Small Area Estimation for State and Local Health Departments

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PROJECT SUMMARY Researchers at state and local health departments producing small area estimates often face a lose-lose situation. On one hand, there is a wealth of evidence of racial disparities in many health outcomes and their risk factors, but stratifying data by space and race (in addition to factors such as age and sex) only exacerbates the issues associated with small area estimation by dividing a dataset with small sample sizes into a larger dataset with smaller sample sizes. On the other hand, while the use of complex statistical models can be used to produce more precise estimates from limited data, estimates produced by state and local health departments may be treated as “official statistics” and thus these agencies may be reluctant to rely too heavily on statistical models for fear of the bias they may introduce. The objective of the proposed work is three-fold. Our first task will be to develop statistical models for the analysis of multivariate spatial data that allow users to pre-specify an upper bound on the model's informativeness — i.e., a measure of the weight given to the model as compared to the data when producing model-based estimates. This work will build on the rich spatial statistics literature and recent research that provides insight into how to quantify the informativeness of spatial models. We will extend this approach to the setting of multivariate spatial data for the purposes of calculating demographic group-specific estimates and age-adjusted estimates. Because we envision these methods being useful for researchers at state and local health departments, we believe a thorough case study of our methods should be conducted to assess their suitability. To this end, our second task will be to partner with the Philadelphia Department of Public Health and use the methods we've developed to conduct a rigorous analysis of heart disease mortality and its risk factors in Philadelphia. This analysis will produce yearly census tract-level estimates for rates of death due to several forms of heart disease and estimates of the prevalence of key risk factors by age, gender, and race/ethnicity. The product of this research will include a collection of reports — one focused on city-level trends and one focused on neighborhood-level trends — an interactive online dashboard, and peer-reviewed publications that add context to our findings. Finally, we recognize that few state and local health departments have staff who are trained in advanced spatial Bayesian statistical methods, a fact that could serve as an impediment to the use of the methods we develop. To remedy this, our third task will be to partner with the CDC-funded GIS Capacity Building Project, which provides training in geospatial analyses to state and local health departments. This month-long training program begins by introducing users to the ArcGIS software package and concludes with an overview of a tool created by the GIS Capacity Building Project — the Rate Stabilizing Tool (RST). For this project, we will partner with the GIS Capacity Building Project to incorporate the methods we develop into the RST in a “black-box” framework and provide additional training on the use of spatial Bayesian methods in disease surveillance.