Combining survey, clinical, and health care claims data to produce state-level estimates of diabetes prevalence, including both diagnosed and undiagnosed cases
University of Michigan School of Public Health
3755 SPH I, 1415 Washington Heights Ann Arbor, MI 48109-2029
This talk summarizes research conducted by Westat for the U.S. Centers for Disease Control and Prevention (CDC). The goal was to develop surveillance methods that could be used by state health departments to monitor diabetes and prediabetes prevalence rates in their state, including undiagnosed cases. This involved combining national and state-level surveys with clinical and health care claims data. The talk describes operational issues in combining these data sources along with statistical issues of adjusting for non-representative data and how best to combine the different data sources. This work includes recently published articles in Preventing Chronic Disease (Mardon, Marker, Nooney, et al., 2017) and Statistics In Medicine (Marker, Mardon, Jenkins, et al., 2018). Department of Biostatistics

Combining survey, clinical, and health care claims data to produce state-level estimates of diabetes prevalence, including both diagnosed and undiagnosed cases

David Marker, PhD, - Associate Director and Senior Statistician - Westat

icon to add this event to your google calendarMarch 14, 2019
3:30 pm - 5:00 pm
3755 SPH I
1415 Washington Heights
Ann Arbor, MI 48109-2029
Sponsored by: Department of Biostatistics
Contact Information: Zhenke Wu (zhenkewu@umich.edu) & Peisong Han (peisong@umich.edu)

This talk summarizes research conducted by Westat for the U.S. Centers for Disease Control and Prevention (CDC). The goal was to develop surveillance methods that could be used by state health departments to monitor diabetes and prediabetes prevalence rates in their state, including undiagnosed cases. This involved combining national and state-level surveys with clinical and health care claims data. The talk describes operational issues in combining these data sources along with statistical issues of adjusting for non-representative data and how best to combine the different data sources. This work includes recently published articles in Preventing Chronic Disease (Mardon, Marker, Nooney, et al., 2017) and Statistics In Medicine (Marker, Mardon, Jenkins, et al., 2018).