PREVALENCE OF MICRONUTRIENT DEFICIENCIES IN NON PREGNANT WOMEN OF REPRODUCTIVE AGE
Bibliographic record
Abstract
Background: Micronutrient deficiencies remain a significant public health issue, particularly among women of reproductive age. Despite their importance in preconception and overall health, non-pregnant women are often overlooked in nutritional surveillance programs, especially in South Asian settings. Objective: To determine the prevalence and patterns of iron, vitamin D, folate, and vitamin B12 deficiencies among non-pregnant women aged 15–45 years attending outpatient clinics in Lahore, Pakistan. Methods: A cross-sectional study was conducted over six months in three outpatient clinics. A total of 360 non-pregnant women aged 15–45 years were recruited. Data collection included demographic profiling, anthropometric measurements, and biochemical assessment of serum ferritin, 25-hydroxyvitamin D, serum folate, and vitamin B12. Deficiency thresholds were based on WHO criteria. Descriptive and inferential statistics were applied using SPSS v26, and subgroup analysis was conducted by age group. Results: The mean age of participants was 29.4 years. Vitamin D deficiency was most prevalent (52.8%), followed by iron (41.5%), folate (23.6%), and vitamin B12 (19.7%) deficiencies. The youngest age group (15–24 years) demonstrated the highest burden across all micronutrients. Mean serum levels for ferritin, vitamin D, folate, and B12 were below optimal in a substantial proportion of participants. Significant age-wise differences in serum ferritin and vitamin D levels were observed (p < 0.01). Conclusion: A high prevalence of micronutrient deficiencies exists among non-pregnant women of reproductive age in Lahore, with younger women being most affected. These findings support the need for integrated nutritional screening and public health interventions, including food fortification and education programs, targeting this at-risk group.
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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 | 0.000 | 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) | 0.002 | 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".