State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.

TitleState-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.
Publication TypeJournal Article
Year of Publication2018
AuthorsMarker, DA, Mardon, R, Jenkins, F, Campione, J, Nooney, J, Li, J, Saydeh, S, Zhang, X, Shrestha, S, Rolka, DB
JournalStatistics in Medicine
Volume37
Issue27
Pagination3975-3990
ISSN Number1097-0258
KeywordsBias, California, Diabetes Mellitus, Electronic Health Records, Health Surveys, Humans, Insurance Claim Review, New York City, Nutrition Surveys, Prediabetic State, Prevalence, Statistics as Topic, United States
Abstract

Many statisticians and policy researchers are interested in using data generated through the normal delivery of health care services, rather than carefully designed and implemented population-representative surveys, to estimate disease prevalence. These larger databases allow for the estimation of smaller geographies, for example, states, at potentially lower expense. However, these health care records frequently do not cover all of the population of interest and may not collect some covariates that are important for accurate estimation. In a recent paper, the authors have described how to adjust for the incomplete coverage of administrative claims data and electronic health records at the state or local level. This article illustrates how to adjust and combine multiple data sets, namely, national surveys, state-level surveys, claims data, and electronic health record data, to improve estimates of diabetes and prediabetes prevalence, along with the estimates of the method's accuracy. We demonstrate and validate the method using data from three jurisdictions (Alabama, California, and New York City). This method can be applied more generally to other areas and other data sources.

DOI10.1002/sim.7848
Citation Key11264
PubMed ID29931829
Grant List2002014F61238 / CC / CDC HHS / United States