Characterizing Micronutrient Status and Risk Factors among Late Adolescent and Young Women in Rural Pakistan: A Cross-Sectional Assessment of the MaPPS Trial
Bibliographic record
Abstract
Nutritional deficiencies are a leading underlying risk factor contributing to the global burden of disease. In Pakistan, late adolescence is considered a nutritionally vulnerable period, as micronutrient requirements are increased to support maturation, and dietary staples are nutrient poor. However, there has been limited evaluation of micronutrient status beyond anemia and its determinants. Using cross-sectional data from late adolescent and young women (15-23 years) at enrolment in the Matiari emPowerment and Preconception Supplementation (MaPPS) Trial, we aimed to describe the prevalence of key micronutrient deficiencies of public health concern, and generate hierarchical models to examine associations with proxies for social determinants of health (SDoH). The prevalence of micronutrient deficiencies was high: 53.6% (95% confidence interval (CI): 53.0-54.3%) had anemia; 38.0% (95% CI: 36.4-39.6%) iron deficiency anemia; 31.8% (95% CI: 30.2-33.3%) vitamin A deficiency; and 81.1% (95% CI: 79.8-82.4%) vitamin D deficiency. At least one deficiency was experienced by 91.0% (95% CI: 90.1-92.0%). Few SDoH were maintained in the final hierarchical models, although those maintained were often related to socioeconomic status (e.g., education, occupation). To improve the micronutrient status of late adolescent and young women in Pakistan, a direct micronutrient intervention is warranted, and should be paired with broader poverty alleviation methods.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".