Exploring stigma of physical health conditions across low and lower-middle income countries: a secondary analysis of a global systematic review.

Exploring stigma of physical health conditions across low and lower-middle income countries: a secondary analysis of a global systematic review

Introduction

Stigma undermines health equity by impeding engagement in care and worsening outcomes. Systematic reviews of stigma are primarily from high-income countries, focus on specific conditions, especially mental illness, and employ heterogeneous stigma measures. Despite bearing a disproportionate burden of disease, low-income (LIC) and lower-middle-income (LMIC) countries remain underrepresented in existing reviews. This study characterizes stigma definitions, measures, impacts, and interventions in physical health conditions across LICs and LMICs.

Methods

This secondary analysis included 137 publications, previously identified through a global systematic review, reporting on patient or caregiver stigma of physical health conditions among LIC/LMIC populations. Data on definitions, measurements, and interventions were utilized from a primary review. Two reviewers extracted supplemental data regarding the study’s conceptualization and outcomes and conducted a quality appraisal.

Results

Among the 137 studies (32 LIC, 105 LMIC), 75.2% used cross-sectional designs, 67.2% focused on adults, and 75.9% included populations with communicable diseases, including HIV, TB, and leprosy. Stigma definitions and measures varied widely, with uneven coverage of stigma dimensions. Negative mental health outcomes were commonly associated with stigma related to physical conditions (57.7%). Only 13.1% of studies evaluated stigma interventions, primarily including social support or health education, with heterogenous intervention designs, outcome definitions, and follow-up periods. Our quality appraisal demonstrated limited use of population-validated and culturally adapted stigma tools.

Conclusion

Stigma research in LIC/LMICs has primarily been conducted in middle-income countries and focused on adults with communicable diseases, leaving gaps for stigma research in non-communicable diseases and pediatric populations. Future work should prioritize development of culturally adapted stigma frameworks and measures to improve patient outcomes in LIC/LMICs.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/view/CRD42023433176.

Introduction

Stigma is the co-occurrence of stereotyping, discrimination, labeling, and status loss (1, 2). It contributes to negative health outcomes by exacerbating mental and physical health burdens and is a barrier to treatment engagement and adherence. The pervasive impact of stigma makes it a critical determinant of health globally, especially in settings with constrained health systems.

Systematic reviews with data largely from high-income countries (HICs) have explored stigma across various health conditions and provided an evidence base for psychosocial interventions. These reviews have highlighted the effects of stigma and identified key factors that could affect stigma, such as ethnicity, age, and co-morbid conditions (3–9). However, most stigma research has focused on specific disease categories and populations, such as mental health disorders, Human Immunodeficiency Virus (HIV), and cancer (8–11). The studies described in these reviews use multiple different frameworks, measures, and intervention strategies to reduce stigma. Prior work has highlighted the need for more standardized approaches to studying stigma, which would enable widespread application.

Despite efforts to understand stigma globally, there is a gap in understanding how stigma is studied and conceptualized in low-income countries (LICs) and lower-middle income countries (LMICs) (8, 12–14). Evidence from HICs may not adequately capture the dynamics of stigma in LICs and LMICs, where differing social norms, cultural contexts, and structural conditions, including health system constraints, shape stigma experiences (15). Additionally, existing stigma research under-represents non-communicable diseases (NCD) that are common, chronic, visible, and socially misunderstood within these settings (16–18). The aim of our study is to characterize definitions and approaches to measuring stigma across physical health conditions in LIC and LMIC studies, evaluate methodological rigor, examine associations with stigma, and highlight priorities for future studies on stigma and stigma-reduction strategies in these settings.

Methods

This in-depth analysis of stigma measurement in LIC/LMICs was a secondary analysis of a larger systematic review that explored stigma measurement globally (19). The primary review was registered in PROSPERO (CRD42023433176) and followed PRISMA guidelines. This secondary analysis was not separately registered and was conducted as a post hoc analysis of the parent dataset. A medical librarian created search strategies and conducted a comprehensive database search for concepts of scales, instruments, measurements, outcomes, patient stigma, and self-stigma across Embase, Ovid Medline, Scopus, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Cumulated Index to Nursing and Allied Health, APA PsycInfo, Global Health, and Clinicaltrials.gov. With no limits applied, 14,663 records were returned on the initial search. After de-duplication using EndNote and Covidence.org and two search rounds, the primary review extracted data from 2,267 full-text articles (20, 21). The primary review was designed to characterize how stigma is measured across health conditions and geographic settings globally.

This secondary analysis included full-length, peer-reviewed papers from the primary review if they quantitatively measured stigma surrounding a physical health condition in LICs and/or LMICs. This analysis was undertaken to specifically characterize patterns in stigma literature within LIC and LMIC resource settings. A focused analysis was necessary to further contextualize stigma and its distribution in these settings, where differences in resources, research capacity, and sociocultural contexts may shape and change the way stigma is experienced and studied. LICs and LMICs were defined using the 2025 World Bank classifications regardless of the publication year (22). There were no restrictions on study design or publication dates, and publications had to be written in English, Spanish, or Arabic due to language limitations of the research team. Papers were excluded if they were review/conceptual articles, abstracts only, or conference proceedings. Data was managed using Research Electronic Data Capture (REDCap) tools hosted at Washington University in St. Louis. Studies in the primary review focused on either tool development/validation and/or tool application/use. For this analysis, papers that exclusively focused on tool development were excluded, as they primarily assessed tool metrics rather than categorizing stigma, resulting in 137 publications for final inclusion (Figure 1; Appendix 1). Two authors (BR, VE) extracted additional data on study tools and results specific to this analysis. Data for the first 21 articles (15%) were independently extracted by both authors, with disagreements resolved through consensus. During this process, variable definitions were refined for clarity to support consistency in extraction between reviewers. The remaining studies were extracted by one of the two authors, with ambiguous cases discussed with the second reviewer and resolved through consensus to maintain consistency.

Data analysis

Data utilized from the primary review included: World Health Organization (WHO) region and country of each study (23–25), the health condition under investigation, stigma definition, and any interventions designed and implemented. For application studies, sample size, study design, and stigma-focused intervention data were extracted.

For this analysis, additional data extraction included: the reported prevalence of stigma and correlate variables of stigma. We also extracted data related to the construct of stigma studied, such as domains and levels. We defined domains, in accordance with prior literature, as the distinct ways stigma is experienced, categorized as self/internalized stigma, perceived stigma, enacted stigma, or anticipated stigma (26). To describe where stigma operates in society, data was collected regarding the level at which stigma was measured and described by the subjects, and categorized as individual, interpersonal, community, or structural aligning with prior literature (27). Finally, data was collected regarding how stigma was conceptualized for the study (driver, outcome, and mediator). If stigma was described as a contributing factor of the outcome being reported, it was categorized as a driver. If it was considered an impact of the primary measure of the study, stigma was categorized as a reported outcome. If stigma was neither the driver nor the outcome, it was categorized as a mediator. Additionally, for each study, correlations, or characteristics that were significantly associated with an individual’s experience of stigma, were defined. Overall, there were 13 correlation categories. The complete list of variables we included in correlation analysis can be found in Table 1. Finally, physical health conditions were categorized as either communicable or non-communicable in accordance with WHO standards (28). We also collected the efficacy of the intervention data, defined as a statistically significant or otherwise established improvement in the study outcome, that was described the methods of the study. Because outcome measures, intervention types, and follow-up were heterogenous and often descriptive, we did not estimate an overall intervention effect, but rather summarized findings descriptively.

Table 1Correlations.

Correlation Total N = 137

LICN = 32 (23.4%) LMICN = 105 (76.6%)

Age 3 27 30 (21.9%)

Disease severity 3 24 27 (19.7%)

Education 6 20 26 (19.0%)

Employment 1 10 11 (8.0%)

Gender 1 25 26 (19.0%)

Income 0 9 9 (6.6%)

Intrapersonal indicators 9 13 22 (16.1%)

Mental health 21 58 79 (57.7%)

Relationship status 6 21 27 (19.7%)

Religion 1 4 5 (3.6%)

Social/cultural structure 9 16 25 (18.2%)

Treatment outcome 7 14 21 (15.3%)

Treatment adherence 9 24 33 (24.1%)

To evaluate methodological rigor, a quality appraisal of each paper was conducted using nine questions with dichotomous yes or no scoring (Table 2). Due to the heterogeneity of studies and study designs, no single quality appraisal could uniformly be applied to satisfy the needs of this study. Instead, tools from Joanna Briggs Institute (29) were used to create a single set of questions that could be applied regardless of study type. Specifically, we included six general methodological items that assessed clarity of inclusion criteria, description of settings and participants, validity and reliability of the stigma measurement, use of statistical analyses, reporting of ethical considerations, and clarity in stigma-related results reporting. Because stigma research poses unique measurement challenges, particularly across diverse cultural contexts, three questions (6,7,8) were added to evaluate the rigor of stigma-specific aspects of these studies, including clarity in stigma conceptualization, the validation of the stigma measurement within the population, and if the study explicitly considered the cultural relevance of stigma or its measurement. These criteria were added because stigma is a context-dependent construct, where meaning and manifestations of stigma can vary across health conditions, populations and sociocultural settings. These factors can substantially affect interpretation and comparability of stigma estimates across settings (12, 30). This appraisal was intended to characterize the methodological strengths and limitation across our studies rather than generate a validated summary score for each study.

Table 2Quality appraisal.

Questions Total papersN = 137 LICsN = 32 LMICsN = 105

1. Was the criteria for inclusion in the sample clearly defined? 131 (95.6%) 32 (100.0%) 99 (94.3%)

2. Were the study participants and setting described in detail? 137 (100%) 32 (100.0%) 105 (100%)

3. Was stigma measured in a valid and reliable way? 83 (60.6%) 18 (56.3%) 65 (61.9%)

4. Were statistical analyses used (more than descriptive statistics)? 134 (97.8%) 32 (100.0%) 102 (97.1%)

5. Were ethical considerations described? (Intro/Con) 123 (90.5%) 30 (93.8%) 93 (88.6%)

6. Was stigma clearly conceptualized and defined? 101 (73.7%) 16 (50.0%) 85 (81.0%)

7. Was the stigma measure validated for this population? 40 (29.2%) 12 (37.5%) 28 (26.7%)

8. Did the study consider cultural relevance of stigma? 58 (42.3%) 15 (46.9%) 43 (41.0%)

9. Were results of stigma clearly presented in results? 135 (98.5%) 32 (100.0%) 103 (98.1%)

Analyses were descriptive in nature and univariate statistics were used to summarize study characteristics, stigma definitions, stigma constructs, and outcome measures. Meta-analysis was not feasible due to the heterogeneity across studies.

Results

Overall, 137 papers were included in this analysis (Figure 1). Most studies (n = 105, 76.6%) were conducted in LMICs with the remaining (n = 32, 23.4%) in LICs. Studies exclusively focused on the adult population predominated (n = 92/137, 67.2%), with 22 of the LIC papers (22/32, 68.8%) and 70 of the LMIC papers (70/105, 66.7%) focused on adult populations. Only eight papers (5.8%) measured stigma exclusively in pediatric populations, and 24 papers (17.5%) studied both adult and pediatric populations. Studies were conducted across all WHO regions, except the European region, which contains no LICs and only three LMICs (Figure 2). Globally, most LICs and many LMICs are in the African WHO region. Correspondingly, the African region was the most represented in our review, with all but one LIC paper (n = 31/32, 96.9%) and almost half of the papers from LMICs (n = 42/105, 40.0%) coming from the African region. The remaining LIC paper came from Yemen in the Eastern Mediterranean region. LMICs in the Southeast Asia region were also well represented within the review (n = 35/105, 33.3%). Additional regional data is provided in Table 3.

Table 3Study characteristics stratified by LIC or LMIC (N = 137 papers).

LIC papersN = 32 (23.4%) LMIC papersN = 105 (76.6%)

Population of interest

Adult (18+) 22 (68.8%) 70 (66.7%)

Pediatric (<18) 2 (6.3%) 6 (5.7%)

Both 7(21.9%) 17 (16.2%)

Not specified 1 (3.1%) 12 (11.4%)

Respondent

Patient Stigma 31 (96.9%) 100 (95.2%)

Patient Proxy 0 (0.0%) 1 (1.0%)

Caregiver Stigma 1 (3.1%) 8 (7.6%)

Total participants

0–100 4 (12.5%) 16 (15.2%)

101–250 7 (21.9%) 46 (43.8%)

251–500 19 (59.4%) 21 (20.0%)

501–1,000 2 (6.3%) 12 (11.4%)

1,001 – 2,000 0 (0.0%) 9 (8.6%)

2,001+ 0 (0.0%) 1 (1.0%)

Number of stigma tools used

1 30 (93.8%) 91 (86.7%)

2 0 (0.0%) 10 (9.5%)

3+ 2 (6.3%) 4 (3.8%)

WHO Region

African 31 (96.9%) 42 (40.0%)

Region of Americas 0 (0.0%) 2 (1.9%)

South-East Asia 0 (0.0%) 35 (33.3%)

European 0 (0.0%) 0 (0.0%)

Eastern Mediterranean 1 (3.1%) 16 (15.2%)

Western pacific 0 (0.0%) 10 (9.5%)

Health condition*

*Some studies analyzed multiple health conditions and double counted

**Conditions such as arthritis, metabolic syndrome and podoconiosis

HIV 20 (62.5%) 61 (58.1%)

Epilepsy 5 (15.6%) 9 (8.6%)

Parkinsons 0 (0.0%) 2 (1.9%)

Leprosy 1 (3.1%) 7 (6.7%)

Obesity 0 (0.0%) 3 (2.9%)

Dermatological conditions 1 (3.1%) 5 (4.8%)

TB 2 (6.3%) 9 (8.6%)

COVID-19 1 (3.1%) 4 (3.8%)

Cancer 0 (0.0%) 4 (3.8%)

T1DM 0 (0.0%) 1 (1.0%)

Asthma/COPD 0 (0.0%) 1 (1.0%)

Other** 3 (9.4%) 2 (1.9%)

Study design

Cross-sectional 21 (65.6%) 82 (78.1%)

Randomized study 4 (12.5%) 8 (7.6%)

Case–control 3 (9.4%) 3 (2.9%)

Longitudinal 1 (3.1%) 3 (2.9%)

Mixed methods 1 (3.1%) 3 (2.9%)

Pre and post-test 0 (0.0%) 4 (3.8%)

Non-randomized experimental Treatment/intervention 1 (3.1%) 2 (1.9%)

Prospective cohort 1 (3.1%) 0 (0.0%)

Stigma studies from LICs and LMICs predominantly focused on communicable diseases (n = 104/137, 75.9%), specifically HIV (n = 81/137, 59.1%) (Figure 3). This was true in LICs, where the most frequently studied communicable diseases were HIV (n = 20/32, 62.5%), tuberculosis (TB) (n = 2/32, 6.3%), leprosy (n = 1/32, 3.1%), and COVID-19 (n = 1/32, 3.1%), and epilepsy was the most studied non-communicable disease (n = 5/32, 15.6%). In LMICs, a similar proportion of studies focused on communicable diseases (n = 80/105, 76.2%). Studies from LMICs also included those exploring stigma in a wider variety of non-communicable diseases such as dermatologic conditions (n = 5/105, 4.8%), cancer (n = 4/105, 3.8%), obesity (n = 3/105, 2.9%), asthma (n = 1/105, 1.0%), and Type 1 Diabetes Mellitus (T1DM) (n = 1/105, 1.0%) (Table 4).

Table 4Health conditions categorized into region*.

LICN = 32 LMICN = 105

African Region

HIV 20 37

Epilepsy 5 3

Leprosy 1 1

Dermatological conditions 1 –

Obesity – 1

TB 2 1

Cancer – 1

Other 3 –

Region of Americas

HIV – 1

TB – 1

Eastern Mediterranean

HIV – 3

Epilepsy – 3

Obesity – 1

Dermatological conditions – 2

TB – 3

COVID-19 1 2

Other – 2

Southeast Asia

HIV – 12

Epilepsy – 2

Leprosy – 6

Obesity – 1

Dermatological conditions – 3

TB – 4

COVID-19 – 2

Cancer – 3

T1DM – 1

Asthma/COPD – 1

Western Pacific

HIV – 8

Epilepsy – 1

Parkinson’s – 2

*Some studies analyzed multiple health conditions and double counted.

Most studies used a cross-sectional design (n = 103/137, 75.2%) and many studies reported on stigma prevalence (n = 55/137; 40.1%) (Table 5). However, estimates of stigma prevalence varied widely across studies and were difficult to compare because methods for measuring and reporting stigma were not standardized. Within these unclear reports of stigma (n = 82/137; 59.9%), stigma was described categorically without clear definitions or thresholds for those rankings. Other papers (n = 11; 8.0%) reported multiple prevalences within a single paper, for example, providing a prevalence for each domain or level of stigma assessed. Despite the heterogeneity in reporting, of studies reporting stigma prevalence as a percentage, 18 studies (n = 18/44, 40.9%) reported >50.0% of their study population experiencing stigma.

Table 5Stigma definitions, prevalence, domain, and levels.

Stigma definition LICN = 6 (12.8%) LMICN = 41 (87.2%) Definitions

Goffman1 2 (33.3%) 8 (19.5%) “The situation of the individual who is disqualified from full social acceptance due to a trait which is deeply discrediting.”

Link and Phelan2 1 (16.7%) 3 (7.3%) “The co-occurrence of labeling, stereotyping, separation, status loss, and discrimination.”

Turan29 0 (0.0%) 2 (4.9%) “The acceptance, acknowledgement and application of negative labels, stereotypes, and societal perceptions.”

Did not use pre-established definition 1 (16.7%) 6 (14.6%)

Other with one citation 1 (16.7%) 15 (36.6%)

Other with multiple citations 1 (16.7%) 7 (17.1%)

LICN = 32 LMICN = 105

Stigma prevalence

Report a percent 17 (53.1%) 27 (25.7%)

Report multiple types 1 (3.1%) 10 (9.5%)

Not clearly reported 14 (43.8%) 68 (64.8%)

Stigma domain

Self/Internalized 13 (40.6%) 62 (59.0%)

Perceived 19 (59.4%) 76 (72.4%)

Enacted 6 (18.8%) 30 (28.6%)

Anticipated 2 (6.3%) 15 (14.3%)

Stigma level

Individual 24 (75.0%) 90 (85.7%)

Interpersonal 10 (31.3%) 64 (61.0%)

Community 3 (9.4%) 22 (21.0%)

Structural 2 (6.3%) 7 (6.7%)

Stigma usage

Driver 26 (81.3%) 50 (47.6%)

Outcome 5 (15.6%) 54 (51.4%)

Mediator 1 (3.1%) 1 (1.0%)

Defining stigma

Most papers (n = 90/137, 65.7%) did not explicitly define stigma (Table 5). Of the 47 papers that provided a stigma definition (LIC: n = 6/47, 12.8%; LMIC: n = 41/47, 87.2%), 40 papers (40/47, 85.1%) used established definitions of stigma, including the Goffman definition (10/40; 25.0%) (1), the Link and Phelan definition (4/40; 10.0%) (2), and the Turan definition (2/40; 5.0%) (31). Some papers provided their own definition of stigma, with no references, which described discriminatory acts, rejection, or exclusion and emphasized stigma being a negative social phenomenon or experience (n = 7/47, 14.9%).

Exploring the conceptualization of stigma

All four stigma domains describing the distinct ways stigma is experienced, (internalized, perceived, enacted, anticipated) were measured, and many papers studied stigma across more than one domain (n = 65/137, 47.4%). The most frequently studied domain was perceived stigma (n = 95/137, 69.3%), while anticipated stigma was the least measured domain (n = 17/137, 12.4%) (Table 5).

Similarly, most papers studied stigma at more than one level (n = 79/137, 57.7%). Stigma was most commonly studied at an individual level (n = 114/137, 83.2%), followed by interpersonal (n = 74/137, 54.0%), community (n = 25/137, 18.2%), and structural (n = 9, 6.6%) levels. Stigma was studied as the driver, or contributing factor of the outcome being reported, in 55.5% of papers (n = 76), while it was studied as an outcome in 43.1% (n = 59). In two papers (1.5%) stigma was a mediator, meaning it was neither a driver nor an outcome (Table 5).

Correlations of stigma

All 137 papers described stigma correlates, within 13 different categories (Table 1). Across all papers, mental health was the most common correlation category (n = 79/137, 57.7%) (Table 2). Many studies in both LICs and LMICs demonstrated how stigma around a physical health disorder was associated with poor mental health outcomes. For example, in a study in Ethiopia, higher levels of HIV-related perceived stigma were associated with significantly increased rates of depression (32–36). Similarly, among patients with epilepsy in Pakistan, there was a positive correlation between perceptions of stigma and depression and anxiety (37). In a study of cancer patients in India, elevated internalized stigma was linked with increased anxiety, depression, and hopelessness (36).

The second most common correlate was treatment adherence (n = 33/137, 24.1%). Higher stigma measures were found to be associated with poor treatment adherence in many settings. In a study from Ethiopia, patients who experienced higher levels of perceived stigma were more likely to be treatment nonadherent (38). Similarly, in HIV patients in Tanzania, individuals reporting lower internalized HIV stigma demonstrated a greater likelihood of initiating antiretroviral treatment (39).

Patient age was also commonly studied as a correlate (n = 30/137, 21.9%), although the ways in which age correlated with stigma were nuanced, with varied patterns across studies. In two studies from Bengal, India, older patients with leprosy and HIV were found to have higher stigma scores (40, 41). In contrast, a study in Kenya found that older HIV patients reported lower levels of internalized stigma (42). Gender was also correlated with stigma (n = 25/137, 18.2%), especially in LMICs. In a study of psoriasis patients from India and a study from COVID-19 patients in Egypt, female gender was significantly associated with increased levels of stigma (43, 44).

HIV study predominance

To assess whether the predominance of HIV studies influenced key aggregate findings of our study, we conducted a stratification analysis comparing HIV (n = 81) and non-HIV (n = 56) studies (Table 6). Several significant differences emerged. HIV studies more frequently assessed internalized stigma than non-HIV studies (67.9% vs. 44.7%, p ≤ 0.05), whereas perceived stigma was more frequently assessed among non-HIV studies (83.9% vs. 59.3%, p ≤ 0.05). Mental health was less frequently examined as a correlate of stigma among HIV than non-HIV studies (50.6% vs. 69.6%, p ≤ 0.05). Finally, studies of HIV conditions were less likely to examine stigma as an outcome (38.3% v. 60.7%, p ≤ 0.05). No significant HIV versus non-HIV differences were identified for treatment adherence, enacted or anticipated stigma, individual, interpersonal, community, or structural levels of stigma, or stigma examined as a driver or mediator.

Table 6HIV vs. Non-HIV study stratification analysis for key findings.

HIVN = 81 (59.1%) Non-HIVN = 56 (40.9%) p-value of Z score(*significant)

Correlation

Mental health 41 (50.6%) 39 (69.6%) p < 0.05*

Treatment adherence 22 (27.2%) 13 (23.2%) p ≥ 0.05

Stigma domain

Internalized 55 (67.9%) 21 (44.7%) p < 0.05*

Perceived 48 (59.3%) 47 (83.9%) p < 0.05*

Enacted 25 (30.9%) 11 (19.6%) p ≥ 0.05

Anticipated 13 (16.0%) 5 (8.9%) p ≥ 0.05

Stigma level

Individual 71 (87.7%) 49 (87.5%) p ≥ 0.05

Interpersonal 44 (54.3%) 34 (60.7%) p ≥ 0.05

Community 18 (22.2%) 7 (13.0%) p ≥ 0.05

Structural 6 (7.4%) 3 (5.4%) p ≥ 0.05

Stigma usage

Driver 56 (69.1%) 46 (82.1%) p ≥ 0.05

Outcome 31 (38.3%) 34 (60.7%) p < 0.05*

Mediator 1 (1.2%) 1 (1.8%) p ≥ 0.05

Intervention efficacy HIVN = 13 (72.2%) Non-HIVN = 5 (27.8%) p-value of Z score(*significant)

Effective 8 (61.5%) 5 (100.0%) p ≥ 0.05

Non-effective 5 (38.5%) 0 (0.00%) p ≥ 0.05

Stigma interventions

Only 18 studies (13.1%) evaluated stigma interventions (Table 7). Most of these (n = 14/18, 77.8%) were conducted in LMICs. Interventions included support groups with peers and family (45, 46), health education from trained individuals (47), medication distribution (39), cognitive-behavioral therapy (48), and agricultural and financial management aid (49). All interventions targeted the patient. The majority focused on patients with HIV (n = 13/18, 72.2%), but others targeted patients with TB (n = 2/18, 11.1%), epilepsy (n = 1/18, 5.6%), Hepatitis B Virus (n = 1/18, 5.6%), and leprosy (n = 1/18, 5.6%). Thirteen of these studies (n = 13/18, 72.2%) reported effective interventions, demonstrating significant or pre-specified improvement in their study outcome measure. Five studies conducted pre- and post-intervention measures, with no subsequent follow-up (5/18, 27.8%). The longest follow-up period was 2 years (1/18, 5.6%). The rest of the studies had 1 year or less follow-up (12/18, 66.7%). Furthermore, as 13 studies were HIV studies (72.2%), we again conducted a stratification analysis of HIV vs. non-HIV studies, and found no statistical significance in efficacy (p > 0.05) (Table 6).

Table 7Interventions.

LICN = 4 (22.2%) LMICN = 14 (77.8%)

Effective Not effective Effective Not effective

Intervention papers total 3 (75%) 1 (25%) 10 (71.4%) 4 (28.6%)

Intervention types

Support groups 1 – 4 3

Health education 2 – 2 1

Treatment distribution – 1 1 –

Other* – - 3 –

*Individual counseling, cognitive behavioral therapy, and financial management.

Quality appraisal

Our quality appraisal showed variability in study quality, but generally acceptable methodological rigor (Table 2). Only 21 of the studies (21/137, 15.3%) scored 100%, while 84 studies (84/137, 61.3%) scored above 70% on the nine-question appraisal. In LICs and LMICs, questions about stigma measure validation and exploring the cultural relevance of stigma had the lowest ratings. Only 40 studies (40/137, 29.2%) reported utilizing a validated stigma measure for study population, and only 58 studies considered the cultural relevance of stigma (58/137, 42.3%).

Discussion

This systematic review synthesizes existing evidence on stigma in physical health conditions across LICs and LMICs, demonstrating that although stigma is widespread in low resource settings it remains understudied. The existing stigma literature disproportionally reports on communicable diseases, especially HIV, within the African and Southeast Asian WHO regions. Due to multiple and inconsistent stigma definitions and measurement tools, the current evidence remains fragmented. Additionally, the studies lack culturally adapted and population-specific stigma measures. Despite these limitations, our findings reveal multiple constructs that correlate with stigma and could be crucial targets for future stigma reduction strategies.

One of the most striking gaps in the stigma literature from LICs and LMICs is the limited representation of NCDs and pediatric populations. This should be interpreted as a gap in the evidence base that has been identified and not across all settings. The predominance of communicable-disease stigma research in the LIC and LMIC studies likely reflects the high infectious-disease burden in these settings and historical funding priorities for transmissible conditions such as HIV and TB (28, 50, 51). However, NCDs, such as cancer, cardiovascular disease, chronic respiratory disease, and diabetes, account for over 70% of global deaths, with 82% of premature NCD fatalities occurring in Low and Middle Income Countries (28). There are many reasons stigma around non-communicable diseases may be under-studied in LICs and LMICs including less urgency surrounding chronic conditions in these regions, limited research capacity, and fewer dedicated public health response systems (52, 53). Importantly, the small number of NCD studies that have been identified within our evidence base, limits our ability to characterize stigma with the same confidence as for more extensively studied communicable diseases. However, stigma related to NCDs is consequential, especially in LICs and LMICs, where effective management requires sustained, recurrent engagement with often fragmented health care systems (54). This represents a critical gap that could undermine long-term care-seeking, adherence, and health outcomes.

The predominance of HIV (59.1%) within the identified evidence base raises the possibility that overall patterns of physical health-related stigma may disproportionately reflect the established HIV stigma literature. Our stratification analysis showed that HIV studies were significantly more likely to assess internalized stigma, whereas non-HIV studies more frequently assessed perceived stigma, mental health correlates, and stigma as an outcome. These differences suggest that the conceptualization and study of stigma within this evidence base varies across health conditions. Aggregate estimates should not be assumed to characterize physical health-related stigma uniformly across diseases. Consistent with prior literature, shared stigma processes may coexist with condition-specific drivers and manifestations (12). HIV-related stigma, for example, may be shaped by mechanisms related to transmissibility, disclosure, sexual behavior, and social marginalization that may not apply in the same way to cancer, diabetes, epilepsy, or other physical health conditions (55). Importantly, several other dimensions, including enacted and anticipated stigma and the individual, interpersonal, community, and structural levels, did not differ significantly between HIV and non-HIV studies, suggesting some commonality in how stigma has been examined across conditions. Greater representation of noncommunicable diseases is crucial to determine which stigma mechanisms are shared across diseases and which may be more condition specific.

Furthermore, the infrequent representation of pediatric population studies is consistent with findings from the broader stigma literature (19, 56); additionally, the need is even more pronounced in LICs and LMICs, where children face heightened vulnerability due to constrained health systems and limited social protections (15). This lack of pediatric-centered stigma research in these settings potentially obscures the understanding of early life processes that could affect long-term health outcomes (57).

Importantly, aligning with prior reviews, included studies generally adhered to basic methodological standards, but lacked usage of unified, culturally-adapted stigma frameworks and measures (8, 13, 19). Developing standardized, yet flexible approaches to defining and measuring stigma may be particularly valuable in LICs and LMICs, where research capacity constraints increase the need for comparable, efficient, and contextually valid tools (5, 27). Additionally, the implications of heterogeneity may be more consequential where inconsistencies hinder comparability of stigma prevalence and implementation of interventions across settings with diverse languages, cultures, regions, and health systems. The differences in reported stigma prevalence could reflect a variation in how stigma was conceptualized and measured, including scoring thresholds, translation, cultural adaptation, or even instrument content, rather than true differences in stigma burden. This is particularly relevant for cross-country comparisons that may be done in the future where the same instrument may not capture the social specific aspects of stigma consistently across populations.

Structural stigma was captured in only a small proportion of studies identified in this analysis. Structural stigma can be difficult to capture with patient-reported stigma measures, which were the focus of the primary systematic review. Therefore, our finding of limited structural-level studies in this analysis could be a limited representation within the identified literature, rather than evidence that structural stigma itself being uncommon in LIC/LMICs.

Studies consistently demonstrated strong associations between stigma and mental health outcomes as well as poor treatment adherence. These findings are aligned with a prior review demonstrating that stigma harms psychosocial well-being and hinders help-seeking in limited resource contexts (51). In LICs and LMICs, there is often limited access to mental health services and follow-up care, which could magnify the adverse effects of stigma (58–60). Additional associations, including age, were more nuanced. It is possible that this nuance is related to the heath condition studied. For example, studies have shown that some patients with HIV learn to live with their disease and build life experience and social support overtime, which may buffer the stigma they experience as they age (61). There are also likely regional and cultural factors influencing the age-stigma association. For example, South Asian and African regions are more age-reverent regions, which could buffer the stigma experienced in older populations (62, 63). Demographic correlative findings, such as age and gender, should be further examined in future work as they may provide insights regarding vulnerable populations.

Although relatively few studies evaluated stigma-reduction interventions, most that did reported short-term effectiveness, particularly those incorporating psychosocial assistance, counseling, and group-based support (45, 64, 65). These approaches could be promising because they target mechanisms such as internalized stigma, misinformation, social isolation, and resource disparities. This aligns with other studies that aimed to reduce stigma across HIV and mental health conditions, which have similarly used counseling, social support, and health education to address stigma at individual and interpersonal levels (13, 66). However, it is crucial to note that this evidence does not permit comparison of intervention modalities as the predominance of HIV-focused studies limits generalizability to NCDs and other physical conditions. Implementation studies are particularly lacking in LICs and LMICs, compared with HICs (12, 13). In low-resource settings most interventions are smaller scale and short-term, leading to less follow-up and sustained stigma reduction (67). Long-term sustainability should be further explored, as prior literature demonstrates that community engagement and integration within existing systems is crucial in allowing implementation work to be sustained and provide long-term impact (8). Bridging this gap requires not only adapting approaches from HICs to the local contexts of LICs and LMICs, but also developing context-specific interventions from the ground up, with a focus on sustainability and investment in local infrastructure.

Key strengths of this study include its broad scope across physical health conditions and focus on vulnerable and understudied populations in LICs and LMICs, as well as rigorous PRISMA-compliant methodology, structured data extraction with dual review for consensus when necessary, and quality appraisal. However, several limitations must be considered. Language restrictions may have resulted in missed studies published in languages outside of the included languages, which could be relevant to this study given the diversity of lingual backgrounds represented in LIC/LMICs. This also could influence conclusions regarding geographic representation itself, as excluding the primary language of specific geographic regions could lead to them being considered under-represented. Furthermore, we used the country-income classification from the 2025 World Bank, regardless of the study publication year. Although this provides a consistent contemporary definition for income settings across the analysis, it does not account for countries that transitioned between income categories during the period represented by our analysis. Future studies could incorporate historical income classifications and examine whether transitions between income categories may influence patterns in distribution and characteristics of stigma research. Additionally, heterogeneity in study designs and measures and a predominance of cross-sectional studies limited comparability and hindered meta-analysis. The pre-dominance of cross-sectional studies also limits the causal inference regarding the associations between stigma and mental health, treatment adherence, or other study outcomes. Furthermore, the primary systematic review was not specifically conducted to comprehensively identify stigma-reduction interventions, structural or provider level stigma, or community level discrimination. The frequency of these topics within our current dataset should be interpreted as the representation among the studies identified within our primary review rather than a comprehensive estimate of the broader literature. Additionally, the frequent use of non-validated or poorly adapted stigma measures could obscure true effect sizes and impacts of stigma. These limitations highlight broader challenges in stigma research in LICs and LMICs and reinforce the need for more rigorous, longitudinal, and context-sensitive study designs.

This review demonstrates that stigma is consistently associated with adverse health outcomes among individuals with physical health conditions in LICs and LMICs, highlighting it importance as a potential consequential and pervasive barrier to positive health outcomes in these settings. Additionally, it reveals critical gaps that limit the ability of current research to inform effective and scalable interventions, while highlighting particularly vulnerable populations in which stigma remains understudied. Standardized and culturally adapted stigma frameworks and measures, as well as investment in long-term stigma interventions to improve patient outcomes in the most limited resource settings are needed.

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