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.

Architecting the Data Loading Process for an i2b2 Research Data Warehouse: Full Reload versus Incremental Updating.

Research data warehouses integrate research and patient data from one or more sources into a single data model that is designed for research. Typically, institutions update their warehouse by fully reloading it periodically. The alternative is to update the warehouse incrementally with new, changed and/or deleted data. Full reloads avoid having to correct and add to a live system, but they can render the data outdated for clinical trial accrual. They place a substantial burden on source systems, involve intermittent work that is challenging to resource, and may involve tight coordination across IT and informatics units. We have implemented daily incremental updating for our i2b2 data warehouse. Incremental updating requires substantial up-front development, and it can expose provisional data to investigators. However, it may support more use cases, it may be a better fit for academic healthcare IT organizational structures, and ongoing support needs appear to be similar or lower.

Metadata-driven Clinical Data Loading into i2b2 for Clinical and Translational Science Institutes.

Clinical and Translational Science Award (CTSA) recipients have a need to create research data marts from their clinical data warehouses, through research data networks and the use of i2b2 and SHRINE technologies. These data marts may have different data requirements and representations, thus necessitating separate extract, transform and load (ETL) processes for populating each mart. Maintaining duplicative procedural logic for each ETL process is onerous. We have created an entirely metadata-driven ETL process that can be customized for different data marts through separate configurations, each stored in an extension of i2b2 's ontology database schema. We extended our previously reported and open source Eureka! Clinical Analytics software with this capability. The same software has created i2b2 data marts for several projects, the largest being the nascent Accrual for Clinical Trials (ACT) network, for which it has loaded over 147 million facts about 1.2 million patients.

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.

Serum mineral status and climacteric symptoms in perimenopausal women before and after Yoga therapy, an ongoing study.

BACKGROUND: Many women report an increased level of anxiety, irritability and mood swings during their perimenopausal state. Studies show that physically active people can reduce their anxiety and depression by practicing yoga. Serum minerals such as calcium, copper and magnesium and the ferro-oxidase, ceruloplasmin play an important role in the body during the perimenopausal period. OBJECTIVE: The objective of this study is to assess the serum mineral status, anthropometric parameters and climacteric symptoms in perimenopausal women before and after yoga intervention. SUBJECTS AND METHODS: A total of 30 subjects with perimenopausal symptoms, aged between 40 and 60 years (49.43 ± 6.15) were included in the study. Yoga intervention was given on a daily basis (45 min duration) for 12 weeks. The climacteric symptoms were assessed by Greene's climacteric scale and biochemical parameters were analyzed spectrophotometrically. RESULTS: A significant decrease in the waist hip ratio (P < 0.036) and body mass index (P < 0.036) was observed after yoga intervention. Systolic (P < 0.064) and diastolic (P < 0.082) blood pressure (BP) showed marginal decrease after yoga therapy. Climacteric symptoms improved significantly (P < 0.001) after yoga intervention. A significant increase (P < 0.001) in serum calcium and copper and a marked decrease in serum magnesium (P < 0.05) and ceruloplasmin (P < 0.028) levels was observed, post yoga therapy. Serum magnesium negatively correlated (r = -0.467, P < 0.035) with systolic BP after yoga intervention. CONCLUSION: The overall changes observed in the mineral status and climacteric symptoms suggest that yoga therapy protocol can be effectively used to improve the quality of life in perimenopausal women.

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.