In order to mitigate any bias arising from systematic differences in the characteristics of the intervention and control village adults (e.g. due to non-random attrition or other reasons), the investigators will also use quasi-experimental matching methods. One widely used methodology is matching based on propensity scores. The investigators will first conduct a selection analysis in which the likelihood of birth in an intervention village is regressed (probit or logit model) on the set of participant, parental, and household covariates mentioned above. Then, based on the predicted probability of intervention assignment (called propensity score) obtained from this model, participants in the intervention group will be matched with similar participant(s) in the control group. The investigators will use a variety of matching algorithms in order to test the sensitivity of the results, e.g. one-to-one nearest neighbor, Kernel, and multiple nearest neighbors matching. Common metrics for the quality of matching exercise and covariate balancing tests will be reported.