Abstract
Objectives
Stigma is multidimensional and undermines HIV care. We examined socio‐environmental pathways linking HIV‐related stigma, everyday discrimination and structural stigma to antiretroviral therapy (ART) adherence among men who have sex with men (MSM) and female sex workers (FSW) living with HIV in Kampala, Uganda.
Methods
We conducted a cross‐sectional survey (06/2024–02/2025) among MSM and FSW aged 18–35 receiving HIV services from a key population clinic in Kampala. ART adherence was assessed using the Wilson 3‐item adherence scale. We conducted stratified multivariable linear regression and multi‐group structural equation modelling (SEM) to estimate direct and indirect associations from stigma to adherence through socio‐environmental stressors (traumatic exposure severity to extreme weather [TESS], food insecurity, water insecurity), adjusted for socio‐demographic variables.
Results
Participants included MSM (n = 225, mean age: 27.65, standard deviation [SD] = 3.54) and FSW (n = 240, mean age: 25.68, SD: 2.90). SEM showed acceptable fit. In adjusted SEM analyses (a) among MSM, higher TESS, HIV‐related stigma and everyday discrimination were directly associated with lower ART adherence and (b) among FSW, higher TESS, HIV‐related stigma, water and food insecurity were directly associated with lower ART adherence. There were significant indirect associations between HIV‐related stigma and ART adherence via TESS for FSW and MSM, and between everyday discrimination and structural stigma with ART adherence via TESS for MSM.
Conclusions
Stigma and socio‐environmental stressors, including traumatic exposure to extreme weather events, were associated with reduced ART adherence among MSM and FSW participants. Population‐tailored HIV care interventions may benefit from integrating intersectional stigma reduction with economic and disaster preparedness supports.
Keywords: female sex workers, food insecurity, HIV, men who have sex with men, stigma, Uganda, water insecurity
INTRODUCTION
HIV‐related stigma drives poorer HIV care outcomes among people living with HIV (PLHIV), including reduced antiretroviral therapy (ART) adherence [1, 2]. Conceptual frameworks identify pathways linking stigma to adverse outcomes through stressful living conditions such as food insecurity [3, 4], limited social support [5] and constrained adaptive coping resources [5]. Key populations disproportionately affected by HIV, including men who have sex with men (MSM) and female sex workers (FSW), face intersecting stigma related to sexual orientation [6] and sex work [7]. Structural stigma is enacted through the criminalisation of same‐sex practices [6, 8] and sex work [7, 8, 9], which is linked with poorer HIV prevention and care outcomes. For instance, countries criminalising sex work and same‐sex practices reported lower HIV status knowledge and viral suppression than non‐criminalising settings [8]. Yet pathways from multiple forms of stigma to ART outcomes among key populations in high HIV prevalence, criminalized contexts remain understudied, including among MSM and FSW living with HIV in Uganda.
Uganda has one of the world's largest HIV epidemics, with approximately 1.5 million PLHIV [10]. HIV prevalence is substantially higher among MSM (12.2%) [11] and FSW (37%) [12] compared with the general population (5.9%) [13]. Criminalisation of sex work drives sex‐work related stigma [14], and limits FSW's access to HIV testing [15], PrEP [12], and ART [16]. Sex work stigma in Uganda manifests through community rejection, healthcare mistreatment and internalised stigma [17]. Criminalisation also adversely affects MSM in Uganda; for instance, following the passage of the 2023 Anti‐Homosexuality Bill, declines in MSM engaging with HIV services and support groups have been reported [18]. These structural contexts of stigma may interact with socio‐environmental stressors to limit HIV prevention and care engagement.
Extreme weather events (EWE), including droughts and floods, were linked with worse HIV care outcomes in sub‐Saharan Africa through disrupted livelihoods, food and water insecurity, reduced social support and psychosocial stress [19, 20]. Evidence from Southern Africa shows that low rainfall is associated with higher mortality, unsuppressed viral loads and reduced HIV care engagement [21]. Research in Uganda similarly links EWE exposure with poorer HIV prevention outcomes [22]. Poverty, including food insecurity, is associated with harmful social and health impacts on PLHIV in Uganda, including among FSW [16] and MSM [23]. Although studies from Kenya [24] and Nigeria [25] document co‐occurring food and water insecurity among PLHIV, water insecurity is understudied in Uganda. Key populations may be particularly vulnerable to EWE [26] and related food and water insecurity due to structural inequities [26, 27], yet they remain largely excluded from EWE‐HIV research. This is a key knowledge gap in a climate‐affected setting such as Kampala where 60% of residents live in informal settlements, many in flood‐prone wetland areas [28], with limited access to safe, reliable water and sanitation [29]. Food [30] and water insecurity [31] are also widespread in Uganda, exacerbated by droughts and floods. Concurrent water and food insecurity may be amplified by stigma‐related stressors and affect HIV care, yet this remains understudied.
Few models integrate multiple forms of stigma with socio‐environmental stressors to understand ART adherence. Turan et al.'s HIV‐related Stigma Framework conceptualizes pathways through stress processes (e.g. poverty), psychological resources (e.g. resilience) and interpersonal factors (e.g. social support) [5]. The Health Stigma and Discrimination Framework examines how intersecting stigma related to marginalized social categories (e.g. sexual orientation) and health conditions (e.g. HIV) restrict access to power and resources and how criminalising legal contexts facilitate these stigma processes [32]. Beyond HIV‐related stigma, PLHIV may experience ‘everyday discrimination’ related to other social categories such as sexual orientation and material insecurities [33]. While attribution‐free measures such as everyday discrimination can provide insight into intersecting stigma, they are rarely examined alongside HIV‐related stigma to understand ART adherence.
To address this gap, this study examined direct and indirect associations from stigma (HIV‐related stigma, structural stigma, everyday discrimination) to ART adherence through socio‐environmental stressors (food insecurity, water insecurity, traumatic exposure to extreme weather events) among MSM and FSW living with HIV in Kampala, Uganda. We also conducted exploratory supplementary analyses to examine whether psychological and interpersonal resources modified selected associations.
METHODS
Study design and setting
We conducted a cross‐sectional study to examine associations among stigma, socio‐environmental stressors (extreme weather event exposure, resource insecurities) and ART adherence among young (18–35 years) MSM and FSW living with HIV in Kampala, Uganda. Data collection occurred between June 2024 and February 2025.
Participants, recruitment and data collection
Participants were recruited through a key population HIV clinic in Kampala that provides specialized key population services (e.g. specific hours and days, trained staff) for FSW and MSM. Recruitment occurred on‐site, where trained research staff approached individuals referred by clinical providers at specialized clinics during routine care appointments. We employed a stratified random sampling strategy to achieve a target sample of 450 participants, with strata defined by gender and age band to ensure balanced representation of individuals aged 18–23 and 24–35 years. A total of 465 participants were enrolled at baseline.
Eligible participants were required to be 18–35 years old; have a confirmed HIV‐positive status verified through clinic records; self‐identify as MSM or FSW; be actively receiving HIV care at the clinic; fluent in English or Luganda; and have the capacity to provide informed consent. Screening was conducted via email, SMS, or telephone prior to scheduling study visits. Following written or thumbprint consent, participants completed interviewer‐administered surveys in private rooms within the clinic, in either English or Luganda according to preference. Trained research assistants entered responses directly into secure electronic data capture systems using the SurveyCTO platform to ensure data quality and confidentiality.
Ethical considerations
Ethical approval for this study was granted by the University of Toronto Research Ethics Board, the Mildmay Uganda Research Ethics Council and the Uganda National Council for Science and Technology. Given the criminalisation of sex work and same‐sex practices in Uganda, participant privacy and safety were prioritized throughout recruitment, consent, data collection, storage and reporting. Recruitment occurred through the MARPI Clinic during routine visits to specialized MSM and FSW services on specific days/times rather than through public outreach. Eligibility screening and informed consent were conducted privately by trained study staff. No identifying information, including names, date of birth, contact details, or sexual orientation, was collected on surveys. Participants provided written or thumbprint consent in private settings, were assured of confidentiality, and informed that participation was voluntary and could be withdrawn at any time. Study staff received training on confidentiality, respectful engagement with key populations and stigma reduction. Survey data were collected using secure electronic systems, deidentified for analysis and accessible only to authorized study team members. Findings are reported in aggregate form to minimize risks of disclosure. Participants received an honorarium of 26 000 UGX (approximately $10 CAD).
Measures
Outcome variable
ART adherence: Antiretroviral therapy adherence was assessed using the Wilson 3‐item adherence scale, [34] which captures adherence over the past 30 days. The scale includes the number of days on which at least one ART dose was missed, the rating of how good a job they did taking their ART as prescribed, and how often they took their ART as prescribed. Following Wilson et al., each item was transformed to a 0 to 100 metric, with higher scores indicating better adherence. The missed dose item was reverse‐scored so that fewer missed dose days indicated higher adherence. The two Likert‐type items were transformed linearly from 0 to 100, with the lowest adherence response coded as 0 and the highest adherence response coded as 100. The final adherence score was calculated as the mean of the three transformed items and treated as a continuous outcome ranging from 0 to 100. Internal consistency in the present sample was modest (Cronbach's α = 0.66).
Exposure variables
Stigma: HIV‐related stigma was measured using a 10‐item scale adapted from the Berger HIV Stigma Scale [35] (score range: 10–40; Cronbach's α = 0.86). Everyday Discrimination was assessed with the 9‐item Everyday Discrimination Scale [36] (score range: 9–54; Cronbach's α = 0.93). Structural stigma was measured using a 7‐item instrument assessing fears or experiences related to arrest, blackmail, employment loss, or health care avoidance (score range: 0–7; Cronbach's α = 0.82) [37].
Socio‐environmental stressors: Traumatic exposure to extreme weather events (EWE) was assessed using the 6‐item Resource loss/being in need subscale and one item from the Damage to home and goods subscale from the Traumatic Exposure Scale (TESS) [38], adapted to assess self‐reported disruptions related to past 12 month EWE (e.g. flooding) (score range: 0–7; Cronbach's α = 0.84). Food insecurity was assessed using the 9 item Household Food Insecurity Access Scale (HFIAS; score range: 0–27; Cronbach's α = 0.90) [39]. Water insecurity was measured using the 12‐item Household Water Insecurity Experiences Scale (HWISE; range: 0–36; Cronbach's α = 0.97) [40].
Psychological resources: Resilience was measured using the 12‐item short form of the Child and Youth Resilience Measure [41] (range: 12–36; Cronbach's α = 0.78). Perceived social support was measured using the 12‐item Multidimensional Scale of Perceived Social Support [42], which assesses support from family, friends and a significant other (range: 12–84; Cronbach's α = 0.89). Community cohesion was assessed using six items capturing perceptions of trust, reciprocity and solidarity within the community [43] (range: 6–18; Cronbach's α = 0.87).
Sociodemographic characteristics: Participants reported age, education level, relationship status, employment status, sex work employment, parental status and duration lived in Uganda, using items developed for this study. The season in which surveys were administered was also reported, including drought, light rains and heavy rains.
Statistical analysis
Descriptive statistics were used to summarize sociodemographic characteristics, extreme weather‐related exposures, stigma, resource insecurity, protective factors and HIV care outcomes. Continuous variables were summarized using means and standard deviations, and categorical variables were summarized using frequencies and percentages. Group differences between MSM and FSW were examined using t‐tests for continuous variables and chi square tests for categorical variables. ART adherence was examined as a continuous outcome using the Wilson 3‐item adherence score. Bivariate linear regression models assessed unadjusted associations between each independent variable and ART adherence, stratified by population group. Multivariable linear regression models were then conducted separately for MSM and FSW, adjusting for age, education, relationship status, parental status and season of survey administration. Results are reported as beta coefficients with 95% confidence intervals.
Structural equation modelling was then used to assess direct and indirect associations from multiple types of stigma (HIV‐related stigma, structural stigma, everyday discrimination) to ART adherence through socio‐environmental stressors (TESS, food insecurity, water insecurity), stratified by key population (FSW, MSM). Continuous variables were standardized and missing data were addressed using full information maximum likelihood. The proposed conceptual model is illustrated in Figure S1. All models were adjusted for sociodemographic factors (age, education level, relationship status, parental status and seasons). Model fit was evaluated using Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR). Acceptable model fit was defined as RMSEA <0.08, CFI/TLI >0.90 and SRMR <0.08 [44]. Multi group models compared MSM and FSW using unconstrained and constrained specifications, with likelihood ratio tests used to assess group differences. Standardized coefficients are reported for direct SEM paths. Indirect effects are reported using model based estimates from nlcom in Stata.
Exploratory moderation analyses were conducted as supplementary analyses to examine whether resilience, perceived social support and community cohesion modified selected associations in the model. Each interaction was tested in a separate regression model stratified by key population group. Analyses were conducted in Stata version 17 [45].
RESULTS
Sample characteristics
Among 465 participants (225 MSM and 240 FSW), FSW were younger than MSM (25.68 years vs. 27.65 years). Table 1 reports differences in socio‐demographic factors, stigma, socio‐environmental stressors, psychological resources and HIV care outcomes between populations. Viral suppression exceeded 93% in both groups (Table 1).
TABLE 1.
Sociodemographic characteristics, stigma, ecosocial stressors, psychological resources and HIV care outcomes among young participants living with HIV in Kampala, Uganda (N = 465).
| Overall (N = 465) | Men who have sex with men (n = 225) | Female sex workers (n = 240) | p‐Value | |
|---|---|---|---|---|
| Sociodemographic factors | ||||
| Age (years) | 26.63 (3.37) | 27.65 (3.54) | 25.68 (2.90) | <0.001 |
| Education | <0.001 | |||
| Less than primary school | 114 (24.5) | 20 (8.9) | 94 (39.2) | |
| Completed primary school | 153 (32.9) | 70 (31.1) | 83 (34.6) | |
| Completed secondary school | 121 (26.0) | 80 (35.6) | 41 (17.1) | |
| Completed post‐secondary school | 77 (16.6) | 55 (24.4) | 22 (9.2) | |
| Place of birth (Uganda vs. Other) | 447 (96.1) | 220 (97.8) | 227 (94.6) | 0.123 |
| Relationship status | <0.001 | |||
| No partner | 140 (30.1) | 45 (20.0) | 95 (39.6) | |
| Dating/casual | 215 (46.2) | 124 (55.1) | 91 (37.9) | |
| Married/living together | 41 (8.8) | 31 (13.8) | 10 (4.2) | |
| Multiple partners | 69 (14.8) | 25 (11.1) | 44 (18.3) | |
| Sex work employment (Yes vs. No) | 264 (56.8) | 87 (38.7) | 177 (73.8) | <0.001 |
| Employment status | 391 (84.1) | 178 (79.1) | 213 (88.8) | 0.007 |
| Have children a | 260 (55.9) | 69 (30.7) | 191 (79.6) | <0.001 |
| Time lived in Uganda ≥5 years | 461 (99.1) | 224 (99.6) | 237 (98.8) | 0.662 |
| Season a | <0.001 | |||
| Dry | 149 (32.0) | 75 (33.3) | 74 (30.8) | |
| Rainy, light | 186 (40.0) | 70 (31.1) | 116 (48.3) | |
| Rainy, heavy | 125 (26.9) | 79 (35.1) | 46 (19.2) | |
| Stigma factors | ||||
| HIV‐related stigma | 28.23 (7.22) | 28.90 (7.44) | 27.60 (6.96) | 0.051 |
| Everyday discrimination | 26.35 (14.12) | 25.97 (13.68) | 26.71 (14.54) | 0.573 |
| Structural stigma | 3.43 (2.26) | 3.84 (2.19) | 3.05 (2.27) | <0.001 |
| Ecosocial stressors | ||||
| Food insecurity (HFIAS) | 12.33 (6.29) | 11.87 (6.31) | 12.76 (6.25) | 0.127 |
| Water insecurity (HWISE) | 9.28 (7.86) | 9.69 (7.45) | 8.89 (8.22) | 0.272 |
| Traumatic exposure severity scale (TESS) | 3.57 (2.70) | 3.79 (2.75) | 3.37 (2.64) | 0.093 |
| Psychological resources | ||||
| Resilience | 30.63 (4.15) | 30.17 (4.20) | 31.06 (4.06) | 0.021 |
| Social support | 32.92 (13.47) | 31.00 (12.01) | 34.71 (14.51) | <0.01 |
| Community cohesion | 12.72 (3.44) | 12.59 (3.14) | 12.84 (3.71) | 0.213 |
| HIV care outcomes | ||||
| Viral load (clinic data) b | 0.995 | |||
| Detectable (≥50) | 30 (6.5) | 14 (6.2) | 16 (6.7) | |
| Undetectable (<50) | 435 (93.5) | 211 (93.8) | 224 (93.3) | |
| Number of clinic visits (past 12 months) b | 2.49 (0.86) | 2.52 (0.80) | 2.45 (0.92) | 0.380 |
| ART adherence (Wilson's 3‐item scale) | 77.66 (18.01) | 77.44 (18.30) | 77.86 (17.77) | 0.802 |
Note: Values are presented as mean (SD) or n (%), as appropriate.
Variable contains missing values.
Based on clinic record data for the past 12 months.
Linear regression analyses
MSM findings
As displayed in Table 2, among MSM in adjusted models, higher TESS (β = −1.81, 95% confidence interval [CI]: −2.70, −0.92) and Everyday Discrimination (β = −0.23, 95%CI: −0.46, −0.01) were associated with lower ART adherence.
TABLE 2.
Unadjusted and adjusted linear regression findings of factors associated with antiretroviral therapy adherence among young participants living with HIV in Kampala, Uganda (N = 465).
| Parameter | MSM (N = 225) | FSW (N = 240) | ||
|---|---|---|---|---|
| Unadjusted coefficient (95% CI) | Adjusted coefficient (95% CI) | Unadjusted coefficient (95% CI) | Adjusted coefficient (95% CI) | |
| Traumatic exposure severity scale (TESS) | −3.31 (−4.07, −2.55)*** | −1.81 (−2.70, −0.92)*** | −2.00 (−2.83, −1.18)*** | −1.21 (−2.08, −0.35)** |
| HIV‐related stigma | −1.19 (−1.48, −0.91)*** | −0.30 (−0.70, 0.11) | −0.81 (−1.12, −0.50)*** | −0.54 (−0.93, −0.15)** |
| Everyday discrimination | −0.66 (−0.81, −0.51)*** | −0.23 (−0.46, −0.01)* | −0.27 (−0.42, −0.11)*** | −0.16 (−0.36, 0.03) |
| Structural stigma | −2.41 (−3.46, −1.35)*** | 0.51 (−0.64, 1.67) | −1.96 (−2.92, −0.99)*** | −0.49 (−1.72, 0.74) |
| Food insecurity (HFIAS) | −0.57 (−0.94, −0.19)** | −0.08 (−0.43, 0.26) | −0.14 (−0.51, 0.22) | 0.29 (−0.09, 0.66) |
| Water insecurity (HWISE) | −0.75 (−1.06, −0.44)*** | −0.14 (−0.44, 0.15) | −0.57 (−0.83, −0.30)*** | −0.39 (−0.68, −0.09)* |
| Resilience | 1.07 (0.52, 1.63)*** | 0.11 (−0.48, 0.70) | 0.55 (−0.01, 1.10) | 0.17 (−0.50, 0.84) |
| Social support | 0.21 (0.01, 0.41)* | 0.18 (−0.01, 0.37) | −0.05 (−0.21, 0.10) | −0.02 (−0.20, 0.16) |
| Community cohesion | 0.18 (−0.59, 0.95) | 0.21 (−0.48, 0.90) | 0.47 (−0.14, 1.08) | 0.20 (−0.46, 0.85) |
Note: Missing values were excluded listwise within each regression model. Bold values reflect statistically significant associations, with asterisks indicating significance levels (*p < 0.05, **p < 0.01, ***p < 0.001).
Abbreviation: CI, Confidence interval.
Female sex workers (FSW) findings
Among FSW, in adjusted models, higher TESS (β = −1.21, 95%CI: −2.08, −0.35), HIV‐related stigma (β = −0.54, 95%CI: −0.93, −0.15) and water insecurity (β = −0.39, 95%CI: −0.68, −0.09) were associated with lower ART adherence (Table 2).
Structural equation modelling findings
Structural equation modelling was used to estimate a multiple group path model for MSM and FSW with ART adherence. The final SEM demonstrated acceptable fit to the data (χ 2(36) = 40.27, p = 0.287; CFI = 0.992; TLI = 0.980; RMSEA = 0.023, 90% CI [0.000, 0.054]; SRMR = 0.028). Figure S1 depicts the standardized pathways, and Table 3 and Figure 1 present final SEM results stratified by key populations.
TABLE 3.
Path analysis of associations with ART adherence among young participants living with HIV in Kampala, Uganda (N = 465).
| Parameter | MSM (N = 225) | FSW (N = 240) | ||
|---|---|---|---|---|
| Coefficient (SE; 95% CI) | Standardised coefficient (SE; 95% CI) | Coefficient (SE; 95% CI) | Standardised coefficient (SE; 95% CI) | |
| TESS ON a | ||||
| HIV‐related stigma | 0.10 (0.03; 0.04, 0.16)*** | 0.27 (0.08; 0.12, 0.43)*** | 0.08 (0.03; 0.03, 0.14)** | 0.22 (0.07; 0.08, 0.37)** |
| Everyday Discrimination | 0.05 (0.02; 0.02, 0.09)*** | 0.27 (0.08; 0.11,0.42)** | 0.02 (0.01; −0.01, 0.05) | 0.11 (0.08; −0.05, 0.26) |
| Structural stigma | 0.20 (0.08; 0.04, 0.37)* | 0.16 (0.07; 0.03, 0.29)* | 0.14 (0.09; −0.04, 0.31) | 0.12 (0.08; −0.03, 0.27) |
| Water insecurity ON | ||||
| TESS | 0.61 (0.20; 0.22, 1.01)** | 0.23 (0.07; 0.08, 0.37)** | 0.64 (0.19; 0.27, 1.00)** | 0.20 (0.06; 0.09, 0.32)** |
| HIV‐related stigma | 0.01 (0.09; −0.16, 0.19) | 0.01 (0.09; −0.16, 0.19) | 0.12 (0.08; −0.04, 0.29) | 0.10 (0.07; −0.04, 0.24) |
| Everyday Discrimination | 0.19 (0.05; 0.09, 0.29)*** | 0.34 (0.09; 0.17, 0.52)*** | 0.13 (0.04; 0.05, 0.21)** | 0.23 (0.07; 0.09, 0.37)** |
| Structural stigma | −0.14 (0.25; −0.73, 0.26) | −0.07 (0.07; −0.21, 0.08) | 0.48 (0.26; −0.03, 0.98) | 0.13 (0.07; −0.01, 0.27) |
| Food insecurity ON | ||||
| TESS | 0.60 (0.18; 0.25, 0.96)** | 0.26 (0.08; 0.11, 0.42)** | 0.14 (0.15; −0.17, 0.44) | 0.11 (0.08; −0.09, 0.32) |
| HIV‐related stigma | 0.03 (0.08; −0.13, 0.19) | 0.04 (0.10; −0.15, 0.23) | 0.24 (0.07; 0.10, 0.38)*** | 0.27 (0.08; 0.12, 0.42)*** |
| Everyday Discrimination | −0.02 (0.05; −0.10, 0.07) | −0.03 (0.10; −0.23, 0.16) | −0.01 (0.03; −0.07, 0.06) | −0.02 (0.08; −0.17, 0.14) |
| Structural stigma | 0.34 (0.23; −0.11, 0.79) | 0.12 (0.08; −0.04, 0.27) | 0.26 (0.21; −0.16, 0.68) | 0.10 (0.08; −0.06, 0.25) |
| ART adherence (Wilson 3‐item scale) ON | ||||
| HIV‐related stigma | −0.38 (0.18; −0.77, −0.13)* | −0.15 (0.07; −0.31, −0.01)* | −0.51 (0.19; −0.89, −0.14)** | −0.20 (0.07; −0.34, −0.06)** |
| Everyday Discrimination | −0.30 (0.11; −0.51, −0.08)** | −0.22 (0.08; −0.38, −0.06)** | 0.10 (0.10; −0.08, 0.29) | 0.08 (0.08; −0.07, 0.23) |
| Structural stigma | 0.75 (0.54; −0.31, 1.81) | 0.09 (0.06; −0.04, 0.22) | −0.46 (0.58; −1.46, 0.67) | −0.06 (0.07; −0.20, 0.08) |
| TESS | −1.71 (0.44; −2.58, −0.84)*** | −0.26 (0.07; −0.39, −0.13)*** | −1.29 (0.42; −2.12, −0.46)** | −0.19 (0.06; −0.31, −0.07)** |
| Water insecurity | −0.06 (0.15; −0.35, 0.23) | −0.03 (0.06; −0.14, 0.09) | −0.39 (0.14; −0.67, −0.11)** | −0.18 (0.06; −0.31, −0.05)** |
| Food insecurity | −0.11 (0.16; −0.43, 0.21) | −0.04 (0.06; −0.15, 0.08) | −0.40 (0.18; −0.75, −0.06)* | −0.14 (0.06; −0.26, −0.02)* |
Note: Bold values reflect statistically significant associations, with asterisks indicating significance levels (*p < 0.05, **p < 0.01, ***p < 0.001).
‘ON’ indicates the dependent variable in each regression path. Adjusted for sociodemographic factors (age, education level, relationship status, parental status and seasons).
FIGURE 1.
Final path analysis of direct and indirect associations linking stigma, socio‐environmental stressors, and ART adherence among men who have sex with men and female sex workers living with HIV in Kampala, Uganda. (a) Final path analysis of ART adherence among men who have sex with men living with HIV in Kampala, Uganda (N = 225). (b) Final path analysis of ART adherence among female sex workers living with HIV in Kampala, Uganda (N = 240). Solid line indicates significant direct associations and dashed lines indicate significant indirect associations. ART adherence was assessed using the Wilson 3‐item adherence scale. Analyses were adjusted for sociodemographic factors (age, education level, relationship status, parental status, and seasons). Results are reported as standardized coefficients with standard errors and 95% confidence intervals. Statistical significance is denoted as follows: *p < 0.05, **p < 0.01, ***p < 0.001.
Direct effects
MSM findings: Higher scores of HIV‐related stigma (β = −0.15, p < 0.05), TESS (β = −0.26, p < 0.001), and everyday discrimination (β = −0.22, p < 0.01) were associated with lower ART adherence scores. Food insecurity, water insecurity and structural stigma were not directly associated with adherence (Figure 1a).
FSW findings: Higher scores of HIV‐related stigma (β = −0.20, p < 0.01), TESS (β = −0.19, p < 0.01), water insecurity (β = −0.18, p < 0.01) and food insecurity (β = −0.14, p < 0.05) were inversely associated with ART adherence. Everyday discrimination and structural stigma were not directly associated with ART adherence (Figure 1b).
Indirect and Total effects
MSM findings: HIV‐related stigma had a significant negative total association with ART adherence (total effect = −0.57, p < 0.01). The overall indirect association was also significant (indirect effect = −0.19, p < 0.01), and the indirect component accounted for 33% of the total association. Path‐specific estimates showed a significant indirect association through traumatic exposure severity (HIV‐related stigma→TESS→ART adherence: indirect effect = −0.17, p < 0.05). HIV‐related stigma was also indirectly associated with food insecurity (B = 0.061, SE = 0.026, p = 0.016) and water insecurity (B = 0.062, SE = 0.027, p = 0.022) through TESS.
Everyday discrimination had a significant negative total association with ART adherence scores (total effect = −0.40, p < 0.001). The overall indirect association was also significant (indirect effect = −0.11, p < 0.05), and the indirect component accounted for 26% of the total association. Path‐specific estimates showed a significant indirect association through TESS (Everyday discrimination→TESS→ART adherence: indirect effect = −0.09, p = 0.012). Everyday discrimination was also indirectly associated with food insecurity (B = 0.032, SE = 0.014, p = 0.020) and water insecurity (B = 0.033, SE = 0.015, p = 0.026) through TESS.
Structural stigma had a significant overall indirect association with ART adherence scores (indirect effect = −0.39, p < 0.05), although the total association was not statistically significant (total effect = 0.36, p = 0.518). Path‐specific estimates showed a significant indirect association through TESS (structural stigma→TESS→ART adherence: indirect effect = −0.35, p = 0.039). Structural stigma was also indirectly associated with food insecurity through TESS (B = 0.123, SE = 0.062, p = 0.049). The indirect association between structural stigma and water insecurity through TESS approached statistical significance but was not significant (B = 0.124, SE = 0.065, p = 0.056).
TESS had a significant negative total association with ART adherence scores (total effect = −1.81, p < 0.001), while its indirect association through food and water insecurity was not statistically significant (indirect effect = −0.10, p = 0.372).
FSW findings: HIV‐related stigma had a significant negative total association with ART adherence scores (total effect = −0.59, p < 0.01). The overall indirect association was not statistically significant (indirect effect = −0.07, p = 0.352). Path‐specific estimates showed a significant indirect association through TESS (HIV‐related stigma→TESS→ART adherence: indirect effect = −0.11, p < 0.05). HIV‐related stigma was also indirectly associated with water insecurity through TESS (B = 0.053, SE = 0.024, p < 0.05), but not with food insecurity through TESS (B = 0.011, SE = 0.014, p = 0.397).
Everyday discrimination had a significant negative indirect association with ART adherence scores (indirect effect = −0.08, p < 0.05), although the total association was not statistically significant (total effect = 0.02, p = 0.825). Path‐specific estimates showed a significant indirect association through water insecurity (Everyday discrimination→water insecurity→ART adherence: indirect effect = −0.05, p < 0.05). The indirect associations from everyday discrimination to food insecurity and water insecurity through TESS were not statistically significant.
Structural stigma was not significantly associated with ART adherence scores through either total or indirect associations among FSW (total effect = −0.74, p = 0.213; indirect effect = −0.28, p = 0.164). The indirect associations from structural stigma to food insecurity and water insecurity through TESS were also not statistically significant.
TESS had a significant negative total association with ART adherence scores (total effect = −1.48, p < 0.001), while its indirect association through food and water insecurity was not statistically significant (indirect effect = −0.19, p = 0.131).
Group differences
Tests of parameter invariance showed significant group differences for three structural paths: everyday discrimination to ART adherence (Wald χ 2(1) = 7.53, p = 0.006), food insecurity to ART adherence (Wald χ 2(1) = 4.65, p = 0.031) and structural stigma to water insecurity (Wald χ 2(1) = 3.86, p = 0.049). Other tested adherence paths, including TESS, HIV‐related stigma, structural stigma and water insecurity to ART adherence, did not differ significantly between MSM and FSW.
Exploratory moderation analyses were conducted as supplementary analyses to examine whether psychological and interpersonal resources modified selected associations in the model. Among FSW, perceived social support moderated the association between HIV‐related stigma and TESS (B = 0.046, SE = 0.020, p < 0.05). Among MSM, resilience moderated the associations of HIV‐related stigma (B = 0.040, SE = 0.014, p < 0.01), everyday discrimination (B = 0.018, SE = 0.008, p < 0.05) and TESS (B = 0.141, SE = 0.037, p < 0.001) with food insecurity. Community cohesion also moderated the association between everyday discrimination and water insecurity among MSM (B = 0.025, SE = 0.011, p < 0.05).
DISCUSSION
This study examined associations among multiple forms of stigma and discrimination, traumatic exposure severity to extreme weather (TESS), resource insecurities, and ART adherence among young MSM and FSW living with HIV in Kampala. HIV‐related stigma and TESS were associated with lower ART adherence among both MSM and FSW, underscoring the continued relevance of HIV‐related stigma for poor health [1, 32, 46] while highlighting extreme weather‐related disruptions as a potential mechanism linking social inequities and HIV treatment outcomes. Group differences between MSM and FSW signal distinct pathways linking stigma to ART adherence and reinforce the importance of examining heterogeneity within key populations [47]. Everyday discrimination was associated with reduced adherence among MSM, while food and water insecurity were associated with lower adherence among FSW, highlighting the importance of material needs security for women living with HIV [48]. Findings advance stigma frameworks [32] by demonstrating how stigma may operate through socio‐environmental pathways, including extreme weather‐related disruptions and resource insecurity.
Aligned with the HIV‐related stigma framework [5], HIV‐related stigma was associated with lower ART adherence among both MSM and FSW, including indirect associations through extreme weather‐related stressors and resource insecurity. Our findings suggest that traumatic extreme weather exposure may represent an additional pathway linking stigma to HIV care outcomes. This corroborates growing evidence that EWE‐related shocks disrupt HIV care engagement through livelihood instability and competing survival needs [19, 20]. Consistent with research showing that disasters disproportionately affect marginalized populations [27], including sexually diverse persons [49] and people with chronic conditions [50], our findings suggest that environmental disruptions may contribute to how stigma becomes translated into poorer ART adherence. Exploratory moderation findings suggested that resilience, perceived social support and community cohesion may modify selected associations involving stigma, TESS and resource insecurity, aligning with evidence that interpersonal and psychological resources can buffer the effects of stigma and socio‐environmental stressors on HIV outcomes [5].
The observed group differences suggest that stigma and socio‐environmental stressors may be patterned differently for MSM and FSW. Among MSM, everyday discrimination was directly and indirectly associated with adherence via TESS, suggesting that chronic discrimination may shape HIV care engagement in ways that extend beyond HIV‐related stigma [18, 23]. In criminalized contexts such as Uganda, MSM may experience intersecting stigma [46, 51] related to sexual orientation, poverty and HIV serostatus, contributing to chronic stress and barriers to HIV care engagement.
Among FSW, adherence was more strongly associated with HIV‐related stigma and resource insecurities. Direct associations between food and water insecurity and reduced adherence suggest that economic and environmental stressors may produce immediate barriers to treatment engagement. Livelihood disruptions may reduce the ability to meet basic needs to sustain HIV treatment [19, 21, 26] including among FSW [16]. This reflects the importance of addressing economic precarity and criminalisation among FSW, particularly in Uganda where most FSW use sex work as their primary income source [52], and suggests that HIV‐related stigma may operate through both psychosocial and material need pathways. Together, these findings indicate that adherence interventions can integrate disaster preparedness [53, 54] in addition to population‐specific approaches, with greater focus on discrimination and intersecting stigma‐related stressors with MSM, and on stigma reduction combined with food and water security among FSW [55, 56].
This study has limitations. The cross‐sectional data preclude causal inference and cannot establish temporal ordering. ART adherence was assessed using self‐report and may be influenced by recall and social desirability bias. Although the Wilson scale is validated, internal consistency was modest, which may reflect the small number of items, and findings should be interpreted as associations with a brief self‐reported adherence measure rather than objective adherence. Participants were recruited from a specialized clinic and may not represent people living with HIV disengaged from care or receiving care elsewhere. Although SEM allowed for the simultaneous estimation of multiple pathways, unmeasured confounding and measurement limitations may have influenced results. Despite these limitations, this study advances stigma and adherence research by integrating multiple stigma constructs with socio‐environmental stressors that are rarely examined with key populations. Findings extend existing stigma frameworks by documenting how environmental conditions may function as pathways through which stigma can be embodied into HIV treatment outcomes.
Findings may inform HIV research, practice and policy. HIV care interventions could consider integrating disaster preparedness and resource security programs, particularly for key populations in low‐income urban informal settlements [20, 53, 54, 57]. Findings also support the need for legal and policy environments that reduce criminalisation, stigma and barriers to HIV care. Finally, differentiated service delivery models co‐created with affected communities may help address population specific needs, including disaster preparedness, resource security and community cohesion.
CONCLUSION
ART adherence among MSM and FSW living with HIV in Kampala was associated with multiple forms of stigma and socio‐environmental stressors, including traumatic exposure to extreme weather and resource insecurity. Optimising HIV outcomes for key populations in criminalized contexts may require integrated approaches that address stigma, EWE preparedness and material needs security.
FUNDING INFORMATION
This project was funded by the Canadian Institutes of Health Research (CIHR), Project Grant: 479681. Logie's efforts were in part supported by the Canada Research Chairs Program (SSHRC, Social Sciences & Humanities Research Council of Canada [SSHRC] CRC 2 in Global Health Equity and Social Justice with Marginalized Populations).
CONFLICT OF INTEREST STATEMENT
The authors have no conflicts of interest to declare.
ETHICS STATEMENT
Ethical approval was obtained from the University of Toronto Research Ethics Board, the Mildmay Uganda Research Ethics Council and the Uganda National Council for Science and Technology. Informed consent, provided either in writing or via a thumbprint, was obtained in private settings. Participants were informed of the study's confidentiality measures, the voluntary nature of their participation and their right to withdraw at any time without consequence.
Supporting information
Data S1: Supporting information
Data S2. Proposed conceptual model of direct and indirect associations linking stigma, socio‐environmental stressors, and ART adherence.
ACKNOWLEDGEMENTS
We would like to thank all participants who contributed their time and energy to the study, the research coordinators and data collectors, as well as colleagues at the Ugandan Ministry of Health, and Most at Risk Population Initiative Clinic at Mulago Hospital for their help and support.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1: Supporting information
Data S2. Proposed conceptual model of direct and indirect associations linking stigma, socio‐environmental stressors, and ART adherence.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.