The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India
Bibliographic record
Abstract
The importance of data quality to correctly determine prevalence estimates of child anthropometric failures has been a contentious issue among policymakers and researchers. Our research objective was to ascertain the impact of improved DHS data quality on the prevalence estimates of stunting, wasting, and underweight. The study also looks for the drivers of data quality. Using five data quality indicators based on age, sex, anthropometric measurements, and normality distribution, we arrive at two datasets of differential data quality and their estimates of anthropometric failures. For this purpose, we use the 2005-2006 and 2015-2016 NFHS data covering 311,182 observations from India. The prevalence estimates of stunting and underweight were virtually unchanged after the application of quality checks. The estimate of wasting had fallen 2 percentage points, indicating an overestimation of the true prevalence. However, this differential impact on the estimate of wasting was driven by the flagging procedure's sensitivity and was in accordance with empirical evidence from existing literature. We found DHS data quality to be of sufficiently high quality for the prevalence estimates of stunting and underweight, to not change significantly after further improving the data quality. The differential estimate of wasting is attributable to the sensitivity of the flagging procedure.
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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 | 0.067 | 0.283 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".