HIV risk segmentation in microplanning with female sex workers in Zimbabwe: an observational study

HIV risk segmentation in microplanning with female sex workers in Zimbabwe: an observational study | 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 HIV risk segmentation in microplanning with female sex workers in Zimbabwe: an observational study Primrose Matambanadzo, Harriet S. Jones, Albert Takaruza, Sungai Chabata, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8316815/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Introduction HIV acquisition and transmission risk among female sex workers (FSWs) is heterogeneous and shaped by overlapping behavioural, social and structural factors. Person-centred HIV programming requires tailoring support to FSWs’ needs. In the A dapted M icroplanning: E liminating T ransmissible HI V In S ex T ransactions (AMETHIST) cluster randomised trial, we implemented peer-led HIV risk assessments among FSWs in Zimbabwe enrolled in microplanning, a form of enhanced peer outreach. We examined how effectively HIV risk among FSWs was assessed. Methods At 11 sites, peer microplanners administered a risk assessment to FSWs based on six binary indicators, classifying them as at lower (score 0), moderate (score 1–2), or higher (score 3–6) risk. The risk assessment included information on both proximal ( condom use consistency, weekly client volume ) and distal ( young age, duration in sex work, substance and alcohol use, and violence ) factors. Risk was reassessed quarterly and FSWs were considered at higher risk until first assessment. Data from enrolment, quarterly assessments, and outreach data were analysed to assess: 1) risk assessment coverage, 2) risk heterogeneity between and within groups, 3) the association between factors included in the score, and 4) risk heterogeneity between sites. Results Among 7012 FSWs enrolled, 86.2% (n=6,045) completed ≥1 assessment, and 62% (29,163/47,101) of all expected assessments were completed. Exposure to violence and duration in sex work ≤6 months was far more likely in the higher risk group vs the moderate risk group. Risk factors were associated with each other as expected: for example, inconsistent condom use was associated with substance use (OR=3.05, 2.73–3.40), and violence (OR=2.16, CI 1.68–2.78). FSWs aged ≤24 years had higher odds of recent sex work entry (OR=3.91, 95% CI 3.48–4.38). Overall, 39.7% of FSWs were at higher risk with site level differences ranging from 22.8-74.9% across sites. Conclusion Peer microplanners effectively delivered risk assessments within microplanning with high coverage. The simple six factor tool captured both individual and contextual differences in risk. Risk segmentation by peer microplanners has the potential to enhance equity and efficiency in HIV services. Refinements including digital tools could further focus support. Trial registration : Pan African Clinical Trials Registry PACTR202007818077777 . Registered on 2 July 2020. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Female sex workers (FSW) in sub-Saharan Africa are disproportionately affected by HIV.[ 1 , 2 ] In Zimbabwe, HIV prevalence among FSWs is four times higher than among all women, and annual incidence among FSWs is estimated at 3–6%, a rate 5–10 times than among all women.[ 3 – 5 , 5 ] Interventions focused on FSWs remain imperative and evidence supports these being led by sex workers themselves.[ 6 – 9 ] Additionally, given that there is heterogeneity of HIV risk within sex worker populations, and additionally geospatial heterogeneity of risk, it is necessary to understand these variations in risk in order to efficiently prioritise those who need greater support.[ 10 – 13 ] Importantly, effective risk segmentation requires valid and deployable assessment tools.[ 14 – 18 ] Most of the existing assessment tools are facility based and provider delivered or retrospectively applied to data collected from those attending facilities.[ 19 , 20 ] In sub-Saharan Africa, various measures have been used to screen people ‘in or out’ of HIV interventions or to predict outcomes.[ 14 , 19 – 23 ] Yet, risk segmentation among FSWs should be used to determine who needs additional support, rather than restrict services. This is because all active sex workers require HIV and sexual and reproductive health services due to the ongoing multiple concurrent partnerships inherent in their work that place them at heightened risk of HIV infection.[ 8 , 24 ] Risk assessment tools specific to sex workers, and deployable within community settings are needed.[ 19 , 25 , 26 ] FSWs’ HIV risk reflects a mix of proximal and distal behavioural, biological and structural factors.[ 8 , 24 , 27 – 35 ] Proximal factors directly facilitate HIV acquisition or transmission such as condomless sex and high client numbers.[ 33 , 34 ] Distal factors are characteristics such as young age, duration in sex work, problematic drinking or substance use, and experience of violence that influence the proximal factors.[ 33 , 34 , 36 ] Both proximal and distal factors have been demonstrated to increase the likelihood of HIV acquisition and transmission. [ 24 , 27 , 29 , 33 – 35 , 37 ] However, due to limited available evidence, it remains to be determined whether sex worker led HIV programmes could effectively assess these risk factors for prioritising intensive support among FSWs with greatest need. Microplanning, a peer outreach approach that uses standardised tools to systematically engage and support FSWs, may offer a potential mechanism for such risk assessments within a community setting. Microplanning has been reported to result in higher population coverage, improved uptake of services and declines in STI and HIV prevalence and reduced risk of HIV transmission in India, Kenya and Zimbabwe.[ 3 , 38 – 41 ] The A dapted M icroplanning: E liminating T ransmissible HI V In S ex T ransactions ( AMETHIST ) cluster randomised trial, conducted between June 2019 and October 2021, sought to show whether integrating risk segmentation into microplanning could prioritise FSWs at highest risk for more support. Here we examine how effectively HIV risk assessment by peer microplanners among FSWs identified distinct subgroups requiring different levels of support. We assess this through the following indicators of effectiveness: (1) feasibility as demonstrated by the proportion of enrolled FSWs who completed at least one assessment and the overall proportion of quarterly assessments completed; (2) risk heterogeneity between and within risk groupings as shown by the distribution of FSWs across risk segments and the prevalence of the risk factors within risk segments; (3) coherence of the risk assessment tool (by examining whether and to what extent the factors included were associated as expected); and (4) risk heterogeneity between sites .[ 42 ] Methods The intervention Within an established nationally scaled sex worker programme in Zimbabwe, the AMETHIST cluster randomised trial evaluated the effect of risk differentiated microplanning among FSWs in Zimbabwe between June 2019–October 2021.[ 3 , 25 , 43 ] In 11 intervention sites, peer microplanners mapped locations and estimated the age, type, and number of female sex workers. This data was aggregated to generate a population size estimate (PSE) used to set intervention goals. Each peer microplanner was assigned to a specific familiar location in which they were well-networked. They then enrolled FSWs into a location diary using a unique identifier. Existing IDs were recorded for those already accessing services in the programme. Caseloads were capped at 50–80 FSWs per peer, based on prior data indicating poorer outcomes when higher numbers of FSWs were supported.[ 38 ] Risk assessment Once peer microplanners had established rapport, they conducted risk assessments with each FSW in their caseload using a six-factor tool (Fig. 1). The tool, adapted with peer microplanners, assessed both proximal (condomless sex, high client numbers) and distal (young age, recent entry into sex work, substance use, violence) factors of HIV risk, as shown in Fig. 2. At trial start in June 2019 probelamtic alcohol or substance use and violence were combined, resulting in a five-factor tool. In March 2020, peer microplanner feedback prompted separation into six distinct factors. Based on total risk scores, FSWs were allocated to lower (0), moderate (1–2), or higher (3–6) risk segments. Peer microplanners used these segments to tailor the frequency of outreach contact: weekly for higher, bi-weekly for moderate, and monthly for lower risk. FSWs not yet assessed were to be treated as higher risk. Outreach contacts included peer counselling, condom and lubricant distribution, and clinic referrals. Risk assessments were repeated quarterly to keep the understanding of individual needs current. Outreach workers supported peer microplanners in weekly supervision meetings. Study design Study setting and participants The study was implemented across 11 sites (selected through restricted randomisation) in Zimbabwe spanning six provinces (Manicaland, Mashonaland East and West, Matabeleland North and South and Midlands). The contexts were diverse including rural farming areas (Birchenough Bridge, Murehwa, Chipinge), mining towns (Hwange, Gwanda, Zvishavane), transport corridors to neighbouring countries (Rusape, Ngundu), a fishing and tourist hub (Kariba), and university towns (Lupane, Chinhoyi). FSWs were enrolled into AMETHIST on an ongoing basis between June 2019 and October 2021.[ 3 ] Study implementation was affected by COVID-19 lockdowns and disruptions from March 2020 onwards. From March 2021, following the lifting of COVID-19 restrictions, we undertook an optimisation exercise that used microplanning data to identify site-specific gaps and strengthen AMETHIST implementation.[ 43 ] Data analysis Our analysis assessed whether HIV risk was effectively segmented through proxy indicators. We examined feasibility by calculating the proportion of FSWs enrolled who completed at least one risk assessment, the proportion of all expected assessments completed overall (based on individual enrolment time) and the median number of quarterly assessments per person for those with ≥ 1 assessment. We defined a quarter as three months within the period of the study. The number of enrolment quarters was dependent on the FSW’s enrolment date with a maximum of nine full quarters of enrolment possible in the 29-month study period. Each FSW was expected to have one assessment completed per quarter of follow up time. Only one risk assessment was counted per quarter to avoid over representation of assessments per FSW where a repeat assessment occurred within a three month period. The final truncated quarter (Q10) was excluded from analysis because participants did not accrue a full three-month follow-up period. To assess risk heterogeneity between and within risk segments, we described the distribution of FSWs across risk segments at first assessment and summarised the prevalence of each of the 6 risk factors within and between segments and used modified Poisson regression to describe the differences in the proportion of FSWs scoring positive on each risk factor between moderate and higher risk segments. Coherence of the risk assessment tool was examined by testing bi-variate associations between the six factors included in the tool using logistic regression to see if the factors were associated as expected in line with what we know from the literature on HIV risk among FSWs.[ 24 , 29 , 44 , 45 ] We examined the internal coherence of the risk score by verifying that the included factors showed associations consistent with published evidence, as a check on the tool’s validity. For geographical differences, we calculated site specific distributions of risk segments and factors, and tested differences using Pearson χ² tests. This allowed us to examine whether the tool could detect meaningful site level variation.[ 46 ] All analyses were conducted in Stata 19. Reporting followed STROBE guidelines.[ 47 , 48 ] Results Coverage and frequency of risk assessments Between June 2019 and October 2021, 7,012 FSWs were enrolled in AMETHIST. Of these, 86.2% (6,045/7,012; range 71.2–94.9%) completed at least one risk assessment, leaving 13.8% (n = 967; range 5.1%-28.8%) unassessed. As per study implementation 47,101 quarterly risk assessments were expected based on the quarter in which the FSWs were enrolled (Table 1 ). Overall, 62% (n = 29,163) of expected assessments were completed. FSWs enrolled early in the implementation period (study Q1–Q3), had the highest completion rate for all expected assessments, ranging from 55–69%. Quarterly assessment completion rates declined substantially among FSWs enrolled during COVID-19 lockdowns and service disruptions (study Q4–Q8), reaching a low of 30–41%. The proportion of enrolled FSWs that received at least one assessment fell from 98.2% (3,114/3,170) for those enrolled in Q1 to 64.7% for those enrolled in Q7 (211/326) and 30.5% for those enrolled by Q9 (49/151). Table 1 Coverage and frequency of risk assessments by FSWs enrolment period Quarter of enrolment N = FSW enrolled per quarter # Expected quarterly risk assessments† Expected total quarterly assessments for FSWs enrolled Actual total quarterly assessments for FSWs enrolled % expected quarterly assessments completed (actual/expected) FSW enrolled with ≥ 1 assessment n (%) Median actual assessments for FSW with ≥ 1 assessment m [IQR] Context Q1 (Jun – Aug ’19) 3,170 9 28,530 19,609 69% 3114 (98.2%) 7 (6–8) Early implementation period Q2 (Sept – Nov ’19) 825 8 6,600 4,126 63% 808 (97.9%) 6 (5–7) Q3 (Dec ’19 – Feb ’20) 638 7 4,466 2,476 55% 606 (95.0%) 5 (4–6) Q4 (Mar – May ’20) 147 6 882 360 41% 102 (69.4%) 5 (4–5) Covid-19 Lockdowns and movement restrictions with service disruptions and clinic decongestion Q5 (Jun – Aug ’20) 442 5 2,210 1,001 45% 381 (86.2%) 3 (3–4) Q6 (Sept – Nov ’20) 363 4 1,452 664 46% 321 (88.4%) 2 (2–3) Q7 (Dec ’20 – Feb ’21) 326 3 978 296 30% 211 (64.7%) 2 (1–2) Q8 (Mar – May ’21) 916 2 1,832 582 32% 456 (49.8%) 1 (1–2) Optimise AMETHIST period Q9 (Jun – Aug ’21) 151 1 151 49 32% 46 (30.5%) Q10: incomplete ‡ (Sept – Oct ’21) 34 Total 7,012 47,101 29,163 62% 6045 (86.2%) †based on full quarters of follow-up from start of trial to end of trial, reflecting implementation approach ‡ Final period truncated and therefore excluded. 34 FSWs recruited in final 2 months (study Q10) did not complete a three month period of follow up Risk heterogeneity between and within risk segments at first assessment Among the women with ≥ 1 risk assessment, there were 45.8% (2,769/6,045) FSWs in the higher risk segment (score 3–6); 42.3% (2,560/6,045) FSWs in the moderate risk segment (score 1–2) and 11.8% (716/6,045) in lower risk segment at first assessment. Overall risk scores were distributed as follows: score = 0 (11.8%; n = 716), score = 1 (16.4%; n = 994), score = 2 (25.8%; n = 1,566), score = 3 (26.7%; n = 1,613), score = 4 (14.6%; n = 882), score = 5 (4.2%; n = 253), and score = 6 (0.4%; n = 21), demonstrating a skewed distribution with the majority of FSWs (69.0%; n = 4,173) concentrated between scores 2–4 which overlapped the moderate and higher risk segments. A small proportion (4.6%; n = 274) exhibited highly concentrated risk with a score of 5 or 6 (see Supplementary Table 3). As expected, all six behavioural, biological and structural factors were more prevalent in the higher risk group than in the moderate risk group. However, the differences in the proportion of FSWs scoring positive on each risk factor between moderate and higher risk segments provide some insight into the contribution of each factor to the overall risk strata. As shown in Table 2 , violence was 3.54 times more likely in the higher risk group compared to the moderate risk group (48.7%; (370/760) vs 11.0%; (53/481)) and duration in sex work ≤ 6 months 2.85 times more likely (51.8% (1,433/2,769) vs 17.5% (447/2,560)). A lower but still marked difference was also noted in problematic alcohol and substance use (67.4% (1,866/2769) for the higher risk group vs 26.2% (670/2,560) for moderate risk) and for young age ≤ 24 years (60.4% (1,672/2,769) vs 24.3% (623/2,560)). However, risk ratios were lower for both high weekly client numbers and inconsistent condom use where the prevalence was high in both the moderate and higher risk group (77.5% (2,145/2,769) vs 42.8% (1,096/2,560) and (82.1% (2,272/2,769) vs 48.3% (1,237/2,560)) for inconsistent condom use. Table 2 Risk distribution by factor and category at first assessment Risk factor FSW in low risk segment at first assessment (n/%) FSW at moderate risk scoring ≥ 1 on factor (n/%) # of FSW at higher risk scoring 1 on factor (n/%) adjusted Risk Ratio* CI N = 716 N = 2560 N = 2796 Young age ≤ 24 years 0 0% 623 24.3% 1672 60.4% 2.43 [2.25–2.61] New to SW ≤ 6 months 0 0% 447 17.5% 1433 51.8% 2.85 [2.60–3.12] High client #s 0 0% 1096 42.8% 2145 77.5% 1.87 [1.78–1.97] Inconsistent condom use 0 0% 1237 48.3% 2272 82.1% 1.67 [1.60–1.75] Problematic alcohol or substance use 0 0% 670 26.2% 1866 67.4% 2.51 [2.34–2.70] N = 150 N = 481 N = 760 Violence § 0 0% 53 11.0% 370 48.7% 3.54 [2.76–4.55] *modified Poisson regression for each factor at first assessment adjusted for enrolment quarter (higher vs moderate) § Problematic alcohol or substance use and violence initially combined factor (5 factor tool) at trial start in June 2019. Separated into 2 distinct factors (six factor tool) in March 2020, in response to peer microplanner feedback. Only 1401 FSWs were scored on disaggregated violence factor at first assessment. Association between risk factors at first assessment Bivariate logistic regression (Fig. 3 ) showed that FSWs aged ≤ 24 years had markedly higher odds of being recent entrants to sex work (OR = 3.91, 95% CI 3.48–4.38; p < 0.01), and higher odds of high client numbers (OR = 1.40, CI 1.26–1.55; p < 0.01) and inconsistent condom use (OR = 1.23, CI 1.10–1.37; p < 0.01). There was no statistical evidence of an association between young age and problematic alcohol and substance use (OR = 1.03, CI 0.92–1.14; p = 0.62). Recent entrants (≤ 6 months) were more likely to report high client volumes (OR = 1.80, CI 1.61–2.01; p < 0.01) and inconsistent condom use (OR = 1.12, CI 1.01–1.26; p < 0.01), and showed no association with problematic drinking or substance use (OR = 0.97, CI 0.87–1.08; p = 0.55). Being young and a new entrant to sex work both had lower odds of violence. High client numbers co-occurred with inconsistent condom use (OR = 1.76, CI 1.59–1.95; p < 0.01), problematic drinking or substance use (OR = 1.71, CI 1.55–1.90; p < 0.01), and violence (OR = 1.37, CI 1.09–1.73; p < 0.01). Inconsistent condom use was significantly associated with problematic drinking or substance use (OR = 3.05, CI 2.73–3.40; p < 0.01) and violence (OR = 2.16, CI 1.68–2.78; p < 0.01). The strongest association was between problematic alcohol or substance use and violence (OR = 8.50, CI 6.57–11.01; p < 0.01). Geographic variation in risk factors and groups Between site heterogeneity in risk factors was substantial, revealing marked differences between geographical contexts (see Fig. 4 ). Risk group distributions differed significantly across the 11 sites with the mean proportion at higher risk at 39.7% (range 22.8–74.9%; (p < 0.001) (see supplementary table). As shown in Table 3 the proportion at low risk (scoring zero on all factors) ranged from 1.7% in Site 2 (n = 19/1132) to 22.8% in Site 9 (n = 76/334), while experience of violence varied from 0% (n = 0/204) in Site 7 to 22.1% in Site 5 (n = 99/447). Site 2, a rural and peri-urban setting, demonstrated concentrated and overlapping risks and recorded the highest proportion of young FSWs (55.9%, 633/1132) and of recent entrants into sex work (49.2%, 557/1132). This occurred alongside problematic drinking and substance use (59,9%, 678/1132) which was also high in rural Site 1 (46.8%, 125/267) and Site 8 (38.1%, 138/362). In mining towns, risk patterns were divergent. Site 11 reported high client volume (69.9%, 364/521) and inconsistent condom use (72%, 375/521), while Site 5 had comparatively lower client volume (29.3%, 131/447) and lower inconsistent condom use (38.9%, 175/447). However, Site 5 had the highest reported experience of violence (22%, 99/447). University towns demonstrated relatively protective environments, with violence nearly absent in Site 2 and Site 7 at 1.7% (13/782) and 0% respectively, though both had moderate numbers of younger FSWs at 28.1% (n = 220/782) and 36.8% (75/204). Table 3: Heterogeneity of risk factors by siteSite Total % with zero risk score Young age ≤ 24 years New to SW ≤ 6 months High client #s Inconsistent condom use Problematic drinking/ substance use Violence Context N = 6,045 N = 1,401¶ Overall 6045 Site 1 267 3.7% (n = 10) 34.5% (n = 92) 26.2% (n = 70) 62.2% (n = 166) 69.7% (n = 186) 46.8% (n = 125) 5.2% (n = 14) Rural farming Site 2 782 19.6% (n = 153) 28.1% (n = 220) 27.5% (n = 215) 46.2% (n = 361) 61.6% (n = 482) 44.3% (n = 346) 1.7% (n = 13) University town, transport corridor Site 3 1132 1.7% (n = 19) 55.9% (n = 633) 49.2% (n = 557) 66.3% (n = 750) 71.0% (n = 804) 59.9% (n = 678) 13.6% (n = 154) Rural and peri-urban farming Site 4 450 17.0% (n = 77) 35.6% (n = 160) 25.6% (n = 115) 41.8% (n = 188) 61.6% (482) 30.7% (n = 138) 2.0% (n = 9) Mining town Site 5 447 20.1% (n = 90) 17.0% (n = 76) 9.0% (n = 40) 29.3% (n = 131) 38.9% (n = 175) 45.6% (n = 204) 22.1% (n = 99) Mining town Site 6 279 14.7% (n = 41) 35.1% (n = 98) 27.2% (n = 76) 54.8% (n = 153) 62.2% (n = 278) 22.9% (n = 64) 1.8% (n = 5) Fishing and tourist town Site 7 204 15.7% (n = 32) 36.8% (n = 75) 57.4% (n = 117) 33.8% (n = 69) 42.3% (n = 118) 18.1% (n = 37) 0.0% (n = 0) University town Site 8 362 19.9% (n = 72) 29.8% (n = 108) 34.5% (n = 125) 48.1% (n = 174) 35.8% (n = 73) 38.1% (n = 138) 6.4% (n = 23) Rural Site 9 334 22.8% (n = 76) 20.7% (n = 70) 12.3% (n = 41) 58.1% (n = 194) 47.8% (n = 173) 18.6% (n = 62) 1.5% (n = 5) Transport corridor Site 10 1267 10.3% (n = 131) 48.1% (n = 610) 32.1% (n = 407) 54.5% (n = 691) 56.1% (n = 711) 38.0% (n = 482) 7.2% (n = 91) Transport corridor Site 11 521 2.9% (n = 15) 29.4% (n = 153) 22.5% (n = 117) 69.9% (n = 364) 72.0% (n = 375) 50.3% (n = 262) 1.9% (n = 10) Mining town ¶ Problematic alcohol or substance use and violence initially combined factor (5 factor tool) at trial start in June 2019. Separated into 2 distinct factors (six factor tool) in March 2020, in response to peer microplanner feedback. Only 1401 FSWs were scored on disaggregated violence factor at first assessment. Discussion Our study examined whether risk assessments by peer microplanners could effectively segment HIV risk among FSWs. We sought to do this by assessing four indicators of effective risk segmentation: 1) the feasibility of delivering assessments, 2) the heterogeneity of risk between and within groups, 3) the coherence of the assessment tool, and 4) differences in risk across diverse contexts. We demonstrated that ongoing risk assessment was feasible at scale with > 85% of FSWs completing at least one risk assessment. However, initial and repeat risk assessment completion rates were lower among those enrolled later in the study and during COVID-19 pandemic related disruptions. We hypothesised that FSWs’ risk scores would show associations between risk factors in line with published literature. We confirmed these expected patterns, providing preliminary evidence that the tool measures what it is intended to measure. Risk could be assessed at the point of first contact in the community, effectively detecting heterogeneity and flagging both individual and contextual differences. Risk-differentiated microplanning was actionable, providing a better understanding of the risk of both individuals and the locations that they were in at the same time, to inform prioritisation of support and services. Interventions targeting service-distant populations are necessary to reduce high risk in groups not yet reached by services.[ 49 ] Waiting for individuals to engage in HIV services before assessing their HIV risk misses earlier risk exposure periods, and the opportunity to respond to them. In our study peer microplanners assessed HIV risk among FSWs and segmented them into risk strata outside of the formal health system. They then provided more intensive support to those at higher risk even before linkage to clinical services.[ 50 , 51 ] Additionally, the tool was well suited to detecting context specific HIV risks requiring community level interventions thereby addressing gaps previously identified in the literature in understanding of geospatial risk heterogeneity.[ 12 , 13 , 46 , 52 ] HIV programmers and policy makers should explore deploying risk differentiated microplanning to characterise and respond to HIV risk among FSWs and other populations known to be at higher risk. While high client numbers and inconsistent condom use were more common in the higher risk group, they were also highly prevalent among FSWs at moderate risk. This HIV prevention gap persists widely despite condom knowledge, efficacy and access within FSW programmes.[ 53 – 55 ] Dependence on sex work income for survival likely limits the power to avoid unsafe sex and often supersedes knowledge and access in shaping prevention behaviour, requiring renewed community informed approaches to address this gap.[ 53 – 56 ] Additionally, the need for comprehensive and integrated HIV prevention approaches for FSWs was underscored by the strong association between proximal factors ( client volume, inconsistent condom use ) and distal factors ( violence, substance use, young age, recent entry ).[ 6 , 8 , 57 , 58 ] For example the overlaps observed between problematic drinking and substance use and inconsistent condom use affirmed that these factors operate synergistically.[ 36 , 59 , 60 ] More intensive harm reduction and violence prevention interventions should be prioritised in areas where these are indicated as more prevalent. Our findings also reinforce the need to prioritise support for younger women who have newly entered sex work for more intense services and support. We noted similar patterns in association between being young (< 25 years) and having recently entered into sex work, having high client numbers and inconsistent condom use as other studies have documented.[ 29 , 53 , 61 , 62 ] A key strength of our study is that it adds to the existing literature by demonstrating real world utility of ongoing community based risk assessment by peer microplanners; peer microplanners can effectively segment and respond to vulnerabilities within routine programming. Our study advances the programme science in this area by demonstrating that peer microplanners could stratify FSWs risk using a simple tool to tailor outreach intensity accordingly. An additional strength is the diversity of contexts in which tool was implemented. This geographic heterogeneity demonstrates that the tool performs can be used across markedly different contexts. A limitation of our study is that, while it demonstrates tool as coherent and effective for segmentation, we did not correlate risk strata with subsequent HIV seroconversion, STI outcomes, or engagement in HIV prevention and treatment over time. As a result, we cannot directly assess the predictive utility of the score or determine whether the chosen thresholds optimally discriminate future HIV-related outcomes.[ 20 ] Future work should link these risk segments to clinical endpoints to inform refining cut points for the risk assessment tool (particularly given the strata overlapping concentration of risk among those who scored 2–4) and possibly explore evidence based weighting of risk factors, to more fully inform the tool’s utility for precision HIV prevention.[ 63 , 64 ] It would be beneficial for future research to longitudinally explore FSWs’ transitions between risk groups as captured in these assessments to better understand what influences these changes over time and inform responses. This will facilitate context tailored programming where resource allocation in HIV interventions that continually reflects site level risk profiles. Mixed-methods research exploring what influences different risk patterns across settings may also usefully inform tailored programming. Conclusions By stratifying FSWs risk using a simple tool and tailoring outreach intensity accordingly, risk differentiated microplanning aims to use granular data to focus HIV programmes more effectively to the unique needs of people, communities and locations as recommended by UNAIDS in its Global AIDS Strategy 2021-2026.[65] HIV risk segmentation by peer microplanners, updated regularly, could inform triaging of outreach, resource allocation, and focus referrals for clinic and support services. Declarations Ethics approval The Medical Research Council of Zimbabwe (MRCZ/A/2559) and the Liverpool School of Tropical Medicine Research Ethics Committee (Ref 19-115RS) granted ethical approval for this study. All procedures involving human participants were conducted in accordance with the ethical standards of these institutional and national research committees and with the principles of the 2013 revision of the Declaration of Helsinki.[66] Before enrolment, participants provided verbal informed consent, and risk assessment and programme data used in this analysis were de-identified prior to use. Consent for Publication Not applicable. Funding The Elton John AIDS Foundation funded the implementation of the intervention, and the Wellcome Trust [Grant number: 214280/Z/18/Z] provided funding for the study. The Key Populations programme is funded through the Global Fund to fight AIDS, Tuberculosis, and Malaria and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) through USAID. Authors’ Contributions PM led the study's conception and design, data analysis, and interpretation. HJ supported data management and analysis. AT supported data management. PM drafted and revised the article with the involvement of ES, JB, RS, HJ, SC, JH, and FMC. All authors approved the version to be submitted. Acknowledgements We acknowledge the members of the research team who collected the data for this study (Fortunate Machingura, Sungai Chabata, Memory Makamba, Gracious Madimutsa, Adrian Chikeya, Jaspar Maguma) and those who supported the implementation of AMETHIST and data cleaning (Sithembile Musemburi, Rumbidzai Makandwa, Florence Mutevedzi, and Sitholubuhle Zitha). We would also like to acknowledge the participants in this study for their contribution and the Ministry of Health and Child Care and National AIDS Council in Zimbabwe, who supported trial implementation. Conflicts of Interest The authors declare that they have no competing interests. Data availability statement All relevant data are presented in the manuscript and online supplementary materials. Any further details can be obtained by contacting the corresponding author. References Jones HS, Anderson RL, Cust H, McClelland RS, Richardson BA, Thirumurthy H, et al. HIV incidence among women engaging in sex work in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob Health 2024;12:e1244–60. https://doi.org/10.1016/S2214-109X(24)00227-4. Stevens O, Sabin K, Anderson RL, Garcia SA, Willis K, Rao A, et al. Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data, 2010–23. Lancet Glob Health 2024;12:e1400–12. https://doi.org/10.1016/S2214-109X(24)00236-5. Cowan FM, Machingura F, Ali MS, Chabata ST, Takaruza A, Dirawo J, et al. A risk-differentiated, community-led intervention to strengthen uptake and engagement with HIV prevention and care cascades among female sex workers in Zimbabwe (AMETHIST): a cluster randomised trial. Lancet Glob Health 2024;12:e1424–35. https://doi.org/10.1016/S2214-109X(24)00235-3. Ali MS, Wit MDE, Chabata ST, Magutshwa S, Musemburi S, Dirawo J, et al. Estimation of HIV incidence from analysis of HIV prevalence patterns among female sex workers in Zimbabwe. AIDS Lond Engl 2022;36:1141–50. https://doi.org/10.1097/QAD.0000000000003198. Ministry of Health and Child Care (MoHCC). Zimbabwe Population-based HIV Impact Assessment 2020 (ZIMPHIA 2020): Final Report. 2021. World Health Organization, United Nations Population Fund, Joint United Nations Programme on HIV/AIDS, Global Network of Sex Work Projects, The World Bank. Implementing comprehensive HIV/STI programmes with sex workers: practical approaches from collaborative interventions 2013. Matambanadzo P, Otiso L, Kavhaza S, Bhattacharjee P, Cowan FM. Community leadership is key to effective HIV service engagement for female sex workers in Africa. J Int AIDS Soc 2025;28:e26425. https://doi.org/10.1002/jia2.26425. Bekker L-G, Johnson L, Cowan F, Overs C, Besada D, Hillier S, et al. Combination HIV prevention for female sex workers: what is the evidence? The Lancet 2015;385:72–87. https://doi.org/10.1016/S0140-6736(14)60974-0. Kerrigan D, Kennedy CE, Morgan-Thomas R, Reza-Paul S, Mwangi P, Win KT, et al. A community empowerment approach to the HIV response among sex workers: effectiveness, challenges, and considerations for implementation and scale-up. The Lancet 2015;385:172–85. https://doi.org/10.1016/S0140-6736(14)60973-9. Mamulwar M, Godbole S, Bembalkar S, Kamble P, Dulhani N, Yadav R, et al. Differing HIV vulnerability among female sex workers in a high HIV burden Indian state. PloS One 2018;13:e0192130. https://doi.org/10.1371/journal.pone.0192130. Melesse DY, Shafer LA, Shaw SY, Thompson LH, Achakzai BK, Furqan S, et al. Heterogeneity Among Sex Workers in Overlapping HIV Risk Interactions With People Who Inject Drugs: A Cross-Sectional Study From 8 Major Cities in Pakistan. Medicine (Baltimore) 2016;95:e3085. https://doi.org/10.1097/MD.0000000000003085. Cuadros DF, Chowdhury T, Milali M, Citron DT, Nyimbili S, Vlahakis N, et al. Geospatial patterns of progress towards UNAIDS “95-95-95” targets and community vulnerability in Zambia: insights from population-based HIV impact assessments. BMJ Glob Health 2023;8:e012629. https://doi.org/10.1136/bmjgh-2023-012629. Cuadros DF, Huang Q, Musuka G, Dzinamarira T, Moyo BK, Mpofu A, et al. Moving beyond hotspots of HIV prevalence to geospatial hotspots of UNAIDS 95-95-95 targets in sub-Saharan Africa. Lancet HIV 2024;11:e479–88. https://doi.org/10.1016/S2352-3018(24)00102-4. Balkus JE, Brown ER, Palanee-Phillips T, Matovu Kiweewa F, Mgodi N, Naidoo L, et al. Performance of a validated risk score to predict HIV-1 acquisition among African women participating in a trial of the dapivirine vaginal ring. J Acquir Immune Defic Syndr 1999 2018;77:e8–10. https://doi.org/10.1097/QAI.0000000000001556. Towards a people-centered precision prevention approach: Considerations for prioritization | GPC n.d. https://hivpreventioncoalition.unaids.org/en/resources/towards-people-centered-precision-prevention-approach-considerations-prioritization (accessed March 9, 2025). Wang B, Liu F, Deveaux L, Ash A, Gosh S, Li X, et al. Adolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention. AIDS Lond Engl 2021;35:S75–84. https://doi.org/10.1097/QAD.0000000000002867. Precision Response Needed For HIV/AIDS | Health Affairs n.d. https://www.healthaffairs.org/doi/10.1377/hlthaff.2017.1311 (accessed March 9, 2025). McGrath C, Palmarella G, Solomon S, Dupuis R. Precision Prevention and Public Health 2017. Willcox AC, Richardson BA, Shafi J, Kabare E, Kinuthia J, Jaoko W, et al. Derivation of an HIV Risk Score for African Women Who Engage in Sex Work. AIDS Behav 2021;25:3292–302. https://doi.org/10.1007/s10461-021-03235-7. Jia KM, Eilerts H, Edun O, Lam K, Howes A, Thomas ML, et al. Risk scores for predicting HIV incidence among adult heterosexual populations in sub-Saharan Africa: a systematic review and meta-analysis. J Int AIDS Soc 2022;25:e25861. https://doi.org/10.1002/jia2.25861. Castor D, Burgess EK, Yende-Zuma N, Heck CJ, Abdool Karim Q. Age-Restriction of a Validated Risk Scoring Tool Better Predicts HIV Acquisition in South African Women: CAPRISA 004. AIDS Behav 2022;26:3300–10. https://doi.org/10.1007/s10461-022-03664-y. Giovenco D, Pettifor A, MacPhail C, Kahn K, Wagner R, Piwowar-Manning E, et al. Assessing risk for HIV infection among adolescent girls in South Africa: an evaluation of the VOICE risk score (HPTN 068). J Int AIDS Soc 2019;22:e25359. https://doi.org/10.1002/jia2.25359. Ong JJ, Coulthard K, Quinn C, Tang MJ, Huynh T, Jamil MS, et al. Risk-Based Screening Tools to Optimise HIV Testing Services: a Systematic Review. Curr HIV/AIDS Rep 2022;19:154–65. https://doi.org/10.1007/s11904-022-00601-5. Scorgie F, Chersich MF, Ntaganira I, Gerbase A, Lule F, Lo Y-R. Socio-Demographic Characteristics and Behavioral Risk Factors of Female Sex Workers in Sub-Saharan Africa: A Systematic Review. AIDS Behav 2012;16:920–33. https://doi.org/10.1007/s10461-011-9985-z. Cowan FM, Machingura F, Chabata ST, Ali MS, Busza J, Steen R, et al. Differentiated prevention and care to reduce the risk of HIV acquisition and transmission among female sex workers in Zimbabwe: study protocol for the “AMETHIST” cluster randomised trial. Trials 2022;23:209. https://doi.org/10.1186/s13063-022-06119-w. Baral S, Beyrer C, Muessig K, Poteat T, Wirtz AL, Decker MR, et al. Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Infect Dis 2012;12:538–49. https://doi.org/10.1016/S1473-3099(12)70066-X. Abdella S, Demissie M, Worku A, Dheresa M, Berhane Y. HIV prevalence and associated factors among female sex workers in Ethiopia, east Africa: A cross-sectional study using a respondent-driven sampling technique. eClinicalMedicine 2022;51. https://doi.org/10.1016/j.eclinm.2022.101540. Longo JDD, Simaleko MM, Diemer HS-C, Grésenguet G, Brücker G, Belec L. Risk factors for HIV infection among female sex workers in Bangui, Central African Republic. PLoS ONE 2017;12:e0187654. https://doi.org/10.1371/journal.pone.0187654. Jones HS, Hensen B, Musemburi S, Chinyanganya L, Takaruza A, Chabata ST, et al. Temporal trends in, and risk factors for, HIV seroconversion among female sex workers accessing Zimbabwe’s national sex worker programme, 2009-19: a retrospective cohort analysis of routinely collected HIV testing data. Lancet HIV 2023;10:e442–52. https://doi.org/10.1016/S2352-3018(23)00110-8. Lämmle L, Woll A, Mensink GBM, Bös K. Distal and Proximal Factors of Health Behaviors and Their Associations with Health in Children and Adolescents. Int J Environ Res Public Health 2013;10:2944–78. https://doi.org/10.3390/ijerph10072944. Shannon K, Strathdee SA, Goldenberg SM, Duff P, Mwangi P, Rusakova M, et al. Global epidemiology of HIV among female sex workers: influence of structural determinants. Lancet Lond Engl 2015;385:55–71. https://doi.org/10.1016/S0140-6736(14)60931-4. Proximal and Distal Predictors of AIDS Risk Behaviors among Inner-city African American and European American Women n.d. https://doi.org/10.1177/135910530100600207. Shannon K, Goldenberg SM, Deering KN, Strathdee SA. HIV infection among female sex workers in concentrated and high prevalence epidemics: why a structural determinants framework is needed. Curr Opin HIV AIDS 2014;9:174–82. https://doi.org/10.1097/COH.0000000000000042. Mishra S, Boily M-C, Schwartz S, Beyrer C, Blanchard JF, Moses S, et al. Data and methods to characterize the role of sex work and to inform sex work programmes in generalized HIV epidemics: evidence to challenge assumptions. Ann Epidemiol 2016;26:557–69. https://doi.org/10.1016/j.annepidem.2016.06.004. Baral S, Logie CH, Grosso A, Wirtz AL, Beyrer C. Modified social ecological model: a tool to guide the assessment of the risks and risk contexts of HIV epidemics. BMC Public Health 2013;13:482. https://doi.org/10.1186/1471-2458-13-482. Beksinska A, Karlsen O, Gafos M, Beattie TS. Alcohol use and associated risk factors among female sex workers in low- and middle-income countries: A systematic review and meta-analysis. PLOS Glob Public Health 2023;3:e0001216. https://doi.org/10.1371/journal.pgph.0001216. Atuhaire L, Adetokunboh O, Shumba C, Nyasulu PS. Effect of community-based interventions targeting female sex workers along the HIV care cascade in sub-Saharan Africa: a systematic review and meta-analysis. Syst Rev 2021;10:137. https://doi.org/10.1186/s13643-021-01688-4. Bhattacharjee P, Musyoki H, Prakash R, Malaba S, Dallabetta G, Wheeler T, et al. Micro-planning at scale with key populations in Kenya: Optimising peer educator ratios for programme outreach and HIV/STI service utilisation. PloS One 2018;13:e0205056. https://doi.org/10.1371/journal.pone.0205056. Blanchard JF, Bhattacharjee P, Kumaran S, Ramesh BM, Kumar NS, Washington RG, et al. Concepts and strategies for scaling up focused prevention for sex workers in India. Sex Transm Infect 2008;84 Suppl 2:ii19-23. https://doi.org/10.1136/sti.2008.033134. Reza-Paul S, Beattie T, Syed HUR, Venukumar KT, Venugopal MS, Fathima MP, et al. Declines in risk behaviour and sexually transmitted infection prevalence following a community-led HIV preventive intervention among female sex workers in Mysore, India. AIDS Lond Engl 2008;22 Suppl 5:S91-100. https://doi.org/10.1097/01.aids.0000343767.08197.18. Thilakavathi S, Boopathi K, Girish Kumar CP, Santhakumar A, Senthilkumar R, Eswaramurthy C, et al. Assessment of the scale, coverage and outcomes of the Avahan HIV prevention program for female sex workers in Tamil Nadu, India: is there evidence of an effect? BMC Public Health 2011;11 Suppl 6:S3. https://doi.org/10.1186/1471-2458-11-S6-S3. Chowdhury MT, Bershteyn A, Milali M, Citron D, Nyimbili S, Musuka G, et al. Progress Towards UNAIDS’s 95-95-95 Targets in Zimbabwe: Sociodemographic Constraints and Geospatial Heterogeneity. medRxiv 2023:2023.07.26.23293207. https://doi.org/10.1101/2023.07.26.23293207. Cowan FM, Musemburi S, Matambanadzo P, Chida P, Steen R, Makandwa R, et al. Using a Programme Science approach to substantially reduce the risk of HIV transmission and acquisition in sex transactions among female sex workers in Zimbabwe. J Int AIDS Soc 2024;27 Suppl 2:e26262. https://doi.org/10.1002/jia2.26262. Strauss ME, Smith GT. Construct Validity: Advances in Theory and Methodology. Annu Rev Clin Psychol 2009;5:1–25. https://doi.org/10.1146/annurev.clinpsy.032408.153639. Chapman H, Gillespie SM. The Revised Conflict Tactics Scales (CTS2): A review of the properties, reliability, and validity of the CTS2 as a measure of partner abuse in community and clinical samples. Aggress Violent Behav 2019;44:27–35. https://doi.org/10.1016/j.avb.2018.10.006. Wand H, Ramjee G. Spatial clustering of “measured” and “unmeasured” risk factors for HIV infections in hyper-endemic communities in KwaZulu-Natal, South Africa: results from geoadditive models. AIDS Care 2015;27:1375–81. https://doi.org/10.1080/09540121.2015.1096896. Cuschieri S. The STROBE guidelines. Saudi J Anaesth 2019;13:S31–4. https://doi.org/10.4103/sja.SJA_543_18. Ghaferi AA, Schwartz TA, Pawlik TM. STROBE Reporting Guidelines for Observational Studies. JAMA Surg 2021;156:577–8. https://doi.org/10.1001/jamasurg.2021.0528. Steen R, Wheeler T, Gorgens M, Mziray E, Dallabetta G. Feasible, Efficient and Necessary, without Exception – Working with Sex Workers Interrupts HIV/STI Transmission and Brings Treatment to Many in Need. PLOS ONE 2015;10:e0121145. https://doi.org/10.1371/journal.pone.0121145. Cowan FM, Machingura F, Ali MS, Chabata ST, Takaruza A, Dirawo J, et al. A risk-differentiated, community-led intervention to strengthen uptake and engagement with HIV prevention and care cascades among female sex workers in Zimbabwe (AMETHIST): a cluster randomised trial. Lancet Glob Health 2024;12:e1424–35. https://doi.org/10.1016/S2214-109X(24)00235-3. Matambanadzo P, Jones HS, Takaruza A, Busza J, Sibanda EL, Hargreaves JR, et al. Risk differentiated microplanning among female sex workers in Zimbabwe: a mixed methods evaluation guided by the RE-AIM framework. ResearchGate 2025. https://doi.org/10.21203/rs.3.rs-7304011/v1. Birri Makota R, Musenge E. Spatial heterogeneity in relationship between district patterns of HIV incidence and covariates in Zimbabwe: a multi-scale geographically weighted regression analysis. Geospatial Health 2023;18. https://doi.org/10.4081/gh.2023.1207. Chabata ST, Hensen B, Chiyaka T, Mushati P, Busza J, Floyd S, et al. Condom use among young women who sell sex in Zimbabwe: a prevention cascade analysis to identify gaps in HIV prevention programming. J Int AIDS Soc 2020;23:e25512. https://doi.org/10.1002/jia2.25512. Andrews CH, Faxelid E, Sychaerun V, Phrasisombath K. Determinants of consistent condom use among female sex workers in Savannakhet, Lao PDR. BMC Womens Health 2015;15:63. https://doi.org/10.1186/s12905-015-0215-0. Decker MR, Park JN, Allen ST, Silberzahn B, Footer K, Huettner S, et al. Inconsistent Condom Use Among Female Sex Workers: Partner-specific Influences of Substance Use, Violence, and Condom Coercion. AIDS Behav 2020;24:762–74. https://doi.org/10.1007/s10461-019-02569-7. Bandyopadhyay K, Banerjee S, Goswami DN, Dasgupta A, Jana S. Predictors of Inconsistent Condom Use among Female Sex Workers: A Community-Based Study in a Red-Light Area of Kolkata, India. Indian J Community Med Off Publ Indian Assoc Prev Soc Med 2018;43:274–8. https://doi.org/10.4103/ijcm.IJCM_84_18. Cowan FM, Chabata ST, Musemburi S, Fearon E, Davey C, Ndori-Mharadze T, et al. Strengthening the scale-up and uptake of effective interventions for sex workers for population impact in Zimbabwe. J Int AIDS Soc 2019;22 Suppl 4:e25320. https://doi.org/10.1002/jia2.25320. Vassall A, Chandrashekar S, Pickles M, Beattie TS, Shetty G, Bhattacharjee P, et al. Community Mobilisation and Empowerment Interventions as Part of HIV Prevention for Female Sex Workers in Southern India: A Cost-Effectiveness Analysis. PLoS ONE 2014;9:e110562. https://doi.org/10.1371/journal.pone.0110562. Bazzi AR, Yotebieng K, Otticha S, Rota G, Agot K, Ohaga S, et al. PrEP and the syndemic of substance use, violence, and HIV among female and male sex workers: a qualitative study in Kisumu, Kenya. J Int AIDS Soc 2019;22:e25266. https://doi.org/10.1002/jia2.25266. Mooney A, Kidanu A, Bradley HM, Kumoji EK, Kennedy CE, Kerrigan D. Work-related violence and inconsistent condom use with non-paying partners among female sex workers in Adama City, Ethiopia. BMC Public Health 2013;13:771. https://doi.org/10.1186/1471-2458-13-771. Bowring AL, Ketende S, Rao A, Mfochive Njindam I, Decker MR, Lyons C, et al. Characterising unmet HIV prevention and treatment needs among young female sex workers and young men who have sex with men in Cameroon: a cross-sectional analysis. Lancet Child Adolesc Health 2019;3:482–91. https://doi.org/10.1016/S2352-4642(19)30123-3. Crankshaw TL, Chareka S, Zambezi P, Poku NK. Age Matters: Determinants of sexual and reproductive health vulnerabilities amongst young women who sell sex (16–24 years) in Zimbabwe. Soc Sci Med 2021;270:113597. https://doi.org/10.1016/j.socscimed.2020.113597. KAHLE EM, HUGHES JP, LINGAPPA JR, JOHN-STEWART G, CELUM C, NAKKU-JOLOBA E, et al. An empiric risk scoring tool for identifying high-risk heterosexual HIV-1 serodiscordant couples for targeted HIV-1 prevention. J Acquir Immune Defic Syndr 1999 2013;62:339–47. https://doi.org/10.1097/QAI.0b013e31827e622d. Balkus JE, Brown E, Palanee T, Nair G, Gafoor Z, Zhang J, et al. An Empiric HIV Risk Scoring Tool to Predict HIV-1 Acquisition in African Women. J Acquir Immune Defic Syndr 1999 2016;72:333–43. https://doi.org/10.1097/QAI.0000000000000974. Global AIDS Strategy 2021-2026 — End Inequalities. End AIDS. | UNAIDS n.d. https://www.unaids.org/en/resources/documents/2021/2021-2026-global-AIDS-strategy (accessed November 15, 2025). World Medical Association. World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA 2013;310:2191–4. https://doi.org/10.1001/jama.2013.281053. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviews received at journal 05 Mar, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviewers agreed at journal 31 Dec, 2025 Reviewers invited by journal 30 Dec, 2025 Editor assigned by journal 30 Dec, 2025 Editor invited by journal 21 Dec, 2025 Submission checks completed at journal 19 Dec, 2025 First submitted to journal 19 Dec, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8316815","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":567906346,"identity":"a1a3bdd9-a62f-46ef-b331-616e317a18d9","order_by":0,"name":"Primrose Matambanadzo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABSElEQVRIie2RMUsDMRSAcwh2aXGTlmDzFxIOWg5q9af0CFyXgIrLDXJNEW9qnSsV/QuRQsAtEnA62rUHHZzORaHFqYPgtVjwjtaugvcNyUvyPt5LAkBGxp/E4D9XajFgANx4Kua3KotopQS/KUn5WzH8zUoV6quB63plsK9fX+Zsgqo5PZjO7upl1O8+fbigdoKOE4p1bfthEGgTQIe0uzIijx3nlNxIauLJkJYC4FgPKqHgwPDDtq9sDhuEF6Q2hGKYFqSyRZHhEgcaE75O8VocNmftT6mPxOgN68Ktat33mDnfrOw0AGTkMq5ii/Ei4KoBxqyyrIKS17c6scIDTXzIzvoHMqJiHJ0bvWdKYrdicexgnHqxfC4KueuhPdgUs3c5ORQjOphOL+oIxY3FRzWMUo2tgt21f7BMwCq9s5VUlYyMjIx/xxfzRII9JLfv4wAAAABJRU5ErkJggg==","orcid":"","institution":"Centre for Sexual Health and HIV AIDS Research","correspondingAuthor":true,"prefix":"","firstName":"Primrose","middleName":"","lastName":"Matambanadzo","suffix":""},{"id":567906350,"identity":"9cc3fbe0-5b21-4f62-b107-70d6c24280a4","order_by":1,"name":"Harriet S. Jones","email":"","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Harriet","middleName":"S.","lastName":"Jones","suffix":""},{"id":567906352,"identity":"59192671-da2a-43e8-a504-4b6ab6e0a91a","order_by":2,"name":"Albert Takaruza","email":"","orcid":"","institution":"Centre for Sexual Health and HIV AIDS Research","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"","lastName":"Takaruza","suffix":""},{"id":567906355,"identity":"3759c937-222b-467b-ab34-04853e837c46","order_by":3,"name":"Sungai Chabata","email":"","orcid":"","institution":"Centre for Sexual Health and HIV AIDS Research","correspondingAuthor":false,"prefix":"","firstName":"Sungai","middleName":"","lastName":"Chabata","suffix":""},{"id":567906356,"identity":"52fa1d7b-26ad-46da-ac62-06379d3c4069","order_by":4,"name":"Joanna Busza","email":"","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Joanna","middleName":"","lastName":"Busza","suffix":""},{"id":567906357,"identity":"3a6f7fd5-a162-42df-b333-c34a62620175","order_by":5,"name":"Euphemia L Sibanda","email":"","orcid":"","institution":"Centre for Sexual Health and HIV AIDS Research","correspondingAuthor":false,"prefix":"","firstName":"Euphemia","middleName":"L","lastName":"Sibanda","suffix":""},{"id":567906358,"identity":"56a70f41-b526-4e39-9129-ef4476a11dd5","order_by":6,"name":"Richard Steen","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Steen","suffix":""},{"id":567906359,"identity":"591b5024-0238-44f9-a47c-903a4585ed24","order_by":7,"name":"Frances M Cowan","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Frances","middleName":"M","lastName":"Cowan","suffix":""},{"id":567906360,"identity":"c51db2e3-a09b-456e-94c2-e3b13f4c7683","order_by":8,"name":"James R Hargreaves","email":"","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"R","lastName":"Hargreaves","suffix":""}],"badges":[],"createdAt":"2025-12-09 11:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8316815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8316815/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99352693,"identity":"cc293cae-849f-4620-a84c-c04f171f23f9","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":493778,"visible":true,"origin":"","legend":"","description":"","filename":"19122025RiskCharacterisationPapersubmitted.docx","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/7d6f09edd5c766181e3dcea0.docx"},{"id":99789174,"identity":"82b290ca-7192-4687-8ff4-1bcf1e105b96","added_by":"auto","created_at":"2026-01-08 12:48:57","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11336,"visible":true,"origin":"","legend":"","description":"","filename":"bf5fbb6a9747465d931be3f8bacbf121.json","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/783530352d3c5db8ac338ca6.json"},{"id":99352696,"identity":"57f1408d-784d-42c4-819d-1e8bc6413fe8","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217476,"visible":true,"origin":"","legend":"","description":"","filename":"bf5fbb6a9747465d931be3f8bacbf1211enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/d147a63d7d0b815f829fe0bc.xml"},{"id":99788475,"identity":"b9980e60-d4da-4fc6-b50a-b37ec7788d99","added_by":"auto","created_at":"2026-01-08 12:46:51","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106847,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/49db7433280a7037575263e0.png"},{"id":99352697,"identity":"240e7e6f-d432-4e92-9a00-f3025fa70d0e","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86166,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/24e252ba33834d44c44bed3a.png"},{"id":99788850,"identity":"858e7afb-d138-45ee-8fd1-8ed5f81f59fc","added_by":"auto","created_at":"2026-01-08 12:48:04","extension":"emf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27136,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.emf","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/a394837a4b3aad7c62875885.emf"},{"id":99788491,"identity":"2e223c72-fd70-4b5c-9485-50b0f1333829","added_by":"auto","created_at":"2026-01-08 12:46:55","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32268,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/171fd7b577f5b3a894a4692f.png"},{"id":99352699,"identity":"26d97347-dabe-4ca4-bccd-d209ed969480","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22114,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/3339bf4b62d38fbf79513ce2.png"},{"id":99352703,"identity":"35e02fcf-da92-4c62-9236-f986e64f81dc","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35968,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/7ed9b3a7964281b1176a0832.png"},{"id":99352700,"identity":"6d96f484-313b-44f8-97ed-943f9b3fd9c6","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212170,"visible":true,"origin":"","legend":"","description":"","filename":"bf5fbb6a9747465d931be3f8bacbf1211structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/d60d7c5d10d0f01e52892b25.xml"},{"id":99352701,"identity":"d3d980a4-6c3f-4c41-8370-b2917a94865a","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":231848,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/d4cf126b4ab3716cf550abc8.html"},{"id":99788997,"identity":"c093013a-1aad-44bd-ac6d-2c6f555558a2","added_by":"auto","created_at":"2026-01-08 12:48:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129128,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/060e75a10589dbc8d88bc71a.png"},{"id":99352689,"identity":"3011f8c0-bb47-4b50-9719-e2b70343fca3","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109596,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/88e8ed2b0eec1e20745c0f70.png"},{"id":99789247,"identity":"6922c833-7110-4688-94c6-4c89ecce84dc","added_by":"auto","created_at":"2026-01-08 12:49:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between factors at first assessment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/927371f70d92b23ffd7e96e6.png"},{"id":99352691,"identity":"3cb78cea-a70c-42c6-a063-027eb27c5041","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of FSWs across risk segments by site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/c6ad2aa73c67927eeda3a589.png"},{"id":99801657,"identity":"9b92976d-dfe8-4f8a-8353-eb0fb7f2fe3a","added_by":"auto","created_at":"2026-01-08 14:07:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1608744,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/78d2c07d-1e33-4585-9864-1f2853679601.pdf"},{"id":99352687,"identity":"b140c717-b558-4bd1-835a-00208c654530","added_by":"auto","created_at":"2026-01-01 13:15:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27640,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8316815/v1/645353be3c8b40051d2212c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"HIV risk segmentation in microplanning with female sex workers in Zimbabwe: an observational study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFemale sex workers (FSW) in sub-Saharan Africa are disproportionately affected by HIV.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] In Zimbabwe, HIV prevalence among FSWs is four times higher than among all women, and annual incidence among FSWs is estimated at 3\u0026ndash;6%, a rate 5\u0026ndash;10 times than among all women.[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Interventions focused on FSWs remain imperative and evidence supports these being led by sex workers themselves.[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Additionally, given that there is heterogeneity of HIV risk within sex worker populations, and additionally geospatial heterogeneity of risk, it is necessary to understand these variations in risk in order to efficiently prioritise those who need greater support.[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eImportantly, effective risk segmentation requires valid and deployable assessment tools.[\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Most of the existing assessment tools are facility based and provider delivered or retrospectively applied to data collected from those attending facilities.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] In sub-Saharan Africa, various measures have been used to screen people \u0026lsquo;in or out\u0026rsquo; of HIV interventions or to predict outcomes.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Yet, risk segmentation among FSWs should be used to determine who needs additional support, rather than restrict services. This is because all active sex workers require HIV and sexual and reproductive health services due to the ongoing multiple concurrent partnerships inherent in their work that place them at heightened risk of HIV infection.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Risk assessment tools specific to sex workers, and deployable within community settings are needed.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFSWs\u0026rsquo; HIV risk reflects a mix of proximal and distal behavioural, biological and structural factors.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31 CR32 CR33 CR34\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Proximal factors directly facilitate HIV acquisition or transmission such as condomless sex and high client numbers.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Distal factors are characteristics such as young age, duration in sex work, problematic drinking or substance use, and experience of violence that influence the proximal factors.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Both proximal and distal factors have been demonstrated to increase the likelihood of HIV acquisition and transmission. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] However, due to limited available evidence, it remains to be determined whether sex worker led HIV programmes could effectively assess these risk factors for prioritising intensive support among FSWs with greatest need.\u003c/p\u003e \u003cp\u003eMicroplanning, a peer outreach approach that uses standardised tools to systematically engage and support FSWs, may offer a potential mechanism for such risk assessments within a community setting. Microplanning has been reported to result in higher population coverage, improved uptake of services and declines in STI and HIV prevalence and reduced risk of HIV transmission in India, Kenya and Zimbabwe.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eA\u003c/b\u003edapted \u003cb\u003eM\u003c/b\u003eicroplanning: \u003cb\u003eE\u003c/b\u003eliminating \u003cb\u003eT\u003c/b\u003eransmissible \u003cb\u003eHI\u003c/b\u003eV In \u003cb\u003eS\u003c/b\u003eex \u003cb\u003eT\u003c/b\u003eransactions (\u003cb\u003eAMETHIST\u003c/b\u003e) cluster randomised trial, conducted between June 2019 and October 2021, sought to show whether integrating risk segmentation into microplanning could prioritise FSWs at highest risk for more support. Here we examine how effectively HIV risk assessment by peer microplanners among FSWs identified distinct subgroups requiring different levels of support. We assess this through the following indicators of effectiveness: (1) \u003cb\u003efeasibility\u003c/b\u003e as demonstrated by the proportion of enrolled FSWs who completed at least one assessment and the overall proportion of quarterly assessments completed; (2) \u003cb\u003erisk heterogeneity between and within risk groupings\u003c/b\u003e as shown by the distribution of FSWs across risk segments and the prevalence of the risk factors within risk segments; (3) \u003cb\u003ecoherence of the risk assessment tool\u003c/b\u003e (by examining whether and to what extent the factors included were associated as expected); and (4) \u003cb\u003erisk heterogeneity between sites\u003c/b\u003e.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe intervention\u003c/h2\u003e \u003cp\u003eWithin an established nationally scaled sex worker programme in Zimbabwe, the AMETHIST cluster randomised trial evaluated the effect of risk differentiated microplanning among FSWs in Zimbabwe between June 2019\u0026ndash;October 2021.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] In 11 intervention sites, peer microplanners mapped locations and estimated the age, type, and number of female sex workers. This data was aggregated to generate a population size estimate (PSE) used to set intervention goals. Each peer microplanner was assigned to a specific familiar location in which they were well-networked. They then enrolled FSWs into a location diary using a unique identifier. Existing IDs were recorded for those already accessing services in the programme. Caseloads were capped at 50\u0026ndash;80 FSWs per peer, based on prior data indicating poorer outcomes when higher numbers of FSWs were supported.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk assessment\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOnce peer microplanners had established rapport, they conducted risk assessments with each FSW in their caseload using a six-factor tool (Fig.\u0026nbsp;1). The tool, adapted with peer microplanners, assessed both proximal \u003cem\u003e(condomless sex, high client numbers)\u003c/em\u003e and distal \u003cem\u003e(young age, recent entry into sex work, substance use, violence)\u003c/em\u003e factors of HIV risk, as shown in Fig.\u0026nbsp;2. At trial start in June 2019 probelamtic alcohol or substance use and violence were combined, resulting in a five-factor tool. In March 2020, peer microplanner feedback prompted separation into six distinct factors.\u003c/p\u003e \u003cp\u003e Based on total risk scores, FSWs were allocated to lower (0), moderate (1\u0026ndash;2), or higher (3\u0026ndash;6) risk segments. Peer microplanners used these segments to tailor the frequency of outreach contact: weekly for higher, bi-weekly for moderate, and monthly for lower risk. FSWs not yet assessed were to be treated as higher risk. Outreach contacts included peer counselling, condom and lubricant distribution, and clinic referrals. Risk assessments were repeated quarterly to keep the understanding of individual needs current. Outreach workers supported peer microplanners in weekly supervision meetings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting and participants\u003c/h2\u003e \u003cp\u003eThe study was implemented across 11 sites (selected through restricted randomisation) in Zimbabwe spanning six provinces (Manicaland, Mashonaland East and West, Matabeleland North and South and Midlands). The contexts were diverse including rural farming areas (Birchenough Bridge, Murehwa, Chipinge), mining towns (Hwange, Gwanda, Zvishavane), transport corridors to neighbouring countries (Rusape, Ngundu), a fishing and tourist hub (Kariba), and university towns (Lupane, Chinhoyi). FSWs were enrolled into AMETHIST on an ongoing basis between June 2019 and October 2021.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Study implementation was affected by COVID-19 lockdowns and disruptions from March 2020 onwards. From March 2021, following the lifting of COVID-19 restrictions, we undertook an optimisation exercise that used microplanning data to identify site-specific gaps and strengthen AMETHIST implementation.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eOur analysis assessed whether HIV risk was effectively segmented through proxy indicators. We examined feasibility by calculating the proportion of FSWs enrolled who completed at least one risk assessment, the proportion of all expected assessments completed overall (based on individual enrolment time) and the median number of quarterly assessments per person for those with \u0026ge;\u0026thinsp;1 assessment. We defined a quarter as three months within the period of the study. The number of enrolment quarters was dependent on the FSW\u0026rsquo;s enrolment date with a maximum of nine full quarters of enrolment possible in the 29-month study period. Each FSW was expected to have one assessment completed per quarter of follow up time. Only one risk assessment was counted per quarter to avoid over representation of assessments per FSW where a repeat assessment occurred within a three month period. The final truncated quarter (Q10) was excluded from analysis because participants did not accrue a full three-month follow-up period.\u003c/p\u003e \u003cp\u003eTo assess risk heterogeneity between and within risk segments, we described the distribution of FSWs across risk segments at first assessment and summarised the prevalence of each of the 6 risk factors within and between segments and used modified Poisson regression to describe the differences in the proportion of FSWs scoring positive on each risk factor between moderate and higher risk segments. Coherence of the risk assessment tool was examined by testing bi-variate associations between the six factors included in the tool using logistic regression to see if the factors were associated as expected in line with what we know from the literature on HIV risk among FSWs.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] We examined the internal coherence of the risk score by verifying that the included factors showed associations consistent with published evidence, as a check on the tool\u0026rsquo;s validity. For geographical differences, we calculated site specific distributions of risk segments and factors, and tested differences using Pearson χ\u0026sup2; tests. This allowed us to examine whether the tool could detect meaningful site level variation.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] All analyses were conducted in Stata 19. Reporting followed STROBE guidelines.[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCoverage and frequency of risk assessments\u003c/h2\u003e \u003cp\u003eBetween June 2019 and October 2021, 7,012 FSWs were enrolled in AMETHIST. Of these, 86.2% (6,045/7,012; range 71.2\u0026ndash;94.9%) completed at least one risk assessment, leaving 13.8% (n\u0026thinsp;=\u0026thinsp;967; range 5.1%-28.8%) unassessed. As per study implementation 47,101 quarterly risk assessments were expected based on the quarter in which the FSWs were enrolled (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, 62% (n\u0026thinsp;=\u0026thinsp;29,163) of expected assessments were completed. FSWs enrolled early in the implementation period (study Q1\u0026ndash;Q3), had the highest completion rate for all expected assessments, ranging from 55\u0026ndash;69%. Quarterly assessment completion rates declined substantially among FSWs enrolled during COVID-19 lockdowns and service disruptions (study Q4\u0026ndash;Q8), reaching a low of 30\u0026ndash;41%. The proportion of enrolled FSWs that received at least one assessment fell from 98.2% (3,114/3,170) for those enrolled in Q1 to 64.7% for those enrolled in Q7 (211/326) and 30.5% for those enrolled by Q9 (49/151).\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\u003eCoverage and frequency of risk assessments by FSWs enrolment period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuarter of enrolment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;FSW enrolled per quarter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# Expected quarterly risk assessments\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected total quarterly assessments for FSWs enrolled\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eActual total quarterly assessments for FSWs enrolled\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% expected quarterly assessments completed\u003c/p\u003e \u003cp\u003e(actual/expected)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFSW enrolled with \u0026ge;\u0026thinsp;1 assessment\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMedian actual assessments for FSW with \u0026ge;\u0026thinsp;1 assessment\u003c/p\u003e \u003cp\u003em [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eContext\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Jun \u0026ndash; Aug \u0026rsquo;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19,609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3114 (98.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (6\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEarly implementation period\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Sept \u0026ndash; Nov \u0026rsquo;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e808 (97.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (5\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Dec \u0026rsquo;19 \u0026ndash; Feb \u0026rsquo;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e606 (95.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (4\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Mar \u0026ndash; May \u0026rsquo;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e102 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCovid-19 Lockdowns and movement restrictions with service disruptions and clinic decongestion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Jun \u0026ndash; Aug \u0026rsquo;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e381 (86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (3\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ6\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Sept \u0026ndash; Nov \u0026rsquo;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e321 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ7\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Dec \u0026rsquo;20 \u0026ndash; Feb \u0026rsquo;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e211 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ8\u003c/b\u003e (Mar \u0026ndash; May \u0026rsquo;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e456 (49.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOptimise AMETHIST period\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ9\u003c/b\u003e (Jun \u0026ndash; Aug \u0026rsquo;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ10: incomplete \u0026Dagger;\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Sept \u0026ndash; Oct \u0026rsquo;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47,101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6045 (86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026dagger;based on full quarters of follow-up from start of trial to end of trial, reflecting implementation approach\u003c/p\u003e \u003cp\u003e\u0026Dagger; Final period truncated and therefore excluded. 34 FSWs recruited in final 2 months (study Q10) did not complete a three month period of follow up\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk heterogeneity between and within risk segments at first assessment\u003c/h3\u003e\n\u003cp\u003eAmong the women with \u0026ge;\u0026thinsp;1 risk assessment, there were 45.8% (2,769/6,045) FSWs in the higher risk segment (score 3\u0026ndash;6); 42.3% (2,560/6,045) FSWs in the moderate risk segment (score 1\u0026ndash;2) and 11.8% (716/6,045) in lower risk segment at first assessment. Overall risk scores were distributed as follows: score\u0026thinsp;=\u0026thinsp;0 (11.8%; n\u0026thinsp;=\u0026thinsp;716), score\u0026thinsp;=\u0026thinsp;1 (16.4%; n\u0026thinsp;=\u0026thinsp;994), score\u0026thinsp;=\u0026thinsp;2 (25.8%; n\u0026thinsp;=\u0026thinsp;1,566), score\u0026thinsp;=\u0026thinsp;3 (26.7%; n\u0026thinsp;=\u0026thinsp;1,613), score\u0026thinsp;=\u0026thinsp;4 (14.6%; n\u0026thinsp;=\u0026thinsp;882), score\u0026thinsp;=\u0026thinsp;5 (4.2%; n\u0026thinsp;=\u0026thinsp;253), and score\u0026thinsp;=\u0026thinsp;6 (0.4%; n\u0026thinsp;=\u0026thinsp;21), demonstrating a skewed distribution with the majority of FSWs (69.0%; n\u0026thinsp;=\u0026thinsp;4,173) concentrated between scores 2\u0026ndash;4 which overlapped the moderate and higher risk segments. A small proportion (4.6%; n\u0026thinsp;=\u0026thinsp;274) exhibited highly concentrated risk with a score of 5 or 6 (see Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eAs expected, all six behavioural, biological and structural factors were more prevalent in the higher risk group than in the moderate risk group. However, the differences in the proportion of FSWs scoring positive on each risk factor between moderate and higher risk segments provide some insight into the contribution of each factor to the overall risk strata. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, violence was 3.54 times more likely in the higher risk group compared to the moderate risk group (48.7%; (370/760) vs 11.0%; (53/481)) and duration in sex work\u0026thinsp;\u0026le;\u0026thinsp;6 months 2.85 times more likely (51.8% (1,433/2,769) vs 17.5% (447/2,560)). A lower but still marked difference was also noted in problematic alcohol and substance use (67.4% (1,866/2769) for the higher risk group vs 26.2% (670/2,560) for moderate risk) and for young age\u0026thinsp;\u0026le;\u0026thinsp;24 years (60.4% (1,672/2,769) vs 24.3% (623/2,560)). However, risk ratios were lower for both high weekly client numbers and inconsistent condom use where the prevalence was high in both the moderate and higher risk group (77.5% (2,145/2,769) vs 42.8% (1,096/2,560) and (82.1% (2,272/2,769) vs 48.3% (1,237/2,560)) for inconsistent condom use.\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\u003eRisk distribution by factor and category at first assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFSW in low risk segment at first assessment\u003c/p\u003e \u003cp\u003e(n/%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFSW at moderate risk scoring\u0026thinsp;\u0026ge;\u0026thinsp;1 on factor\u003c/p\u003e \u003cp\u003e(n/%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e# of FSW at higher risk scoring 1 on factor\u003c/p\u003e \u003cp\u003e(n/%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eadjusted Risk Ratio*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI\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 \u003cp\u003eN\u0026thinsp;=\u0026thinsp;716\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2560\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2796\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung age\u0026thinsp;\u0026le;\u0026thinsp;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[2.25\u0026ndash;2.61]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew to SW\u0026thinsp;\u0026le;\u0026thinsp;6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[2.60\u0026ndash;3.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh client #s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[1.78\u0026ndash;1.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInconsistent condom use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e82.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[1.60\u0026ndash;1.75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProblematic alcohol or substance use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[2.34\u0026ndash;2.70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;150\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;481\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;760\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViolence \u0026sect;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e[2.76\u0026ndash;4.55]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*modified Poisson regression for each factor at first assessment adjusted for enrolment quarter (higher vs moderate)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026sect;\u003c/b\u003e Problematic alcohol or substance use and violence initially combined factor (5 factor tool) at trial start in June 2019. Separated into 2 distinct factors (six factor tool) in March 2020, in response to peer microplanner feedback. Only 1401 FSWs were scored on disaggregated violence factor at first assessment.\u003c/p\u003e\n\u003ch3\u003eAssociation between risk factors at first assessment\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBivariate logistic regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that FSWs aged\u0026thinsp;\u0026le;\u0026thinsp;24 years had markedly higher odds of being recent entrants to sex work (OR\u0026thinsp;=\u0026thinsp;3.91, 95% CI 3.48\u0026ndash;4.38; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and higher odds of high client numbers (OR\u0026thinsp;=\u0026thinsp;1.40, CI 1.26\u0026ndash;1.55; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and inconsistent condom use (OR\u0026thinsp;=\u0026thinsp;1.23, CI 1.10\u0026ndash;1.37; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There was no statistical evidence of an association between young age and problematic alcohol and substance use (OR\u0026thinsp;=\u0026thinsp;1.03, CI 0.92\u0026ndash;1.14; p\u0026thinsp;=\u0026thinsp;0.62). Recent entrants (\u0026le;\u0026thinsp;6 months) were more likely to report high client volumes (OR\u0026thinsp;=\u0026thinsp;1.80, CI 1.61\u0026ndash;2.01; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and inconsistent condom use (OR\u0026thinsp;=\u0026thinsp;1.12, CI 1.01\u0026ndash;1.26; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and showed no association with problematic drinking or substance use (OR\u0026thinsp;=\u0026thinsp;0.97, CI 0.87\u0026ndash;1.08; p\u0026thinsp;=\u0026thinsp;0.55). Being young and a new entrant to sex work both had lower odds of violence. High client numbers co-occurred with inconsistent condom use (OR\u0026thinsp;=\u0026thinsp;1.76, CI 1.59\u0026ndash;1.95; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), problematic drinking or substance use (OR\u0026thinsp;=\u0026thinsp;1.71, CI 1.55\u0026ndash;1.90; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and violence (OR\u0026thinsp;=\u0026thinsp;1.37, CI 1.09\u0026ndash;1.73; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Inconsistent condom use was significantly associated with problematic drinking or substance use (OR\u0026thinsp;=\u0026thinsp;3.05, CI 2.73\u0026ndash;3.40; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and violence (OR\u0026thinsp;=\u0026thinsp;2.16, CI 1.68\u0026ndash;2.78; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The strongest association was between problematic alcohol or substance use and violence (OR\u0026thinsp;=\u0026thinsp;8.50, CI 6.57\u0026ndash;11.01; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeographic variation in risk factors and groups\u003c/h2\u003e \u003cp\u003eBetween site heterogeneity in risk factors was substantial, revealing marked differences between geographical contexts (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Risk group distributions differed significantly across the 11 sites with the mean proportion at higher risk at 39.7% (range 22.8\u0026ndash;74.9%; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (see supplementary table). As shown in Table\u0026nbsp;3 the proportion at low risk (scoring zero on all factors) ranged from 1.7% in Site 2 (n\u0026thinsp;=\u0026thinsp;19/1132) to 22.8% in Site 9 (n\u0026thinsp;=\u0026thinsp;76/334), while experience of violence varied from 0% (n\u0026thinsp;=\u0026thinsp;0/204) in Site 7 to 22.1% in Site 5 (n\u0026thinsp;=\u0026thinsp;99/447). Site 2, a rural and peri-urban setting, demonstrated concentrated and overlapping risks and recorded the highest proportion of young FSWs (55.9%, 633/1132) and of recent entrants into sex work (49.2%, 557/1132). This occurred alongside problematic drinking and substance use (59,9%, 678/1132) which was also high in rural Site 1 (46.8%, 125/267) and Site 8 (38.1%, 138/362). In mining towns, risk patterns were divergent. Site 11 reported high client volume (69.9%, 364/521) and inconsistent condom use (72%, 375/521), while Site 5 had comparatively lower client volume (29.3%, 131/447) and lower inconsistent condom use (38.9%, 175/447). However, Site 5 had the highest reported experience of violence (22%, 99/447). University towns demonstrated relatively protective environments, with violence nearly absent in Site 2 and Site 7 at 1.7% (13/782) and 0% respectively, though both had moderate numbers of younger FSWs at 28.1% (n\u0026thinsp;=\u0026thinsp;220/782) and 36.8% (75/204).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;3: Heterogeneity of risk factors by siteSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% with zero risk score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYoung age\u0026thinsp;\u0026le;\u0026thinsp;24 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNew to SW\u0026thinsp;\u0026le;\u0026thinsp;6 months\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh client #s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInconsistent condom use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProblematic drinking/ substance use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eViolence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eContext\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;6,045\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,401\u0026para;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6045\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.5%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.2%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.2%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.2%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRural farming\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6% (n\u0026thinsp;=\u0026thinsp;153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.5%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.2%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.6%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.3%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUniversity town, transport corridor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.9%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.2%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.3%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.0%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.9%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.6%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRural and peri-urban farming\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.6%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.6%\u003c/p\u003e \u003cp\u003e(482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.7%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.0%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMining town\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.0%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.0%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.3%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.9%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.6%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMining town\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.2%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.2%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.9%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFishing and tourist town\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.7%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.4%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.3%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUniversity town\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.9%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.5%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.7%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.8%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.6%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTransport corridor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;610)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;407)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.5%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.1%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.0%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.2%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTransport corridor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.4%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.5%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.9%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.0%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.3%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMining town\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 \u003cb\u003e\u0026para;\u003c/b\u003e Problematic alcohol or substance use and violence initially combined factor (5 factor tool) at trial start in June 2019. Separated into 2 distinct factors (six factor tool) in March 2020, in response to peer microplanner feedback. Only 1401 FSWs were scored on disaggregated violence factor at first assessment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study examined whether risk assessments by peer microplanners could effectively segment HIV risk among FSWs. We sought to do this by assessing four indicators of effective risk segmentation: 1) the feasibility of delivering assessments, 2) the heterogeneity of risk between and within groups, 3) the coherence of the assessment tool, and 4) differences in risk across diverse contexts. We demonstrated that ongoing risk assessment was feasible at scale with \u0026gt;\u0026thinsp;85% of FSWs completing at least one risk assessment. However, initial and repeat risk assessment completion rates were lower among those enrolled later in the study and during COVID-19 pandemic related disruptions. We hypothesised that FSWs\u0026rsquo; risk scores would show associations between risk factors in line with published literature. We confirmed these expected patterns, providing preliminary evidence that the tool measures what it is intended to measure. Risk could be assessed at the point of first contact in the community, effectively detecting heterogeneity and flagging both individual and contextual differences. Risk-differentiated microplanning was actionable, providing a better understanding of the risk of both individuals and the locations that they were in at the same time, to inform prioritisation of support and services.\u003c/p\u003e \u003cp\u003eInterventions targeting service-distant populations are necessary to reduce high risk in groups not yet reached by services.[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] Waiting for individuals to engage in HIV services before assessing their HIV risk misses earlier risk exposure periods, and the opportunity to respond to them. In our study peer microplanners assessed HIV risk among FSWs and segmented them into risk strata outside of the formal health system. They then provided more intensive support to those at higher risk even before linkage to clinical services.[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] Additionally, the tool was well suited to detecting context specific HIV risks requiring community level interventions thereby addressing gaps previously identified in the literature in understanding of geospatial risk heterogeneity.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] HIV programmers and policy makers should explore deploying risk differentiated microplanning to characterise and respond to HIV risk among FSWs and other populations known to be at higher risk.\u003c/p\u003e \u003cp\u003eWhile high client numbers and inconsistent condom use were more common in the higher risk group, they were also highly prevalent among FSWs at moderate risk. This HIV prevention gap persists widely despite condom knowledge, efficacy and access within FSW programmes.[\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] Dependence on sex work income for survival likely limits the power to avoid unsafe sex and often supersedes knowledge and access in shaping prevention behaviour, requiring renewed community informed approaches to address this gap.[\u003cspan additionalcitationids=\"CR54 CR55\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAdditionally, the need for comprehensive and integrated HIV prevention approaches for FSWs was underscored by the strong association between proximal factors (\u003cem\u003eclient volume, inconsistent condom use\u003c/em\u003e) and distal factors (\u003cem\u003eviolence, substance use, young age, recent entry\u003c/em\u003e).[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] For example the overlaps observed between problematic drinking and substance use and inconsistent condom use affirmed that these factors operate synergistically.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] More intensive harm reduction and violence prevention interventions should be prioritised in areas where these are indicated as more prevalent. Our findings also reinforce the need to prioritise support for younger women who have newly entered sex work for more intense services and support. We noted similar patterns in association between being young (\u0026lt;\u0026thinsp;25 years) and having recently entered into sex work, having high client numbers and inconsistent condom use as other studies have documented.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA key strength of our study is that it adds to the existing literature by demonstrating real world utility of ongoing community based risk assessment by peer microplanners; peer microplanners can effectively segment and respond to vulnerabilities within routine programming. Our study advances the programme science in this area by demonstrating that peer microplanners could stratify FSWs risk using a simple tool to tailor outreach intensity accordingly. An additional strength is the diversity of contexts in which tool was implemented. This geographic heterogeneity demonstrates that the tool performs can be used across markedly different contexts. A limitation of our study is that, while it demonstrates tool as coherent and effective for segmentation, we did not correlate risk strata with subsequent HIV seroconversion, STI outcomes, or engagement in HIV prevention and treatment over time. As a result, we cannot directly assess the predictive utility of the score or determine whether the chosen thresholds optimally discriminate future HIV-related outcomes.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Future work should link these risk segments to clinical endpoints to inform refining cut points for the risk assessment tool (particularly given the strata overlapping concentration of risk among those who scored 2\u0026ndash;4) and possibly explore evidence based weighting of risk factors, to more fully inform the tool\u0026rsquo;s utility for precision HIV prevention.[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIt would be beneficial for future research to longitudinally explore FSWs\u0026rsquo; transitions between risk groups as captured in these assessments to better understand what influences these changes over time and inform responses. This will facilitate context tailored programming where resource allocation in HIV interventions that continually reflects site level risk profiles. Mixed-methods research exploring what influences different risk patterns across settings may also usefully inform tailored programming.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy stratifying FSWs risk using a simple tool and tailoring outreach intensity accordingly, risk differentiated microplanning aims to use granular data to focus HIV programmes more effectively to the unique needs of people, communities and locations as recommended by UNAIDS in its Global AIDS Strategy 2021-2026.[65] HIV risk segmentation by peer microplanners, updated regularly, could inform triaging of outreach, resource allocation, and focus referrals for clinic and support services.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Medical Research Council of Zimbabwe (MRCZ/A/2559) and the Liverpool School of Tropical Medicine Research Ethics Committee (Ref 19-115RS) granted ethical approval for this study. All procedures involving human participants were conducted in accordance with the ethical standards of these institutional and national research committees and with the principles of the 2013 revision of the Declaration of Helsinki.[66]\u0026nbsp;Before enrolment, participants provided verbal informed consent, and risk assessment and programme data used in this analysis were de-identified prior to use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Elton John AIDS Foundation funded the implementation of the intervention, and the Wellcome Trust [Grant number: 214280/Z/18/Z] provided funding for the study. The Key Populations programme is funded through the Global Fund to fight AIDS, Tuberculosis, and Malaria and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) through USAID.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM led the study's conception and design, data analysis, and interpretation. HJ supported data management and analysis. AT supported data management. PM drafted and revised the article with the involvement of ES, JB, RS, HJ, SC, JH, and FMC. All authors approved the version to be submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the members of the research team who collected the data for this study (Fortunate Machingura, Sungai Chabata, Memory Makamba, Gracious Madimutsa, Adrian Chikeya, Jaspar Maguma) and those who supported the implementation of AMETHIST and data cleaning (Sithembile Musemburi, Rumbidzai Makandwa, Florence Mutevedzi, and Sitholubuhle Zitha). We would also like to acknowledge the participants in this study for their contribution and the Ministry of Health and Child Care and National AIDS Council in Zimbabwe, who supported trial implementation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are presented in the manuscript and online supplementary materials. Any further details can be obtained by contacting the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJones HS, Anderson RL, Cust H, McClelland RS, Richardson BA, Thirumurthy H, et al. HIV incidence among women engaging in sex work in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob Health 2024;12:e1244\u0026ndash;60. https://doi.org/10.1016/S2214-109X(24)00227-4.\u003c/li\u003e\n\u003cli\u003eStevens O, Sabin K, Anderson RL, Garcia SA, Willis K, Rao A, et al. Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data, 2010\u0026ndash;23. Lancet Glob Health 2024;12:e1400\u0026ndash;12. https://doi.org/10.1016/S2214-109X(24)00236-5.\u003c/li\u003e\n\u003cli\u003eCowan FM, Machingura F, Ali MS, Chabata ST, Takaruza A, Dirawo J, et al. A risk-differentiated, community-led intervention to strengthen uptake and engagement with HIV prevention and care cascades among female sex workers in Zimbabwe (AMETHIST): a cluster randomised trial. Lancet Glob Health 2024;12:e1424\u0026ndash;35. https://doi.org/10.1016/S2214-109X(24)00235-3.\u003c/li\u003e\n\u003cli\u003eAli MS, Wit MDE, Chabata ST, Magutshwa S, Musemburi S, Dirawo J, et al. Estimation of HIV incidence from analysis of HIV prevalence patterns among female sex workers in Zimbabwe. AIDS Lond Engl 2022;36:1141\u0026ndash;50. https://doi.org/10.1097/QAD.0000000000003198.\u003c/li\u003e\n\u003cli\u003eMinistry of Health and Child Care (MoHCC). Zimbabwe Population-based HIV Impact Assessment 2020 (ZIMPHIA 2020): Final Report. 2021.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, United Nations Population Fund, Joint United Nations Programme on HIV/AIDS, Global Network of Sex Work Projects, The World Bank. Implementing comprehensive HIV/STI programmes with sex workers: practical approaches from collaborative interventions 2013.\u003c/li\u003e\n\u003cli\u003eMatambanadzo P, Otiso L, Kavhaza S, Bhattacharjee P, Cowan FM. Community leadership is key to effective HIV service engagement for female sex workers in Africa. J Int AIDS Soc 2025;28:e26425. https://doi.org/10.1002/jia2.26425.\u003c/li\u003e\n\u003cli\u003eBekker L-G, Johnson L, Cowan F, Overs C, Besada D, Hillier S, et al. Combination HIV prevention for female sex workers: what is the evidence? The Lancet 2015;385:72\u0026ndash;87. https://doi.org/10.1016/S0140-6736(14)60974-0.\u003c/li\u003e\n\u003cli\u003eKerrigan D, Kennedy CE, Morgan-Thomas R, Reza-Paul S, Mwangi P, Win KT, et al. A community empowerment approach to the HIV response among sex workers: effectiveness, challenges, and considerations for implementation and scale-up. The Lancet 2015;385:172\u0026ndash;85. https://doi.org/10.1016/S0140-6736(14)60973-9.\u003c/li\u003e\n\u003cli\u003eMamulwar M, Godbole S, Bembalkar S, Kamble P, Dulhani N, Yadav R, et al. Differing HIV vulnerability among female sex workers in a high HIV burden Indian state. PloS One 2018;13:e0192130. https://doi.org/10.1371/journal.pone.0192130.\u003c/li\u003e\n\u003cli\u003eMelesse DY, Shafer LA, Shaw SY, Thompson LH, Achakzai BK, Furqan S, et al. Heterogeneity Among Sex Workers in Overlapping HIV Risk Interactions With People Who Inject Drugs: A Cross-Sectional Study From 8 Major Cities in Pakistan. Medicine (Baltimore) 2016;95:e3085. https://doi.org/10.1097/MD.0000000000003085.\u003c/li\u003e\n\u003cli\u003eCuadros DF, Chowdhury T, Milali M, Citron DT, Nyimbili S, Vlahakis N, et al. Geospatial patterns of progress towards UNAIDS \u0026ldquo;95-95-95\u0026rdquo; targets and community vulnerability in Zambia: insights from population-based HIV impact assessments. BMJ Glob Health 2023;8:e012629. https://doi.org/10.1136/bmjgh-2023-012629.\u003c/li\u003e\n\u003cli\u003eCuadros DF, Huang Q, Musuka G, Dzinamarira T, Moyo BK, Mpofu A, et al. Moving beyond hotspots of HIV prevalence to geospatial hotspots of UNAIDS 95-95-95 targets in sub-Saharan Africa. Lancet HIV 2024;11:e479\u0026ndash;88. https://doi.org/10.1016/S2352-3018(24)00102-4.\u003c/li\u003e\n\u003cli\u003eBalkus JE, Brown ER, Palanee-Phillips T, Matovu Kiweewa F, Mgodi N, Naidoo L, et al. Performance of a validated risk score to predict HIV-1 acquisition among African women participating in a trial of the dapivirine vaginal ring. J Acquir Immune Defic Syndr 1999 2018;77:e8\u0026ndash;10. https://doi.org/10.1097/QAI.0000000000001556.\u003c/li\u003e\n\u003cli\u003eTowards a people-centered precision prevention approach: Considerations for prioritization | GPC n.d. https://hivpreventioncoalition.unaids.org/en/resources/towards-people-centered-precision-prevention-approach-considerations-prioritization (accessed March 9, 2025).\u003c/li\u003e\n\u003cli\u003eWang B, Liu F, Deveaux L, Ash A, Gosh S, Li X, et al. Adolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention. AIDS Lond Engl 2021;35:S75\u0026ndash;84. https://doi.org/10.1097/QAD.0000000000002867.\u003c/li\u003e\n\u003cli\u003ePrecision Response Needed For HIV/AIDS | Health Affairs n.d. https://www.healthaffairs.org/doi/10.1377/hlthaff.2017.1311 (accessed March 9, 2025).\u003c/li\u003e\n\u003cli\u003eMcGrath C, Palmarella G, Solomon S, Dupuis R. Precision Prevention and Public Health 2017.\u003c/li\u003e\n\u003cli\u003eWillcox AC, Richardson BA, Shafi J, Kabare E, Kinuthia J, Jaoko W, et al. Derivation of an HIV Risk Score for African Women Who Engage in Sex Work. AIDS Behav 2021;25:3292\u0026ndash;302. https://doi.org/10.1007/s10461-021-03235-7.\u003c/li\u003e\n\u003cli\u003eJia KM, Eilerts H, Edun O, Lam K, Howes A, Thomas ML, et al. Risk scores for predicting HIV incidence among adult heterosexual populations in sub-Saharan Africa: a systematic review and meta-analysis. J Int AIDS Soc 2022;25:e25861. https://doi.org/10.1002/jia2.25861.\u003c/li\u003e\n\u003cli\u003eCastor D, Burgess EK, Yende-Zuma N, Heck CJ, Abdool Karim Q. Age-Restriction of a Validated Risk Scoring Tool Better Predicts HIV Acquisition in South African Women: CAPRISA 004. AIDS Behav 2022;26:3300\u0026ndash;10. https://doi.org/10.1007/s10461-022-03664-y.\u003c/li\u003e\n\u003cli\u003eGiovenco D, Pettifor A, MacPhail C, Kahn K, Wagner R, Piwowar-Manning E, et al. Assessing risk for HIV infection among adolescent girls in South Africa: an evaluation of the VOICE risk score (HPTN 068). J Int AIDS Soc 2019;22:e25359. https://doi.org/10.1002/jia2.25359.\u003c/li\u003e\n\u003cli\u003eOng JJ, Coulthard K, Quinn C, Tang MJ, Huynh T, Jamil MS, et al. Risk-Based Screening Tools to Optimise HIV Testing Services: a Systematic Review. Curr HIV/AIDS Rep 2022;19:154\u0026ndash;65. https://doi.org/10.1007/s11904-022-00601-5.\u003c/li\u003e\n\u003cli\u003eScorgie F, Chersich MF, Ntaganira I, Gerbase A, Lule F, Lo Y-R. Socio-Demographic Characteristics and Behavioral Risk Factors of Female Sex Workers in Sub-Saharan Africa: A Systematic Review. AIDS Behav 2012;16:920\u0026ndash;33. https://doi.org/10.1007/s10461-011-9985-z.\u003c/li\u003e\n\u003cli\u003eCowan FM, Machingura F, Chabata ST, Ali MS, Busza J, Steen R, et al. Differentiated prevention and care to reduce the risk of HIV acquisition and transmission among female sex workers in Zimbabwe: study protocol for the \u0026ldquo;AMETHIST\u0026rdquo; cluster randomised trial. Trials 2022;23:209. https://doi.org/10.1186/s13063-022-06119-w.\u003c/li\u003e\n\u003cli\u003eBaral S, Beyrer C, Muessig K, Poteat T, Wirtz AL, Decker MR, et al. Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Infect Dis 2012;12:538\u0026ndash;49. https://doi.org/10.1016/S1473-3099(12)70066-X.\u003c/li\u003e\n\u003cli\u003eAbdella S, Demissie M, Worku A, Dheresa M, Berhane Y. HIV prevalence and associated factors among female sex workers in Ethiopia, east Africa: A cross-sectional study using a respondent-driven sampling technique. eClinicalMedicine 2022;51. https://doi.org/10.1016/j.eclinm.2022.101540.\u003c/li\u003e\n\u003cli\u003eLongo JDD, Simaleko MM, Diemer HS-C, Gr\u0026eacute;senguet G, Br\u0026uuml;cker G, Belec L. Risk factors for HIV infection among female sex workers in Bangui, Central African Republic. PLoS ONE 2017;12:e0187654. https://doi.org/10.1371/journal.pone.0187654.\u003c/li\u003e\n\u003cli\u003eJones HS, Hensen B, Musemburi S, Chinyanganya L, Takaruza A, Chabata ST, et al. Temporal trends in, and risk factors for, HIV seroconversion among female sex workers accessing Zimbabwe\u0026rsquo;s national sex worker programme, 2009-19: a retrospective cohort analysis of routinely collected HIV testing data. Lancet HIV 2023;10:e442\u0026ndash;52. https://doi.org/10.1016/S2352-3018(23)00110-8.\u003c/li\u003e\n\u003cli\u003eL\u0026auml;mmle L, Woll A, Mensink GBM, B\u0026ouml;s K. Distal and Proximal Factors of Health Behaviors and Their Associations with Health in Children and Adolescents. Int J Environ Res Public Health 2013;10:2944\u0026ndash;78. https://doi.org/10.3390/ijerph10072944.\u003c/li\u003e\n\u003cli\u003eShannon K, Strathdee SA, Goldenberg SM, Duff P, Mwangi P, Rusakova M, et al. Global epidemiology of HIV among female sex workers: influence of structural determinants. Lancet Lond Engl 2015;385:55\u0026ndash;71. https://doi.org/10.1016/S0140-6736(14)60931-4.\u003c/li\u003e\n\u003cli\u003eProximal and Distal Predictors of AIDS Risk Behaviors among Inner-city African American and European American Women n.d. https://doi.org/10.1177/135910530100600207.\u003c/li\u003e\n\u003cli\u003eShannon K, Goldenberg SM, Deering KN, Strathdee SA. HIV infection among female sex workers in concentrated and high prevalence epidemics: why a structural determinants framework is needed. Curr Opin HIV AIDS 2014;9:174\u0026ndash;82. https://doi.org/10.1097/COH.0000000000000042.\u003c/li\u003e\n\u003cli\u003eMishra S, Boily M-C, Schwartz S, Beyrer C, Blanchard JF, Moses S, et al. Data and methods to characterize the role of sex work and to inform sex work programmes in generalized HIV epidemics: evidence to challenge assumptions. Ann Epidemiol 2016;26:557\u0026ndash;69. https://doi.org/10.1016/j.annepidem.2016.06.004.\u003c/li\u003e\n\u003cli\u003eBaral S, Logie CH, Grosso A, Wirtz AL, Beyrer C. Modified social ecological model: a tool to guide the assessment of the risks and risk contexts of HIV epidemics. BMC Public Health 2013;13:482. https://doi.org/10.1186/1471-2458-13-482.\u003c/li\u003e\n\u003cli\u003eBeksinska A, Karlsen O, Gafos M, Beattie TS. Alcohol use and associated risk factors among female sex workers in low- and middle-income countries: A systematic review and meta-analysis. PLOS Glob Public Health 2023;3:e0001216. https://doi.org/10.1371/journal.pgph.0001216.\u003c/li\u003e\n\u003cli\u003eAtuhaire L, Adetokunboh O, Shumba C, Nyasulu PS. Effect of community-based interventions targeting female sex workers along the HIV care cascade in sub-Saharan Africa: a systematic review and meta-analysis. Syst Rev 2021;10:137. https://doi.org/10.1186/s13643-021-01688-4.\u003c/li\u003e\n\u003cli\u003eBhattacharjee P, Musyoki H, Prakash R, Malaba S, Dallabetta G, Wheeler T, et al. Micro-planning at scale with key populations in Kenya: Optimising peer educator ratios for programme outreach and HIV/STI service utilisation. PloS One 2018;13:e0205056. https://doi.org/10.1371/journal.pone.0205056.\u003c/li\u003e\n\u003cli\u003eBlanchard JF, Bhattacharjee P, Kumaran S, Ramesh BM, Kumar NS, Washington RG, et al. Concepts and strategies for scaling up focused prevention for sex workers in India. Sex Transm Infect 2008;84 Suppl 2:ii19-23. https://doi.org/10.1136/sti.2008.033134.\u003c/li\u003e\n\u003cli\u003eReza-Paul S, Beattie T, Syed HUR, Venukumar KT, Venugopal MS, Fathima MP, et al. Declines in risk behaviour and sexually transmitted infection prevalence following a community-led HIV preventive intervention among female sex workers in Mysore, India. AIDS Lond Engl 2008;22 Suppl 5:S91-100. https://doi.org/10.1097/01.aids.0000343767.08197.18.\u003c/li\u003e\n\u003cli\u003eThilakavathi S, Boopathi K, Girish Kumar CP, Santhakumar A, Senthilkumar R, Eswaramurthy C, et al. Assessment of the scale, coverage and outcomes of the Avahan HIV prevention program for female sex workers in Tamil Nadu, India: is there evidence of an effect? BMC Public Health 2011;11 Suppl 6:S3. https://doi.org/10.1186/1471-2458-11-S6-S3.\u003c/li\u003e\n\u003cli\u003eChowdhury MT, Bershteyn A, Milali M, Citron D, Nyimbili S, Musuka G, et al. Progress Towards UNAIDS\u0026rsquo;s 95-95-95 Targets in Zimbabwe: Sociodemographic Constraints and Geospatial Heterogeneity. medRxiv 2023:2023.07.26.23293207. https://doi.org/10.1101/2023.07.26.23293207.\u003c/li\u003e\n\u003cli\u003eCowan FM, Musemburi S, Matambanadzo P, Chida P, Steen R, Makandwa R, et al. Using a Programme Science approach to substantially reduce the risk of HIV transmission and acquisition in sex transactions among female sex workers in Zimbabwe. J Int AIDS Soc 2024;27 Suppl 2:e26262. https://doi.org/10.1002/jia2.26262.\u003c/li\u003e\n\u003cli\u003eStrauss ME, Smith GT. Construct Validity: Advances in Theory and Methodology. Annu Rev Clin Psychol 2009;5:1\u0026ndash;25. https://doi.org/10.1146/annurev.clinpsy.032408.153639.\u003c/li\u003e\n\u003cli\u003eChapman H, Gillespie SM. The Revised Conflict Tactics Scales (CTS2): A review of the properties, reliability, and validity of the CTS2 as a measure of partner abuse in community and clinical samples. Aggress Violent Behav 2019;44:27\u0026ndash;35. https://doi.org/10.1016/j.avb.2018.10.006.\u003c/li\u003e\n\u003cli\u003eWand H, Ramjee G. Spatial clustering of \u0026ldquo;measured\u0026rdquo; and \u0026ldquo;unmeasured\u0026rdquo; risk factors for HIV infections in hyper-endemic communities in KwaZulu-Natal, South Africa: results from geoadditive models. AIDS Care 2015;27:1375\u0026ndash;81. https://doi.org/10.1080/09540121.2015.1096896.\u003c/li\u003e\n\u003cli\u003eCuschieri S. The STROBE guidelines. Saudi J Anaesth 2019;13:S31\u0026ndash;4. https://doi.org/10.4103/sja.SJA_543_18.\u003c/li\u003e\n\u003cli\u003eGhaferi AA, Schwartz TA, Pawlik TM. STROBE Reporting Guidelines for Observational Studies. JAMA Surg 2021;156:577\u0026ndash;8. https://doi.org/10.1001/jamasurg.2021.0528.\u003c/li\u003e\n\u003cli\u003eSteen R, Wheeler T, Gorgens M, Mziray E, Dallabetta G. Feasible, Efficient and Necessary, without Exception \u0026ndash; Working with Sex Workers Interrupts HIV/STI Transmission and Brings Treatment to Many in Need. PLOS ONE 2015;10:e0121145. https://doi.org/10.1371/journal.pone.0121145.\u003c/li\u003e\n\u003cli\u003eCowan FM, Machingura F, Ali MS, Chabata ST, Takaruza A, Dirawo J, et al. A risk-differentiated, community-led intervention to strengthen uptake and engagement with HIV prevention and care cascades among female sex workers in Zimbabwe (AMETHIST): a cluster randomised trial. Lancet Glob Health 2024;12:e1424\u0026ndash;35. https://doi.org/10.1016/S2214-109X(24)00235-3.\u003c/li\u003e\n\u003cli\u003eMatambanadzo P, Jones HS, Takaruza A, Busza J, Sibanda EL, Hargreaves JR, et al. Risk differentiated microplanning among female sex workers in Zimbabwe: a mixed methods evaluation guided by the RE-AIM framework. ResearchGate 2025. https://doi.org/10.21203/rs.3.rs-7304011/v1.\u003c/li\u003e\n\u003cli\u003eBirri Makota R, Musenge E. Spatial heterogeneity in relationship between district patterns of HIV incidence and covariates in Zimbabwe: a multi-scale geographically weighted regression analysis. Geospatial Health 2023;18. https://doi.org/10.4081/gh.2023.1207.\u003c/li\u003e\n\u003cli\u003eChabata ST, Hensen B, Chiyaka T, Mushati P, Busza J, Floyd S, et al. Condom use among young women who sell sex in Zimbabwe: a prevention cascade analysis to identify gaps in HIV prevention programming. J Int AIDS Soc 2020;23:e25512. https://doi.org/10.1002/jia2.25512.\u003c/li\u003e\n\u003cli\u003eAndrews CH, Faxelid E, Sychaerun V, Phrasisombath K. Determinants of consistent condom use among female sex workers in Savannakhet, Lao PDR. BMC Womens Health 2015;15:63. https://doi.org/10.1186/s12905-015-0215-0.\u003c/li\u003e\n\u003cli\u003eDecker MR, Park JN, Allen ST, Silberzahn B, Footer K, Huettner S, et al. Inconsistent Condom Use Among Female Sex Workers: Partner-specific Influences of Substance Use, Violence, and Condom Coercion. AIDS Behav 2020;24:762\u0026ndash;74. https://doi.org/10.1007/s10461-019-02569-7.\u003c/li\u003e\n\u003cli\u003eBandyopadhyay K, Banerjee S, Goswami DN, Dasgupta A, Jana S. Predictors of Inconsistent Condom Use among Female Sex Workers: A Community-Based Study in a Red-Light Area of Kolkata, India. Indian J Community Med Off Publ Indian Assoc Prev Soc Med 2018;43:274\u0026ndash;8. https://doi.org/10.4103/ijcm.IJCM_84_18.\u003c/li\u003e\n\u003cli\u003eCowan FM, Chabata ST, Musemburi S, Fearon E, Davey C, Ndori-Mharadze T, et al. Strengthening the scale-up and uptake of effective interventions for sex workers for population impact in Zimbabwe. J Int AIDS Soc 2019;22 Suppl 4:e25320. https://doi.org/10.1002/jia2.25320.\u003c/li\u003e\n\u003cli\u003eVassall A, Chandrashekar S, Pickles M, Beattie TS, Shetty G, Bhattacharjee P, et al. Community Mobilisation and Empowerment Interventions as Part of HIV Prevention for Female Sex Workers in Southern India: A Cost-Effectiveness Analysis. PLoS ONE 2014;9:e110562. https://doi.org/10.1371/journal.pone.0110562.\u003c/li\u003e\n\u003cli\u003eBazzi AR, Yotebieng K, Otticha S, Rota G, Agot K, Ohaga S, et al. PrEP and the syndemic of substance use, violence, and HIV among female and male sex workers: a qualitative study in Kisumu, Kenya. J Int AIDS Soc 2019;22:e25266. https://doi.org/10.1002/jia2.25266.\u003c/li\u003e\n\u003cli\u003eMooney A, Kidanu A, Bradley HM, Kumoji EK, Kennedy CE, Kerrigan D. Work-related violence and inconsistent condom use with non-paying partners among female sex workers in Adama City, Ethiopia. BMC Public Health 2013;13:771. https://doi.org/10.1186/1471-2458-13-771.\u003c/li\u003e\n\u003cli\u003eBowring AL, Ketende S, Rao A, Mfochive Njindam I, Decker MR, Lyons C, et al. Characterising unmet HIV prevention and treatment needs among young female sex workers and young men who have sex with men in Cameroon: a cross-sectional analysis. Lancet Child Adolesc Health 2019;3:482\u0026ndash;91. https://doi.org/10.1016/S2352-4642(19)30123-3.\u003c/li\u003e\n\u003cli\u003eCrankshaw TL, Chareka S, Zambezi P, Poku NK. Age Matters: Determinants of sexual and reproductive health vulnerabilities amongst young women who sell sex (16\u0026ndash;24 years) in Zimbabwe. Soc Sci Med 2021;270:113597. https://doi.org/10.1016/j.socscimed.2020.113597.\u003c/li\u003e\n\u003cli\u003eKAHLE EM, HUGHES JP, LINGAPPA JR, JOHN-STEWART G, CELUM C, NAKKU-JOLOBA E, et al. An empiric risk scoring tool for identifying high-risk heterosexual HIV-1 serodiscordant couples for targeted HIV-1 prevention. J Acquir Immune Defic Syndr 1999 2013;62:339\u0026ndash;47. https://doi.org/10.1097/QAI.0b013e31827e622d.\u003c/li\u003e\n\u003cli\u003eBalkus JE, Brown E, Palanee T, Nair G, Gafoor Z, Zhang J, et al. An Empiric HIV Risk Scoring Tool to Predict HIV-1 Acquisition in African Women. J Acquir Immune Defic Syndr 1999 2016;72:333\u0026ndash;43. https://doi.org/10.1097/QAI.0000000000000974.\u003c/li\u003e\n\u003cli\u003eGlobal AIDS Strategy 2021-2026 \u0026mdash; End Inequalities. End AIDS. | UNAIDS n.d. https://www.unaids.org/en/resources/documents/2021/2021-2026-global-AIDS-strategy (accessed November 15, 2025).\u003c/li\u003e\n\u003cli\u003eWorld Medical Association. World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA 2013;310:2191\u0026ndash;4. https://doi.org/10.1001/jama.2013.281053.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8316815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8316815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003cbr\u003e\n HIV acquisition and transmission risk among female sex workers (FSWs) is heterogeneous and shaped by overlapping behavioural, social and structural factors. Person-centred HIV programming requires tailoring support to FSWs’ needs. In the \u003cstrong\u003eA\u003c/strong\u003edapted \u003cstrong\u003eM\u003c/strong\u003eicroplanning: \u003cstrong\u003eE\u003c/strong\u003eliminating \u003cstrong\u003eT\u003c/strong\u003eransmissible \u003cstrong\u003eHI\u003c/strong\u003eV In \u003cstrong\u003eS\u003c/strong\u003eex \u003cstrong\u003eT\u003c/strong\u003eransactions (AMETHIST) cluster randomised trial, we implemented peer-led HIV risk assessments among FSWs in Zimbabwe enrolled in microplanning, a form of enhanced peer outreach. We examined how effectively HIV risk among FSWs was assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\n At 11 sites, peer microplanners administered a risk assessment to FSWs based on six binary indicators, classifying them as at lower (score 0), moderate (score 1–2), or higher (score 3–6) risk. The risk assessment included information on both proximal (\u003cem\u003econdom use consistency, weekly client volume\u003c/em\u003e) and distal (\u003cem\u003eyoung age, duration in sex work, substance and alcohol use, and violence\u003c/em\u003e) factors. Risk was reassessed quarterly and FSWs were considered at higher risk until first assessment. Data from enrolment, quarterly assessments, and outreach data were analysed to assess: 1) risk assessment coverage, 2) risk heterogeneity between and within groups, 3) the association between factors included in the score, and 4) risk heterogeneity between sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\n Among 7012 FSWs enrolled, 86.2% (n=6,045) completed ≥1 assessment, and 62% (29,163/47,101) of all expected assessments were completed. Exposure to violence and duration in sex work ≤6 months was far more likely in the higher risk group vs the moderate risk group. Risk factors were associated with each other as expected: for example, inconsistent condom use was associated with substance use (OR=3.05, 2.73–3.40), and violence (OR=2.16, CI 1.68–2.78). FSWs aged ≤24 years had higher odds of recent sex work entry (OR=3.91, 95% CI 3.48–4.38). Overall, 39.7% of FSWs were at higher risk with site level differences ranging from 22.8-74.9% across sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nPeer microplanners effectively delivered risk assessments within microplanning with high coverage. The simple six factor tool captured both individual and contextual differences in risk. Risk segmentation by peer microplanners has the potential to enhance equity and efficiency in HIV services. Refinements including digital tools could further focus support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e: Pan African Clinical Trials Registry \u003ca href=\"https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=9777\" target=\"_blank\"\u003ePACTR202007818077777\u003c/a\u003e. Registered on 2 July 2020.\u003c/p\u003e","manuscriptTitle":"HIV risk segmentation in microplanning with female sex workers in Zimbabwe: an observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-01 13:15:05","doi":"10.21203/rs.3.rs-8316815/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T04:32:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T22:57:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296005773580302822014889197157291680429","date":"2026-03-11T20:59:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T10:24:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243435433081289732605824008340299458697","date":"2026-02-19T09:50:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130490931473912637390310765263157939311","date":"2026-01-26T14:40:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161801802182537485123287757832181313415","date":"2026-01-06T18:35:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282524611511800720460985057358830309640","date":"2026-01-06T16:34:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T18:42:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16405903135067165434915369997666073063","date":"2025-12-31T14:11:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-30T11:38:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T09:55:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-22T03:42:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-19T17:33:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-19T17:26:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af86c062-8840-4523-9d6c-4d49ec9fa022","owner":[],"postedDate":"January 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T04:39:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-01 13:15:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8316815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8316815","identity":"rs-8316815","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}