Prevalence of Body Mass Index Lower Than 16 Among Women in Low- and Middle-Income Countries
Bibliographic record
Abstract
IMPORTANCE: Body mass index (BMI) lower than 16 is the most severe category of adult undernutrition and is associated with substantial morbidity, increased mortality, and poor maternal-fetal outcomes such as low-birth-weight newborns. Little is known about the prevalence and distribution of BMI lower than 16 in low- and middle-income countries (LMIC). OBJECTIVE: To determine the prevalence and distribution of BMI lower than 16 and its change in prevalence over time in women in LMIC. DESIGN, SETTINGS, AND PARTICIPANTS: Cross-sectional data analysis composed of nationally representative surveys from 1993 through 2012 from the Demographic and Health Surveys Program. Women aged 20 through 49 years from 60 LMIC (N = 500,761) and a subset of 40 countries with repeated surveys (N = 604,144) were examined. EXPOSURES: Wealth was measured using a validated asset index, age was categorized in deciles, education by highest completed level (none, primary, secondary, or greater), and place of residence as urban vs rural. MAIN OUTCOMES AND MEASURES: The primary outcome was BMI lower than 16. Analyses assessed the prevalence of BMI lower than 16, its association with sociodemographic factors, and change in prevalence. Logistic regression models were used to calculate odds ratios (ORs), adjusting for survey design and age structure. RESULTS: Among countries examined, the pooled, weighted, and age-standardized prevalence of BMI lower than 16 was 1.8% (95% CI, 1.7% to 1.8%) with the highest prevalence in India (6.2% [95% CI, 5.9% to 6.5%]), followed by Bangladesh (3.9% [95% CI, 3.4% to 4.3%]), Madagascar (3.4% [95% CI, 2.8% to 4.0%], Timor-Leste (2.9% [95% CI, 2.4% to 3.2%]), Senegal (2.5% [95% CI, 1.9% to 3.2%]), and Sierra Leone (2.2% [95% CI, 1.3% to 3.0%]); and 6 countries had prevalences lower than 0.1% (Albania, Bolivia, Egypt, Peru, Swaziland, and Turkey). The prevalence of BMI lower than 16 in women with a secondary or higher education level was 0.51% (95% CI, 0.47% to 0.55%), and in mutually adjusted models, a less than primary education level was associated with an OR of 1.4 (95% CI, 1.2 to 1.6). The prevalence of BMI lower than 16 was 0.43% (95% CI, 0.37% to 0.48%) in the highest wealth quintile with an OR of 3.0 (95% CI, 2.4 to 3.7) in the lowest wealth quintile. Among the 24 of 39 countries with repeated surveys, there was no decrease in prevalence. In Bangladesh and India, rates were declining with an average absolute change annually of -0.52% (95% CI, -0.58% to -0.46%) in Bangladesh and -0.11% (95% CI, -0.12% to -0.10%) in India. CONCLUSIONS AND RELEVANCE: Among women in 60 LMIC, the prevalence of BMI lower than 16 was 1.8%, and was associated with poverty and low education levels. Prevalence of BMI lower than 16 did not decrease over time in most countries studied.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".