Tuberculosis in pregnancy: an estimate of the global burden of disease
Bibliographic record
Abstract
BACKGROUND: The estimated number of maternal deaths in 2013 worldwide was 289 000, a 45% reduction from 1990. Non-obstetric causes such as infectious diseases including tuberculosis now account for 28% of maternal deaths. In 2013, 3·3 million cases of tuberculosis were estimated to occur in women globally. During pregnancy, tuberculosis is associated with poor outcomes, including increased mortality in both the neonate and the pregnant woman. The aim of our study was to estimate the burden of tuberculosis disease among pregnant women, and to describe how maternal care services could be used as a platform to improve case detection. METHODS: We used publicly accessible country-level estimates of the total population, distribution of the total population by age and sex, crude birth rate, estimated prevalence of active tuberculosis, and case notification data by age and sex to estimate the number of pregnant women with active tuberculosis for 217 countries. We then used indicators of health system access and tuberculosis diagnostic test performance obtained from published literature to determine how many of these cases could ultimately be detected. FINDINGS: We estimated that 216 500 (95% uncertainty range 192 100-247 000) active tuberculosis cases existed in pregnant women globally in 2011. The greatest burdens were in the WHO African region with 89 400 cases and the WHO South East Asian region with 67 500 cases in pregnant women. Chest radiography or Xpert RIF/MTB, delivered through maternal care services, were estimated to detect as many as 114 100 and 120 300 tuberculosis cases, respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".