Burden of tuberculosis among vulnerable populations worldwide: an overview of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Tuberculosis is a communicable disease of public health concern that inequitably impacts the most vulnerable populations worldwide. Vulnerable populations are those with a high risk for tuberculosis disease and whose disadvantaged or marginalised socioeconomic position limits their access to the health system. We conducted an overview of reviews that aimed to assess the burden (ie, prevalence and incidence) of tuberculosis disease among 12 vulnerable populations globally. METHODS: We did an overview of reviews using a systematic search in MEDLINE, Embase, and the Cochrane Database for Systematic Reviews for articles published in English, French, and Chinese, from Jan 1, 2010 to March 8, 2023. We did an initial search on Oct 28, 2021, and updated our search on March 8, 2023. We included systematic and scoping reviews reporting on the prevalence or incidence of active tuberculosis among 12 vulnerable populations. Evidence gaps were supplemented with primary or secondary database studies. Study characteristics and outcome data related to tuberculosis burden were tabulated, including prevalence ratios and incidence rate ratios, and evidence was synthesised narratively. This trial is registered with PROSPERO (CRD42022324421). FINDINGS: We screened 13 169 citations and included 44 publications (23 reviews and 21 primary or database studies) in the final synthesis. The comprehensiveness and methodological quality of the evidence differed across population groups. Prevalence of more than 1000 cases per 100 000 were reported in all vulnerable populations. On the basis of pooled estimates, prevalence ratios were often more than 25 among people experiencing homelessness, incarcerated populations, refugees, asylum seekers, and people living with HIV compared with the general population. Incidence was infrequently reported, with the best-available incidence rate ratios documented for people who were incarcerated. There was scarce evidence specific to miners, nomadic populations, sex workers, men who have sex with men, and transgender individuals. INTERPRETATION: The burden of tuberculosis is substantially higher among vulnerable populations than general populations, suggesting a need for improved integration of these groups, including dedicated efforts for their identification, targeted screening and prevention measures, as well as treatment support. FUNDING: WHO.
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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 | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".