CSDE Fellow Jessica Godwin and CSDE Affiliate Jon Wakefield just published their research on “Space-Time Modeling of Child Mortality at the Admin-2 Level in Low and Middle Income Countries Context” in Statistics in Medicine.  Their work demonstrates innovative approaches in statistical methods for estimating child mortality when data are sparse or variably observed at different levels of aggregation and collected through different methodologies. They extend earlier work to provide estimates at the Admin-2 Level so as to improve information for targeted interventions to reduce child mortality. In the paper they describe four extensions to previous work: (i) combining summary birth history (SBH) data with first birth history (FBH) data, (ii) modeling on a yearly scale, to combine data on a yearly scale with data at coarser time scales, (iii) adjusting direct estimates in Admin‐2 areas where we do not observe any deaths due to small sample sizes, (iv) acknowledge differences in data sources by modeling potential bias arising from the various data sources. The methods are illustrated using household survey and census data from Kenya and Malawi, to produce mortality estimates from 1980 to the time of the most recent survey, and predictions to 2020. To read the article, click here.

CSDE Fellow Neal Marquez and CSDE Affiliate Jon Wakefield recently published “Harmonizing Child Mortality Data at Disparate Geographic Levels” in Statistical Methods in Medical Research. The authors present a new method for analyzing masked survey data, using an approach that is consistent with the data-generating process. In addition, they critique two previously proposed approaches to analyzing masked data and illustrate that they are fundamentally flawed methodologically. To validate their method, they compare their approach with previously formulated solutions in several realistic simulation environments in which the underlying structure of the risk field is known. They simulate samples from spatiotemporal fields in a way that mimics the sampling frame implemented in the most common health surveys in low- and middle-income countries, the Demographic and Health Surveys and Multiple Indicator Cluster Surveys. In simulations, the newly proposed approach outperforms previously proposed approaches in terms of minimizing error while increasing the precision of estimates. The approaches are subsequently compared using child mortality data from the Dominican Republic where their findings are reinforced. The ability to accurately increase precision of child mortality estimates, and health outcomes in general, by leveraging various types of data, improves our ability to implement precision public health initiatives and better understand the landscape of geographic health inequalities. To read the article, click here.

CSDE Trainee Aasli Nur and co-authors recently published a practice article in BMJ Global Health titled “Behavior Adoption Approaches During Public Health Emergencies: Implications for the COVID 19 Pandemic and Beyond.” The authors  draw from the emerging evidence on behavior adoption during diverse public health emergencies to develop a framework that contextualizes behavior adoption vis-à-vis a combination of top-down, intermediary and bottom-up approaches. Using the COVID-19 pandemic as a case study, they operationalize the contextual framework to demonstrate how these three approaches differ in terms of their implementation, underlying drivers of action, enforcement, reach and uptake. They illustrate how blended strategies that include all three approaches can help accelerate and sustain protective behaviors that will remain important even when safe and effective vaccines become more widely available. They conclude that as the world grapples with the COVID-19 pandemic and prepares to respond to (re)emerging public health threats, a contextual framework can inform the design, implementation, tracking and evaluation of comprehensive public health and social measures during health emergencies. To read the article, click here.