Comparison of 1-week and 2-week recall periods for caregiver-reported diarrhoeal illness in children, using nationally representative household surveys
Bibliographic record
Abstract
BACKGROUND: Diarrhoeal outcomes in children are often ascertained using caregiver-reported symptoms, which are subject to a variety of biases and methodological challenges. One source of bias is the time window used for reporting diarrhoeal illness and the ability of caregivers to accurately recall episodes in children. METHODS: Diarrhoea period prevalence in children under five was determined using two similarly administered, nationally representative household surveys: Performance Monitoring and Accountability 2020 (PMA2020) (1-week recall, N = 14 603) and Demographic and Health Surveys (DHS) (2-week recall, N = 66 717). Countries included in the analysis were the Democratic Republic of the Congo, Ethiopia, Ghana, Kenya and Uganda. Diarrhoea period prevalence estimates were compared and water, sanitation and hygiene risk factors were analysed. RESULTS: Childhood diarrhoea prevalence using 1-week recall (PMA2020) pooled across countries was 21.4% [95% confidence interval (CI): 19.9%, 22.9%] versus 16.0% using 2-week recall (DHS) (95% CI: 15.4%, 16.5%). In stratified analyses for all five countries, the number of diarrhoea cases detected was consistently higher using 1-week recall versus 2-week recall. The key risk factors identified in the PMA2020 data that were not associated with diarrhoeal episodes or were attenuated in the DHS data included: the main sanitation classifications for households, disposal method used for child faeces, number of household members and wealth quintiles. CONCLUSIONS: For nationally representative household surveys assessing childhood diarrhoea period prevalence, a 2-week recall period may underestimate diarrhoea prevalence compared with a 1-week period. The household sanitation facility and practices remain key risk factors for diarrhoeal disease in children under five.
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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 | 0.053 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".