Journal of rehabilitation research and development
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Within the Veterans Health Administration (VHA), anthropometric measurements entered into the electronic medical record are stored in local information systems, the national Corporate Data Warehouse (CDW), and in some regional data warehouses. This article describes efforts to examine the quality of weight and height data within the CDW and to compare CDW data with data from warehouses maintained by several of VHA's regional groupings of healthcare facilities (Veterans Integrated Service Networks [VISNs]). We found significantly fewer recorded heights than weights in both the CDW and VISN data sources. ⋯ Implausible variation in same-day and same-year heights and weights was noted, suggesting measurement or data-entry errors. Our work suggests that the CDW, over time and through validation, has become a generally reliable source of anthropometric data. Researchers should assess the reliability of data contained within any source and apply strategies to minimize the impact of data errors appropriate to their study population.
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The Department of Veterans Affairs (VA) provides integrated services to more than 25,000 veterans with spinal cord injuries and disorders (SCI/D). VA data offer great potential for providing insights into healthcare utilization and morbidity, and these capabilities are central to efforts to improve healthcare for veterans with SCI/D. The objective of this article is to introduce researchers to the use of VA data to examine questions related to SCI/D using examples from Spinal Cord Injury (SCI) Quality Enhancement Research Initiative studies. ⋯ Methods used to identify veterans with SCI/D include the Allocation Resource Center cohort, the Spinal Cord Dysfunction (SCD) Registry, and the VA inpatient SCI flag; only 33% of veterans were included in all three groups (n = 12,306). While neurological level of SCI was unknown for approximately a third of veterans (from SCD Registry data alone), the percent decreased to 13% when augmented with diagnostic codes. Primary data can be used to augment other missing SCI data and to provide more detailed information about complications commonly associated with SCI/D.
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We evaluated the improvement in Department of Veterans Affairs (VA) race data completeness that could be achieved by linking VA data with data from Medicare and the Department of Defense (DOD) and examined agreement in values across the data sources. After linking VA with Medicare and DOD records for a 10% sample of VA patients, we calculated the percentage for which race could be identified in those sources. To evaluate race agreement, we calculated sensitivities, specificities, positive predictive values (PPVs), negative predictive values, and kappa statistics. ⋯ Kappa statistics reflected these patterns. Supplementing VA with Medicare and DOD data improves VA race data completeness substantially. More study is needed to understand poor rates of agreement between VA and external sources in identifying non-African-American minority individuals.
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This article is the first to describe Department of Veterans Affairs (VA) patients' use of Medicaid at a national level. We obtained 1999 national VA enrollment and utilization data, Centers for Medicare and Medicaid Services enrollment and claims, and Medicare information from the VA Information Resource Center. The research team created files for program characteristics and described the VA-Medicaid dually enrolled population, healthcare utilization, and costs. ⋯ Dually enrolled women veterans cost ~55% less than men. Medicaid benefits complement VA and are more accessible in many states. VA researchers need to consider including Medicaid utilization and costs in their studies if they target populations or programs related to long-term care or mental disorders.
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Access to appropriate and timely healthcare is critical to the overall health and well-being of patients with chronic diseases. In this study, we used geographic information system (GIS) tools to map Veterans Health Administration (VHA) patients with multiple sclerosis (MS) and their access to MS specialty care. ⋯ We demonstrate the utility of using GIS tools in decision-making by providing three examples of how patients' access to care is affected when additional specialty clinics are added. The mapping technique used in this study provides a powerful and valuable tool for policy and planning personnel who are evaluating how to address underserved populations and areas within the VHA healthcare system.