Determinants of childhood anaemia in India from 2005 to 2021: insights from the last three rounds of India National Family Health Surveys
Bibliographic record
Abstract
Anaemia is a global public health problem affecting 800 million women and children globally. Anaemia is associated with perinatal mortality, child morbidity and mortality, mental development, immune competence, susceptibility to lead poisoning, and performance at work. This paper used data from India National Family Health Surveys (INFHS) carried out in 2005-06, 2015-16 and 2019-21 to identify the factors associated with childhood anaemia among children, adjusting for a range of covariates. In NFHS rounds of 3 and 4 after adjusting for standard covariates, in the probit model, it was found according to marginal effects, the probability of children being anaemic in urban areas is lower. Those belonging to the scheduled tribes have a higher probability of being anaemic in the two survey rounds of NFHS-3 and NFHS-4. Literate mothers were found to have a lower probability of anaemia in their children. In NFHS-5, in the binary logit model after adjusting for standard covariates, it was found that those belonging to the scheduled tribes were 1.348 times more likely to be anaemic compared to the scheduled castes. Maternal education was another significant factor determining the likelihood of being anaemic. Literate mothers were less likely to have anaemic children. This study provides crucial insights into the dynamic nature of childhood anaemia in India, utilizing data from three decades of NFHS surveys. It highlights the need for comprehensive interventions addressing socio-economic determinants, education of women, and behaviour change interventions to mitigate the burden of childhood anaemia in India.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".