Unlocking Self-Testing: Predictors of HIV Self-Testing Kit Use Among Reproductive-Aged Women in Tanzania; A Multilevel Analysis of the 2022 Demographic and Health Survey

Unlocking Self-Testing: Predictors of HIV Self-Testing Kit Use Among Reproductive-Aged Women in Tanzania; A Multilevel Analysis of the 2022 Demographic and Health Survey | 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 Unlocking Self-Testing: Predictors of HIV Self-Testing Kit Use Among Reproductive-Aged Women in Tanzania; A Multilevel Analysis of the 2022 Demographic and Health Survey Elihuruma Eliufoo, Tegemea Patrick Mwalingo, Shazra Kazumari, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6551004/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Aug, 2025 Read the published version in AIDS Research and Therapy → Version 1 posted 10 You are reading this latest preprint version Abstract Background The introduction of HIV self-test (HIVST) kits offered a significant advancement in fighting HIV/AIDS, particularly for vulnerable populations. Despite its potential, self-testing uptake varies. In Tanzania, where HIV prevalence among women remains a concern, this study aimed to identify the socio-demographic and behavioural predictors of self-testing for effective prevention. Methods We conducted a cross-sectional analysis using data from the 2022 Tanzania Demographic and Health Survey. Data management and analysis were performed using Stata 18.5. Given the survey’s complex design, a multilevel mixed-effect logistic regression model was used to identify predictors of HIVST kit use, with results presented as adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Statistical significance was set at p < 0.05. Results The prevalence of HIVST kit usage among women of reproductive age in Tanzania was 3.2% (95% CI: 2.8–3.7%). Individual-level factors associated with a higher likelihood of HIV self-test kit use included age, 25–34 (AOR = 1.93, 95%CI: 1.43–2.60) and 35–49 (AOR = 1.60, 95%CI:1.43–2.26), secondary/higher education (AOR = 2.77, 95%CI: 1.57–4.85), belonging to the rich wealth quintile (AOR = 2.69, 95%CI: 1.52–4.77), internet use (AOR = 3.04, 95%CI: 2.04–4.52), awareness of sexually transmitted infections (STIs) (AOR = 2.03, 95%CI: 1.21–3.42), and one or higher number of sexual partners. At the community level, geographical zone was associated with increased odds of use, while living in a high-poverty community (AOR = 0.52, 95%CI: 0.30–0.89) was associated with a lower likelihood of HIV self-test kit use. Conclusion This study reveals a low uptake of HIVST kits among women of reproductive age in Tanzania, highlighting the influence of both individual-level factors, such as age, education, wealth, internet access, STI awareness, and sexual behaviours, as well as community-level factors like geographical zone and community poverty. These findings underscore the importance of considering both individual characteristics and the broader community context when designing targeted interventions to promote HIVST kits. HIV Self Testing Reproductive-Aged Women Tanzania Background Globally, Human Immunodeficiency Virus (HIV) persists as a major public health challenge, with approximately 40 million people living with the virus in 2023, including 5.5 million individuals unaware of their status and lacking access to testing services [ 1 , 2 ]. Sub-Saharan Africa (SSA) continues to bear the heaviest burden of the epidemic, while the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets for 2030 emphasize that 95% of people living with HIV should know their status [ 3 ]. This goal requires a significant expansion of HIV testing efforts to reach those currently undiagnosed [ 4 ]. HIV self-testing (HIVST) has emerged as a critical tool in global HIV prevention and treatment strategies [ 2 ]. The evolution of HIVST began with the United States Food and Drug Administration’s (FDA) 1996 approval of home collection tests requiring laboratory processing and telephone results delivery [ 5 ]. A significant advancement came in 2002 with the approval of OraQuick's rapid HIV-1 blood test, followed by expanded approvals for venipuncture specimens in 2003 and for oral fluid, plasma, and HIV-2 testing in 2004 [ 5 ]. In 2012, the FDA approved the OraQuick In-Home HIV Test as the first over-the-counter rapid HIV self-test, establishing HIVST as a viable testing approach that has since expanded globally with numerous kit options [ 5 , 6 ]. Since the World Health Organization's (WHO) recommendation in 2016, more than 80 countries have adopted HIVST policies, with implementation varying widely across regions [ 7 ]. Evidence from multiple countries demonstrates that HIVST effectively reaches individuals who might otherwise not test, particularly key populations and men [ 8 ]. In SSA, where the HIV burden remains disproportionately high, countries like Kenya, Malawi, and South Africa have led adoption efforts, showing promising results with increased testing rates, earlier diagnosis, and linkage to care [ 8 – 10 ]. In Tanzania specifically, HIVST was officially introduced in 2018 through pilot programs in selected regions following the country's updated HIV testing guidelines [ 11 ]. This introduction came relatively later than in neighboring countries, with the Ministry of Health incorporating self-testing into its national HIV strategy in 2019 [ 12 , 13 ]. Before the introduction of HIVST, the 2008 National HIV Act required HIV testing to be carried out by a healthcare provider in healthcare settings [ 12 ]. Initial implementation focused on high-burden regions and targeted key populations, including female sex workers, men who have sex with men, and partners of people living with HIV [ 14 ]. Despite policy adoption, HIVST coverage and availability of kits in Tanzania remain limited compared to other SSA countries, with uneven distribution across regions and persistent challenges in supply chain management, awareness, and community acceptance [ 15 ]. Current estimates from HIVST kits sales research suggest that while knowledge about HIVST is growing, actual utilization rates of HIVST kits remain low among the general population, with reproductive-aged women representing an underserved demographic despite their increased vulnerability to HIV [ 16 ]. While previous research has explored HIVST implementation in various contexts, significant knowledge gaps exist regarding the specific predictors that influence HIVST kit uptake among reproductive-aged women in Tanzania [ 12 , 14 , 15 ]. Another study in 2020 reported the willingness to use the HIVST kit being 69% in Tanzania, while the actual usage remains minimal [ 17 ]. Most existing studies have focused on key populations or urban settings, leaving rural women and those of reproductive age underrepresented in the literature [ 18 ]. Additionally, much of the available evidence comes from small-scale implementation projects rather than nationally representative data, limiting the generalizability of findings [ 12 , 14 , 15 , 18 ]. Understanding the factors that predict HIVST kit use among reproductive-aged women is particularly important given their centrality in family health decision-making and their increased vulnerability during pregnancy and postpartum periods. The Tanzania Demographic and Health Survey (TDHS) provides a unique opportunity to examine these patterns at a national scale. As a nationally representative dataset that includes comprehensive information on HIV knowledge, attitudes, practices, and sociodemographic characteristics, the TDHS allows for robust analysis of factors associated with HIVST kit usage across diverse regions and population segments. A previous study using the most recent TDHS data reported on the usage among men only [ 11 ]. This study aimed to present data that can identify specific individual, household, and community-level predictors that influence women's decisions to use self-testing kits, providing evidence that moves beyond small-scale implementation research to inform national policy and programming. Therefore, this study aimed to assess predictors of HIV self-test kit use among reproductive-aged women in Tanzania by a multilevel analysis of the 2022 TDHS. Given the nature of Demographic Health Survey (DHS) data, a multilevel analysis offers a methodological advantage to this study over the previous studies using TDHS. Methods and findings will serve as a model for similar analyses in other SSA countries seeking to optimize their HIVST kit usage strategies for reproductive-aged women. Methods Data source and design This study was an analytical cross-sectional survey that utilised secondary data from the 2022 TDHS, which conducts nationally representative population-based household surveys typically every five years.​ The survey was carried out by the Tanzania Bureau of Statistics in collaboration with the relevant ministries of in Tanzania Mainland and Zanzibar. Population and sampling Data for this study were obtained from the latest DHS conducted between 24 February to 21 July 2022 across all regions in Tanzania. The target population for the TDHS includes women of reproductive age (15–49 years), men, children, and households across the 32 administrative regions in Tanzania. At the country level, a sampling frame is usually obtained. To minimize sampling errors, the country is stratified by geographic region and by urban/rural areas within each region, followed by a two-stage sampling to select a household to be surveyed. The first sampling is to select a primary sampling unit (PSU) and then select a household. PSUs are survey clusters that are usually based on census enumeration areas (EAs). A probability proportional to size is employed in each stratum to select the PSU. For each selected PSU, a complete household listing is done. This is then followed by selecting a fixed number of households to be surveyed using equal probability systematic sampling. All women who had spent the night before the survey in the selected households were eligible for the survey. From the individual file recode (IR), all 15,254 women were involved in this study. Study Variables Dependent variable This study's outcome variable was the usage of HIV self-test kits, determined by women's knowledge and reported use. Initially, responses were categorized into three groups: (1) those who had never heard of HIV self-test kits, (2) those who had ever used them, and (3) those who knew of them but had never used them. For analytical purposes, these categories were recoded into a binary variable. Women in categories (1) and (3) were combined and assigned the value '0' (never used), while women in category (2) were assigned the value '1' (ever used) [ 19 ]. Independent variables Predictor variables were classified into two categories: socio-demographic characteristics and sexual activity-related variables, as outlined in Table 1 . Table 1 Sociodemographic and sexual activity-related variables used for this study Variable description Coding categories Individual levels Woman’s age group 1 = 15–24, 2 = 25–44 or 3 = 35–49 Highest level of education 1 = No formal education, 2 = Primary education, 3 = Secondary/Higher Current Marital status 1 = Never married, 2 = Married/Cohabiting or 4 = Separated/Widowed Wealth Status 1 = Poor, 2 = Middle or 3 = Rich Working status 1 = Working or 2 = Not working Media exposure: Media exposure was calculated by aggregating TV watching, radio listening, and reading newspapers and woman who has exposure to either of the media sources was categorized as having media exposure and the rest considered as having no media exposure. 1 = Yes or 2 = No Internet use 1 = Yes or 2 = No Visited Health facility in the past 12 moths 1 = Yes or 2 = No Visited by Community Health Workers in the past 12 months 1 = Yes or 2 = No Recent sexual activity 1 = Never had sex, 2 = Active in last 4 weeks or 3 = Not active in last 4 weeks A condom was used during the last sex with the most recent partner 1 = Yes or 2 = No Number of sex partners, excluding a spouse, in the past 12 months 1 = None, 2 = One or 3 = More than one Ever heard Sexually transmitted Infections 1 = Yes or 2 = No Community levels Community poverty: The level of poverty in the community was determined by the proportion of women in the poorer and poorest wealth quintiles, as indicated by the wealth index. It was classified as low (communities where < 50% of women were in the poorer or poorest quintiles) or high (communities where ≥ 50% of women were in the poorer or poorest quintiles). 1 = Low or 2 = High Place of residence 1 = Urban or 2 = Rural Geographical zones 1 = Western, 2 = Northern, 3 = Central, 4 = Southern, 5 = Lake, 6 = Eastern or 7 = Zanzibar Data management and analysis To address the complex survey design of the TDHS, we applied individual sampling weights (v005/1,000,000), accounted for primary sampling units (clusters), and stratified the data to ensure representative estimates and control for sampling biases. Data cleaning, coding, and analysis were performed using STATA 18.5 (STATA Corp, College Station, TX). Descriptive statistics included means and standard deviations for continuous variables, and frequencies with proportions for categorical variables. Considering the hierarchical structure of the data (women within households within clusters), we employed a multilevel mixed-effects logistic regression model to account for non-independence and unequal variance. Our analysis involved four models: a null model (Model 0), a model with individual-level factors (Model I), a model with community-level factors (Model II), and a final model combining both levels (Model III). Random effects and model fitness To assess the variation in HIV self-kit use across clusters, we calculated random effects measures including the Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and Proportion Change in Variance (PCV). Treating clusters as a random effect, the ICC, quantifying the proportion of total variation in HIV self-kit use attributable to between-cluster variations, was computed as ICC = (VC / (VC + 3.29)) * 100. The MOR, representing the median odds ratio between clusters with high and low likelihood of HIV self-kit use, was calculated as MOR = exp(0.95 * sqrt(VC)). The PCV, indicating the reduction in variance across successive models, was calculated as (Ve – Vmi) / Ve, where Ve is the variance in the null model and Vmi is the variance in subsequent models. The association between individual and community-level independent variables and the likelihood of HIV self-kit use was estimated using fixed effects, presented as adjusted odds ratios (AORs) with 95% confidence intervals. Model comparison, due to the nested structure, was based on the deviance statistic (-2 * log-likelihood), with lower deviance and higher log-likelihood indicating a better fit. Multilevel logistic regression analysis was performed using the 'melogit' package in Stata. Before multivariable regression modelling, multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF). All analyses were two-tailed, and statistical significance was set at p < 0.05. Results Characteristics of study participants Table 1 presents demographic and sexually related characteristics of women of reproductive age. Of all 15,254, their mean age was 29.3 years (standard deviation = 9.8), with 38.1% aged 15–24 years. More than half (53.3%) had attained primary education, while 16.1% had no formal education. Nearly three in ten were single (26.5%), and one-third were from poor households. The majority (64.3%) were working, and just 14.3% were internet users. In terms of access to health services, 53.0% had visited a health facility in the past 12 months, and only 3.1% were visited by community healthcare workers during the same period. On sexually related behaviours,78.1% had ever heard of STI, 56.0% were sexually active in the last 4 weeks, and 2.4% had more than one sexual partner, excluding spouse. More than half were from rural (64.3%) and nearly 30.0% from the lake zone of mainland Tanzania. Bivariate analysis revealed a significant association between HIV self-kit use with age, education level, wealth index, working status, internet use, media exposure, visiting health facility in the past 12 months, ever heard of STI, recent sexual activity, number of sexual partner community poverty, residence and geographical zones (p < 0.05), (Table 2 ). Table 2 Sociodemographic characteristics and distribution of HIV self-kit use among women of reproductive age in Tanzania (N = 15,254) Characteristics N (%) HIV self-kit use, n (%) p-value No Yes Age in years < 0.001 15–24 5810 (38.1) 5676 (97.7) 134 (2.3) 25–34 4609 (30.2) 4390 (95.2) 219 (4.8) 35–49 4835 (31.7) 5698 (97.2) 136 (2.8) Mean (± SD) 29.3 (9.8) Education Level < 0.001 No formal education 2450 (16.1) 2429 (99.1) 21 (0.9) Primary 8123 (53.3) 7935 (97.7) 188 (2.3) Secondary/higher 4681 (30.7) 4401 (94.0) 280 (6.0) Marital status 0.215 Never Married 4047 (26.5) 3900 (96.4) 147 (3.6) Married/Cohabiting 6630 (43.5) 6443 (97.2) 187 (2.8) Previously married 4577 (30.0) 4421 (96.6) 156 (3.4) Wealth Index < 0.001 Poor 5044 (33.1) 5013 (99.4) 31 (0.6) Middle 2880 (18.9) 2835 (98.4) 46 (1.6) Rich 7330 (48.1) 6917 (94.4) 413 (5.6) Working status 0.006 Not working 5452 (35.7) 5333 (97.8) 119 (2.2) Working 9802 (64.3) 9431 (96.2) 371 (3.8) Internet use No 13072 (85.7) 12828 (98.1) 244 (1.9) < 0.001 Yes 2182 (14.3) 1936 (88.8) 246 (11.2) Media Exposure < 0.001 No 4690 (30.7) 4638 (98.9) 52 (1.1) Yes 10564 (69.3) 10127 (95.9) 437 (4.1) Visited Health Facility past 12 months 0.011 No 7167 (47) 6982 (97.4) 185 (2.6) Yes 8087 (53) 7782 (96.2) 305 (3.8) Visited by Community Health Worker in the past 12 months 0.274 No 14781 (96.9) 14312 (96.8) 469 (3.2) Yes 473 (3.1) 452 (95.6) 21 (4.4) Ever heard STI < 0.001 No 3336 (21.9) 3305 (99.1) 31 (0.9) Yes 11918 (78.1) 11459 (96.2) 459 (3.8) Recent sexual activity < 0.001 Never had sex 2146 (14.1) 2140 (99.7) 6 (0.3) Active in last 4 weeks 8539 (56) 8257 (96.7) 282 (3.3) Not Active in the last 4 weeks 4569 (30) 4368 (95.6) 201 (4.4) Number of sexual partners excluding spouse < 0.001 None 12108 (79.4) 11823 (97.6) 285 (2.4) One 2774 (18.2) 2612 (94.2) 162 (5.8) More than one 373 (2.4) 330 (88.6) 43 (11.4) Community Poverty < 0.001 Low 10084 (66.1) 9629 (95.5) 455 (4.5) High 5170 (33.9) 5135 (99.3) 25 (0.7) Residence < 0.001 Urban 5446 (35.7) 5141 (94.4) 305 (5.6) Rural 9808 (64.3) 9624 (98.1) 184 (1.9) Geographical zones < 0.001 Western 1268 (8.3) 1237 (97.6) 31 (2.4) Northern 1733 (11.4) 1677 (96.7) 56 (3.2) Central 1573 (10.3) 1535 (97.5) 38 (2.5) Southern 3051 (20) 2998 (98.2) 53 (1.8) Lake 4454 (29.2) 4241 (95.2) 213 (4.8) Eastern 2657 (17.4) 2567 (96.6) 89 (3.4) Zanzibar 517 (3.4) 510 (98.6) 7 (1.4) Multilevel analysis for Predictors of HIV Self-Kit use among women of reproductive age Table 3 presents multilevel analysis findings; women with secondary/higher (AOR = 2.77, 95%CI: 1.57–4.85) had increased odds of using HIV self-kit compared to those with no formal education. Women aged 25–34 (AOR = 1.93, 95%CI: 1.43–2.60) and 35–49 (AOR = 1.60, 95%CI: 1.43–2.26) had a higher likelihood of HIV self-kit use compared to those aged 15–24 years. Regarding socioeconomic status, women in the rich quintile had higher odds of HIV self-kit use compared to those in the poor quintile (AOR = 2.69, 95%CI: 1.52–4.77). Women who use the internet had increased odds of HIV self-kit use compared to their counterparts (3.04 (2.04–4.52). At the community levels, women from communities with high poverty were 48% less likely to use HIV-self kit compared to their counterparts (AOR = 0.52, 95%CI: 0.30–0.89), and all geographical zones in mainland Tanzania had a significantly higher likelihood of HIV self-kit use compared to Zanzibar (Table 3 ). Measures of variation and model fitness To assess the significance of community-level variation in HIV self0kit use, a null model was employed. This model demonstrated significant variation across localities (variance = 1.23, p < 0.001), with 27.3% of the total variation attributed to between-cluster differences and 72.7% to within-cluster differences. The MOR in the null model was 1.40, indicating substantial variation of community-level effects. Model I, which included individual-level variables, showed that 17.4% of the variation was attributable to individual differences, with an MOR of 1.28. Model II, incorporating community-level variables, resulted in an MOR of 1.27 and an ICC of 14.4%, indicating cluster-level variation. The final model (Model III), which included both individual and community-level variables, exhibited the lowest deviance and highest likelihood ratio (LLR), suggesting the best model fit. In Model III, 11.1% of the variation was attributed to both individual and community-level factors, with an MOR of 1.24. (Table 3 ). Table 3 Multilevel logistic regression analysis for Predictors of HIV Self-Kit use among women of reproductive age (N = 15,254) Characteristics Model 0 Model I Model II Model III AOR (95%CI) AOR (95%CI) AOR (95%CI) AOR (95%CI) Age group (years) 15–24 Ref Ref 25–34 1.90 (1.40–2.57)* 1.93 (1.43–2.60)* ≥ 35 1.56 (1.10–2.20)* 1.60 (1.13–2.26)* Education Level No formal education Ref Ref Primary 1.58 (0.97–2.56) 1.60 (0.99–2.58) Secondary/ Higher 2.62 (1.49–4.59)* 2.77 (1.57–4.85)* Working Status Not working Ref Ref Working 1.25 (0.79–1.95) 1.26 (0.81–1.96) Wealth Index Poor Ref Ref Middle 1.84 (0.91–3.75) 1.57 (0.77–3.19) Rich 3.63 (2.27–5.81)* 2.69 (1.52–4.77)* Internet use No Ref Ref Yes 2.95 (1.99–4.37)* 3.04 (2.04–4.52)* Ever heard STI No Ref Yes 2.01 (1.19–3.36)* 2.03 (1.21–3.42)* Number of sexual partners excluding spouse None Ref Ref One 2.32 (1.72–3.13)* 2.26 (1.67–3.07)* More than one 5.17 (2.85–9.38)* 5.10 (2.82–9.21)* Community poverty Low Ref Ref High 0.21 (0.13–0.34)* 0.52 (0.30–0.89)* Residence Urban Ref Ref Rural 0.41 (0.30–0.56)* 0.82 (0.59–1.13) Geographical Zones Western 4.83 (2.46–9.46)* 7.19 (3.80-13.61)* Northern 3.06 (1.76–5.35)* 3.17 (1.77–5.66)* Central 3.06 (1.79–5.22)* 3.36 (2.01–5.61)* Southern 1.71 (1.01–2.91)* 1.98 (1.16–3.37)* Lake 4.23 (2.58–6.94)* 6.26 (3.83–10.21)* Eastern 2.06 (1.23–3.47)* 1.96 (1.14–3.56)* Zanzibar Ref Ref Random effects Variance (SE) 1.23 (0.17) 0.69 (0.12) 0.55 (0.12) 0.41 (0.11) PCV (%) Ref 43.9% 55.3% 66.7% ICC (%) 27.3% 17.4% 14.4% 11.1% MOR 1.40 1.28 1.27 1.24 Model fitness Deviance 4107.94 3573.52 3937.46 3439.39 LLR -2053.97 -1786.76 -1968.73 -1751.28 AIC 4111.94 3599.52 3957.46 3444.56 BIC 4127.21 3698.75 4033.79 3704.84 *p < 0.05, ICC; Intra-class Correlation Coefficient, PCV; Proportional Change in Variance, MOR; Median Odds Ratio, AIC; Akaike Information Criterion, BIC; Bayesian Information Criterion, LLR; Log-likelihood ratio, SE; Standard Error, Ref; Reference category. Discussion This study examined the socio-demographic and behavioral predictors of HIVST kit use among women of reproductive age using the 2022 TDHS. The prevalence of HIV self-test kit use amongst women of reproductive age was 3.2% (95% CI: 2.8–3.7), slightly higher than the 2.4% observed among Ghanaian women of reproductive age during the same DHS period [ 20 ]. Despite the global efforts to promote self-testing as a strategy to increase HIV testing coverage, uptake remains suboptimal in many countries, including Tanzania and Ghana [ 15 , 20 ]. These low prevalence rates indicate limited progress in adopting a critical strategy aimed at increasing accessibility to HIV testing [ 1 , 21 ]. The study findings revealed that several socio-demographic factors, behavioral characteristics, and community-level factors significantly shape the uptake of the HIVST kit among women of reproductive age [ 21 ]. Social demographic factors such as age, education, economic status, internet use, STI awareness, and the number of sexual partners individuals had influenced HIVST kit use. For instance, women who had secondary or higher education were nearly three times more likely to use HIVST kits compared to those with no formal education, aligning with findings in previous studies [ 20 , 22 ]. Empirical evidence exploring the link between education level and health indicators has highlighted that education influences health by exposing educated individuals to skills and knowledge on general health, enhancing their awareness of healthy behaviors and preventive care [ 23 ]. In contrast to the Tanzanian findings, where older women (aged 25–49) were more likely to use self-testing kits than younger women (aged 15–24), the community-based prospective study conducted in Malawi and the Self-testing Africa (STAR) project done in Zambia, Malawi, and Zimbabwe demonstrated higher uptake of HIV self-testing kits among young women [ 24 , 25 ]. This divergence may highlight how structural barriers and access limitations may uniquely suppress testing among Tanzanian adolescents, underscoring the need for adapted, youth accessible testing interventions. Women in the richest wealth quintile demonstrated higher odds of using HIVST kits compared to their poorest counterparts, while those from high-poverty communities were less likely to use the HIVST kit. These finding aligns with studies from similar low-income settings that have consistently identified poverty as a structural barrier at both individual and community levels [ 26 ]. The compounded effect of individual and community barriers to HIV testing services includes both direct costs of the test kits and broader systemic limitations in healthcare infrastructure in impoverished areas [ 27 ]. The cost of HIV self-test kits, coupled with opportunity costs associated with healthcare access, likely creates substantial barriers for economically disadvantaged women and partly may explain the low rate of HIVST kits demonstrated by the younger women (15–24) who may still not have income sources in the Tanzanian context [ 16 ]. This economic gradient in self-testing uptake underscores the need for targeted subsidization or free distribution programs to ensure equitable access across socioeconomic and community strata. Geographical disparities were equally striking, with all mainland Tanzania zones showing significantly higher self-testing uptake compared to Zanzibar. This regional variation may reflect differences in health system organization, distribution networks, or cultural acceptance of self-testing [ 28 ]. The particularly low utilization in Zanzibar warrants targeted investigations into whether this results from supply-side limitations, such as poor kit distribution, or demand-side factors such as lower awareness and greater stigma. Digital access emerged as another powerful predictor, with internet users showing threefold greater odds of self-testing kit use. This finding supports growing evidence that digital platforms can effectively overcome traditional barriers to HIV testing [ 29 ]. However, with only 14.3% of study participants reporting internet use, this pathway remains inaccessible to the majority of Tanzanian women. The digital divide may thus exacerbate existing health inequalities, as women without internet access-typically those in rural areas, with lower education and from the poorest households, are simultaneously excluded from both digital health information and self-testing opportunities. Similar observations were reported in a systematic review [ 30 ]. These findings collectively suggest that while HIV self-testing holds promise for expanding testing coverage, its current implementation in Tanzania may inadvertently widen health disparities. Innovative strategies are needed to reach economically disadvantaged and digitally excluded populations, potentially through community-based distribution systems coupled with targeted digital literacy programs. Strengths and limitations This study benefits from several key strengths. The utilization of nationally representative data from the 2022 TDHS, coupled with a substantial sample size, bolsters the generalizability of our findings to the Tanzanian population of reproductive-aged women. Furthermore, the rigorous data collection protocols, including the engagement of experienced field assistants, contribute to the high quality and reliability of the data. Finally, the application of multilevel binary logistic regression allowed for a robust analysis, effectively addressing potential issues of clustering within the survey design and enhancing the validity of our inferences. However, it is important to interpret these findings, while considering certain limitations. The cross-sectional nature of the TDHS data prevents the establishment of causal relationships between the examined factors and HIV self-test kit use. Additionally, the reliance on self-reported data for several variables introduces the potential for recall and social desirability biases, which could influence the accuracy of responses. Implications for Practice and Policy Recommendations The low prevalence of HIVST kit use (3.2%) among women of reproductive age in Tanzania highlights the need for targeted interventions to increase uptake. To maximize impact, these interventions should prioritize women exhibiting characteristics associated with higher usage, including those with secondary/higher education, higher wealth status, internet access, awareness of STIs, and a greater number of sexual partners. Tailoring messaging and distribution strategies to effectively reach these groups is crucial. Furthermore, the observed geographical variations in self-testing odds necessitate localized approaches to understand and address region-specific barriers and facilitators. Notably, the lower likelihood of uptake in high-poverty communities demands specific strategies to ensure affordability and accessibility, potentially through subsidies or distribution via public health facilities and community-based organizations. Integrating HIV self-testing promotion and distribution within existing reproductive health services, STI prevention programs, and community outreach initiatives would offer a promising avenue to reach women. Leveraging digital platforms, given the positive association with internet use, can enhance information dissemination. Simultaneously, broad public health campaigns would be essential to raise overall awareness and knowledge about HIVST, address misconceptions, and highlight their benefits, like privacy and convenience, aiming to encourage uptake across all demographics. Conclusion This study reveals a low but significant uptake of HIVST kits among Tanzanian women of reproductive age, influenced by a complex interplay of individual-level factors, including younger age, secondary/higher education, higher wealth status, internet access, STI awareness, and a higher number of sexual partners and community-level factors, notably geographical zone and lower uptake in high-poverty areas. These findings underscore the necessity of a dual approach in designing targeted interventions: addressing individual characteristics to reach those more likely to self-test and tackling community-level barriers related to geographical disparities and socioeconomic inequalities to ensure equitable access, ultimately enhancing HIV prevention and control efforts across Tanzania by promoting earlier diagnosis and linkage to care. Abbreviations AIC Akaike Information Criterion AOR Adjusted Odds Ratio BIC Bayesian Information Criterion CIs Confidence Intervals EA Enumeration areas FDA Food and Drug Administration’s HIV Human Immunodeficiency Virus HIVST HIV self-test ICC Intra-class Correlation Coefficient LLR Log-likelihood ratio MOR Median Odds Ratio PCV Proportion Change in Variance PSU Primary sampling unit SD Standard deviation SE Standard error SSA Sub-Saharan Africa STI Sexually Transmitted Infections TDHS Tanzania Demographic and Health Survey UNAIDS United Nations Programme on HIV/AIDS VIF Variance Inflation Factor WHO World Health Organization's Declarations Acknowledgements We thank the DHS program for making the data available for this study and TILAM International for statistical consultation. Authors’ Contribution MJM and EE conceptualized the idea, conducted formal analysis. EE, TGM, SK, EJN, VGM and MJM interpreted the results, drafted the manuscript and reviewed all versions of the manuscript. All authors read and approved the final manuscript. Funding Not Applicable. Availability of data and materials The dataset used for this study is openly available and can be accessed via https://dhsprogram.com/data/dataset_admin/login_main.cfm;jsessionid=6BB6964E691F9D481F42CEF7601D7945.cfusion?CFID=410453820&CFTOKEN=d339e221faf5ad06-4F68E5CB-E1B5-3181-B3272BB523352435 Ethics approval and consent to participate The study is based on the publicly available 2022 TDHS datasets, which are accessible online and have been de-identified. The initial survey was approved by the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. We obtained permission to use the DHS data from MEASURE Tanzania Demographic and Health Surveys after submitting a request outlining our data analysis plan. Upon receiving approval, we downloaded the dataset from the DHS Program’s website. Informed consent was obtained from participants before the interviews. All methods were conducted according to the relevant guidelines and regulations. Consent for publication Not applicable. Competing interests None declared. References Joint United Nations Programme on HIV/AIDS (UNAIDS). Global HIV & AIDS statistics — Fact sheet [Internet]. [cited 2024 Oct 3]. Available from: https://www.unaids.org/en/resources/fact-sheet Nduhukyire L, Semitala FC, Mutanda JN, Muramuzi D, Ipola PA, Owori B, et al. Prevalence, associated factors, barriers and facilitators for oral HIV self-testing among partners of pregnant women attending antenatal care clinics in Wakiso, Uganda. AIDS Research and Therapy. 2024;21:82. 2024 global AIDS report — The Urgency of Now: AIDS at a Crossroads | UNAIDS [Internet]. [cited 2025 Apr 17]. Available from: https://www.unaids.org/en/resources/documents/2024/global-aids-update-2024 Global HIV Programme [Internet]. [cited 2025 Apr 17]. Available from: https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/hiv/testing-diagnostics/hiv-testing-services Hurt CB, Powers KA. Self-testing for HIV and its impact on public health. Sexually transmitted diseases. 2014;41:10–2. Stevens DR, Vrana CJ, Dlin RE, Korte JE. A Global Review of HIV Self-testing: Themes and Implications. AIDS Behav. 2018;22:497–512. Kadye T, Jamil MS, Johnson C, Baggaley R, Barr-DiChiara M, Cambiano V. Country uptake of WHO recommendations on differentiated HIV testing services approaches: a global policy review. BMJ Open. 2024;14:e058098. Johnson C, Neuman M, MacPherson P, Choko A, Quinn C, Wong VJ, et al. Use and awareness of and willingness to self-test for HIV: an analysis of cross-sectional population-based surveys in Malawi and Zimbabwe. BMC Public Health. 2020;20:779. Zishiri V, Conserve DF, Haile ZT, Corbett E, Hatzold K, Meyer-Rath G, et al. Secondary distribution of HIV self-test kits by HIV index and antenatal care clients: implementation and costing results from the STAR Initiative in South Africa. BMC Infectious Diseases. 2023;22:971. Mwangi J, Miruka F, Mugambi M, Fidhow A, Chepkwony B, Kitheka F, et al. Characteristics of users of HIV self-testing in Kenya, outcomes, and factors associated with use: results from a population-based HIV impact assessment, 2018. BMC Public Health. 2022;22:643. Aloni MS. Drivers of HIV self-test kit among Tanzanian men aged 15–49: findings from the 2022 TDHS-MIS cross-sectional study. AIDS Research and Therapy. 2025;22:3. Conserve DF, Abu-Ba’are GR, Janson S, Mhando F, Munisi GV, Drezgic B, et al. Peer-based Promotion and Nurse-led Distribution of HIV Self-Testing Among Networks of Men in Dar es Salaam, Tanzania: Development and Feasibility Results of the STEP Intervention. Res Sq. 2023;rs.3.rs-3283552. Mbita G, Mwanamsangu A, Plotkin M, Casalini C, Shao A, Lija G, et al. Consistent condom use and dual protection among female sex workers: surveillance findings from a large-scale, community-based combination HIV prevention program in Tanzania. AIDS and Behavior. 2020;24:802–11. Conserve DF, Muessig KE, Maboko LL, Shirima S, Kilonzo MN, Maman S, et al. Mate Yako Afya Yako: formative research to develop the Tanzania HIV self-testing education and promotion (Tanzania STEP) project for men. PloS one. 2018;13:e0202521. Conserve DF, Abu-Ba’are GR, Janson S, Mhando F, Munisi GV, Drezgic B, et al. Development and feasibility of the peer and nurse-led HIV Self-Testing Education and Promotion (STEP) intervention among social networks of men in Dar es Salaam, Tanzania: application of the ADAPT-ITT model. BMC Health Services Research. 2024;24:1166. Chiu C, Hunter LA, McCoy SI, Mfaume R, Njau P, Liu JX. Sales and pricing decisions for HIV self-test kits among local drug shops in Tanzania: a prospective cohort study. BMC Health Services Research. 2021;21:434. Ashburn K, Antelman G, N’Goran MK, Jahanpour O, Yemaneberhan A, N’Guessan Kouakou B, et al. Willingness to use HIV self‐test kits and willingness to pay among urban antenatal clients in Cote d’Ivoire and Tanzania: a cross‐sectional study. Trop Med Int Health. 2020;25:1155–65. Hunter LA, Rao A, Napierala S, Kalinjila A, Mnyippembe A, Hassan K, et al. Reaching adolescent girls and young women with HIV self-testing and contraception at girl-friendly drug shops: A randomized trial in Tanzania. J Adolesc Health. 2023;72:64–72. Mohamud LA, Hassan AM, Nasir JA. Determinants of HIV/Aids Knowledge Among Females in Somalia: Findings from 2018 to 2019 SDHS Data. HIV. 2023;15:435–44. Akweh TY, Adoku E, Mbiba F, Teyko F, Brinsley TY, Boakye Jr BA, et al. Prevalence and factors associated with knowledge of HIV Self-Test kit and HIV-Self Testing among Ghanaian women: multi-level analyses using the 2022 Ghana demographic and health survey. BMC Public Health. 2025;25:1161. HIV/AIDS JUNP on, others. Global AIDS strategy 2021-2026: End inequalities. End AIDS. 2021; Sabo KG, Seifu BL, Kase BF, Asebe HA, Asmare ZA, Asgedom YS, et al. Factors influencing HIV testing uptake in Sub-Saharan Africa: a comprehensive multi-level analysis using demographic and health survey data (2015–2022). BMC Infectious Diseases. 2024;24:821. Raghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Archives of public health. 2020;78:1–18. Choko AT, MacPherson P, Webb EL, Willey BA, Feasy H, Sambakunsi R, et al. Uptake, accuracy, safety, and linkage into care over two years of promoting annual self-testing for HIV in Blantyre, Malawi: a community-based prospective study. PLoS medicine. 2015;12:e1001873. Hatzold K, Gudukeya S, Mutseta MN, Chilongosi R, Nalubamba M, Nkhoma C, et al. HIV self-testing: breaking the barriers to uptake of testing among men and adolescents in sub-Saharan Africa, experiences from STAR demonstration projects in Malawi, Zambia and Zimbabwe. Journal of the International AIDS Society. 2019;22:e25244. Njau B, Covin C, Lisasi E, Damian D, Mushi D, Boulle A, et al. A systematic review of qualitative evidence on factors enabling and deterring uptake of HIV self-testing in Africa. BMC public health. 2019;19:1–16. Johnson C, Dalal S, Baggaley R, Hogan D, Parrott G, Mathews R, et al. Systematic review of HIV testing costs in high and low income settings. Consolidated Guidelines on HIV Testing Services: 5Cs: Consent, Confidentiality, Counselling, Correct Results and Connection 2015 [Internet]. World Health Organization; 2015 [cited 2025 Apr 26]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK316032/ Mkopi A, Korte ,Jeffrey E., Lesslie ,Virginia, diNapoli ,Marisa, Mutiso ,Fedelis, Mwajubwa ,Shabani, et al. Acceptability and uptake of oral HIV self-testing among rural community members in Tanzania: a pilot study. AIDS Care. 2023;35:1338–45. Romero RA, Klausner JD, Marsch LA, Young SD. Technology-delivered intervention strategies to bolster HIV testing. Current HIV/AIDS Reports. 2021;18:391–405. Saeed SA, Masters RM. Disparities in Health Care and the Digital Divide. Curr Psychiatry Rep. 2021;23:61. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Aug, 2025 Read the published version in AIDS Research and Therapy → Version 1 posted Editorial decision: Revision requested 23 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviews received at journal 23 May, 2025 Reviews received at journal 15 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers invited by journal 01 May, 2025 Editor assigned by journal 30 Apr, 2025 Submission checks completed at journal 30 Apr, 2025 First submitted to journal 28 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6551004","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452210767,"identity":"40c31096-573e-4e65-b8b6-09ae44e08b1d","order_by":0,"name":"Elihuruma Eliufoo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDAC5gNAokCChx/ESSggRgtbApAwkJCRbABpMSBeC4ONAcg2BmK0GBxjPvjgg4EFj/H51YkfHhgwyPOLHSCkhS3ZcIaBBI/ZjbebJYAOM5w5O4GAlvs9ZtI8YC1nN4C0JBjcJqTlGI+Z9B+gFuMZZzf/IF4LMMR4DPh7txFniyTILz1ALRI3eLdZJBhIEPYLHyjEflTU2fP3n91880eFjTy/NAEtCCABVilBrHIQ4D9AiupRMApGwSgYSQAApSo968jTgS0AAAAASUVORK5CYII=","orcid":"","institution":"University of Dodoma","correspondingAuthor":true,"prefix":"","firstName":"Elihuruma","middleName":"","lastName":"Eliufoo","suffix":""},{"id":452210768,"identity":"163d6fc5-dc5e-4c8d-888a-6ff1e12da700","order_by":1,"name":"Tegemea Patrick Mwalingo","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Tegemea","middleName":"Patrick","lastName":"Mwalingo","suffix":""},{"id":452210769,"identity":"23253532-4454-45e3-9b3c-6677aada59c4","order_by":2,"name":"Shazra Kazumari","email":"","orcid":"","institution":"Dodoma Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shazra","middleName":"","lastName":"Kazumari","suffix":""},{"id":452210770,"identity":"2d30e4c0-a119-460f-a104-f3a4fda2e629","order_by":3,"name":"Emanuel James Nkuwi","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Emanuel","middleName":"James","lastName":"Nkuwi","suffix":""},{"id":452210771,"identity":"340eb2da-65d4-4ed5-b684-fcaa181c061f","order_by":4,"name":"Victoria Godfrey Majengo","email":"","orcid":"","institution":"Dodoma Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"Godfrey","lastName":"Majengo","suffix":""},{"id":452210772,"identity":"6c256f2f-c4b7-45e2-a81c-366f06578e2f","order_by":5,"name":"Mtoro J. Mtoro","email":"","orcid":"","institution":"TILAM International","correspondingAuthor":false,"prefix":"","firstName":"Mtoro","middleName":"J.","lastName":"Mtoro","suffix":""}],"badges":[],"createdAt":"2025-04-28 22:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6551004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6551004/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12981-025-00774-0","type":"published","date":"2025-08-04T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88814109,"identity":"17384200-3666-42e8-b886-a847a514065f","added_by":"auto","created_at":"2025-08-11 16:06:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1398090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6551004/v1/c9e3692c-69e5-4b57-8a34-5118a63e1b26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unlocking Self-Testing: Predictors of HIV Self-Testing Kit Use Among Reproductive-Aged Women in Tanzania; A Multilevel Analysis of the 2022 Demographic and Health Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobally, Human Immunodeficiency Virus (HIV) persists as a major public health challenge, with approximately 40\u0026nbsp;million people living with the virus in 2023, including 5.5\u0026nbsp;million individuals unaware of their status and lacking access to testing services [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Sub-Saharan Africa (SSA) continues to bear the heaviest burden of the epidemic, while the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets for 2030 emphasize that 95% of people living with HIV should know their status [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This goal requires a significant expansion of HIV testing efforts to reach those currently undiagnosed [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHIV self-testing (HIVST) has emerged as a critical tool in global HIV prevention and treatment strategies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The evolution of HIVST began with the United States Food and Drug Administration\u0026rsquo;s (FDA) 1996 approval of home collection tests requiring laboratory processing and telephone results delivery [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A significant advancement came in 2002 with the approval of OraQuick's rapid HIV-1 blood test, followed by expanded approvals for venipuncture specimens in 2003 and for oral fluid, plasma, and HIV-2 testing in 2004 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In 2012, the FDA approved the OraQuick In-Home HIV Test as the first over-the-counter rapid HIV self-test, establishing HIVST as a viable testing approach that has since expanded globally with numerous kit options [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the World Health Organization's (WHO) recommendation in 2016, more than 80 countries have adopted HIVST policies, with implementation varying widely across regions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Evidence from multiple countries demonstrates that HIVST effectively reaches individuals who might otherwise not test, particularly key populations and men [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In SSA, where the HIV burden remains disproportionately high, countries like Kenya, Malawi, and South Africa have led adoption efforts, showing promising results with increased testing rates, earlier diagnosis, and linkage to care [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Tanzania specifically, HIVST was officially introduced in 2018 through pilot programs in selected regions following the country's updated HIV testing guidelines [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This introduction came relatively later than in neighboring countries, with the Ministry of Health incorporating self-testing into its national HIV strategy in 2019 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Before the introduction of HIVST, the 2008 National HIV Act required HIV testing to be carried out by a healthcare provider in healthcare settings [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Initial implementation focused on high-burden regions and targeted key populations, including female sex workers, men who have sex with men, and partners of people living with HIV [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Despite policy adoption, HIVST coverage and availability of kits in Tanzania remain limited compared to other SSA countries, with uneven distribution across regions and persistent challenges in supply chain management, awareness, and community acceptance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Current estimates from HIVST kits sales research suggest that while knowledge about HIVST is growing, actual utilization rates of HIVST kits remain low among the general population, with reproductive-aged women representing an underserved demographic despite their increased vulnerability to HIV [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile previous research has explored HIVST implementation in various contexts, significant knowledge gaps exist regarding the specific predictors that influence HIVST kit uptake among reproductive-aged women in Tanzania [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Another study in 2020 reported the willingness to use the HIVST kit being 69% in Tanzania, while the actual usage remains minimal [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Most existing studies have focused on key populations or urban settings, leaving rural women and those of reproductive age underrepresented in the literature [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, much of the available evidence comes from small-scale implementation projects rather than nationally representative data, limiting the generalizability of findings [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Understanding the factors that predict HIVST kit use among reproductive-aged women is particularly important given their centrality in family health decision-making and their increased vulnerability during pregnancy and postpartum periods.\u003c/p\u003e \u003cp\u003eThe Tanzania Demographic and Health Survey (TDHS) provides a unique opportunity to examine these patterns at a national scale. As a nationally representative dataset that includes comprehensive information on HIV knowledge, attitudes, practices, and sociodemographic characteristics, the TDHS allows for robust analysis of factors associated with HIVST kit usage across diverse regions and population segments. A previous study using the most recent TDHS data reported on the usage among men only [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This study aimed to present data that can identify specific individual, household, and community-level predictors that influence women's decisions to use self-testing kits, providing evidence that moves beyond small-scale implementation research to inform national policy and programming. Therefore, this study aimed to assess predictors of HIV self-test kit use among reproductive-aged women in Tanzania by a multilevel analysis of the 2022 TDHS. Given the nature of Demographic Health Survey (DHS) data, a multilevel analysis offers a methodological advantage to this study over the previous studies using TDHS. Methods and findings will serve as a model for similar analyses in other SSA countries seeking to optimize their HIVST kit usage strategies for reproductive-aged women.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and design\u003c/h2\u003e \u003cp\u003eThis study was an analytical cross-sectional survey that utilised secondary data from the 2022 TDHS, which conducts nationally representative population-based household surveys typically every five years.​ The survey was carried out by the Tanzania Bureau of Statistics in collaboration with the relevant ministries of in Tanzania Mainland and Zanzibar.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation and sampling\u003c/h3\u003e\n\u003cp\u003eData for this study were obtained from the latest DHS conducted between 24 February to 21 July 2022 across all regions in Tanzania. The target population for the TDHS includes women of reproductive age (15\u0026ndash;49 years), men, children, and households across the 32 administrative regions in Tanzania. At the country level, a sampling frame is usually obtained. To minimize sampling errors, the country is stratified by geographic region and by urban/rural areas within each region, followed by a two-stage sampling to select a household to be surveyed. The first sampling is to select a primary sampling unit (PSU) and then select a household. PSUs are survey clusters that are usually based on census enumeration areas (EAs). A probability proportional to size is employed in each stratum to select the PSU. For each selected PSU, a complete household listing is done. This is then followed by selecting a fixed number of households to be surveyed using equal probability systematic sampling. All women who had spent the night before the survey in the selected households were eligible for the survey. From the individual file recode (IR), all 15,254 women were involved in this study.\u003c/p\u003e\n\u003ch3\u003eStudy Variables\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDependent variable\u003c/h2\u003e \u003cp\u003eThis study's outcome variable was the usage of HIV self-test kits, determined by women's knowledge and reported use. Initially, responses were categorized into three groups: (1) those who had never heard of HIV self-test kits, (2) those who had ever used them, and (3) those who knew of them but had never used them. For analytical purposes, these categories were recoded into a binary variable. Women in categories (1) and (3) were combined and assigned the value '0' (never used), while women in category (2) were assigned the value '1' (ever used) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent variables\u003c/h3\u003e\n\u003cp\u003ePredictor variables were classified into two categories: socio-demographic characteristics and sexual activity-related variables, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eSociodemographic and sexual activity-related variables used for this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoding categories\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIndividual levels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoman\u0026rsquo;s age group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;15\u0026ndash;24, 2\u0026thinsp;=\u0026thinsp;25\u0026ndash;44 or 3\u0026thinsp;=\u0026thinsp;35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest level of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;No formal education, 2\u0026thinsp;=\u0026thinsp;Primary education, 3\u0026thinsp;=\u0026thinsp;Secondary/Higher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Never married, 2\u0026thinsp;=\u0026thinsp;Married/Cohabiting or 4\u0026thinsp;=\u0026thinsp;Separated/Widowed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Poor, 2\u0026thinsp;=\u0026thinsp;Middle or 3\u0026thinsp;=\u0026thinsp;Rich\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Working or 2\u0026thinsp;=\u0026thinsp;Not working\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia exposure: Media exposure was calculated by aggregating TV watching, radio listening, and reading newspapers and woman who has exposure to either of the media sources was categorized as having media exposure and the rest considered as having no media exposure.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisited Health facility in the past 12 moths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisited by Community Health Workers in the past 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent sexual activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Never had sex, 2\u0026thinsp;=\u0026thinsp;Active in last 4\u0026nbsp;weeks or 3\u0026thinsp;=\u0026thinsp;Not active in last 4\u0026nbsp;weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA condom was used during the last sex with the most recent partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sex partners, excluding a spouse, in the past 12\u0026nbsp;months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;None, 2\u0026thinsp;=\u0026thinsp;One or 3\u0026thinsp;=\u0026thinsp;More than one\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver heard Sexually transmitted Infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes or 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity levels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity poverty: The level of poverty in the community was determined by the proportion of women in the poorer and poorest wealth quintiles, as indicated by the wealth\u003c/p\u003e \u003cp\u003eindex. It was classified as low (communities where \u0026lt;\u0026thinsp;50% of women were in the poorer or poorest quintiles) or high (communities where \u0026ge;\u0026thinsp;50% of women were in the poorer or poorest quintiles).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Low or 2\u0026thinsp;=\u0026thinsp;High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Urban or 2\u0026thinsp;=\u0026thinsp;Rural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographical zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Western, 2\u0026thinsp;=\u0026thinsp;Northern, 3\u0026thinsp;=\u0026thinsp;Central, 4\u0026thinsp;=\u0026thinsp;Southern,\u003c/p\u003e \u003cp\u003e5\u0026thinsp;=\u0026thinsp;Lake, 6\u0026thinsp;=\u0026thinsp;Eastern or 7\u0026thinsp;=\u0026thinsp;Zanzibar\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData management and analysis\u003c/h2\u003e \u003cp\u003eTo address the complex survey design of the TDHS, we applied individual sampling weights (v005/1,000,000), accounted for primary sampling units (clusters), and stratified the data to ensure representative estimates and control for sampling biases. Data cleaning, coding, and analysis were performed using STATA 18.5 (STATA Corp, College Station, TX). Descriptive statistics included means and standard deviations for continuous variables, and frequencies with proportions for categorical variables. Considering the hierarchical structure of the data (women within households within clusters), we employed a multilevel mixed-effects logistic regression model to account for non-independence and unequal variance. Our analysis involved four models: a null model (Model 0), a model with individual-level factors (Model I), a model with community-level factors (Model II), and a final model combining both levels (Model III).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRandom effects and model fitness\u003c/h3\u003e\n\u003cp\u003eTo assess the variation in HIV self-kit use across clusters, we calculated random effects measures including the Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and Proportion Change in Variance (PCV). Treating clusters as a random effect, the ICC, quantifying the proportion of total variation in HIV self-kit use attributable to between-cluster variations, was computed as ICC = (VC / (VC\u0026thinsp;+\u0026thinsp;3.29)) * 100. The MOR, representing the median odds ratio between clusters with high and low likelihood of HIV self-kit use, was calculated as MOR\u0026thinsp;=\u0026thinsp;exp(0.95 * sqrt(VC)). The PCV, indicating the reduction in variance across successive models, was calculated as (Ve \u0026ndash; Vmi) / Ve, where Ve is the variance in the null model and Vmi is the variance in subsequent models.\u003c/p\u003e \u003cp\u003eThe association between individual and community-level independent variables and the likelihood of HIV self-kit use was estimated using fixed effects, presented as adjusted odds ratios (AORs) with 95% confidence intervals. Model comparison, due to the nested structure, was based on the deviance statistic (-2 * log-likelihood), with lower deviance and higher log-likelihood indicating a better fit. Multilevel logistic regression analysis was performed using the 'melogit' package in Stata. Before multivariable regression modelling, multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF). All analyses were two-tailed, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents demographic and sexually related characteristics of women of reproductive age. Of all 15,254, their mean age was 29.3 years (standard deviation\u0026thinsp;=\u0026thinsp;9.8), with 38.1% aged 15\u0026ndash;24 years. More than half (53.3%) had attained primary education, while 16.1% had no formal education. Nearly three in ten were single (26.5%), and one-third were from poor households. The majority (64.3%) were working, and just 14.3% were internet users. In terms of access to health services, 53.0% had visited a health facility in the past 12 months, and only 3.1% were visited by community healthcare workers during the same period. On sexually related behaviours,78.1% had ever heard of STI, 56.0% were sexually active in the last 4 weeks, and 2.4% had more than one sexual partner, excluding spouse. More than half were from rural (64.3%) and nearly 30.0% from the lake zone of mainland Tanzania. Bivariate analysis revealed a significant association between HIV self-kit use with age, education level, wealth index, working status, internet use, media exposure, visiting health facility in the past 12 months, ever heard of STI, recent sexual activity, number of sexual partner community poverty, residence and geographical zones (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eSociodemographic characteristics and distribution of HIV self-kit use among women of reproductive age in Tanzania (N\u0026thinsp;=\u0026thinsp;15,254)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHIV self-kit use, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge in years\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5810 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5676 (97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4609 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4390 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e219 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4835 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5698 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.3 (9.8)\u003c/p\u003e \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\u0026nbsp;\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 \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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2450 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2429 (99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8123 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7935 (97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary/higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4681 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4401 (94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e280 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4047 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3900 (96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6630 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6443 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreviously married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4577 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4421 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e156 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5044 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5013 (99.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2880 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2835 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7330 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6917 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e413 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking status\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5452 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5333 (97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9802 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9431 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e371 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInternet use\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13072 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12828 (98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e244 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2182 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1936 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4690 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4638 (98.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10564 (69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10127 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e437 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVisited Health Facility past 12 months\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7167 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6982 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e185 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8087 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7782 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e305 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVisited by Community Health Worker in the past 12 months\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14781 (96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14312 (96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e469 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e473 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e452 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver heard STI\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3336 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3305 (99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11918 (78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11459 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecent sexual activity\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever had sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2146 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2140 (99.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive in last 4 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8539 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8257 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e282 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Active in the last 4 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4569 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4368 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of sexual partners excluding spouse\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12108 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11823 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e285 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2774 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2612 (94.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e330 (88.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity Poverty\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10084 (66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9629 (95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e455 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5170 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5135 (99.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5446 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5141 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e305 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9808 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9624 (98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e184 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical zones\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1268 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1237 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1733 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1677 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1573 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1535 (97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3051 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2998 (98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4454 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4241 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2657 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2567 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZanzibar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e517 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e510 (98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultilevel analysis for Predictors of HIV Self-Kit use among women of reproductive age\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents multilevel analysis findings; women with secondary/higher (AOR\u0026thinsp;=\u0026thinsp;2.77, 95%CI: 1.57\u0026ndash;4.85) had increased odds of using HIV self-kit compared to those with no formal education. Women aged 25\u0026ndash;34 (AOR\u0026thinsp;=\u0026thinsp;1.93, 95%CI: 1.43\u0026ndash;2.60) and 35\u0026ndash;49 (AOR\u0026thinsp;=\u0026thinsp;1.60, 95%CI: 1.43\u0026ndash;2.26) had a higher likelihood of HIV self-kit use compared to those aged 15\u0026ndash;24 years. Regarding socioeconomic status, women in the rich quintile had higher odds of HIV self-kit use compared to those in the poor quintile (AOR\u0026thinsp;=\u0026thinsp;2.69, 95%CI: 1.52\u0026ndash;4.77). Women who use the internet had increased odds of HIV self-kit use compared to their counterparts (3.04 (2.04\u0026ndash;4.52). At the community levels, women from communities with high poverty were 48% less likely to use HIV-self kit compared to their counterparts (AOR\u0026thinsp;=\u0026thinsp;0.52, 95%CI: 0.30\u0026ndash;0.89), and all geographical zones in mainland Tanzania had a significantly higher likelihood of HIV self-kit use compared to Zanzibar (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMeasures of variation and model fitness\u003c/h2\u003e \u003cp\u003eTo assess the significance of community-level variation in HIV self0kit use, a null model was employed. This model demonstrated significant variation across localities (variance\u0026thinsp;=\u0026thinsp;1.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with 27.3% of the total variation attributed to between-cluster differences and 72.7% to within-cluster differences. The MOR in the null model was 1.40, indicating substantial variation of community-level effects. Model I, which included individual-level variables, showed that 17.4% of the variation was attributable to individual differences, with an MOR of 1.28. Model II, incorporating community-level variables, resulted in an MOR of 1.27 and an ICC of 14.4%, indicating cluster-level variation. The final model (Model III), which included both individual and community-level variables, exhibited the lowest deviance and highest likelihood ratio (LLR), suggesting the best model fit. In Model III, 11.1% of the variation was attributed to both individual and community-level factors, with an MOR of 1.24. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eMultilevel logistic regression analysis for Predictors of HIV Self-Kit use among women of reproductive age (N\u0026thinsp;=\u0026thinsp;15,254)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90 (1.40\u0026ndash;2.57)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.93 (1.43\u0026ndash;2.60)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56 (1.10\u0026ndash;2.20)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60 (1.13\u0026ndash;2.26)*\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 \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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58 (0.97\u0026ndash;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60 (0.99\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary/\u003c/p\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62 (1.49\u0026ndash;4.59)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.77 (1.57\u0026ndash;4.85)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking Status\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (0.79\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26 (0.81\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84 (0.91\u0026ndash;3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57 (0.77\u0026ndash;3.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63 (2.27\u0026ndash;5.81)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.69 (1.52\u0026ndash;4.77)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInternet use\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95 (1.99\u0026ndash;4.37)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.04 (2.04\u0026ndash;4.52)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver heard STI\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01 (1.19\u0026ndash;3.36)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.03 (1.21\u0026ndash;3.42)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of sexual partners excluding spouse\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32 (1.72\u0026ndash;3.13)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.26 (1.67\u0026ndash;3.07)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than\u003c/p\u003e \u003cp\u003eone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.17 (2.85\u0026ndash;9.38)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.10 (2.82\u0026ndash;9.21)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity poverty\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\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\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\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\u003e0.21 (0.13\u0026ndash;0.34)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52 (0.30\u0026ndash;0.89)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\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\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\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\u003e0.41 (0.30\u0026ndash;0.56)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82 (0.59\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical Zones\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\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\u003e4.83 (2.46\u0026ndash;9.46)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.19 (3.80-13.61)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\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\u003e3.06 (1.76\u0026ndash;5.35)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.17 (1.77\u0026ndash;5.66)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\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\u003e3.06 (1.79\u0026ndash;5.22)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.36 (2.01\u0026ndash;5.61)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern\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\u003e1.71 (1.01\u0026ndash;2.91)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.98 (1.16\u0026ndash;3.37)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake\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\u003e4.23 (2.58\u0026ndash;6.94)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.26 (3.83\u0026ndash;10.21)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\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\u003e2.06 (1.23\u0026ndash;3.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96 (1.14\u0026ndash;3.56)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZanzibar\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\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRandom effects\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41 (0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\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 \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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeviance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4107.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3573.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3937.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3439.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2053.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1786.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1968.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1751.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4111.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3599.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3957.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3444.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4127.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3698.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4033.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3704.84\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*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ICC; Intra-class Correlation Coefficient, PCV; Proportional Change in Variance, MOR; Median Odds Ratio, AIC; Akaike Information Criterion, BIC; Bayesian Information Criterion, LLR; Log-likelihood ratio, SE; Standard Error, Ref; Reference category.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the socio-demographic and behavioral predictors of HIVST kit use among women of reproductive age using the 2022 TDHS. The prevalence of HIV self-test kit use amongst women of reproductive age was 3.2% (95% CI: 2.8\u0026ndash;3.7), slightly higher than the 2.4% observed among Ghanaian women of reproductive age during the same DHS period [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Despite the global efforts to promote self-testing as a strategy to increase HIV testing coverage, uptake remains suboptimal in many countries, including Tanzania and Ghana [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These low prevalence rates indicate limited progress in adopting a critical strategy aimed at increasing accessibility to HIV testing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The study findings revealed that several socio-demographic factors, behavioral characteristics, and community-level factors significantly shape the uptake of the HIVST kit among women of reproductive age [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial demographic factors such as age, education, economic status, internet use, STI awareness, and the number of sexual partners individuals had influenced HIVST kit use. For instance, women who had secondary or higher education were nearly three times more likely to use HIVST kits compared to those with no formal education, aligning with findings in previous studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Empirical evidence exploring the link between education level and health indicators has highlighted that education influences health by exposing educated individuals to skills and knowledge on general health, enhancing their awareness of healthy behaviors and preventive care [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast to the Tanzanian findings, where older women (aged 25\u0026ndash;49) were more likely to use self-testing kits than younger women (aged 15\u0026ndash;24), the community-based prospective study conducted in Malawi and the Self-testing Africa (STAR) project done in Zambia, Malawi, and Zimbabwe demonstrated higher uptake of HIV self-testing kits among young women [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This divergence may highlight how structural barriers and access limitations may uniquely suppress testing among Tanzanian adolescents, underscoring the need for adapted, youth accessible testing interventions.\u003c/p\u003e \u003cp\u003eWomen in the richest wealth quintile demonstrated higher odds of using HIVST kits compared to their poorest counterparts, while those from high-poverty communities were less likely to use the HIVST kit. These finding aligns with studies from similar low-income settings that have consistently identified poverty as a structural barrier at both individual and community levels [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The compounded effect of individual and community barriers to HIV testing services includes both direct costs of the test kits and broader systemic limitations in healthcare infrastructure in impoverished areas [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The cost of HIV self-test kits, coupled with opportunity costs associated with healthcare access, likely creates substantial barriers for economically disadvantaged women and partly may explain the low rate of HIVST kits demonstrated by the younger women (15\u0026ndash;24) who may still not have income sources in the Tanzanian context [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This economic gradient in self-testing uptake underscores the need for targeted subsidization or free distribution programs to ensure equitable access across socioeconomic and community strata.\u003c/p\u003e \u003cp\u003eGeographical disparities were equally striking, with all mainland Tanzania zones showing significantly higher self-testing uptake compared to Zanzibar. This regional variation may reflect differences in health system organization, distribution networks, or cultural acceptance of self-testing [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The particularly low utilization in Zanzibar warrants targeted investigations into whether this results from supply-side limitations, such as poor kit distribution, or demand-side factors such as lower awareness and greater stigma.\u003c/p\u003e \u003cp\u003eDigital access emerged as another powerful predictor, with internet users showing threefold greater odds of self-testing kit use. This finding supports growing evidence that digital platforms can effectively overcome traditional barriers to HIV testing [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, with only 14.3% of study participants reporting internet use, this pathway remains inaccessible to the majority of Tanzanian women. The digital divide may thus exacerbate existing health inequalities, as women without internet access-typically those in rural areas, with lower education and from the poorest households, are simultaneously excluded from both digital health information and self-testing opportunities. Similar observations were reported in a systematic review [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings collectively suggest that while HIV self-testing holds promise for expanding testing coverage, its current implementation in Tanzania may inadvertently widen health disparities. Innovative strategies are needed to reach economically disadvantaged and digitally excluded populations, potentially through community-based distribution systems coupled with targeted digital literacy programs.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study benefits from several key strengths. The utilization of nationally representative data from the 2022 TDHS, coupled with a substantial sample size, bolsters the generalizability of our findings to the Tanzanian population of reproductive-aged women. Furthermore, the rigorous data collection protocols, including the engagement of experienced field assistants, contribute to the high quality and reliability of the data. Finally, the application of multilevel binary logistic regression allowed for a robust analysis, effectively addressing potential issues of clustering within the survey design and enhancing the validity of our inferences. However, it is important to interpret these findings, while considering certain limitations. The cross-sectional nature of the TDHS data prevents the establishment of causal relationships between the examined factors and HIV self-test kit use. Additionally, the reliance on self-reported data for several variables introduces the potential for recall and social desirability biases, which could influence the accuracy of responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Practice and Policy Recommendations\u003c/h2\u003e \u003cp\u003eThe low prevalence of HIVST kit use (3.2%) among women of reproductive age in Tanzania highlights the need for targeted interventions to increase uptake. To maximize impact, these interventions should prioritize women exhibiting characteristics associated with higher usage, including those with secondary/higher education, higher wealth status, internet access, awareness of STIs, and a greater number of sexual partners. Tailoring messaging and distribution strategies to effectively reach these groups is crucial. Furthermore, the observed geographical variations in self-testing odds necessitate localized approaches to understand and address region-specific barriers and facilitators. Notably, the lower likelihood of uptake in high-poverty communities demands specific strategies to ensure affordability and accessibility, potentially through subsidies or distribution via public health facilities and community-based organizations. Integrating HIV self-testing promotion and distribution within existing reproductive health services, STI prevention programs, and community outreach initiatives would offer a promising avenue to reach women. Leveraging digital platforms, given the positive association with internet use, can enhance information dissemination. Simultaneously, broad public health campaigns would be essential to raise overall awareness and knowledge about HIVST, address misconceptions, and highlight their benefits, like privacy and convenience, aiming to encourage uptake across all demographics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals a low but significant uptake of HIVST kits among Tanzanian women of reproductive age, influenced by a complex interplay of individual-level factors, including younger age, secondary/higher education, higher wealth status, internet access, STI awareness, and a higher number of sexual partners and community-level factors, notably geographical zone and lower uptake in high-poverty areas. These findings underscore the necessity of a dual approach in designing targeted interventions: addressing individual characteristics to reach those more likely to self-test and tackling community-level barriers related to geographical disparities and socioeconomic inequalities to ensure equitable access, ultimately enhancing HIV prevention and control efforts across Tanzania by promoting earlier diagnosis and linkage to care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eAkaike Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eBayesian Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eConfidence Intervals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eEnumeration areas\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eFDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eFood and Drug Administration\u0026rsquo;s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eHIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eHuman Immunodeficiency Virus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eHIVST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eHIV self-test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eIntra-class Correlation Coefficient\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eLog-likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eMedian Odds Ratio\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eProportion Change in Variance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePSU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003ePrimary sampling unit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eSexually Transmitted Infections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eTDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eTanzania Demographic and Health Survey\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eUNAIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eUnited Nations Programme on HIV/AIDS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eVariance Inflation Factor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 479px;\"\u003e\n \u003cp\u003eWorld Health Organization\u0026apos;s\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the DHS program for making the data available for this study and TILAM International for statistical consultation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJM and EE conceptualized the idea, conducted formal analysis. EE, TGM, SK, EJN, VGM and MJM interpreted the results, drafted the manuscript and reviewed all versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used for this study is openly available and can be accessed via https://dhsprogram.com/data/dataset_admin/login_main.cfm;jsessionid=6BB6964E691F9D481F42CEF7601D7945.cfusion?CFID=410453820\u0026amp;CFTOKEN=d339e221faf5ad06-4F68E5CB-E1B5-3181-B3272BB523352435\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is based on the publicly available 2022 TDHS datasets, which are accessible online and have been de-identified. The initial survey was approved by the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. We obtained permission to use the DHS data from MEASURE Tanzania Demographic and Health Surveys after submitting a request outlining our data analysis plan. Upon receiving approval, we downloaded the dataset from the DHS Program\u0026rsquo;s website. Informed consent was obtained from participants before the interviews. All methods were conducted according to the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJoint United Nations Programme on HIV/AIDS (UNAIDS). Global HIV \u0026amp; AIDS statistics \u0026mdash; Fact sheet [Internet]. [cited 2024 Oct 3]. Available from: https://www.unaids.org/en/resources/fact-sheet\u003c/li\u003e\n\u003cli\u003eNduhukyire L, Semitala FC, Mutanda JN, Muramuzi D, Ipola PA, Owori B, et al. Prevalence, associated factors, barriers and facilitators for oral HIV self-testing among partners of pregnant women attending antenatal care clinics in Wakiso, Uganda. AIDS Research and Therapy. 2024;21:82. \u003c/li\u003e\n\u003cli\u003e2024 global AIDS report \u0026mdash; The Urgency of Now: AIDS at a Crossroads | UNAIDS [Internet]. [cited 2025 Apr 17]. Available from: https://www.unaids.org/en/resources/documents/2024/global-aids-update-2024\u003c/li\u003e\n\u003cli\u003eGlobal HIV Programme [Internet]. [cited 2025 Apr 17]. Available from: https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/hiv/testing-diagnostics/hiv-testing-services\u003c/li\u003e\n\u003cli\u003eHurt CB, Powers KA. Self-testing for HIV and its impact on public health. Sexually transmitted diseases. 2014;41:10\u0026ndash;2. \u003c/li\u003e\n\u003cli\u003eStevens DR, Vrana CJ, Dlin RE, Korte JE. A Global Review of HIV Self-testing: Themes and Implications. AIDS Behav. 2018;22:497\u0026ndash;512. \u003c/li\u003e\n\u003cli\u003eKadye T, Jamil MS, Johnson C, Baggaley R, Barr-DiChiara M, Cambiano V. Country uptake of WHO recommendations on differentiated HIV testing services approaches: a global policy review. BMJ Open. 2024;14:e058098. \u003c/li\u003e\n\u003cli\u003eJohnson C, Neuman M, MacPherson P, Choko A, Quinn C, Wong VJ, et al. Use and awareness of and willingness to self-test for HIV: an analysis of cross-sectional population-based surveys in Malawi and Zimbabwe. BMC Public Health. 2020;20:779. \u003c/li\u003e\n\u003cli\u003eZishiri V, Conserve DF, Haile ZT, Corbett E, Hatzold K, Meyer-Rath G, et al. Secondary distribution of HIV self-test kits by HIV index and antenatal care clients: implementation and costing results from the STAR Initiative in South Africa. BMC Infectious Diseases. 2023;22:971. \u003c/li\u003e\n\u003cli\u003eMwangi J, Miruka F, Mugambi M, Fidhow A, Chepkwony B, Kitheka F, et al. Characteristics of users of HIV self-testing in Kenya, outcomes, and factors associated with use: results from a population-based HIV impact assessment, 2018. BMC Public Health. 2022;22:643. \u003c/li\u003e\n\u003cli\u003eAloni MS. Drivers of HIV self-test kit among Tanzanian men aged 15\u0026ndash;49: findings from the 2022 TDHS-MIS cross-sectional study. AIDS Research and Therapy. 2025;22:3. \u003c/li\u003e\n\u003cli\u003eConserve DF, Abu-Ba\u0026rsquo;are GR, Janson S, Mhando F, Munisi GV, Drezgic B, et al. Peer-based Promotion and Nurse-led Distribution of HIV Self-Testing Among Networks of Men in Dar es Salaam, Tanzania: Development and Feasibility Results of the STEP Intervention. Res Sq. 2023;rs.3.rs-3283552. \u003c/li\u003e\n\u003cli\u003eMbita G, Mwanamsangu A, Plotkin M, Casalini C, Shao A, Lija G, et al. Consistent condom use and dual protection among female sex workers: surveillance findings from a large-scale, community-based combination HIV prevention program in Tanzania. AIDS and Behavior. 2020;24:802\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eConserve DF, Muessig KE, Maboko LL, Shirima S, Kilonzo MN, Maman S, et al. Mate Yako Afya Yako: formative research to develop the Tanzania HIV self-testing education and promotion (Tanzania STEP) project for men. PloS one. 2018;13:e0202521. \u003c/li\u003e\n\u003cli\u003eConserve DF, Abu-Ba\u0026rsquo;are GR, Janson S, Mhando F, Munisi GV, Drezgic B, et al. Development and feasibility of the peer and nurse-led HIV Self-Testing Education and Promotion (STEP) intervention among social networks of men in Dar es Salaam, Tanzania: application of the ADAPT-ITT model. BMC Health Services Research. 2024;24:1166. \u003c/li\u003e\n\u003cli\u003eChiu C, Hunter LA, McCoy SI, Mfaume R, Njau P, Liu JX. Sales and pricing decisions for HIV self-test kits among local drug shops in Tanzania: a prospective cohort study. BMC Health Services Research. 2021;21:434. \u003c/li\u003e\n\u003cli\u003eAshburn K, Antelman G, N\u0026rsquo;Goran MK, Jahanpour O, Yemaneberhan A, N\u0026rsquo;Guessan Kouakou B, et al. Willingness to use HIV self‐test kits and willingness to pay among urban antenatal clients in Cote d\u0026rsquo;Ivoire and Tanzania: a cross‐sectional study. Trop Med Int Health. 2020;25:1155\u0026ndash;65. \u003c/li\u003e\n\u003cli\u003eHunter LA, Rao A, Napierala S, Kalinjila A, Mnyippembe A, Hassan K, et al. Reaching adolescent girls and young women with HIV self-testing and contraception at girl-friendly drug shops: A randomized trial in Tanzania. J Adolesc Health. 2023;72:64\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eMohamud LA, Hassan AM, Nasir JA. Determinants of HIV/Aids Knowledge Among Females in Somalia: Findings from 2018 to 2019 SDHS Data. HIV. 2023;15:435\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eAkweh TY, Adoku E, Mbiba F, Teyko F, Brinsley TY, Boakye Jr BA, et al. Prevalence and factors associated with knowledge of HIV Self-Test kit and HIV-Self Testing among Ghanaian women: multi-level analyses using the 2022 Ghana demographic and health survey. BMC Public Health. 2025;25:1161. \u003c/li\u003e\n\u003cli\u003eHIV/AIDS JUNP on, others. Global AIDS strategy 2021-2026: End inequalities. End AIDS. 2021; \u003c/li\u003e\n\u003cli\u003eSabo KG, Seifu BL, Kase BF, Asebe HA, Asmare ZA, Asgedom YS, et al. Factors influencing HIV testing uptake in Sub-Saharan Africa: a comprehensive multi-level analysis using demographic and health survey data (2015\u0026ndash;2022). BMC Infectious Diseases. 2024;24:821. \u003c/li\u003e\n\u003cli\u003eRaghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995\u0026ndash;2015. Archives of public health. 2020;78:1\u0026ndash;18. \u003c/li\u003e\n\u003cli\u003eChoko AT, MacPherson P, Webb EL, Willey BA, Feasy H, Sambakunsi R, et al. Uptake, accuracy, safety, and linkage into care over two years of promoting annual self-testing for HIV in Blantyre, Malawi: a community-based prospective study. PLoS medicine. 2015;12:e1001873. \u003c/li\u003e\n\u003cli\u003eHatzold K, Gudukeya S, Mutseta MN, Chilongosi R, Nalubamba M, Nkhoma C, et al. HIV self-testing: breaking the barriers to uptake of testing among men and adolescents in sub-Saharan Africa, experiences from STAR demonstration projects in Malawi, Zambia and Zimbabwe. Journal of the International AIDS Society. 2019;22:e25244. \u003c/li\u003e\n\u003cli\u003eNjau B, Covin C, Lisasi E, Damian D, Mushi D, Boulle A, et al. A systematic review of qualitative evidence on factors enabling and deterring uptake of HIV self-testing in Africa. BMC public health. 2019;19:1\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003eJohnson C, Dalal S, Baggaley R, Hogan D, Parrott G, Mathews R, et al. Systematic review of HIV testing costs in high and low income settings. Consolidated Guidelines on HIV Testing Services: 5Cs: Consent, Confidentiality, Counselling, Correct Results and Connection 2015 [Internet]. World Health Organization; 2015 [cited 2025 Apr 26]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK316032/\u003c/li\u003e\n\u003cli\u003eMkopi A, Korte ,Jeffrey E., Lesslie ,Virginia, diNapoli ,Marisa, Mutiso ,Fedelis, Mwajubwa ,Shabani, et al. Acceptability and uptake of oral HIV self-testing among rural community members in Tanzania: a pilot study. AIDS Care. 2023;35:1338\u0026ndash;45. \u003c/li\u003e\n\u003cli\u003eRomero RA, Klausner JD, Marsch LA, Young SD. Technology-delivered intervention strategies to bolster HIV testing. Current HIV/AIDS Reports. 2021;18:391\u0026ndash;405. \u003c/li\u003e\n\u003cli\u003eSaeed SA, Masters RM. Disparities in Health Care and the Digital Divide. Curr Psychiatry Rep. 2021;23:61.\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":"aids-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arty","sideBox":"Learn more about [AIDS Research and Therapy](http://aidsrestherapy.biomedcentral.com/)","snPcode":"12981","submissionUrl":"https://submission.nature.com/new-submission/12981/3","title":"AIDS Research and Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV Self Testing, Reproductive-Aged, Women, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-6551004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6551004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe introduction of HIV self-test (HIVST) kits offered a significant advancement in fighting HIV/AIDS, particularly for vulnerable populations. Despite its potential, self-testing uptake varies. In Tanzania, where HIV prevalence among women remains a concern, this study aimed to identify the socio-demographic and behavioural predictors of self-testing for effective prevention.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional analysis using data from the 2022 Tanzania Demographic and Health Survey. Data management and analysis were performed using Stata 18.5. Given the survey\u0026rsquo;s complex design, a multilevel mixed-effect logistic regression model was used to identify predictors of HIVST kit use, with results presented as adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of HIVST kit usage among women of reproductive age in Tanzania was 3.2% (95% CI: 2.8\u0026ndash;3.7%). Individual-level factors associated with a higher likelihood of HIV self-test kit use included age, 25\u0026ndash;34 (AOR\u0026thinsp;=\u0026thinsp;1.93, 95%CI: 1.43\u0026ndash;2.60) and 35\u0026ndash;49 (AOR\u0026thinsp;=\u0026thinsp;1.60, 95%CI:1.43\u0026ndash;2.26), secondary/higher education (AOR\u0026thinsp;=\u0026thinsp;2.77, 95%CI: 1.57\u0026ndash;4.85), belonging to the rich wealth quintile (AOR\u0026thinsp;=\u0026thinsp;2.69, 95%CI: 1.52\u0026ndash;4.77), internet use (AOR\u0026thinsp;=\u0026thinsp;3.04, 95%CI: 2.04\u0026ndash;4.52), awareness of sexually transmitted infections (STIs) (AOR\u0026thinsp;=\u0026thinsp;2.03, 95%CI: 1.21\u0026ndash;3.42), and one or higher number of sexual partners. At the community level, geographical zone was associated with increased odds of use, while living in a high-poverty community (AOR\u0026thinsp;=\u0026thinsp;0.52, 95%CI: 0.30\u0026ndash;0.89) was associated with a lower likelihood of HIV self-test kit use.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study reveals a low uptake of HIVST kits among women of reproductive age in Tanzania, highlighting the influence of both individual-level factors, such as age, education, wealth, internet access, STI awareness, and sexual behaviours, as well as community-level factors like geographical zone and community poverty. These findings underscore the importance of considering both individual characteristics and the broader community context when designing targeted interventions to promote HIVST kits.\u003c/p\u003e","manuscriptTitle":"Unlocking Self-Testing: Predictors of HIV Self-Testing Kit Use Among Reproductive-Aged Women in Tanzania; A Multilevel Analysis of the 2022 Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 17:44:48","doi":"10.21203/rs.3.rs-6551004/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-23T14:30:05+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"179954602993322350625546757258057751747","date":"2025-05-23T12:05:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T10:03:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T12:22:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118053103838782024421913425035694923023","date":"2025-05-06T07:37:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24720705741872176645380123129285402321","date":"2025-05-05T06:26:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T12:21:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-01T01:49:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-30T11:04:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"AIDS Research and Therapy","date":"2025-04-28T22:16:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aids-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arty","sideBox":"Learn more about [AIDS Research and Therapy](http://aidsrestherapy.biomedcentral.com/)","snPcode":"12981","submissionUrl":"https://submission.nature.com/new-submission/12981/3","title":"AIDS Research and Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a28bd43-8c40-4505-956b-2995019c9b5d","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T15:59:55+00:00","versionOfRecord":{"articleIdentity":"rs-6551004","link":"https://doi.org/10.1186/s12981-025-00774-0","journal":{"identity":"aids-research-and-therapy","isVorOnly":false,"title":"AIDS Research and Therapy"},"publishedOn":"2025-08-04 15:57:10","publishedOnDateReadable":"August 4th, 2025"},"versionCreatedAt":"2025-05-06 17:44:48","video":"","vorDoi":"10.1186/s12981-025-00774-0","vorDoiUrl":"https://doi.org/10.1186/s12981-025-00774-0","workflowStages":[]},"version":"v1","identity":"rs-6551004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6551004","identity":"rs-6551004","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}