Undernutrition and the incidence of tuberculosis in India: national and subnational estimates of the population-attributable fraction related to undernutrition.
Bibliographic record
Abstract
BACKGROUND: India has the largest global burden of tuberculosis (TB)-related morbidity and mortality as well as undernutrition. Undernutrition impairs cell-mediated immunity, is a risk factor for the development of TB, and has the largest potential impact on the incidence of TB in countries with a high burden of TB. METHODS: We refined the national estimate of the population-attributable fraction (PAF) for undernutrition in India to report the first subnational estimates, and stratified these further for age, gender, residence, caste and socioeconomic status. We also compared the PAF related to undernutrition in India with that in 15 other countries with a high burden of TB. We used data on body mass index (BMI) from the National Family Health Survey-3 (NFHS-3), as well as risk estimates for a low BMI from a recently published population-based study which had controlled for several confounders. RESULTS: The overall prevalence of undernutrition in the age group of 15-49 years was 35.6% among women and 34.2% among men. About half (55.4%; 95% CI 27.4- 75.9) of all cases of active TB among women and 54.4% (95% CI 26.5-75.2) of all cases among men were attributable to undernutrition. In the age group of 15-19 years, the PAFs for undernutrition were 62% and 67% among women and men, respectively. The PAF of undernutrition was higher in rural areas, in scheduled castes, scheduled tribes and other backward classes, and in the lower quintiles of the wealth index. The PAF of undernutrition exceeded 50% in most states, and the largest PAFs were seen among women of scheduled tribes in central India. Among countries with a high burden of TB, India had the highest PAF related to undernutrition. CONCLUSION: Addressing the problem of endemic undernutrition among adolescents and adults in India could complement the current TB control strategy based on case management, and help reduce the incidence of TB in India in line with global targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".