Community variability in TB-related stigma in South Africa: an ecologic analysis from the MISSED TB Outcomes Study
Bibliographic record
Abstract
Introduction: Tuberculosis (TB) stigma is a critical barrier to timely diagnosis and treatment. Although stigma originates within communities, few studies have quantified community-level TB stigma or its variability across geographic contexts. This study describes methods for capturing community-level TB stigma and examines stigma variability across 93 urban, peri-urban, and rural communities in Buffalo City Metropolitan Health District, South Africa. Methods: As part of the MISSED TB Outcomes Study, heads of household (HoHs) were surveyed in a geographically clustered random sample of households across demarcated study communities. Validated scales were used to measure perceived community-level TB stigma, HIV stigma, and TB/HIV knowledge. Demographic data, including self-reported household TB and HIV history, were also captured. Community-level data, including TB and HIV stigma, were generated by aggregating individual responses within each study community. Associations between TB stigma and other community-level variables were analyzed using robust linear regression. Results: Surveys were completed by 3,869 households across 93 communities. Median community TB stigma scores varied significantly by community location, with rural communities reporting the lowest stigma and peri-urban communities the highest. TB stigma was positively associated with HIV stigma across all community types, with the strongest associations in urban and rural communities. No associations were observed between TB stigma and TB prevalence, TB knowledge, or household demographics after adjusting for community location. Conclusions: TB stigma varied meaningfully across communities and was influenced by urbanicity and HIV stigma. These findings suggest that stigma-reduction interventions must be tailored to local contexts and consider community-level determinants beyond individual knowledge or TB burden. The identified variability in TB stigma will inform future multilevel analyses of the TB care cascade in South Africa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".