Is the impact of paid maternity leave policy on the prevalence of childhood diarrhoea mediated by breastfeeding duration? A causal mediation analysis using quasi-experimental evidence from 38 low-income and middle-income countries
Bibliographic record
Abstract
OBJECTIVES: Quasi-experimental evidence suggests that extending the duration of legislated paid maternity leave is associated with lower prevalence of childhood diarrhoea in low-income and middle-income countries (LMICs). This could be due to a variety of mechanisms. This study examines whether this effect is mediated by changes in breastfeeding duration. DESIGN AND SETTING: Difference-in-difference approach and causal mediation analysis were used to perform secondary statistical analysis of cross-sectional data from Demographic and Health Surveys (DHSs) in 38 LMICs. PARTICIPANTS: We merged longitudinal data on national maternity leave policies with information on childhood diarrhoea related to 639 153 live births between 1996 and 2014 in 38 LMICs that participated in the DHS at least twice between 1995 and 2015. PRIMARY OUTCOME MEASURE: Our outcome was whether the child had bloody stools in the 2 weeks prior to the interview. This measure was used as an indicator of severe diarrhoea because the frequency of loose stools in breastfed infants can be difficult to distinguish from pathological diarrhoea based on survey data. RESULTS: A 1-month increase in the legislated duration of paid maternity leave was associated with a 34% (risk ratio 0.66, 95% CI 0.47 to 0.91) reduction in the prevalence of bloody diarrhoea. Breast feeding for at least 6 months and 12 months mediated 10.6% and 7.4% of this effect, respectively. CONCLUSION: Extending the duration of paid maternity leave appears to lower diarrhoea prevalence in children under 5 years of age in LMICs. This effect is slightly mediated by changes in breastfeeding duration.
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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 | 0.054 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".