Descriptive epidemiology of epilepsy in the U.S. population: A different approach.

OBJECTIVE: Determine prevalence and incidence of epilepsy within two health insurance claims databases representing large sectors of the U.S. METHODS: A retrospective observational analysis using Commercial Claims and Medicare (CC&M) Supplemental and Medicaid insurance claims data between January 1, 2007 and December 31, 2011. Over 20 million continuously enrolled lives of all ages were included. Our definition of a prevalent case of epilepsy was based on International Classification of Diseases, Ninth Revision, Clinical Modification-coded diagnoses of epilepsy or seizures and evidence of prescribed antiepileptic drugs. Incident cases were identified among prevalent cases continuously enrolled for ≥ 2 years before the year of incidence determination with no epilepsy, seizure diagnoses, or antiepileptic drug prescriptions recorded. RESULTS: During 2010 and 2011, overall age-adjusted prevalence estimate, combining weighted estimates from all datasets, was 8.5 cases of epilepsy/1,000 population. With evaluation of CC&M and Medicaid data separately, age-adjusted prevalence estimates were 5.0 and 34.3/1,000 population, respectively, for the same period. The overall age-adjusted incidence estimate for 2011, combining weighted estimates from all datasets, was 79.1/100,000 population. Age-adjusted incidence estimates from CC&M and Medicaid data were 64.5 and 182.7/100,000 enrollees, respectively. Incidence data should be interpreted with caution due to possible misclassification of some prevalent cases. SIGNIFICANCE: The large number of patients identified as having epilepsy is statistically robust and provides a credible estimate of the prevalence of epilepsy. Our study draws from multiple U.S. population sectors, making it reasonably representative of the U.S.-insured population.

Using the CER Hub to ensure data quality in a multi-institution smoking cessation study.

Comparative effectiveness research (CER) studies involving multiple institutions with diverse electronic health records (EHRs) depend on high quality data. To ensure uniformity of data derived from different EHR systems and implementations, the CER Hub informatics platform developed a quality assurance (QA) process using tools and data formats available through the CER Hub. The QA process, implemented here in a study of smoking cessation services in primary care, used the 'emrAdapter' tool programmed with a set of quality checks to query large samples of primary care encounter records extracted in accord with the CER Hub common data framework. The tool, deployed to each study site, generated error reports indicating data problems to be fixed locally and aggregate data sharable with the central site for quality review. Across the CER Hub network of six health systems, data completeness and correctness issues were prevalent in the first iteration and were considerably improved after three iterations of the QA process. A common issue encountered was incomplete mapping of local EHR data values to those defined by the common data framework. A highly automated and distributed QA process helped to ensure the correctness and completeness of patient care data extracted from EHRs for a multi-institution CER study in smoking cessation.

Temporal abstraction-based clinical phenotyping with Eureka!

Temporal abstraction, a method for specifying and detecting temporal patterns in clinical databases, is very expressive and performs well, but it is difficult for clinical investigators and data analysts to understand. Such patterns are critical in phenotyping patients using their medical records in research and quality improvement. We have previously developed the Analytic Information Warehouse (AIW), which computes such phenotypes using temporal abstraction but requires software engineers to use. We have extended the AIW's web user interface, Eureka! Clinical Analytics, to support specifying phenotypes using an alternative model that we developed with clinical stakeholders. The software converts phenotypes from this model to that of temporal abstraction prior to data processing. The model can represent all phenotypes in a quality improvement project and a growing set of phenotypes in a multi-site research study. Phenotyping that is accessible to investigators and IT personnel may enable its broader adoption.