Modeling and presentation of vaccination coverage estimates using data from household surveys - Dong, Tracy Qi; Wakefield, Jon (Vaccine, 2021)
•Modeling survey stratification and clustering improves coverage estimation.•High-resolution coverage maps based on survey data often have large uncertainties.•Visualizing posterior distributions of coverage estimates help reveal uncertainties.•A new presentation method is proposed to compare and control overall map precision. It is becoming increasingly popular to produce high-resolution maps of vaccination coverage by fitting Bayesian geostatistical models to data from household surveys. Usually, the surveys adopt a stratified cluster sampling design. We discuss a number of crucial choices with respect to two key aspects of the map production process: the acknowledgement of the survey design in modeling, and the appropriate presentation of estimates and their uncertainties. Specifically, we consider the importance of accounting for urban/rural stratification and cluster-level non-spatial excess variation in survey outcomes, when fitting geostatistical models. We also discuss the trade-off between the geographical scale and precision of model-based estimates, and demonstrate visualization methods for mapping and ranking that emphasize the probabilistic interpretation of results. A novel approach to coverage map presentation is proposed to allow comparison and control of the overall map uncertainty. We use measles vaccination coverage in Nigeria as a motivating example and illustrate the different issues using data from the 2018 Nigeria Demographic and Health Survey.