Noting that accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data, Russ Mardon, Ph.D., from Westat Inc. in Rockville, Maryland, and colleagues presented an approach for adjusting new and nontraditional data sources for diabetes surveillance and described results for Alabama and California.
The researchers reweighted surveys and other data sources with population undercoverage to make them more representative of state populations. They also adjusted for nonrandom use of laboratory testing in clinically generated data. The data sources used were the National Health and Nutrition Examination Survey, the Health and Retirement Study, the National Ambulatory Medical Care Survey, and MarketScan. These methods can be applied to other survey, administrative, or clinical data sets.
"Our prevalence estimates have general face validity, but we cannot validate these estimates directly because of the absence of a gold standard," the authors write. "We are testing and validating a method to combine the state-level estimates of diabetes and prediabetes prevalence across these data sets to create a single composite prevalence estimate for a state."
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