Differences in tuberculosis prevalence by sex in low- and middle-income countries over 1993–2025: A systematic review and meta-analysis

This is an uncorrected proof. Figures Abstract Background Global and national initiatives to combat tuberculosis (TB) have expanded over recent years. Despite this, the TB burden remains high in some population groups, with men recognized as having elevated TB risks. Summary measures of sex differences in TB prevalence were last estimated in 2016. Since then, many additional prevalence surveys have been conducted, including in the highest TB burden countries. We conducted a systematic review of sex-stratified TB prevalence survey data published over 1993–2025, to provide updated estimates of male-to-female (M:F) TB prevalence ratios and determine whether sex-related disparities in TB burden have closed over time. Methods and findings We identified surveys reporting community-representative, sex-stratified estimates of pulmonary TB prevalence in low- and middle-income countries (LMICs), including surveys from an earlier review (covering January 1993–March 2016) and a new systematic review (covering 1st December 2015–13th October 2025). This review was prospectively registered with PROSPERO (CRD42024503853) and included searches of PubMed, Embase, Global Health, the Cochrane Library, Africa Index Medicus, LILACS, and SciELO. We extracted data on bacteriologically confirmed and smear-positive TB prevalence among adults (aged ≥ 15 years), stratified by sex. Risk of bias was evaluated using eight criteria specific to prevalence surveys. We fit multi-level Bayesian regression models with study- and country-level random effects to estimate the M:F ratio of TB prevalence (male prevalence divided by female prevalence), overall and for key subgroups. In meta-regression analyses, we estimated how prevalence ratios varied over time and according to known TB risk factors and TB case definitions. We identified 10,124 publications and extracted data from 100 eligible studies representing 102 unique prevalence surveys and 4,658,310 participants (45.6% male) in 33 LMICs. TB prevalence was higher in men than women in 90/102 of the included surveys, with a pooled M:F prevalence ratio of 2.02 (95% credible interval (CrI): 1.71, 2.34) for bacteriologically confirmed TB and 2.38 (95% CrI: 1.91, 2.90) for smear-positive TB. Time trend analyses showed a 2.0% (95% CrI: −0.2, 4.5%) average annual change in the M:F ratio of bacteriologically confirmed TB over the study period. The M:F prevalence ratio was estimated to be higher for countries with greater excess HIV prevalence among men, and countries with greater gender equity (as measured by the United Nation’s Gender Development Index). The estimated M:F prevalence ratio was also higher for surveys that did not restrict testing to individuals reporting TB symptoms. Study limitations include heterogeneity in survey methods and definitions, as well as limited data from the Americas, Eastern Mediterranean, and Europe WHO world regions and post-COVID-19 period. Author summary Why was the study done? - Previous studies, including a 2016 systematic review and meta-analysis, have identified substantial sex differences in tuberculosis (TB) burden, with higher TB infection, prevalence, and mortality consistently observed among men in low- and middle-income countries. - The change in these sex differences over time has not been previously estimated. Recent improvements in TB case detection efforts, diagnostics, and treatment approaches may differentially affect TB epidemiology among men and women. Additionally, newly conducted TB prevalence surveys have increased the duration and diversity of evidence to inform trends in TB burden. What did the researchers do and find? - We systematically identified 102 national and sub-national TB prevalence surveys conducted in low- and middle-income countries and used Bayesian meta-regression models to estimate male-to-female TB prevalence risk ratios overall and among key demographic and epidemiological subgroups. We also estimated how prevalence risk ratios varied according to known TB risk factors, TB case definitions, and over time (1994–2024). - Adult men have over twice the prevalence of pulmonary TB as compared to women in low- and middle-income countries (LMICs). Increased prevalence was associated with greater excess HIV prevalence among men, and countries with greater gender equity (as measured by the United Nation’s Gender Development Index). The estimated M:F prevalence ratio was higher among surveys that did not restrict testing to individuals reporting TB symptoms. - Temporal analysis suggested that male-to-female inequalities in TB prevalence might be growing, particularly in the World Health Organization (WHO) Africa region. What do these findings mean? - Despite global commitments to gender equity in health, men in LMICs continue to bear a disproportionate burden of TB. - If sex-related inequalities in TB burden are growing, developing effective strategies to reduce men’s risk of TB and to engage men in TB prevention and care will be essential to end TB. - Limitations include limited prevalence survey data in the Americas, Eastern Mediterranean, and Europe WHO world regions, methodological heterogeneity across surveys, especially among subnational surveys, and few prevalence surveys from the post-COVID-19 period. Citation: Swartwood NA, Singh N, Mortazavi SA, Can MH, Cui H, Ryuk DK, et al. (2026) Differences in tuberculosis prevalence by sex in low- and middle-income countries over 1993–2025: A systematic review and meta-analysis. PLoS Med 23(5): e1005114. https://doi.org/10.1371/journal.pmed.1005114 Academic Editor: Shenglan Tang, Duke University, UNITED STATES OF AMERICA Received: December 2, 2025; Accepted: May 8, 2026; Published: May 22, 2026 Copyright: © 2026 Swartwood et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All data and code used in this analysis, including the final systematic review dataset, can be found on GitHub: https://github.com/nswartwo/tb-lmic-prevalence-review/ and archived on Zenodo: (DOI: https://doi.org/10.5281/zenodo.20029353). Funding: PM was funded by Wellcome (https://wellcome.org; 304666/Z/23/Z) and an NIHR Global Health Research Professorship (https://www.nihr.ac.uk; NIHR304311). KCH and PM are supported by the UK FCDO (https://www.gov.uk/government/organisations/foreign-commonwealth-development-office; Leaving no one behind: transforming gendered pathways to health for TB). KCH is supported by the U.S. National Institutes of Health (https://www.nih.gov; R-202309-71190). This research has been partially funded by UK aid from the UK government (to KCH and PM). The funders (Wellcome, NIHR, UK FCDO, and U.S. National Institutes of Health) had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: P.M. is a member of the Editorial Board of PLOS Medicine. All other authors declared no competing interests. Abbreviations: AT, anti-retroviral therap; BMI, body mass index; CrI, credible interval; EAPC, estimated annual percentage change; ESS, effective sample size; GBD, Global Burden of Diseases; GDI, Gender Development Index; HDI, human development index; LMICs, low- and middle-income countries; M:F, male-to-female; MOOSE, Meta-analysis of Observational Studies in Epidemiology; NMT, neural machine translation; PPD, probability of positive direction; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-analyses; PROSPERO, International Prospective Register of Systematic Reviews; TB, tuberculosis; WHO, World Health Organization. Introduction Despite sustained global efforts to reduce tuberculosis (TB), it remains the leading cause of death from a single infectious agent globally [1]. In 2024, men were estimated to account for approximately 60% of TB incidence among adults and TB deaths among HIV–negative adults worldwide [1]. The mechanisms underpinning these differences, and the extent to which they vary across time and settings, are not well understood. Gender—defined as the set of socially constructed roles, behaviors, and norms related to perceived sex—is a key determinant of health and shapes TB susceptibility, exposure risks, engagement with prevention and care, and outcomes [2–5]. Gendered health behaviors and systemic healthcare access barriers can lead to delayed diagnosis and worse care engagement among men, contributing to ongoing transmission and under-detection. In addition, sex-assortative mixing among men—particularly in workplaces, social settings, and high-risk congretate settings such as prisons or homeless shelters—can amplify transmission within male networks [2,3]. Men also have a higher prevalence of risk factors that increase both susceptibility to infection (e.g., tobacco use, occupational, and ambient air pollution) and progression from infection to TB disease (e.g., untreated HIV, diabetes, malnutrition, and smoking). Together, sex and gender interact with structural determinants, such as occupational exposures, unequal health system access, and prevailing societal norms, to produce a persistent excess burden of TB among men [4,5]. Conventionally, measures of disease burden are reported by biological sex, not gender. However, given the considerable overlap of sex and gender identities within populations, sex-stratified outcomes will reflect the joint impact of these two related, but distinct, factors. While routinely reported, TB case notifications have marked limitations as measures of TB burden, as they can be affected by incomplete case detection and reporting. National TB prevalence surveys are designed to generate population-representative estimates of TB burden unaffected by these potential biases and have been conducted in a number of high-burden countries with the support of the World Health Organization (WHO). Historically, these surveys have reported higher TB prevalence in men than in women in low- and middle-income countries (LMICs) [6]. A 2016 systematic review and meta-analysis by Horton and colleagues estimated that men in LMICs had more than twice the prevalence of TB compared with women [7]. TB epidemiology is not static and will change under the influence of multiple factors. During the COVID-19 pandemic, many TB resources were reallocated to pandemic response—an effort that led to the pronounced decreases in TB case detection and increases in TB deaths [8,9]. Most recently, global and national commitments to TB elimination have been renewed through accelerated case detection and introduction of new diagnostics and treatment approaches [10]. There has also been a scale-up of interventions to address key risk factors for TB, including increased treatment of HIV [11]. Alongside these changes, a substantial number of new prevalence surveys have been conducted since 2016, including in some of the world’s highest TB burden countries (e.g., India, South Africa) [1]. Sex differences in TB prevalence might have changed in response to these factors, or as a result of the increasing recognition of sex as a TB risk factor [1,12], most notably the addition of men to WHO’s list of populations vulnerable to TB [13]. However, empirical analyses have not described whether sex differences in TB burden have changed over time. In this study, we undertook a systematic review and meta-analysis of sex-disaggregated TB prevalence surveys conducted in LMICs, published between 1st January 1993 and 13th October 2025. Based on this review, we investigated whether sex differences in TB prevalence (operationalized by the male-to-female ratio of TB prevalence) have changed over time. We also explored how this ratio is correlated with key socio-demographic determinants and approaches used to determine TB prevalence (Table 1). Methods We conducted a systematic review and meta-analysis of sex-stratified TB prevalence surveys conducted among people living in LMICs (as defined by the World Bank) [14], We registered the review and meta-analysis with the International Prospective Register of Systematic Reviews (PROSPERO) protocol number CRD42024503853 [15]. We followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines for the conduct of the review (S1 Checklist) and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for preparing the manuscript (S2 Checklist) [16,17]. We used the Covidence online systematic review tool to manage the review [18]. Search strategy We designed the search strategy as an update of a previous systematic review and meta-analysis by Horton and colleagues [7]. Our search included a combination of MeSH terms and keywords specifically related to “tuberculosis,” “prevalence,” and “low- and middle-income countries (LMICs).” We searched four electronic databases—Global Health, the Cochrane Database of Systematic Reviews, PubMed, and Embase—between 1st December 2015 and 13th October 2025 for English language publications. The full search strategy is reported in Table A in S1 Appendix. We additionally evaluated all studies included in the previous analysis for inclusion [7] and compared our included studies against the national TB prevalence surveys listed in WHO Global Tuberculosis Report 2025, adding any survey reports that were not captured by our search [1]. We also examined the citations of all identified publications for additional relevant material. Finally, we screened published abstracts from the 2023 Union World Conference on Lung Health for recent prevalence surveys [19]. Inclusion and exclusion criteria We reviewed all articles and included TB prevalence surveys that reported sex-stratified estimates of adult (aged ≥ 15 years) pulmonary TB prevalence from a population-based sample, conducted in a LMIC (based on 2024 World Bank classification). Extra-pulmonary TB was excluded due to the diagnostic complexity of this condition, which often necessitates invasive diagnostic procedures not typically available in community survey settings. We also excluded studies that only evaluated care-seeking persons, surveys solely reliant on self-reported data without laboratory confirmation, surveys conducted following a community-based active case finding intervention in the same setting (as the intervention may have altered local TB prevalence), and surveys of children aged 14 years or younger, as determining TB status can be challenging in this group. Detailed inclusion and exclusion criteria can be found in Tables B and C in S1 Appendix. Our review team consisted of nine people (NS, NAS, AM, MHC, HC, DKR, PM, KCH, and NAM). Two reviewers (of NS, NAS, AM, MHC, HC, and DKR, allocated at random) independently assessed the titles and abstracts of all studies to identify those requiring full text review. Discrepancies were resolved through consensus discussions, mediated by a third reviewer (NAS, PM, KCH, or NAM). Two reviewers (of NS, NAS, AM, MHC, HC, DKR, PM, and KCH) independently evaluated full texts for study inclusion, with discrepancies being resolved through discussion as outlined above. Data extraction Two reviewers independently extracted data on study methodology, risk of bias, and TB prevalence using a pretested extraction form (S1 Form). Discrepancies between reviewers were resolved by a third reviewer. Where multiple studies reported on the same TB prevalence survey, we merged the studies and extracted all available data; where conflicting data were reported, we extracted data from the most recent publication. Where studies reported multiple prevalence surveys, data were extracted separately for each survey. Therefore, the unit of analysis in our meta-analysis was the prevalence survey as opposed to the publication. Risk of bias We assessed the risk of bias for each included study using eight criteria adapted from the Hoy and colleagues tool for the appraisal of prevalence surveys [20]. This tool examines study population selection, nonresponse bias, data collection methodology, and case definition parameters. The assessment criteria are listed in the extraction form (S1 Form, Section 8). Reviewers were also asked to provide an overall assessment of risk of bias (“low”, “moderate”, or “high”) based on the eight criteria. To evaluate the robustness of our primary English-language search strategy, we also searched three databases—Africa Index Medicus, LILACS, and SciELO—for French, Portuguese, and Spanish publications over the full study period (1st January 2009–13th October 2025). The corresponding search strings are in Table D in S1 Appendix. Non-English titles and abstracts were translated to English using DeepL, a neural machine translation (NMT) tool; studies identified for full-text review were similarly translated. Definitions To be eligible for quantitative analysis, a survey was required to report a measure of bacteriologically confirmed TB. Bacteriologically confirmed TB was defined as TB confirmed through bacteriological methods, requiring at least one positive result of smear microscopy, culture, or a molecular diagnostic (e.g., Xpert MTB/RIF). Where reported, we adopted the study definition of bacteriologically confirmed TB. In the absence of a study definition, we used estimates of culture-positive TB prevalence or smear-positive TB prevalence, prioritized in that order. Smear-positive TB was defined as TB confirmed through sputum smear microscopy, regardless of other diagnostic results. Where studies reported more than one prevalence measure, adjusted TB prevalence estimates with 95% confidence intervals were the preferred measure used for synthesis, followed by crude prevalence estimates with 95% confidence intervals, then counts of individuals with TB and total number tested, or summary prevalence estimates without a confidence interval (Fig A in S1 Appendix). Survey year was set to the end year of data collection; where the end year was missing, we set the survey year to the publication year less the mean observed publication delay in our data, which was 4 years. Participants were classified as male or female by the underlying prevalence review, the basis for which was not commonly reported. As such, we adopted these labels as reported for our analysis. Data synthesis and statistical analysis Reported data from all surveys were converted to prevalence per 100,000 persons. We took one of two approaches to calculate the standard error of each survey-reported prevalence estimate. If confidence intervals were reported, we used these to back-calculate the standard error; for estimates without confidence intervals, we applied the normal approximation method of the binomial standard error. Standard errors were estimated on the log scale. All crude prevalence estimates (i.e., those not already adjusted for features of the survey design) had a variance correction factor applied in order to approximate the additional sampling variance due to complex survey designs. Our primary outcome was the male-to-female (M:F) ratio of bacteriologically confirmed TB prevalence, calculated as TB prevalence among males divided by TB prevalence among females. We used Bayesian multilevel meta-regression models to synthesize evidence from the pooled survey data, with models constructed for the log of the M:F prevalence ratio. This log transformation was adopted to improve the symmetry and homoscedasticity of residuals for the fitted model. This approach also produced a multiplicative relationship between predictor variables and the M:F prevalence ratio, allowing results to be interpreted as risk ratios. We adopted an identity link function and specified a normal likelihood function for the survey data. The standard deviation of individual observations (the log of study-specific M:F prevalence ratios) was calculated from study-reported standard errors. We chose our priors to be weakly informative, based on published guidance [21], and tested the impact of alternative prior specifications on the study results. To estimate overall and study-specific M:F prevalence ratios, we fit models with study- and country-level random effects. To estimate M:F prevalence ratios for each WHO region, we fit models with study- and region-level random effects. For each model, random effects and associated variance parameters were assigned priors and estimated simultaneously, such that uncertainty in these values was fully propagated into posterior inference. We examined these results to report the relative magnitude of random effect variance at the country (or region) and study level. All model equations and priors are reported in Table E in S1 Appendix. To explore how the M:F prevalence ratio changed over time, we revised the regression models to include a term for study year and added random effects for the interaction of this time trend with study country or WHO region, respectively. From the results of these models, we calculated the estimated annual percentage change (EAPC) in the M:F ratio of bacteriologically confirmed TB prevalence, overall and by WHO region. We also calculated the probability that these EAPC estimates were positive (P(EAPC > 0)), as an indicator of whether the magnitude of the M:F prevalence ratio was increasing over time. We extended these regression models to investigate univariable and multivariable country-level exploratory associations between several TB determinants and the M:F ratio of bacteriologically confirmed TB. We included covariates for survey year, United Nations’ Gender Development Index (GDI) [22]. and absolute difference in country-specific male and female levels of prevalence of alcohol use disorder, type II diabetes mellitus, HIV/AIDS, underweight as measured through low body mass index (BMI), and smoking, obtained from the Global Burden of Diseases Project (GBD) [23,24]. Covariates were matched to each survey based on survey country and end year, and standardized globally to a zero mean and unit standard deviation. As such, coefficient estimates reflect the change in the log M:F prevalence ratio associated with a 1 standard deviation change in the predictor. GBD covariates were missing for one survey (2.5%) and GDI was missing for five surveys (12.5%); where available (one of GBD and three of GDI missing surveys), these surveys were assigned the value corresponding to the nearest available year. We list the affected surveys in Table F in S1 Appendix. We fitted these models using data from 38 of 40 nationally-representative surveys (GDI was not available for the Democratic People’s Republic of Korea or Eritrea, and the corresponding surveys were omitted from univariable and multivariable analyses). We applied the main effect model to subgroups of the overall survey data, such as surveys stratified by survey representativeness (national or subnational), risk of bias (low or not low), use of symptom screening (yes or no), and whether bacteriologically confirmed TB required a positive Xpert MTB/RIF or culture, to explore potential differences in the M:F ratio of bacteriologically confirmed TB prevalence. For countries represented by five or more surveys, we estimated country-specific M:F prevalence ratios. We also examined how our results compared with the Horton and colleagues 2016 review by replicating our main effect model for those only those studies included in the previous study. Finally, as an alternative examination of the change in M:F ratios of bacteriologically confirmed TB prevalence over the study period, we fit the main effect model to extracted data grouped into five-year bands (e.g., 2000–2004, 2005–2009, etc.) by survey end year. We tested several alternative model specifications. We revised the main effects and time trend models to include binary covariates representing variation in the use of more sensitive TB diagnostics (i.e., Xpert MTB/RIF or culture) in each survey, and whether symptoms were required for sputum collection. We also explored alternative hierarchical structures, including nested country-random effects within WHO region-random effects. All analyses were conducted in R (version 4.4.2) using the `brms` and `tidybayes` packages [25–27]. The model ran for 10,000 iterations (2,500 burn-in) on 4 chains. Convergence was assessed through inspection of each model’s effective sample size (ESS), potential scale reduction factor (R), and posterior predictive checks. For all outcomes, we report the posterior mean and equal-tailed 95% credible interval (representing an interval with 95% probability of containing the true value) obtained from summarizing 10,000 posterior draws per parameter. Results Of the 10,124 publications screened by title and abstract, 216 had their full text reviewed (Fig 1). Table G in S1 Appendix lists the publications excluded at the full-text review stage with their corresponding exclusion reasons. The most common exclusion reasons were wrong outcome (39%) and wrong study population (34%). We identified 100 English-language publications that described 102 unique surveys reporting sex-stratified bacteriologically confirmed TB prevalence estimates [28–127]. We did not find any relevant surveys in the examined French, Portuguese, or Spanish studies. The characteristics of included surveys and participants are reported in Tables H and I in S1 Appendix. These surveys were conducted in 33 countries across five WHO world regions: 16 in Africa region, seven in the South-East Asia region, six in the Western Pacific region, two in the Eastern Mediterranean region, and two in the region of the Americas (Fig 2). All included countries were classified as WHO high TB incidence countries. Overall, 100/102 surveys reported total participant numbers, giving 4,658,310 participants; 96/102 surveys reported sex-stratified participant numbers, with 45.6% male participants. Panel A: Geographic distribution of surveys, including the total number of surveys in each country. Panel B: Number of subnational and national TB prevalence surveys per country. Note: Map was generated using the maps R package [156]. Among the identified surveys, 90 (88%) reported greater bacteriologically confirmed TB prevalence among men than women; all of the 40 nationally representative surveys reported greater male bacteriologically confirmed TB prevalence. The overall pooled M:F ratio of bacteriologically confirmed TB was 2.02 (95% credible interval: 1.71, 2.34; Fig 3). Posterior predictive checks did not reveal any major systematic discrepancies between model estimates and the underlying data. Details on model performance can be found in Table J and Fig B in S1 Appendix. Table K in S1 Appendix contains estimated M:F ratios from an alternative model hierarchical structure. We also estimated the impact of requiring symptoms for sputum collection and differential diagnostic algorithms on the male-to-female ratio of bacteriologically confirmed TB prevalence; these estimates are reported in Table L in S1 Appendix. Surveys that required symptoms for sputum collection had lower M:F ratios compared to surveys that did not (multiplicative effect: 0.64; 95% CrI: 0.51, 0.80). Surveys that used Xpert MTB/RIF or sputum culture as a component of the diagnostic cascade had higher M:F ratios compared to surveys that only used sputum smear microscopy (multiplicative effect: 1.48; 95% CrI: 1.12, 1.94). In each WHO region, bacteriologically confirmed TB prevalence was higher among males compared with females, ranging from 1.76 (95% CrI: 1.17, 2.49) in the Eastern Mediterranean to 2.91 (95% CrI: 2.51, 3.33) in South-East Asia. Posterior summaries indicated that the between-country and between-region variance exceeded within-county or within-region (i.e., survey-level) variance, respectively (Fig C and Table M in S1 Appendix). This reallocation of variance is consistent with country and region random effects capturing genuine structural differences in TB epidemiology and health system context, rather than representing arbitrary aggregation. Of the identified surveys, 64 also reported sex-stratified smear-positive TB prevalence. All these studies reported the number of participants, for a total of 2,825,955 (45.6% male). The estimated M:F ratio of smear-positive TB was higher than the bacteriologically confirmed TB estimate for the pooled survey data (2.38; 95% CrI: 1.91, 2.90) and for each world region, when estimated using only those surveys that reported both bacteriologically confirmed and smear-positive TB prevalence (Table N in S1 Appendix). The M:F ratios of smear-positive TB prevalence varied among WHO regions—Americas: 1.26 (95% CrI: 0.51, 2.42), Africa: 1.82 (95% CrI: 1.42, 2.26), Eastern Mediterranean: 2.09 (95% CrI: 1.20, 3.31), Western Pacific: 2.58 (95% CrI: 1.95, 3.33), and South-East Asia: 3.65 (95% CrI: 2.97, 4.38). Survey year, defined as the last year of data collection, ranged from 1994 to 2024; five surveys did not report the survey end year (two in Africa, two in South-East Asia, and one in Western Pacific regions). We estimated a likely increasing, but uncertain, time trend in the M:F ratio of bacteriologically confirmed TB (Fig 4). On average, the M:F prevalence ratio increased by 2.0% (95% CrI: −0.2, 4.5%) annually, with a probability of positive direction (PPD) of 0.96. The Africa region had the greatest annual rate of increase, with an average annual change of 2.9% (95% CrI: 0.2, 6.0%) and a PPD of 0.98, while trends in other regions were more uncertain (Table O in S1 Appendix). In exploratory subgroup analyses of 5-year bands, the M:F ratio ranged from 1.68 (95% CrI: 1.01, 2.58) during 2005–2010 to 2.84 (95% CrI: 1.50, 4.64) during 2020–2024; however, these years were among those with the fewest number of prevalence surveys (2005–2010: 18 total, 6 nationally-representative; 2020–2024: 6 total; 2 nationally-representative). We found no evidence that the use of Xpert MTB/RIF or culture or requiring symptoms for sputum collection modified the estimated annual percentage change (Table P and Fig D in S1 Appendix). Panel A: All included surveys. Panel B and C: World regions. Notes: Shaded area represents 95% credible interval. Points on the plot show the empirical male-to-female prevalence ratio for each study, with size corresponding to 1/the standard error of the log odds ratio of male-to-female prevalence, which is used to weight the relative influence of each data point on the regression. Due to the small number of surveys and subsequent uncertainty, we do not present temporal analysis for the Americas (N = 2) or Eastern Mediterranean (N = 4) regions. In univariable exploratory analyses using data from 38 of 40 nationally-representative surveys, we estimated a positive association between the M:F ratio and GDI (multiplicative effect: 1.15; 95% CrI: 1.03, 1.28). In multivariable analysis, the uncertainty in this estimate widened (1.16; 95% CrI: 1.00, 1.34). We also estimated a positive association with excess HIV/AIDS prevalence in men (1.18; 95% CrI: 1.03, 1.35). Together, the included covariates accounted for 60% (95% CrI: 38%, 78%) of the variation observed, as measured by Bayes R2. These estimates were robust to prior choice (Table Q in S1 Appendix). We assessed the correlation between covariates and found minimal evidence of strong correlation. The strongest correlation was observed between alcohol use disorder and GDI (0.36; 95% CrI: 0.10, 0.58); Fig E in S1 Appendix presents the estimated covariate correlations. We also estimated M:F ratios for each of the countries included in the multivariable analysis (Table R in S1 Appendix); these ranged from 1.80 (95% CrI: 1.15, 2.69) in Eswatini to 3.79 (95% CrI: 2.48, 5.57) in Viet Nam. In subgroup analysis, surveys that were nationally representative (N = 40), with low risk of bias (N = 46), or that did not require symptom screening for sputum collection (N = 66) all had M:F ratios of bacteriologically confirmed TB prevalence greater than 2.0 (Table 2). All of the five countries that had five or more surveys—India (N = 27), Ethiopia (N = 11), China (N = 6), Bangladesh (N = 5), Viet Nam (N = 5)—had mean M:F ratios greater than 1.0, but only India, Bangladesh, and China had M:F ratios for which the 95% credible interval excluded the null (1.0), with 3.28 (95% CrI: 2.68, 3.91), 3.25 (95% CrI: 1.54, 5.57), and 2.65 (95% CrI: 1.77, 3.57) times higher prevalence among males compared to females, respectively. We also found that surveys published after the Horton and colleagues (2016) review had a higher pooled M:F ratio of 2.34 (95% CrI: 1.95, 2.79) compared with those included in the previous review, which had a pooled M:F ratio of 1.79 (95% CrI: 1.39, 2.25). Among the 102 surveys included in this analysis, 46, 41, and 14 were assessed to have low, moderate, and high risk of bias, respectively; one survey did not report sufficient information to assess the risk of bias. 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