PRAYAS: Cohort profile for Pooled Research and Analysis for Yielding Anemia-free Solutions in India
Bibliographic record
Abstract
Abstract Purpose This cohort would aim to estimate the prevalence of anemia among children under 18 years, non-pregnant and non-lactating (NPNL) women, and pregnant women (by trimester), with further stratification by age group, year, and region of India. Cohort would also help address extended deliberation concerning etiological fraction of iron and other key erythropoietic micronutrient deficiencies contributing to anemia in India. Additionally, this will help assess the effectiveness of existing anaemia prevention and treatment interventions and examine factors associated with non-response, thereby supporting the “test–treat–track” approach. Participants Children under 18 years, pregnant women, and non-pregnant-non-lactating women (NPNL) in India. Findings to date This cohort profile comprises 88 datasets spanning between 1994 to 2023, encompassing a total of 319,721 participants for prevalence analysis [children(19,762), NPNL(17,883), and pregnant women(282,076)]. Additionally, 59,292 participants were included in intervention studies [children(13,435), NPNL(11,594), and pregnant women(34,263)]. RCTs comprised 55.7% (49/88) of the datasets whereas observational studies comprise 35.2% (31/88) of the datasets. Majority of the studies were from the norther region - 38 studies (43.2%), followed by the western part - 20 studies (22.7%). The southern part contributed 16 studies (18.2%). Major [59/88 (67%)] datasets in cohort were from community-based studies. The sample included NPNL and pregnant women with a median age of 26 years (IQR 23-32), and 23 years (IQR 21-25) respectively. Information from 6 months up to 18 years was pooled within the children’s cohort. Within the pregnancy cohort the mean gestational age at enrollment was 10.24 weeks(SD-17.65). Of total 10.8% (34,442/ 319,721), 9% (28,672), 4.5% (14,240) of the sample had information on complete blood count, ferritin and vitamin B12 respectively. A total of 33 datasets (sample - 59,292) were from intervention studies. Among pregnant women, a broader range of interventions was implemented, including intravenous iron sucrose, ferric carboxymaltose, iron isomaltoside, IV iron combined with vitamin B12, folic acid, and niacinamide, integrated interventions, as well as low-dose calcium supplementation. A similar set of interventions were delivered to NPNL group with being distinct which compared Ferrous sulfate tablets of 60 mg elemental iron daily with a control of 120 mg on alternate days. Ferrous sulfate was the major interventions amongst children along with food supplements and some were Ayush trials. Future plans The PRAYAS will provide robust, high-quality evidence to inform public health policy in India. The findings will feed into the Anemia Mukt Bharat program recommendations for pregnant women, NPNL women and children to guide targeted strategies for reduction of anemia and its associated health burdens across vulnerable populations. Strengths and limitations of this study The harmonized PRAYAS pooled Indian dataset is one of the largest, reliable and most comprehensive datasets on pregnant/ non-pregnant and non-lactating women and children. One of its kind of dataset with information on hemoglobin levels, relevant biochemical and key micronutrients parameters and varied interventions from across India. Heterogeneity of interventions, dosage, duration and data collection approaches. Studies lack critical parameters needed to assess changes in haemoglobin concentration like non-availability of key erythropoietic micronutrients in most of the studies, limiting the scope of certain analyses.
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".