Replication data for: Modeling global health indicators: missing data imputation and accounting for ‘double uncertainty’

Global health indicators such as infant and maternal mortality are important for informing priorities for health research, policy development, and resource allocation. However, due to inconsistent reporting within and across nations, construction of comparable indicators often requires extensive data imputation and complex modeling from limited observed data. We draw on Ahmed et al.’s 2012 paper – an analysis of maternal deaths averted by contraceptive use for 172 countries in 2008 – as an exemplary case of the challenge of building reliable models with scarce observations. The authors’ employ a counterfactual modeling approach using regression imputation on the independent variable which assumes no estimation uncertainty in the final model and does not address the potential for scattered missingness in the predictor variables. We replicate their results and test the sensitivity of their published estimates to the use of an alternative method for imputing missing data, multiple imputation. We also calculate alternative estimates of standard errors for the model estimates that more appropriately account for the uncertainty introduced through data imputation of multiple predictor variables. Based on our results, we discuss the risks associated with the missing data practices employed and evaluate the appropriateness of multiple imputation as an alternative for data imputation and uncertainty estimation for models of global health indicators.