Multilevel determinants of self-reported sexually transmitted infections symptoms among men engaged in transactional sex in 26 Sub-Saharan African countries Issifou Yaya, Ter Tiero Elias Dah, Panawé Kassang, Kouamé Mathias N’dri, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8127004/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Apr, 2026 Read the published version in BMC Public Health → Version 1 posted 17 You are reading this latest preprint version Abstract Background Men engaged in transactional sex (METS) represent a neglected key population in sub-Saharan Africa (SSA), yet little is known about their burden of sexually transmitted infections (STIs) and associated factors. This study assessed the prevalence of self-reported STIs (SR-STI) and identified individual, community, and country-level determinants among this group. Methods We analyzed pooled recent nationally representative Demographic and Health Survey (DHS) data from 26 SSA countries. This study included 10,128 men who reported engagement in transactional sex within the past 12 months. Weighted prevalence estimates were calculated, and multilevel logistic regression models were applied to examine individual-, community-, and country-level determinants of SR-STI symptoms, adjusting for survey year. Model fit was assessed using Akaike Information Criterion. Results The participants’ mean (± SD) age was 29.8 (± 10.2) years. The overall weighted prevalence of SR-STIs among METS was 19.5% (95%CI: 18.3–20.7%), nearly threefold higher than among men not reporting transactional sex (7.2%; 95%CI: 6.9–7.5%; p for difference < 0.001). Prevalence varied substantially across countries, from 5.6% in Niger to 36.9% in Liberia (p < 0.001). At the individual-level, younger age, lower education, employment, risky sexual behavior, middle household wealth, heard about STI, HIV testing, and media exposure were associated with higher odds of SR-STI, while circumcision, HIV knowledge, and Christian affiliation were protective. At the community-level, men from poorer communities were less likely to report SR-STI symptoms (aOR = 0.79; 95%CI: 0.66–0.87). At the country-level, participants from Southern Africa had lower odds (aOR = 0.49; 95%CI: 0.24–0.97) compared to those in West Africa. Significant between-country and -community heterogeneity was observed, but variance decreased with the inclusion of individual and contextual predictors. Conclusion SR-STIs are highly prevalent among METS in SSA, with marked heterogeneity across countries and multiple individual and structural determinants. These findings underscore the need for targeted, context-specific interventions integrating biomedical, behavioral, and structural approaches to reduce STI burden in this population. Transactional sex Men Sexually transmitted infections Self-reported symptoms Sub-Saharan Africa Public health Figures Figure 1 Figure 2 Background Sexually transmitted infections (STIs), including chlamydia, gonorrhea, trichomoniasis and syphilis, remain a persistent public health concern in sub-Saharan Africa (SSA), where limited access to diagnostic testing, stigma, and fragile health systems undermine effective prevention and control strategies [ 1 , 2 ]. Despite progress in HIV care and surveillance, STIs receive comparatively less attention, although they contribute substantially to morbidity, infertility, and increased HIV transmission risk [ 3 ]. In most SSA settings, syndromic management, largely dependent on self-reported symptoms, remains the primary diagnostic approach due to resource constraints [ 2 , 4 , 5 ]. While pragmatic, this strategy underestimates asymptomatic infections and often misclassifies other genitourinary conditions, leading to both under- and over-treatment [ 6 ]. Men, in particular, tend to underutilize sexual and reproductive health services, and surveillance systems rarely capture their STI burden adequately [ 7 ]. Transactional sex, broadly defined as the exchange of money, goods, or favors for sexual relations [ 8 , 9 ], has been widely studied in relation to women and female sex workers in SSA [ 10 ]. However, less is known about its prevalence and implications among men, despite growing evidence that men who engage in transactional sex (METS) experience heightened vulnerability to HIV and other STIs [ 11 , 12 ]. The intersecting risks of multiple sexual partnerships, inconsistent condom use, and structural barriers to care amplify this vulnerability [ 13 – 16 ]. While a growing body of research documents STI symptoms among sexually active men in general [ 17 , 18 ], little is known about those engaged specifically in transactional sex, a subgroup often overlooked in epidemiological studies. Analyses of Demographic and Health Survey (DHS) data provide valuable insights into self-reported STI (SR-STI) symptoms across SSA [ 17 – 19 ]. A pooled study of 27 countries reported a pooled prevalence of 3.8% among sexually active men, with risk factors including younger age, urban residence, multiple partners, payment for sex, and limited HIV knowledge [ 17 ]. Similarly, a recent multilevel analysis from East Africa highlighted the role of both individual and contextual determinants, showing that community factors such as residence type, media exposure, and regional norms significantly shape STI risk [ 20 ]. Nevertheless, these studies grouped all men together, thereby obscuring differences for those engaging in transactional sex. Given the social and cultural complexity of transactional sex, and its links to gender norms, economic vulnerability, and mobility [ 3 , 21 , 22 ], a focused analysis of this subgroup is warranted. This study addresses a critical evidence gap by focusing specifically on METS, a population rarely studied in STI epidemiology despite their heightened vulnerability [ 14 , 15 ]. The study goes beyond individual-level analysis to account for contextual factors shaping STI risk. This approach enables cross-country comparisons and the identification of both structural and behavioral drivers of SR-STI symptoms. Therefore, the objective of this study is to estimate the prevalence and identify the individual, community, and country-level determinants of SR-STI symptoms among METS in sub-Saharan African countries. These questions were addressed using pooled and harmonized data from 26 DHS data. Methods Study setting, design and participants The study used data from the Demographic and Health Survey (DHS). The DHS is a cross-sectional nationwide representative household survey conducted in over 85 low- and middle-income countries. The survey used a stratified two-stage probability sampling approach, selecting enumeration areas and households within each enumeration area. From households included in the nationally representative household survey, male individuals aged 15 to 59 years are recruited [ 23 ]. The dataset is freely accessible at https://dhsprogram.com/data/available-datasets.cfm . More details about the survey procedures are described elsewhere [ 24 ]. We considered data for men from the most recent available DHS-datasets (with information on transactional sex) in twenty-six SSA countries, including Angola, Benin, Burundi, Cameroon, Chad, Comoros, Congo, Democratic Republic of the Congo (DRC), Ethiopia, Gabon, Gambia, Guinea, Liberia, Madagascar, Malawi, Mali, Namibia, Niger, Nigeria, Rwanda, Sierra Leone, South Africa, Togo, Uganda, Zambia, and Zimbabwe (Table 1 ). All data were combined into a single analytical dataset. Only sexually active men aged 15 years or more, who reported transactional sex in the last 12 months were included in the current analyses. Transactional sex was defined as financial or material goods for exchange sex in a noncommercial or nonmarital context [ 21 ]. Table 1 The Demographic Health and Survey (DHS) years of study and study participants of men engaged in transactional sex in twenty-six Sub-Saharan African Countries Countries DHS year Unweighted n (%) National HIV prevalence (%) GDP per capita ( $ ) Source of data Angola 2015–2016 409 (4.0) 2.0 7,120 https://dhsprogram.com/methodology/survey/survey-display-477.cfm Benin 2017–2018 408 (4.0) 1.2 2,886 https://dhsprogram.com/methodology/survey/survey-display-491.cfm Burundi 2016–2017 121 (1.2) 0.9 772 https://dhsprogram.com/methodology/survey/survey-display-463.cfm Cameroon 2018 516 (5.1) 2.7 4,011 https://dhsprogram.com/methodology/survey/survey-display-511.cfm Chad 2014–2015 141 (1.4) 1.6 2,133 https://dhsprogram.com/methodology/survey/survey-display-465.cfm Comoros 2012 121 (1.2) 0.025 2,679 https://dhsprogram.com/methodology/survey/survey-display-443.cfm Congo 2011–2012 466 (4.6) 3.2 5,175 https://dhsprogram.com/methodology/survey/survey-display-388.cfm DR Congo 2013-14 893 (8.8) 1.2 712 https://dhsprogram.com/methodology/survey/survey-display-421.cfm Ethiopia 2016 192 (1.9) 1.1 1,858 https://dhsprogram.com/methodology/survey/survey-display-478.cfm Gabon 2019–2021 564 (5.6) 4.7 15,950 https://dhsprogram.com/methodology/survey/survey-display-546.cfm Gambia 2019–2020 77 (0.8) 1.5 2,422 https://dhsprogram.com/methodology/survey/survey-display-555.cfm Guinea 2018 199 (2.0) 1.5 2,844 https://dhsprogram.com/methodology/survey/survey-display-539.cfm Liberia 2019–2020 238 (2.4) 1.1 1,900 https://dhsprogram.com/methodology/survey/survey-display-537.cfm Madagascar 2021 1,510 (14.9) 0.4 1,577 https://dhsprogram.com/methodology/survey/survey-display-560.cfm Malawi 2015–2016 703 (6.9) 8.8 1,403 https://dhsprogram.com/methodology/survey/survey-display-483.cfm Mali 2018 164 (1.6) 1.2 2,684 https://dhsprogram.com/methodology/survey/survey-display-517.cfm Namibia 2013 46 (0.5) 10.9 9,699 https://dhsprogram.com/methodology/survey/survey-display-363.cfm Niger 2012 26 (0.3) 0.4 1,162 https://dhsprogram.com/data/dataset_admin/index.cfm Nigeria 2018 660 (6.5) 2.9 5,083 https://dhsprogram.com/methodology/survey/survey-display-528.cfm Rwanda 2019–2020 134 (1.3) 3.0 2,336 https://dhsprogram.com/methodology/survey/survey-display-554.cfm Sierra Leone 2019 478 (4.7) 1.7 2,705 https://dhsprogram.com/methodology/survey/survey-display-545.cfm South Africa 2016 133 (1.3) 21.0 13,513 https://dhsprogram.com/methodology/survey/survey-display-390.cfm Togo 2013-14 39 (0.4) 3.1 1,715 https://dhsprogram.com/methodology/survey/survey-display-328.cfm Uganda 2016 409 (4.0) 6.5 2,165 https://dhsprogram.com/methodology/survey/survey-display-504.cfm Zambia 2018 952 (9.4) 11.1 3,442 https://dhsprogram.com/methodology/survey/survey-display-542.cfm Zimbabwe 2015 529 (5.2) 14.0 2,647 https://dhsprogram.com/methodology/survey/survey-display-475.cfm Table 2 Participants’ characteristics and SR-STI symptoms prevalence among men engaged in transactional sex in twenty-six Sub-Saharan African Countries Characteristics (N = 10,128) Unweighted n (%) Weighted STI symptoms prevalence (95%CI) p-value* Survey year 2011–2015 2016–2021 3373 (33.3) 6755 (66.7) 20.5 (18.3,22.9) 18.9 (17.5,20.3) 0.225 Individual-level factors Respondent’s age (years) 15–24 25–34 35 and more Mean (± SD) 3811 (37.7) 3285 (32.4) 3032 (29.9) 29.8 (± 10.2) 19.8 [18.1,21.8] 23.3 [21.0,25.7] 14.8 [13.1,16.8] < 0.001 Education level No education Primary Secondary and higher 1294 (12.8) 3262 (32.2) 5571 (55.0) 17.8 [15.2,20.8] 20.8 [18.8,22.9] 19.2 [17.5,21.0] 0.245 Literate No Yes 2279 (22.5) 7849 (77.5) 20.2 (18.0,22.7) 19.3 (17.9,20.7) 0.484 Currently working No Yes 1406 (13.9) 8722 (86.1) 16.4 (12.8,20.9) 20.0 (18.7,21.3) 0.128 Living in couple No Yes 5606 (55.4) 4522 (44.6) 20.1 (18.5,21.8) 18.7 (17.0,20.5) 0.258 Religious affiliation Traditional/animist/no religion Christianism Islam 6141 (60.6) 2867 (28.3) 1120 (11.1) 20.8 (19.3,22.5) 16.7 (14.8,18.8) 18.8 (15.3,22.9) 0.010 Households’ wealth index Poorer Middle Richer 3913 (38.6) 2052 (20.3) 4163 (41.1) 18.4 (16.6,20.3) 23.7 (20.7,27.0) 18.3 (16.5,20.2) 0.003 Media exposure No Yes 2086 (20.6) 8031 (79.4) 16.3 (13.9,18.9) 20.2 (18.8,21.6) 0.012 Age at the first sex Less than 18 years 18 years or more 6309 (62.5) 3781 (37.5) 21.0 (19.4,22.7) 16.9 (15.3,18.7) < 0.001 Comprehensive knowledge about HIV No Yes 6986 (69.0) 3142 (31.0) 19.5 (18.1,21.1) 19.3 (17.3,21.5) 0.888 Heard about STI No Yes 180 (1.8) 9948 (98.2) 4.4 (2.0,9.1) 19.7 (18.5,20.7) < 0.001 Circumcision No Yes 3384 (33.4) 6744 (66.6) 22.2 (20.1,24.4) 18.1 (16.6,19.6) 0.001 Risky sexual behavior No Yes 1887 (18.6) 8241 (81.4) 13.4 (11.4,15.8) 20.9 (19.6,22.4) < 0.001 Tested for HIV** No Yes 7803 (77.0) 2325 (33.0) 19.3 (17.9,20.7) 20.3 (18.0,22.7) 0.468 Community-level factors Place of residence Urban Rural 3973 (39.2) 6155 (60.8) 19.8 (17.9,22.0) 19.2 (17.7,20.6) 0.587 Community poverty levels Low High 5099 (50.4) 5029 (49.6) 22.6 (20.8,24.6) 15.9 (14.6,17.3) < 0.001 Community literacy level Low High 7199 (71.1) 2929 (28.9) 19.4 (18.0,20.8) 19.7 (17.4,22.3) 0.793 Level of media exposure Low High 3813 (37.6) 6315 (62.4) 19.2 (17.3,21.1) 19.7 (18.2,21.4) 0.656 Region Western Africa Central Africa Eastern Africa Southern Africa 2289 (22.6) 2701 (26.7) 2366 (23.3) 2772 (27.4) 20.7 [18.2,23.4] 21.1 [18.7,23.6] 18 [15.5,20.8] 18.1 [16.0,20.3] 0.157 * Pearson chi2 ** in the las 12 months Table 3 Multivariable multi-level logistic regression analysis of individual level community level and country level factors associated with STI symptoms among men engaged in transactional sex in Sub-saharan Africa, Characteristics (N = 10,126) Model 0 a Model 1* b aOR (95%CI) Model 2* c aOR (95%CI) Model 3* d aOR (95%CI) Model 4* e aOR (95%CI) p-value Survey year 2011–2015 2016–2021 1 1.07 (0.75,1.53) 1 1.15 (0.80,1.67) 1 1.17 (0.72,1.92) 1 0.97 (0.65,1.45) 0.874 Individual-level factors Respondent’s age (years) 15–24 25–34 35 and more 1 1.12 (0.97,1.30) 0.69 (0.58,0.83) 1 1.11 (0.96,1.29) 0.69 (0.58,0.82) 0.167 < 0.001 Education level No education Primary Secondary and higher 0.93 (0.75,1.14) 0.77 (0.63,0.95) 1 0.94 (0.77,1.16) 0.78 (0.63,0.96) 0.575 0.017 Currently working 1.41 (1.17,1.69) 1.40 (1.17,1.69) < 0.001 Living in couple 0.87 (0.76,1.00) 0.87 (0.76,1.00) 0.057 Religious affiliation Traditional/animist/no religion Christianism Islam 1 0.85 (0.72,1.00) 0.87 (0.71,1.07) 1 0.84 (0.72,0.99) 0.86 (0.70,1.06) 0.044 0.163 Households’ wealth index Poorer Middle Richer 1.31 (1.12,1.53) 1.08 (0.93,1.26) 1 1.31 (1.12,1.54) 1.07 (0.90,1.26) 0.001 0.440 Media exposure 1.19 (1.01,1.39) 1.19 (1.01,1.39) 0.036 Age at the first sex Less than 18 years 18 years or more 1 0.89 (0.78,1.01) 1 0.89 (0.78,1.01) 0.063 Comprehensive knowledge about HIV 0.81 (0.71,0.93) 0.82 (0.72,0.94) 0.004 Heard about STIs 3.21 (1.62–6.35) 3.17 (1.60,6.26) 0.001 Circumcised 0.82 (0.68,0.99) 0.82 (0.68,0.99) 0.041 Risky sexual behavior 1.69 (1.43,2.01) 1.69 (1.43,2.00) < 0.001 Tested for HIV** 1.17 (1.01,1.36) 1.18 (1.02,1.37) 0.029 Community-level factors Place of residence Urban Rural 1 1.05 (0.92,1.19) 1 1.03 (0.88,1.20) 0.737 Community literacy level Low High 1 0.94 (0.76,1.17) 1 0.85 (0.67,1.08) 1 0.192 Community poverty levels Low High 1 0.78 (0.68,0.89) 1 0.76 (0.66,0.87) 1 < 0.001 Community STI prevalence 6.78 (0.85,54,08) 6.53 (0.82,52,35) 0.077 Country-level factors Region Western Africa Central Africa Eastern Africa Southern Africa 1 0.96 (0.57,1.63) 0.75 (0.45,1.23) 0.62 (0.27,1.43) 1 0.80 (0.50,1.27) 0.68 (0.44,1.04) 0.49 (0.25,0.98) 1 0.344 0.078 0.044 National HIV prevalence 1.01 (0.94,1.10) 1.03 (0.96,1.09) 0.427 Condom use prevalence 5.06 (0.19,131.19) 2.41 (0.17,33.76) 0.515 GDP per capita , ( $ ) 0.99 (0.99,1.00) 0.99 (0.99,1.00) 0.352 Model comparison and random effect Variance cluster (95%CI) 0.58 (0.41,0.83) 0.56 (0.39,0.81) 0.54 (0.37,0.79) 0.58 (0.41,0.83) 0.52 (0.35,0.78) Variance country (95%CI) 0.19 (0.09,037) 0.14 (0.07,0.30) 0.16 (0.08,0.32) 0.15 (0.07,0.31) 0.08 (0.03,0.19) PCV cluster Reference 3.45% 6.90% 1.72% 10.34% PCV country Reference 26.32% 15.79% 21.05% 57.89% ICC cluster (95%CI) 18.97% (14.47,24.46%) 17.61% (13.24,23.04%) 17.50% (13.12,22.95%) 18.33% (13.97,23.68%) 15.48% (11.32,20,80%) ICC country (95%CI) 4.60% (2.37,8.76%) 3.60% (1.78,7.15%) 3.94% (1.99,7.64%) 3.80% (1.89,7.51%) 2.02% (0.86,4,69%) MOR cluster (95%CI) 2.42 (2.10,2.88) 2.38 (2.07,2.84) 2.35 (2.03,2.81) 2.40 (2.10,2.88) 2.31 (1.99,2.79) MOR country (95%CI) 1.66 (1.42,2.03) 1.54 (1.36,1.89) 1.59 (1.39,1.93) 1.57 (1.36,1.91) 1.39 (1.22,1.66) Model fitness AIC Log-likelihood 9224.417 -4609.208 9063.554 -4510.777 9217.897 -4600.949 9234.372 -4607.186 9061.025 -4499.513 * adjusted for year of the survey; ** in the las 12 months; Bold: Statistically significant at p-value less than 0.05 a : Model 0, empty null model, baseline model without any explanatory variables (unconditional model); b : Model 1, adjusted for only individual-level factors; c : Model 2, adjusted for only community-level factors; d : Model 3, adjusted for only country-level factors; e : Model 5, adjusted for individual-, community-, and country-level factors (full model) aOR: ajusted Odds-Ratio; 95% CI: 95% confidence interval; AIC: Akaike Information Criterion, ICC: intra-class correlation coefficient, MOR: median odds ratio, PCV: proportional change in variation Study variables Outcome variable: self-reported sexually transmitted infections (SR-STIs) SR-STIs symptoms were defined as any of the following self-reported STI symptoms that occurred in the previous 12 months: i) genital discharge, ii) genital sore/ulcer, or iii) any other symptoms of STI, including pain, rash or itchy genitals. Responses were coded as '0' for participants reporting no STI symptom and '1' for participants reporting at least one STI symptom. Prevalence of SR-STIs was defined as the proportion of participants who reported at least one STI symptom in the previous 12 months among the included participants. Explanatory variables The selection of explanatory variables for this analysis was guided by the scientific literature and their availability within the dataset. To ensure a structured analytical framework, the variables were organized into three broad categories: individual-level variables (capturing personal characteristics and behaviors), community-level variables (reflecting the social and contextual environment in which individuals live), and country-level variables (representing broader national and policy-related factors). This categorization allows for a multilevel perspective on the determinants under investigation. Individual-level variables including: Socio-demographic variables : age groups (15–24, 25–34, and 35 years and more), living in couple (yes/no), education level (primary/secondary and higher/no education), literacy status (read perfectly vs not), religion (Islam/ traditional, animist, no religion/Christianism), currently working (yes/no), household wealth quintiles index were grouped into three categories: poor (poorest and poorer), middle and, rich (richer and richest), media exposure (yes/no), defined as a composite variable obtained by the aggregation of three variables: reading newspaper, listening radio and watching television. Media exposure was coded as “no” if the respondent did not report any exposure to these three media sources. Sexual behavior and practices age at the first sex (less than 18 years vs 18 years or more), circumcised (yes/no), risky sexual behavior (yes/no), heard about STI (yes/no), ever tested for HIV (yes/no) and HIV-prevention knowledge (yes/no). Individuals were considered as having knowledge about HIV/aids if they declared that having just one uninfected faithful partner and consistent condoms use during sexual intercourse could reduce the risk of acquiring HIV. Community-level variables These variables were operationalized to capture the broader sociocultural and economic context. These included: (i) place of residence, dichotomized into rural and urban settings; (ii) community literacy level, categorized as low or high according to the proportion of individuals who were able to read within the cluster; (iii) community poverty level, classified as low or high based on the aggregated household wealth index scores within the community; (iv) community media exposure, defined as low or high according to the proportion of participants within the cluster with regular access to mass media sources such as radio, television, newspapers, or digital platforms; and (v) the prevalence of SR-STI symptoms within each cluster was also included as a proxy for the community sexual health risk environment. Country-level variables The analysis included several country-level contextual variables to account for structural and epidemiological differences across nations: (i) Geographical region: countries were categorized according to their location within the SSA region (Western Africa, Central Africa, Eastern Africa, Southern Africa); (ii) National HIV prevalence in each country, as reported by the most recent national surveillance or epidemiological survey. This variable captures the burden of HIV at the country level; (iii) Gross Domestic Product (GDP) per Capita [ 25 ]: Measured in US dollars ( $ ), GDP per capita was included as an indicator of national economic status and development, which may influence health system capacity and access to care; and (iv) the country prevalence of condom use. Statistical analysis Data from the seven DHS were combined (pooled-data analysis) for all the analyses. Survey weights were applied in the descriptive analyses to account for the unequal selection probability of households resulting from the sample design as well as for non-response according to the DHS program recommendation for the analysis of DHS data [ 26 ]. A detailed explanation of the weighting procedure can be found in the DHS Methodology report [ 27 ]. Analyses were conducted using STATA version 15.1. Due to the complex survey design, all statistical analyses were carried out using the svy procedures to adjust for unequal sampling probabilities and the sample weight to consider generalizability of the findings. We additionally used the "svyset" option of "single unit (certainty)" to consider difference in sample size across the studies. Descriptive Analyses Initial analyses consisted of descriptive statistics to characterise the study population. Frequencies and percentages were used to summarise categorical variables, while measures of central tendency (mean, and standard deviation) were applied for continuous variables. Pearson Chi-square tests or Fisher test were performed if appropriate for groups’ comparisons. We calculated the weighed prevalence of SR-STIs with 95% confidence intervals (95% CI). Modelling approaches To explore associations between SR-STI symptoms and a set of individual, community, and country-level factors, multivariable multi-level logistic regression models were employed. This approach was chosen to account for the hierarchical structure of the data, where individuals (level 1) were nested within communities or clusters (level 2), and communities were nested within countries (level 3). We constructed a series of five sequential models: (i) Empty model (Model 0): An unconditional model without explanatory variables, used to partition the total variance in SR-STI symptoms across the individual, community, and country levels; (ii) Individual-level model (Model 1): included only individual-level factors; (iii) Community-level model (Model 2): incorporated only contextual characteristics measured at the community or cluster level; (iv) Country-level model (Model 3): which included only country-level factors; (v) Full model (Model 4): which integrated explanatory variables from all three levels simultaneously, allowing assessment of the independent and combined contributions of compositional and contextual factors. This stepwise approach facilitated examination of the relative contribution of each level in explaining variation in SR-STI symptoms. To account for potential temporal effects arising from the use of data collected in different survey years, the DHS survey year was included as a control variable in all statistical models. This adjustment helps to mitigate confounding due to variations over time in both exposure and outcome variables. Fixed-effects Fixed effects were reported as adjusted odds ratios (aORs) with corresponding 95% confidence intervals (95%CI). The aORs provide interpretable measures of association, indicating the relative odds of hypertension given specific individual or contextual exposures. aORs > 1 indicated an increased likelihood of reporting SR-STI symptoms, whereas aORs < 1 indicated a protective effect. Random - effects (Measures of variation) To capture contextual effects, we examined both the intraclass correlation coefficient (ICC) and the median odds ratio (MOR). Together, these measures allow disentangling the role of compositional versus contextual determinants of SR-STI symptoms. Model Fit The adequacy and reliability of the multi-level logistic regression models were rigorously assessed using several diagnostic approaches. First, multicollinearity among explanatory variables was evaluated to ensure that parameter estimates were stable and not inflated due to highly correlated predictors. This was done by calculating the variance inflation factor (VIF) for each variable. All values fell within acceptable limits (< 10), indicating that multicollinearity was not a concern and that the variables could be reliably included in the models. Second, overall model fit was evaluated using both the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Lower values of AIC and BIC indicate better-fitting models, facilitating selection of the model that adequately captures the variation in SR-STI symptoms across levels. Ethics approval and consent to participate This study utilized secondary data obtained from the MEASURE DHS program. Access to DHS data requires only registration and submission of a formal data request; therefore, additional ethical approval for this secondary analysis was not required. The DHS program ensures that all surveys are conducted in accordance with internationally recognized ethical standards, including informed consent from participants. For more information on DHS data access procedures and ethical guidelines, please visit: https://www.dhsprogram.com/data/available-datasets.cfm Results Participants’ characteristics of the study Among 235,290 men drawn from DHS conducted between 2011 and 2021 across 26 Sub-Saharan African countries, 10,128 reported engaging in transactional sex (Figure 1). The distribution of respondents varied substantially across countries, reflecting differences in population size, survey sampling strategies, and the prevalence of transactional sex. For instance, the smallest sample was observed in Niger (n = 26), whereas Madagascar contributed the largest number of respondents (n = 1,510) (Table 1). The characteristics of the participants are reported in Table 2. Their mean age was 29.8 years (standard deviation [SD] ±10.2) and 37.7% were aged between 15 and 24 years. Over half (55.0%) of the participants had completed at least secondary education and the large majority of the participants (86.1%) were working at the time of the surveys. The proportion of participants living in couple were 44.6%, 60.6% declared not be affiliated to any conventional religion and 41.1% were living in a rich households. The majority of the respondents (79.4%) were exposed to media information (magazine, radio or TV). Moreover, 98.2% of METS had heard about STI, 31.0% had comprehensive knowledge about HIV, while 66.6% of the participants declared being circumcised, 81.4% of the respondents reported risky sexual behavior and 33.0% declared being tested for HIV for in the last 12 months. While over half (60.8%) of participants lived in a rural area, 27.4% were from Southern African region and 26.6% from Central African region. National HIV prevalence rates spanned from 0.025% in Comoros to 21% in South Africa, while gross domestic product (GDP) per capita ranged from $712 in the Democratic Republic of Congo to $15,950 in Gabon. Prevalence of self-reported STIs The overall weighted prevalence of self-reported STIs in the 12 months before the survey among METS was 19.5% (95%CI: 18.3-20.7%), ranging from 5.6% (1.4-20.0%) in Niger to 36.9% (28.4-46.4%) in Liberia with significant variability across countries (p<0.001). Compared to those who did not engaged in transactional sex, the prevalence of self-reported STI among the participants was significantly high (19.5% (18.3-20.7%) vs 7.2% (6.9-7.5%); p<0.001). This difference was also found within countries (Figure 2). Among METS, the prevalence of self-reported STIs was higher among participants aged 25–34 years (p<0.001), those without a conventional religion (p=0.010), individuals from middle-wealth households (p=0.003), and those with media exposure (p=0.012). Higher prevalence was also observed among participants with sexual debut before age 18 (p<0.001), uncircumcised men (p=0.001), those reporting risky sexual practices (p<0.001), and residents of low-poverty communities (p<0.001). (Table 2). Multilevel logistic regression analysis of SR-STI Symptoms Fixed effects At the individual level, older age (35 years and more : aOR = 0.69; 95% CI: 0.58,0.82; ref=15-24 years), educational attainment (secondary or higher level: aOR = 0.78; 95% CI: 0.63,0.96; ref= no education), religion affiliation (Christian: aOR = 0.84; 95% CI: 0.72,0.99; ref = no conventional religion), comprehensive knowledge about HIV (aOR = 0.82; 95% CI: 0.72,0.94), circumcision (aOR = 0.83; 95% CI: 0.69,0.99) were significantly associated with lower odds of SR-STI symptoms. While, employment (aOR = 1.40; 95% CI: 1.17,1.69), household wealth (middle households : aOR = 1.31; 95% CI: 1.12,1.54; ref = poorer households), media exposure (aOR = 1.19; 95% CI: 1.01–1.39), having heard about STI (aOR = 3.17; 95% CI: 1.60–6.26), risky sexual behavior (aOR = 1.69; 95% CI: 1.43–2.00), being tested for HIV (aOR = 1.18; 95% CI: 1.02–1.37), were significantly associated with higher odds of SR-STI symptoms. At the community level, communities with high poverty levels exhibited less odds (aOR = 0.76; 95% CI: 0.66,0.87). At the country level, respondents living in Southern African region were 51% less likely (aOR = 0.49; 95% CI: 0.25,0.98) to report SR-STI symptoms compared to those in Western African region. Random effects Significant variability was observed across countries and communities. At the country level, the null model variance was 0.19 (95% CI : 0.09,037), decreasing to 0.08 (95% CI: 0.03,0.19) in the full model, indicating that inclusion of predictors accounted for a substantial portion of between-country variance. The ICC decreased from 4.60% (95% CI: 2.37,8.76%) in the null model to 2.02% (95% CI: 0.86,4,69%) in the full model, and the MOR dropped from 1.66 (95% CI: 1.42,2.03) to 1.39 (95% CI: 1.22,1.66), demonstrating reduced country-level differences. The proportional change in variance (PCV) increased from 26.32% in Model 1 to 57.89% in Model 4, confirming substantial explanatory power of the included predictors (Table 3). At the community level, variance decreased from 0.58 (95% CI: 0.41,0.83) in the null model to 0.52 (95% CI: 0.35,0.78) in the full model. The ICC declined from 18.97% (95% CI: 14.47,24.46%) to 15.48% (95% CI: 11.32,20,80%), and the MOR decreased from 2.42 (95% CI: 2.10,2.88) to 2.31 (95% CI: 1.99,2.79), indicating that community-level disparities diminished with the addition of covariates. The PCV at the community level reached 10.34% in the full model (Table 3). Model Fit Model fit improved progressively with the inclusion of additional predictors. The log-likelihood increased from -4609.2085 in the null model to -4499.513 in the full model, while the AIC decreased from 9224.417to 9061.023 suggesting that Model 4 provided the best fit among the compared models (Table 3). Discussion This multi-country analysis provides a comprehensive assessment of the prevalence and multilevel factors associated with SR-STIs among METS in SSA. Our findings reveal a substantial burden of SR-STIs within this group and highlight key individual and contextual determinants, offering critical insights for public health intervention. The analysis demonstrates that the overall prevalence of SR-STIs among METS was 19.5%, a rate nearly threefold higher than that observed among men not reporting transactional sex (7.2%). This profound disparity underscores that METS, encompassing both clients of sex workers and male sex workers themselves, constitute a core group that bears a disproportionately high burden of STIs [28]. This aligns with prior evidence from focused studies in Eastern and Western Africa [12, 29], confirming that this group is an essential component of sexual networks and a critical population for targeted STI control strategies. A key finding is the marked heterogeneity in SR-STI prevalence across countries, ranging from 5.6% in Niger to 36.9% in Liberia. This pattern mirrors trends identified in another study reporting that among all sexually active men in 27 SSA countries, SR-STIs ranged from 0.4% (Niger) to 13.5% (Liberia) [17]. Our study, by focusing specifically on METS, reveals an even starker gradient of risk. Furthermore, in Niger, the SR-STI prevalence among METS (5.6%) was more than ten times higher than the national baseline for all men (0.4%). Conversely, in Liberia, the prevalence among METS (36.9%) nearly tripled the country's already elevated baseline (13.5%). This heterogeneity underscores the interplay between individual risk behaviors and country-level contextual factors, including epidemiological history, healthcare infrastructure, and cultural norms. Our identification of individual-level determinants aligns with established epidemiological patterns across SSA. Younger age, engagement in high-risk sexual practices (e.g., multiple partners, inconsistent condom use), and a lack of comprehensive HIV knowledge were all significantly associated with increased odds of reporting an STI. This is consistent with studies from South Africa, where young people exhibited high prevalence of STI [30], from Nigeria, where early sexual debut was linked to a 1.5–2.0 times higher likelihood of STI symptoms [31], and from Tanzania [19, 32], where multiple sexual partners and poor condom use predicted both self-reported and lab-confirmed STIs. Conversely, higher educational attainment and male circumcision emerged as protective factors. The protective effect of circumcision is well-documented in studies from Ethiopia [33] and Uganda [34], where it was associated with significantly reduced rates of ulcerative STIs. The association between employment and increased risk is notable and parallels findings from Uganda and Zimbabwe [35, 36]. This suggests that disposable income and high mobility among economically active men may facilitate engagement in transactional sex, thereby increasing exposure risk. The positive association between awareness of STI, prior HIV testing and SR-STI reporting warrants careful interpretation. This likely does not indicate that testing causes STIs, but rather reflects two concurrent phenomena: first, men who engage with health services (e.g., for HIV testing) are more likely to be aware of STI and screened for other conditions, leading to greater detection and awareness of STI symptoms [37, 38]; and second, it underscores the shared risk environment and common socio-behavioral determinants for HIV and other STIs [39, 40] Our multilevel analysis revealed important community- and country-level influences. Paradoxically, residing in a higher-poverty community was associated with lower odds of SR-STI reporting. This counterintuitive finding is consistent with other DHS-based analyses [35, 41] and is best explained by significant under-ascertainment in resource-poor settings due to heightened stigma, lower level of health literacy, lack of awareness of STI symptoms, and severely constrained access to diagnostic services [42]. At the country-level, respondents in Southern African countries were less likely to report STIs compared to those in West Africa. This regional disparity persists in DHS data despite the higher underlying HIV prevalence in the south [43, 44]. This may be attributed to regional differences in health system factors (e.g., more robust syndromic management protocols that reduce prolonged symptoms), cultural norms influencing willingness to disclose STI symptoms, or variations in the endemicity of specific bacterial STIs [45, 46]. Our results on individual, community, and country-level variables explained a substantial proportion of the variance (PCV ~58% at country level; ~10% at community level). However, significant residual heterogeneity remained, implying that critical unmeasured structural drivers, such as the quality of STI care, levels of stigma, population mobility patterns, and the enforcement of public health policies, continue to shape the epidemiological landscape [47, 48]. Overall, this study advances the literature by being among the first to systematically quantify the prevalence and determinants of SR-STIs among METS across multiple African countries. It demonstrates that this group faces a uniquely high burden of STIs, with complex socio-behavioral and structural determinants that differ from the general male population and sometimes diverge from patterns observed among female sex workers or other key populations. These findings have several implications for STI and HIV prevention strategies in SSA. First, METS should be explicitly recognized as a key population in regional STI/HIV control frameworks, alongside female sex workers and men who have sex with men. Second, targeted interventions such as tailored health communication, condom promotion, and access to comprehensive sexual health services must be developed to address their specific vulnerabilities. Third, the association of STIs with structural factors such as education, circumcision, and community-level characteristics emphasizes the need for a multilevel approach that combines individual behavioral interventions with broader structural and policy measures. Fourth, the observed inter-country and regional disparities call for context-specific strategies, rather than a uniform regional approach. This study has several limitations inherent to its design and data sources. First, the dependence on self-reported STI symptoms leads to inevitable underestimation of prevalence due to asymptomatic infections and underreporting driven by stigma. Second, the cross-sectional nature of the DHS data precludes any causal inference from the observed associations. Third, the use of surveys conducted across different years may introduce temporal biases if STI prevalence or reporting behaviors changed over time. Fourth, our analysis was constrained to variables available in the DHS, omitting potentially crucial factors such as detailed partner networks, population mobility, and direct measures of access to healthcare. Finally, while we identified significant community- and country-level variance, these effects may be partially confounded by unmeasured contextual factors like the density of health services, cultural norms, and local migration patterns. Conclusion This study found a high prevalence of SR-STIs among METS, with substantial heterogeneity across countries and significant associations with individual, community, and structural determinants. The elevated risk in this population underscores the urgent need for targeted, context-specific interventions that combine biomedical, behavioral, and structural strategies. Strengthening surveillance, improving access to STI diagnostics and treatment, and addressing the social determinants of sexual health are essential to reducing STI burden and advancing sexual health equity in sub-Saharan Africa. Abbreviations Abreviations : not applicable Declarations Ethics approval and consent to participate This study utilized secondary data obtained from the MEASURE DHS program. Access to DHS data requires only registration and submission of a formal data request; therefore, additional ethical approval for this secondary analysis was not required. The DHS program ensures that all surveys are conducted in accordance with internationally recognized ethical standards, including informed consent from participants. For more information on DHS data access procedures and ethical guidelines, please visit: https://www.dhsprogram.com/data/available-datasets.cfm Patient and public involvement : Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research . Consent of publication All authors have give their consent for the publication of the manuscript Availability data and materials statement: The data were obtained from the DHS program (www.dhsprogram.com) under the condition that the authors do not pass the data to other researchers. However, other researchers can apply directly to the DHS program to obtain the data. Competing interests Authors declare no interest conflict Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors Authors’ contribution IY contributed to the conception and design of the study, conducted the data analysis, interpreted the results and drafted the original version of the manuscript. All authors critically reviewed the manuscript for its intellectual content, as well as read the drafts and approved the final version. PK had final responsibility to submit. Acknowledgments The authors thank the Demographic and Health Surveys program (www.dhsprogram.org) for generously providing the data for this study. References World Health Organization (WHO). Report on global sexually transmitted infection surveillance, 2018 . Geneva. World Health Organization (WHO). 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14:54:11","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184510,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8127004/v1/96a57f5e4a920c0dd7fa234d.html"},{"id":97270257,"identity":"98339e3b-bd80-43b7-bac7-6d4776bbf2f8","added_by":"auto","created_at":"2025-12-02 14:54:13","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":379932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of participants’ selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127004/v1/12655c7510825113f8b37ebf.jpeg"},{"id":97270246,"identity":"aa5fdcd3-a4b6-467b-8e77-50b2b5c485ab","added_by":"auto","created_at":"2025-12-02 14:54:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1037386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted-prevalence of self-reported STI according to the transactional sex status\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127004/v1/9fa541ab84b116566e7ade12.jpeg"},{"id":107928078,"identity":"d0751beb-1e50-43e2-bb46-22abad33eb62","added_by":"auto","created_at":"2026-04-27 16:07:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2062045,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8127004/v1/3865fa51-2b8e-4c94-b819-f27acfe7eb68.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"When context matters: Multilevel determinants of self-reported sexually transmitted infections symptoms among men engaged in transactional sex in 26 Sub-Saharan African countries","fulltext":[{"header":"Background","content":"\u003cp\u003eSexually transmitted infections (STIs), including chlamydia, gonorrhea, trichomoniasis and syphilis, remain a persistent public health concern in sub-Saharan Africa (SSA), where limited access to diagnostic testing, stigma, and fragile health systems undermine effective prevention and control strategies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite progress in HIV care and surveillance, STIs receive comparatively less attention, although they contribute substantially to morbidity, infertility, and increased HIV transmission risk [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In most SSA settings, syndromic management, largely dependent on self-reported symptoms, remains the primary diagnostic approach due to resource constraints [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While pragmatic, this strategy underestimates asymptomatic infections and often misclassifies other genitourinary conditions, leading to both under- and over-treatment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Men, in particular, tend to underutilize sexual and reproductive health services, and surveillance systems rarely capture their STI burden adequately [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTransactional sex, broadly defined as the exchange of money, goods, or favors for sexual relations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], has been widely studied in relation to women and female sex workers in SSA [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, less is known about its prevalence and implications among men, despite growing evidence that men who engage in transactional sex (METS) experience heightened vulnerability to HIV and other STIs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The intersecting risks of multiple sexual partnerships, inconsistent condom use, and structural barriers to care amplify this vulnerability [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While a growing body of research documents STI symptoms among sexually active men in general [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], little is known about those engaged specifically in transactional sex, a subgroup often overlooked in epidemiological studies.\u003c/p\u003e\u003cp\u003eAnalyses of Demographic and Health Survey (DHS) data provide valuable insights into self-reported STI (SR-STI) symptoms across SSA [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A pooled study of 27 countries reported a pooled prevalence of 3.8% among sexually active men, with risk factors including younger age, urban residence, multiple partners, payment for sex, and limited HIV knowledge [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, a recent multilevel analysis from East Africa highlighted the role of both individual and contextual determinants, showing that community factors such as residence type, media exposure, and regional norms significantly shape STI risk [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nevertheless, these studies grouped all men together, thereby obscuring differences for those engaging in transactional sex. Given the social and cultural complexity of transactional sex, and its links to gender norms, economic vulnerability, and mobility [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a focused analysis of this subgroup is warranted.\u003c/p\u003e\u003cp\u003eThis study addresses a critical evidence gap by focusing specifically on METS, a population rarely studied in STI epidemiology despite their heightened vulnerability [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The study goes beyond individual-level analysis to account for contextual factors shaping STI risk. This approach enables cross-country comparisons and the identification of both structural and behavioral drivers of SR-STI symptoms.\u003c/p\u003e\u003cp\u003eTherefore, the objective of this study is to estimate the prevalence and identify the individual, community, and country-level determinants of SR-STI symptoms among METS in sub-Saharan African countries. These questions were addressed using pooled and harmonized data from 26 DHS data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy setting, design and participants\u003c/h2\u003e\u003cp\u003eThe study used data from the Demographic and Health Survey (DHS). The DHS is a cross-sectional nationwide representative household survey conducted in over 85 low- and middle-income countries. The survey used a stratified two-stage probability sampling approach, selecting enumeration areas and households within each enumeration area. From households included in the nationally representative household survey, male individuals aged 15 to 59 years are recruited [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The dataset is freely accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/data/available-datasets.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/data/available-datasets.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. More details about the survey procedures are described elsewhere [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe considered data for men from the most recent available DHS-datasets (with information on transactional sex) in twenty-six SSA countries, including Angola, Benin, Burundi, Cameroon, Chad, Comoros, Congo, Democratic Republic of the Congo (DRC), Ethiopia, Gabon, Gambia, Guinea, Liberia, Madagascar, Malawi, Mali, Namibia, Niger, Nigeria, Rwanda, Sierra Leone, South Africa, Togo, Uganda, Zambia, and Zimbabwe (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All data were combined into a single analytical dataset. Only sexually active men aged 15 years or more, who reported transactional sex in the last 12 months were included in the current analyses. Transactional sex was defined as financial or material goods for exchange sex in a noncommercial or nonmarital context [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Demographic Health and Survey (DHS) years of study and study participants of men engaged in transactional sex in twenty-six Sub-Saharan African Countries\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDHS year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnweighted\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNational HIV prevalence (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGDP per capita (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSource of data\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAngola\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e409 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7,120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-477.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-477.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBenin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e408 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-491.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-491.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBurundi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-463.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-463.cfm\" targettype=\"URL\" 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class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthiopia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e192 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-478.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-478.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGabon\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e564 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15,950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-546.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-546.cfm\" targettype=\"URL\" 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class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGuinea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e199 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-539.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-539.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiberia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e238 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-537.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-537.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMadagascar\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,510 (14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-560.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-560.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMalawi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e703 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-483.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-483.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMali\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e164 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-517.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-517.cfm\" targettype=\"URL\" 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class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNigeria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e660 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5,083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-528.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-528.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRwanda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e134 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-554.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-554.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSierra Leone\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e478 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-545.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-545.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSouth Africa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e133 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13,513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-390.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-390.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTogo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2013-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-328.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-328.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUganda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e409 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-504.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-504.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZambia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e952 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3,442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-542.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-542.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZimbabwe\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e529 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-475.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-475.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipants\u0026rsquo; characteristics and SR-STI symptoms prevalence among men engaged in transactional sex in twenty-six Sub-Saharan African Countries\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics (N\u0026thinsp;=\u0026thinsp;10,128)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnweighted\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeighted STI symptoms prevalence\u003c/p\u003e\u003cp\u003e(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSurvey year\u003c/b\u003e\u003c/p\u003e\u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\u003cp\u003e2016\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3373 (33.3)\u003c/p\u003e\u003cp\u003e6755 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.5 (18.3,22.9)\u003c/p\u003e\u003cp\u003e18.9 (17.5,20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndividual-level factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespondent\u0026rsquo;s age\u003c/b\u003e (years)\u003c/p\u003e\u003cp\u003e15\u0026ndash;24\u003c/p\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003cp\u003e35 and more\u003c/p\u003e\u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3811 (37.7)\u003c/p\u003e\u003cp\u003e3285 (32.4)\u003c/p\u003e\u003cp\u003e3032 (29.9)\u003c/p\u003e\u003cp\u003e29.8 (\u0026plusmn;\u0026thinsp;10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.8 [18.1,21.8]\u003c/p\u003e\u003cp\u003e23.3 [21.0,25.7]\u003c/p\u003e\u003cp\u003e14.8 [13.1,16.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo education\u003c/p\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003cp\u003eSecondary and higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1294 (12.8)\u003c/p\u003e\u003cp\u003e3262 (32.2)\u003c/p\u003e\u003cp\u003e5571 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.8 [15.2,20.8]\u003c/p\u003e\u003cp\u003e20.8 [18.8,22.9]\u003c/p\u003e\u003cp\u003e19.2 [17.5,21.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiterate\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2279 (22.5)\u003c/p\u003e\u003cp\u003e7849 (77.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.2 (18.0,22.7)\u003c/p\u003e\u003cp\u003e19.3 (17.9,20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrently working\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1406 (13.9)\u003c/p\u003e\u003cp\u003e8722 (86.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.4 (12.8,20.9)\u003c/p\u003e\u003cp\u003e20.0 (18.7,21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiving in couple\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5606 (55.4)\u003c/p\u003e\u003cp\u003e4522 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.1 (18.5,21.8)\u003c/p\u003e\u003cp\u003e18.7 (17.0,20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligious affiliation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTraditional/animist/no religion\u003c/p\u003e\u003cp\u003eChristianism\u003c/p\u003e\u003cp\u003eIslam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6141 (60.6)\u003c/p\u003e\u003cp\u003e2867 (28.3)\u003c/p\u003e\u003cp\u003e1120 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.8 (19.3,22.5)\u003c/p\u003e\u003cp\u003e16.7 (14.8,18.8)\u003c/p\u003e\u003cp\u003e18.8 (15.3,22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHouseholds\u0026rsquo; wealth index\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3913 (38.6)\u003c/p\u003e\u003cp\u003e2052 (20.3)\u003c/p\u003e\u003cp\u003e4163 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.4 (16.6,20.3)\u003c/p\u003e\u003cp\u003e23.7 (20.7,27.0)\u003c/p\u003e\u003cp\u003e18.3 (16.5,20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedia exposure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2086 (20.6)\u003c/p\u003e\u003cp\u003e8031 (79.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.3 (13.9,18.9)\u003c/p\u003e\u003cp\u003e20.2 (18.8,21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at the first sex\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLess than 18 years\u003c/p\u003e\u003cp\u003e18 years or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6309 (62.5)\u003c/p\u003e\u003cp\u003e3781 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.0 (19.4,22.7)\u003c/p\u003e\u003cp\u003e16.9 (15.3,18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComprehensive knowledge about HIV\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6986 (69.0)\u003c/p\u003e\u003cp\u003e3142 (31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.5 (18.1,21.1)\u003c/p\u003e\u003cp\u003e19.3 (17.3,21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeard about STI\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (1.8)\u003c/p\u003e\u003cp\u003e9948 (98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.4 (2.0,9.1)\u003c/p\u003e\u003cp\u003e19.7 (18.5,20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCircumcision\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3384 (33.4)\u003c/p\u003e\u003cp\u003e6744 (66.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.2 (20.1,24.4)\u003c/p\u003e\u003cp\u003e18.1 (16.6,19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRisky sexual behavior\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1887 (18.6)\u003c/p\u003e\u003cp\u003e8241 (81.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.4 (11.4,15.8)\u003c/p\u003e\u003cp\u003e20.9 (19.6,22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTested for HIV**\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7803 (77.0)\u003c/p\u003e\u003cp\u003e2325 (33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.3 (17.9,20.7)\u003c/p\u003e\u003cp\u003e20.3 (18.0,22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunity-level factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUrban\u003c/p\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3973 (39.2)\u003c/p\u003e\u003cp\u003e6155 (60.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.8 (17.9,22.0)\u003c/p\u003e\u003cp\u003e19.2 (17.7,20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunity poverty levels\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5099 (50.4)\u003c/p\u003e\u003cp\u003e5029 (49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.6 (20.8,24.6)\u003c/p\u003e\u003cp\u003e15.9 (14.6,17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunity literacy level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7199 (71.1)\u003c/p\u003e\u003cp\u003e2929 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.4 (18.0,20.8)\u003c/p\u003e\u003cp\u003e19.7 (17.4,22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevel of media exposure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3813 (37.6)\u003c/p\u003e\u003cp\u003e6315 (62.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.2 (17.3,21.1)\u003c/p\u003e\u003cp\u003e19.7 (18.2,21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWestern Africa\u003c/p\u003e\u003cp\u003eCentral Africa\u003c/p\u003e\u003cp\u003eEastern Africa\u003c/p\u003e\u003cp\u003eSouthern Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2289 (22.6)\u003c/p\u003e\u003cp\u003e2701 (26.7)\u003c/p\u003e\u003cp\u003e2366 (23.3)\u003c/p\u003e\u003cp\u003e2772 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.7 [18.2,23.4]\u003c/p\u003e\u003cp\u003e21.1 [18.7,23.6]\u003c/p\u003e\u003cp\u003e18 [15.5,20.8]\u003c/p\u003e\u003cp\u003e18.1 [16.0,20.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Pearson chi2\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e**\u003c/b\u003e in the las 12 months\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable multi-level logistic regression analysis of individual level community level and country level factors associated with STI symptoms among men engaged in transactional sex in Sub-saharan Africa,\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics (N\u0026thinsp;=\u0026thinsp;10,126)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 1*\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eaOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2*\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eaOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 3*\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eaOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 4*\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eaOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey year\u003c/p\u003e\u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\u003cp\u003e2016\u0026ndash;2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.07 (0.75,1.53)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.15 (0.80,1.67)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.17 (0.72,1.92)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.97 (0.65,1.45)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eIndividual-level factors\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespondent\u0026rsquo;s age\u003c/b\u003e (years)\u003c/p\u003e\u003cp\u003e15\u0026ndash;24\u003c/p\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003cp\u003e35 and more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.12 (0.97,1.30)\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.69 (0.58,0.83)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.11 (0.96,1.29)\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.69 (0.58,0.82)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo education\u003c/p\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003cp\u003eSecondary and higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93 (0.75,1.14)\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.77 (0.63,0.95)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.94 (0.77,1.16)\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.78 (0.63,0.96)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrently working\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.41 (1.17,1.69)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.40 (1.17,1.69)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiving in couple\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87 (0.76,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87 (0.76,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligious affiliation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTraditional/animist/no religion\u003c/p\u003e\u003cp\u003eChristianism\u003c/p\u003e\u003cp\u003eIslam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.85 (0.72,1.00)\u003c/p\u003e\u003cp\u003e0.87 (0.71,1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.84 (0.72,0.99)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.86 (0.70,1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHouseholds\u0026rsquo; wealth index\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.31 (1.12,1.53)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1.08 (0.93,1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.31 (1.12,1.54)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1.07 (0.90,1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedia exposure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.19 (1.01,1.39)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.19 (1.01,1.39)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at the first sex\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLess than 18 years\u003c/p\u003e\u003cp\u003e18 years or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.89 (0.78,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.89 (0.78,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComprehensive knowledge about HIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.81 (0.71,0.93)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.82 (0.72,0.94)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeard about STIs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.21 (1.62\u0026ndash;6.35)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e3.17 (1.60,6.26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCircumcised\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.82 (0.68,0.99)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.82 (0.68,0.99)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRisky sexual behavior\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.69 (1.43,2.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.69 (1.43,2.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTested for HIV**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.17 (1.01,1.36)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.18 (1.02,1.37)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunity-level factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUrban\u003c/p\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.05 (0.92,1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.03 (0.88,1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunity literacy level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.94 (0.76,1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.85 (0.67,1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunity poverty levels\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.78 (0.68,0.89)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.76 (0.66,0.87)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunity STI prevalence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.78 (0.85,54,08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.53 (0.82,52,35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCountry-level factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWestern Africa\u003c/p\u003e\u003cp\u003eCentral Africa\u003c/p\u003e\u003cp\u003eEastern Africa\u003c/p\u003e\u003cp\u003eSouthern Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.96 (0.57,1.63)\u003c/p\u003e\u003cp\u003e0.75 (0.45,1.23)\u003c/p\u003e\u003cp\u003e0.62 (0.27,1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.80 (0.50,1.27)\u003c/p\u003e\u003cp\u003e0.68 (0.44,1.04)\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.49 (0.25,0.98)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.344\u003c/p\u003e\u003cp\u003e0.078\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNational HIV prevalence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01 (0.94,1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03 (0.96,1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCondom use prevalence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.06 (0.19,131.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.41 (0.17,33.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGDP per capita\u003c/b\u003e, (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99 (0.99,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99 (0.99,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel comparison and random effect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVariance\u003c/b\u003e\u003csub\u003e\u003cem\u003ecluster\u003c/em\u003e\u003c/sub\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.58 (0.41,0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56 (0.39,0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.54 (0.37,0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58 (0.41,0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.52 (0.35,0.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVariance\u003c/b\u003e\u003csub\u003e\u003cem\u003ecountry\u003c/em\u003e\u003c/sub\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19 (0.09,037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14 (0.07,0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16 (0.08,0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15 (0.07,0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.08 (0.03,0.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCV\u003c/b\u003e\u003csub\u003e\u003cem\u003ecluster\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.45%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.72%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e10.34%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCV\u003c/b\u003e\u003csub\u003e\u003cem\u003ecountry\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.32%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.79%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.05%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e57.89%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICC\u003c/b\u003e\u003csub\u003e\u003cem\u003ecluster\u003c/em\u003e\u003c/sub\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.97% (14.47,24.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.61% (13.24,23.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.50% (13.12,22.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.33% (13.97,23.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e15.48% (11.32,20,80%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICC\u003c/b\u003e\u003csub\u003e\u003cem\u003ecountry\u003c/em\u003e\u003c/sub\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.60% (2.37,8.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.60% (1.78,7.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.94% (1.99,7.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.80% (1.89,7.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2.02% (0.86,4,69%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMOR\u003c/b\u003e\u003csub\u003e\u003cem\u003ecluster\u003c/em\u003e\u003c/sub\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.42 (2.10,2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.38 (2.07,2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.35 (2.03,2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.40 (2.10,2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2.31 (1.99,2.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMOR\u003c/b\u003e\u003csub\u003e\u003cem\u003ecountry\u003c/em\u003e\u003c/sub\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.66 (1.42,2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.54 (1.36,1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59 (1.39,1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.57 (1.36,1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.39 (1.22,1.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel fitness\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAIC\u003c/p\u003e\u003cp\u003eLog-likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9224.417\u003c/p\u003e\u003cp\u003e-4609.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9063.554\u003c/p\u003e\u003cp\u003e-4510.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9217.897\u003c/p\u003e\u003cp\u003e-4600.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9234.372\u003c/p\u003e\u003cp\u003e-4607.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e9061.025\u003c/p\u003e\u003cp\u003e-4499.513\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e* adjusted for year of the survey; ** in the las 12 months; Bold: Statistically significant at p-value less than 0.05\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e : Model 0, empty null model, baseline model without any explanatory variables (unconditional model); \u003csup\u003eb\u003c/sup\u003e : Model 1, adjusted for only individual-level factors; \u003csup\u003ec\u003c/sup\u003e : Model 2, adjusted for only community-level factors; \u003csup\u003ed\u003c/sup\u003e : Model 3, adjusted for only country-level factors; \u003csup\u003ee\u003c/sup\u003e : Model 5, adjusted for individual-, community-, and country-level factors (full model)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eaOR: ajusted Odds-Ratio; 95% CI: 95% confidence interval; AIC: Akaike Information Criterion, ICC: intra-class correlation coefficient, MOR: median odds ratio, PCV: proportional change in variation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eOutcome variable: self-reported sexually transmitted infections (SR-STIs)\u003c/h2\u003e\u003cp\u003eSR-STIs symptoms were defined as any of the following self-reported STI symptoms that occurred in the previous 12 months: i) genital discharge, ii) genital sore/ulcer, or iii) any other symptoms of STI, including pain, rash or itchy genitals. Responses were coded as '0' for participants reporting no STI symptom and '1' for participants reporting at least one STI symptom. Prevalence of SR-STIs was defined as the proportion of participants who reported at least one STI symptom in the previous 12 months among the included participants.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExplanatory variables\u003c/h3\u003e\n\u003cp\u003eThe selection of explanatory variables for this analysis was guided by the scientific literature and their availability within the dataset. To ensure a structured analytical framework, the variables were organized into three broad categories: individual-level variables (capturing personal characteristics and behaviors), community-level variables (reflecting the social and contextual environment in which individuals live), and country-level variables (representing broader national and policy-related factors). This categorization allows for a multilevel perspective on the determinants under investigation.\u003c/p\u003e\n\u003ch3\u003eIndividual-level variables including:\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eSocio-demographic variables\u003c/em\u003e: age groups (15\u0026ndash;24, 25\u0026ndash;34, and 35 years and more), living in couple (yes/no), education level (primary/secondary and higher/no education), literacy status (read perfectly vs not), religion (Islam/ traditional, animist, no religion/Christianism), currently working (yes/no), household wealth quintiles index were grouped into three categories: poor (poorest and poorer), middle and, rich (richer and richest), media exposure (yes/no), defined as a composite variable obtained by the aggregation of three variables: reading newspaper, listening radio and watching television. Media exposure was coded as \u0026ldquo;no\u0026rdquo; if the respondent did not report any exposure to these three media sources.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSexual behavior and practices\u003c/strong\u003e\u003cp\u003eage at the first sex (less than 18 years vs 18 years or more), circumcised (yes/no), risky sexual behavior (yes/no), heard about STI (yes/no), ever tested for HIV (yes/no) and HIV-prevention knowledge (yes/no). Individuals were considered as having knowledge about HIV/aids if they declared that having just one uninfected faithful partner and consistent condoms use during sexual intercourse could reduce the risk of acquiring HIV.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCommunity-level variables\u003c/h2\u003e\u003cp\u003eThese variables were operationalized to capture the broader sociocultural and economic context. These included: (i) place of residence, dichotomized into rural and urban settings; (ii) community literacy level, categorized as low or high according to the proportion of individuals who were able to read within the cluster; (iii) community poverty level, classified as low or high based on the aggregated household wealth index scores within the community; (iv) community media exposure, defined as low or high according to the proportion of participants within the cluster with regular access to mass media sources such as radio, television, newspapers, or digital platforms; and (v) the prevalence of SR-STI symptoms within each cluster was also included as a proxy for the community sexual health risk environment.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCountry-level variables\u003c/h3\u003e\n\u003cp\u003eThe analysis included several country-level contextual variables to account for structural and epidemiological differences across nations: (i) Geographical region: countries were categorized according to their location within the SSA region (Western Africa, Central Africa, Eastern Africa, Southern Africa); (ii) National HIV prevalence in each country, as reported by the most recent national surveillance or epidemiological survey. This variable captures the burden of HIV at the country level; (iii) Gross Domestic Product (GDP) per Capita [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]: Measured in US dollars (\u003cspan\u003e$\u003c/span\u003e), GDP per capita was included as an indicator of national economic status and development, which may influence health system capacity and access to care; and (iv) the country prevalence of condom use.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData from the seven DHS were combined (pooled-data analysis) for all the analyses. Survey weights were applied in the descriptive analyses to account for the unequal selection probability of households resulting from the sample design as well as for non-response according to the DHS program recommendation for the analysis of DHS data [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A detailed explanation of the weighting procedure can be found in the DHS Methodology report [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Analyses were conducted using STATA version 15.1. Due to the complex survey design, all statistical analyses were carried out using the \u003cem\u003esvy\u003c/em\u003e procedures to adjust for unequal sampling probabilities and the sample weight to consider generalizability of the findings. We additionally used the \"svyset\" option of \"single unit (certainty)\" to consider difference in sample size across the studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Analyses\u003c/h2\u003e\u003cp\u003eInitial analyses consisted of descriptive statistics to characterise the study population. Frequencies and percentages were used to summarise categorical variables, while measures of central tendency (mean, and standard deviation) were applied for continuous variables. Pearson Chi-square tests or Fisher test were performed if appropriate for groups\u0026rsquo; comparisons. We calculated the weighed prevalence of SR-STIs with 95% confidence intervals (95% CI).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eModelling approaches\u003c/h2\u003e\u003cp\u003eTo explore associations between SR-STI symptoms and a set of individual, community, and country-level factors, multivariable multi-level logistic regression models were employed. This approach was chosen to account for the hierarchical structure of the data, where individuals (level 1) were nested within communities or clusters (level 2), and communities were nested within countries (level 3). We constructed a series of five sequential models: (i) Empty model (Model 0): An unconditional model without explanatory variables, used to partition the total variance in SR-STI symptoms across the individual, community, and country levels; (ii) Individual-level model (Model 1): included only individual-level factors; (iii) Community-level model (Model 2): incorporated only contextual characteristics measured at the community or cluster level; (iv) Country-level model (Model 3): which included only country-level factors; (v) Full model (Model 4): which integrated explanatory variables from all three levels simultaneously, allowing assessment of the independent and combined contributions of compositional and contextual factors. This stepwise approach facilitated examination of the relative contribution of each level in explaining variation in SR-STI symptoms.\u003c/p\u003e\u003cp\u003eTo account for potential temporal effects arising from the use of data collected in different survey years, the DHS survey year was included as a control variable in all statistical models. This adjustment helps to mitigate confounding due to variations over time in both exposure and outcome variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFixed-effects\u003c/h2\u003e\u003cp\u003eFixed effects were reported as adjusted odds ratios (aORs) with corresponding 95% confidence intervals (95%CI). The aORs provide interpretable measures of association, indicating the relative odds of hypertension given specific individual or contextual exposures. aORs\u0026thinsp;\u0026gt;\u0026thinsp;1 indicated an increased likelihood of reporting SR-STI symptoms, whereas aORs\u0026thinsp;\u0026lt;\u0026thinsp;1 indicated a protective effect.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRandom - effects (Measures of variation)\u003c/h2\u003e\u003cp\u003eTo capture contextual effects, we examined both the intraclass correlation coefficient (ICC) and the median odds ratio (MOR). Together, these measures allow disentangling the role of compositional versus contextual determinants of SR-STI symptoms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eModel Fit\u003c/h2\u003e\u003cp\u003eThe adequacy and reliability of the multi-level logistic regression models were rigorously assessed using several diagnostic approaches. First, multicollinearity among explanatory variables was evaluated to ensure that parameter estimates were stable and not inflated due to highly correlated predictors. This was done by calculating the variance inflation factor (VIF) for each variable. All values fell within acceptable limits (\u0026lt;\u0026thinsp;10), indicating that multicollinearity was not a concern and that the variables could be reliably included in the models. Second, overall model fit was evaluated using both the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Lower values of AIC and BIC indicate better-fitting models, facilitating selection of the model that adequately captures the variation in SR-STI symptoms across levels.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized secondary data obtained from the MEASURE DHS program. Access to DHS data requires only registration and submission of a formal data request; therefore, additional ethical approval for this secondary analysis was not required. The DHS program ensures that all surveys are conducted in accordance with internationally recognized ethical standards, including informed consent from participants. For more information on DHS data access procedures and ethical guidelines, please visit: https://www.dhsprogram.com/data/available-datasets.cfm\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eParticipants’ characteristics of the study\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAmong 235,290 men drawn from DHS conducted between 2011 and 2021 across 26 Sub-Saharan African countries, 10,128 reported engaging in transactional sex (Figure 1). The distribution of respondents varied substantially across countries, reflecting differences in population size, survey sampling strategies, and the prevalence of transactional sex. For instance, the smallest sample was observed in Niger (n = 26), whereas Madagascar contributed the largest number of respondents (n = 1,510) (Table 1).\u003c/p\u003e\n\u003cp\u003eThe characteristics of the participants are reported in Table 2. Their mean age was 29.8 years (standard deviation [SD] ±10.2) and 37.7% were aged between 15 and 24 years. Over half (55.0%) of the participants had completed at least secondary education and the large majority of the participants (86.1%) were working at the time of the surveys. The proportion of participants living in couple were 44.6%, 60.6% declared not be affiliated to any conventional religion and 41.1% were living in a rich households. The majority of the respondents (79.4%) were exposed to media information (magazine, radio or TV). Moreover, 98.2% of METS had heard about STI, 31.0% had comprehensive knowledge about HIV, while 66.6% of the participants declared being circumcised, 81.4% of the respondents reported risky sexual behavior and 33.0% declared being tested for HIV for in the last 12 months.\u003c/p\u003e\n\u003cp\u003eWhile over half (60.8%) of participants lived in a rural area, 27.4% were from Southern African region and 26.6% from Central African region. National HIV prevalence rates spanned from 0.025% in Comoros to 21% in South Africa, while gross domestic product (GDP) per capita ranged from $712 in the Democratic Republic of Congo to $15,950 in Gabon.\u003c/p\u003e\n\u003ch2\u003ePrevalence of self-reported STIs\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe overall weighted prevalence of self-reported STIs in the 12 months before the survey among METS was 19.5% (95%CI: 18.3-20.7%), ranging from 5.6% (1.4-20.0%) in Niger to 36.9% (28.4-46.4%) in Liberia with significant variability across countries (p\u0026lt;0.001). Compared to those who did not engaged in transactional sex, the prevalence of self-reported STI among the participants was significantly high (19.5% (18.3-20.7%) vs 7.2% (6.9-7.5%); p\u0026lt;0.001). This difference was also found within countries (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong METS, the prevalence of self-reported STIs was higher among participants aged 25–34 years (p\u0026lt;0.001), those without a conventional religion (p=0.010), individuals from middle-wealth households (p=0.003), and those with media exposure (p=0.012). Higher prevalence was also observed among participants with sexual debut before age 18 (p\u0026lt;0.001), uncircumcised men (p=0.001), those reporting risky sexual practices (p\u0026lt;0.001), and residents of low-poverty communities (p\u0026lt;0.001). (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultilevel logistic regression analysis of SR-STI Symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFixed effects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the individual level, older age \u0026nbsp;(35 years and more : aOR = 0.69; 95% CI: 0.58,0.82; ref=15-24 years), educational attainment (secondary or higher level: aOR = 0.78; 95% CI: 0.63,0.96; ref= no education), religion affiliation (Christian: aOR = 0.84; 95% CI: 0.72,0.99; ref = no conventional religion), comprehensive knowledge about HIV (aOR = 0.82; 95% CI: 0.72,0.94), circumcision (aOR = 0.83; 95% CI: 0.69,0.99) were significantly associated with lower odds of SR-STI symptoms. While, employment (aOR = 1.40; 95% CI: 1.17,1.69), household wealth (middle households : aOR = 1.31; 95% CI: 1.12,1.54; ref = poorer households), media exposure (aOR = 1.19; 95% CI: 1.01–1.39), having heard about STI (aOR = 3.17; 95% CI: 1.60–6.26), risky sexual behavior (aOR = 1.69; 95% CI: 1.43–2.00), being tested for HIV (aOR = 1.18; 95% CI: 1.02–1.37), \u0026nbsp;were significantly associated with higher odds of SR-STI symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the community level, communities with high poverty levels exhibited less odds (aOR = 0.76; 95% CI: 0.66,0.87).\u003c/p\u003e\n\u003cp\u003eAt the country level, respondents living in Southern African region were 51% less likely (aOR = 0.49; 95% CI: 0.25,0.98) to report SR-STI symptoms compared to those in Western African region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRandom effects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant variability was observed across countries and communities. At the country level, the null model variance was 0.19 (95% CI : 0.09,037), decreasing to 0.08 (95% CI: 0.03,0.19) in the full model, indicating that inclusion of predictors accounted for a substantial portion of between-country variance. The ICC decreased from 4.60% (95% CI: 2.37,8.76%) in the null model to 2.02% (95% CI: 0.86,4,69%) in the full model, and the MOR dropped from 1.66 (95% CI: 1.42,2.03) to 1.39 (95% CI: 1.22,1.66), demonstrating reduced country-level differences. The proportional change in variance (PCV) increased from 26.32% in Model 1 to 57.89% in Model 4, confirming substantial explanatory power of the included predictors (Table 3).\u003c/p\u003e\n\u003cp\u003eAt the community level, variance decreased from 0.58 (95% CI: 0.41,0.83) in the null model to 0.52 (95% CI: 0.35,0.78) \u0026nbsp;in the full model. The ICC declined from 18.97% (95% CI: 14.47,24.46%) to 15.48% (95% CI: 11.32,20,80%), and the MOR decreased from 2.42 (95% CI: 2.10,2.88) to 2.31 (95% CI: 1.99,2.79), indicating that community-level disparities diminished with the addition of covariates. The PCV at the community level reached 10.34% in the full model (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel Fit\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel fit improved progressively with the inclusion of additional predictors. The log-likelihood increased from -4609.2085 in the null model to -4499.513 in the full model, while the AIC decreased from 9224.417to 9061.023 suggesting that Model 4 provided the best fit among the compared models (Table 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis multi-country analysis provides a comprehensive assessment of the prevalence and multilevel factors associated with SR-STIs among METS in SSA. Our findings reveal a substantial burden of SR-STIs within this group and highlight key individual and contextual determinants, offering critical insights for public health intervention.\u003c/p\u003e\n\u003cp\u003eThe analysis demonstrates that the overall prevalence of SR-STIs among METS was 19.5%, a rate nearly threefold higher than that observed among men not reporting transactional sex (7.2%). This profound disparity underscores that METS, encompassing both clients of sex workers and male sex workers themselves, constitute a core group that bears a disproportionately high burden of STIs [28]. This aligns with prior evidence from focused studies in Eastern and Western Africa [12, 29], confirming that this group is an essential component of sexual networks and a critical population for targeted STI control strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA key finding is the marked heterogeneity in SR-STI prevalence across countries, ranging from 5.6% in Niger to 36.9% in Liberia. This pattern mirrors trends identified in another study reporting that among all sexually active men in 27 SSA countries, SR-STIs ranged from 0.4% (Niger) to 13.5% (Liberia) [17]. Our study, by focusing specifically on METS, reveals an even starker gradient of risk. Furthermore, in Niger, the SR-STI prevalence among METS (5.6%) was more than ten times higher than the national baseline for all men (0.4%). Conversely, in Liberia, the prevalence among METS (36.9%) nearly tripled the country's already elevated baseline (13.5%). This heterogeneity underscores the interplay between individual risk behaviors and country-level contextual factors, including epidemiological history, healthcare infrastructure, and cultural norms.\u003c/p\u003e\n\u003cp\u003eOur identification of individual-level determinants aligns with established epidemiological patterns across SSA. Younger age, engagement in high-risk sexual practices (e.g., multiple partners, inconsistent condom use), and a lack of comprehensive HIV knowledge were all significantly associated with increased odds of reporting an STI. This is consistent with studies from South Africa, where young people exhibited high prevalence of STI [30], from Nigeria, where early sexual debut was linked to a 1.5–2.0 times higher likelihood of STI symptoms [31], and from Tanzania [19, 32], where multiple sexual partners and poor condom use predicted both self-reported and lab-confirmed STIs.\u003c/p\u003e\n\u003cp\u003eConversely, higher educational attainment and male circumcision emerged as protective factors. The protective effect of circumcision is well-documented in studies from Ethiopia [33] and Uganda [34], where it was associated with significantly reduced rates of ulcerative STIs. The association between employment and increased risk is notable and parallels findings from Uganda and Zimbabwe [35, 36]. This suggests that disposable income and high mobility among economically active men may facilitate engagement in transactional sex, thereby increasing exposure risk.\u003c/p\u003e\n\u003cp\u003eThe positive association between awareness of STI, prior HIV testing and SR-STI reporting warrants careful interpretation. This likely does not indicate that testing causes STIs, but rather reflects two concurrent phenomena: first, men who engage with health services (e.g., for HIV testing) are more likely to be aware of STI and screened for other conditions, leading to greater detection and awareness of STI symptoms [37, 38]; and second, it underscores the shared risk environment and common socio-behavioral determinants for HIV and other STIs [39, 40]\u003c/p\u003e\n\u003cp\u003eOur multilevel analysis revealed important community- and country-level influences. Paradoxically, residing in a higher-poverty community was associated with lower odds of SR-STI reporting. This counterintuitive finding is consistent with other DHS-based analyses [35, 41] and is best explained by significant under-ascertainment in resource-poor settings due to heightened stigma, lower level of health literacy, lack of awareness of STI symptoms, and severely constrained access to diagnostic services [42].\u003c/p\u003e\n\u003cp\u003eAt the country-level, respondents in Southern African countries were less likely to report STIs compared to those in West Africa. This regional disparity persists in DHS data despite the higher underlying HIV prevalence in the south [43, 44]. This may be attributed to regional differences in health system factors (e.g., more robust syndromic management protocols that reduce prolonged symptoms), cultural norms influencing willingness to disclose STI symptoms, or variations in the endemicity of specific bacterial STIs [45, 46].\u003c/p\u003e\n\u003cp\u003eOur results on individual, community, and country-level variables explained a substantial proportion of the variance (PCV ~58% at country level; ~10% at community level). However, significant residual heterogeneity remained, implying that critical unmeasured structural drivers, such as the quality of STI care, levels of stigma, population mobility patterns, and the enforcement of public health policies, continue to shape the epidemiological landscape [47, 48].\u003c/p\u003e\n\u003cp\u003eOverall, this study advances the literature by being among the first to systematically quantify the prevalence and determinants of SR-STIs among METS across multiple African countries. It demonstrates that this group faces a uniquely high burden of STIs, with complex socio-behavioral and structural determinants that differ from the general male population and sometimes diverge from patterns observed among female sex workers or other key populations.\u003c/p\u003e\n\u003cp\u003eThese findings have several implications for STI and HIV prevention strategies in SSA. First, METS should be explicitly recognized as a key population in regional STI/HIV control frameworks, alongside female sex workers and men who have sex with men. Second, targeted interventions such as tailored health communication, condom promotion, and access to comprehensive sexual health services must be developed to address their specific vulnerabilities. Third, the association of STIs with structural factors such as education, circumcision, and community-level characteristics emphasizes the need for a multilevel approach that combines individual behavioral interventions with broader structural and policy measures. Fourth, the observed inter-country and regional disparities call for context-specific strategies, rather than a uniform regional approach.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations inherent to its design and data sources. First, the dependence on self-reported STI symptoms leads to inevitable underestimation of prevalence due to asymptomatic infections and underreporting driven by stigma. Second, the cross-sectional nature of the DHS data precludes any causal inference from the observed associations. Third, the use of surveys conducted across different years may introduce temporal biases if STI prevalence or reporting behaviors changed over time. Fourth, our analysis was constrained to variables available in the DHS, omitting potentially crucial factors such as detailed partner networks, population mobility, and direct measures of access to healthcare. Finally, while we identified significant community- and country-level variance, these effects may be partially confounded by unmeasured contextual factors like the density of health services, cultural norms, and local migration patterns.\u003c/p\u003e\n\n"},{"header":"Conclusion","content":"\u003cp\u003eThis study found a high prevalence of SR-STIs among METS, with substantial heterogeneity across countries and significant associations with individual, community, and structural determinants. The elevated risk in this population underscores the urgent need for targeted, context-specific interventions that combine biomedical, behavioral, and structural strategies. Strengthening surveillance, improving access to STI diagnostics and treatment, and addressing the social determinants of sexual health are essential to reducing STI burden and advancing sexual health equity in sub-Saharan Africa.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAbreviations : not applicable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized secondary data obtained from the MEASURE DHS program. Access to DHS data requires only registration and submission of a formal data request; therefore, additional ethical approval for this secondary analysis was not required. The DHS program ensures that all surveys are conducted in accordance with internationally recognized ethical standards, including informed consent from participants. For more information on DHS data access procedures and ethical guidelines, please visit: https://www.dhsprogram.com/data/available-datasets.cfm\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement\u003c/strong\u003e: \u0026nbsp;Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent of publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have give their consent for the publication of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability data and materials statement:\u003c/strong\u003e The data were obtained from the DHS program (www.dhsprogram.com) under the condition that the authors do not pass the data to other researchers. However, other researchers can apply directly to the DHS program to obtain the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no interest conflict\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIY contributed to the conception and design of the study, conducted the data analysis, interpreted the results and drafted the original version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors critically reviewed the manuscript for its intellectual content, as well as read the drafts and approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePK had final responsibility to submit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Demographic and Health Surveys program (www.dhsprogram.org) for generously providing the data for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization (WHO). \u003cem\u003eReport on global sexually transmitted infection surveillance, 2018\u003c/em\u003e. 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Using multilevel models to evaluate the influence of contextual factors on HIV/AIDS, sexually transmitted infections, and risky sexual behavior in sub-Saharan Africa: a systematic review. \u003cem\u003eAnnals of Epidemiology\u003c/em\u003e 2018; 28: 119\u0026ndash;134.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Transactional sex, Men, Sexually transmitted infections, Self-reported symptoms, Sub-Saharan Africa, Public health","lastPublishedDoi":"10.21203/rs.3.rs-8127004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8127004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMen engaged in transactional sex (METS) represent a neglected key population in sub-Saharan Africa (SSA), yet little is known about their burden of sexually transmitted infections (STIs) and associated factors. This study assessed the prevalence of self-reported STIs (SR-STI) and identified individual, community, and country-level determinants among this group.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed pooled recent nationally representative Demographic and Health Survey (DHS) data from 26 SSA countries. This study included 10,128 men who reported engagement in transactional sex within the past 12 months. Weighted prevalence estimates were calculated, and multilevel logistic regression models were applied to examine individual-, community-, and country-level determinants of SR-STI symptoms, adjusting for survey year. Model fit was assessed using Akaike Information Criterion.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe participants\u0026rsquo; mean (\u0026plusmn;\u0026thinsp;SD) age was 29.8 (\u0026plusmn;\u0026thinsp;10.2) years. The overall weighted prevalence of SR-STIs among METS was 19.5% (95%CI: 18.3\u0026ndash;20.7%), nearly threefold higher than among men not reporting transactional sex (7.2%; 95%CI: 6.9\u0026ndash;7.5%; p for difference\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Prevalence varied substantially across countries, from 5.6% in Niger to 36.9% in Liberia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At the individual-level, younger age, lower education, employment, risky sexual behavior, middle household wealth, heard about STI, HIV testing, and media exposure were associated with higher odds of SR-STI, while circumcision, HIV knowledge, and Christian affiliation were protective. At the community-level, men from poorer communities were less likely to report SR-STI symptoms (aOR\u0026thinsp;=\u0026thinsp;0.79; 95%CI: 0.66\u0026ndash;0.87). At the country-level, participants from Southern Africa had lower odds (aOR\u0026thinsp;=\u0026thinsp;0.49; 95%CI: 0.24\u0026ndash;0.97) compared to those in West Africa. Significant between-country and -community heterogeneity was observed, but variance decreased with the inclusion of individual and contextual predictors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSR-STIs are highly prevalent among METS in SSA, with marked heterogeneity across countries and multiple individual and structural determinants. These findings underscore the need for targeted, context-specific interventions integrating biomedical, behavioral, and structural approaches to reduce STI burden in this population.\u003c/p\u003e","manuscriptTitle":"When context matters: Multilevel determinants of self-reported sexually transmitted infections symptoms among men engaged in transactional sex in 26 Sub-Saharan African countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:53:44","doi":"10.21203/rs.3.rs-8127004/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T09:48:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T17:35:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255883493385724931076729107538636081244","date":"2026-03-02T14:01:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T18:28:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131084309005860214003025165156651977982","date":"2026-02-02T15:30:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264803003612027839540635341520528869626","date":"2026-01-06T03:21:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317237281848245645035796534246762087524","date":"2026-01-03T16:24:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-13T23:30:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104723307161517731497067230381911979288","date":"2025-12-09T05:58:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42204627268681805542498428709053117972","date":"2025-12-03T15:31:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119627408286111929043178671370390951867","date":"2025-12-01T22:12:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168995196559851666690941275179034179396","date":"2025-12-01T12:28:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-28T15:24:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-19T09:43:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T10:17:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-18T10:14:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-16T11:49:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ef55958-f0e3-47d0-a2b3-e1952baf0c99","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:04:56+00:00","versionOfRecord":{"articleIdentity":"rs-8127004","link":"https://doi.org/10.1186/s12889-026-27529-4","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-04-25 15:59:01","publishedOnDateReadable":"April 25th, 2026"},"versionCreatedAt":"2025-12-02 14:53:44","video":"","vorDoi":"10.1186/s12889-026-27529-4","vorDoiUrl":"https://doi.org/10.1186/s12889-026-27529-4","workflowStages":[]},"version":"v1","identity":"rs-8127004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8127004","identity":"rs-8127004","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}