@article {12938, title = {State-level metabolic comorbidity prevalence and control among adults age 50-plus with diabetes: estimates from electronic health records and survey data in five states.}, journal = {Population Health Metrics}, volume = {20}, year = {2022}, pages = {22}, abstract = {

BACKGROUND: Although treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically.

METHODS: We analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts.

RESULTS: The estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states.

CONCLUSIONS: Despite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care.

TRIAL REGISTRATION: Not applicable.

}, keywords = {Comorbidity, Diabetes Mellitus, Electronic Health Records, Prevalence, Self Report}, issn = {1478-7954}, doi = {10.1186/s12963-022-00298-z}, author = {Mardon, Russell and Campione, Joanne and Nooney, Jennifer and Merrill, Lori and Johnson, Maurice and Marker, David and Jenkins, Frank and Saydah, Sharon and Rolka, Deborah and Zhang, Xuanping and Shrestha, Sundar and Gregg, Edward} } @article {11264, title = {State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.}, journal = {Statistics in Medicine}, volume = {37}, year = {2018}, pages = {3975-3990}, 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{\textquoteright}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.

}, keywords = {Bias, 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}, issn = {1097-0258}, doi = {10.1002/sim.7848}, author = {David A Marker and Mardon, Russ and Jenkins, Frank and Campione, Joanne and Nooney, Jennifer and Li, Jane and Saydeh, Sharon and Zhang, Xuanping and Shrestha, Sundar and Deborah B. Rolka} }