Relationships among epilepsy self-management, health outcomes, and socioeconomic status

The purpose of this study was to investigate the association between epilepsy self-management and disease control and socio-economic status. Study participants were adult patients at two epilepsy specialty clinics in Houston, Texas that serve demographically and socioeconomically diverse populations. Self-management behaviors- medication, information, safety, seizure, and lifestyle management were tested against emergency room visits, hospitalizations, and seizure occurrence. Overall self-management score was associated with a greater likelihood of hospitalizations over a prior twelve month time frame, but not for three months, and was not associated with seizure occurrence or emergency room visits, at all. Scores on specific self-management behaviors varied in their relationships to the different disease control indicators, over time. Contrary to expectations based on the findings of previous research, higher information management scores were associated with greater likelihood of emergency room visits and hospitalizations, over the study's twelve months. Higher lifestyle management scores were associated with lower likelihood of any emergency room visits, over the preceding twelve months and emergency room visits for the last three months. The positive associations between overall self-management scores and information management behaviors and disease control are contrary to published research. These findings may indicate that those with worse disease control in a prior period employ stronger self-management efforts to better control their epilepsy. Further research is needed to investigate this hypothesis.^

A PROCESS FOR ACHIEVING COMPARABLE DATA FROM HETEROGENEOUS DATABASES

The current state of health and biomedicine includes an enormity of heterogeneous data ‘silos’, collected for different purposes and represented differently, that are presently impossible to share or analyze in toto. The greatest challenge for large-scale and meaningful analyses of health-related data is to achieve a uniform data representation for data extracted from heterogeneous source representations. Based upon an analysis and categorization of heterogeneities, a process for achieving comparable data content by using a uniform terminological representation is developed. This process addresses the types of representational heterogeneities that commonly arise in healthcare data integration problems. Specifically, this process uses a reference terminology, and associated "maps" to transform heterogeneous data to a standard representation for comparability and secondary use. The capture of quality and precision of the “maps” between local terms and reference terminology concepts enhances the meaning of the aggregated data, empowering end users with better-informed queries for subsequent analyses. A data integration case study in the domain of pediatric asthma illustrates the development and use of a reference terminology for creating comparable data from heterogeneous source representations. The contribution of this research is a generalized process for the integration of data from heterogeneous source representations, and this process can be applied and extended to other problems where heterogeneous data needs to be merged.