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Purpose: To assess the association between Veterans affairs (VA) stroke patients' poststroke rehabilitation utilization and their residential settings by using 2 common rural-urban taxonomies., Methods: This retrospective study included all VA stroke inpatients in 2001 and 2002. Rehabilitation utilization referred to rehabilitation therapy received 12-months poststroke hospitalization. Patients' urban, rural, or isolated/highly rural status was determined using the rural-urban commuting areas (RUCA) and VA rural urban (VARU) definitions based on patient residential ZIP code. Logistic regression models were fit for the rehabilitation outcome, adjusting for potential risk factors., Findings: Among the 8,296 stroke patients, 69.6%/61.1% were categorized as urban, 21.3%/37.5% as rural, and 9.1%/1.4% as isolated/highly rural by the RUCA/VARU definitions, respectively. Compared with their urban counterparts, the rural and/or isolated/highly rural patients were significantly more likely to be older, white, married, living further from the VA hospitals, not hospitalized for stroke directly from home, and not intubated. Compared with the rural patients, odds of receiving rehabilitation therapy were 1.2 times (RUCA) and 1.1 times (VARU) by the urban patients, and 0.53 times (VARU only) by the highly rural patients, after risk adjustment. The above comparisons were significant at P < .05., Conclusions: With both taxonomies, the rural patients were less likely to receive postacute stroke rehabilitant therapy than their urban counterparts. With the VARU, the highly rural patients were less likely to receive rehabilitation care than their rural counterparts. Different taxonomy may lead to different rural-urban classification yields and different yields may lead to different outcomes and conclusions., (© 2011 National Rural Health Association.)

Objectives: To examine the impact of comorbidities in predicting stroke rehabilitation outcomes and to examine differences among 3 commonly used comorbidity measures--the Charlson Index, adjusted clinical groups (ACGs), and diagnosis cost groups (DCGs)--in how well they predict these outcomes., Design: Inception cohort of patients followed for 6 months., Setting: Department of Veterans Affairs (VA) hospitals., Participants: A total of 2402 patients beginning stroke rehabilitation at a VA facility in 2001 and included in the Integrated Stroke Outcomes Database., Interventions: Not applicable., Main Outcome Measures: Three outcomes were evaluated: 6-month mortality, 6-month rehospitalization, and change in FIM score., Results: During 6 months of follow-up, 27.6% of patients were rehospitalized and 8.6% died. The mean FIM score increased an average of 20 points during rehabilitation. Addition of comorbidities to the age and sex models improved their performance in predicting these outcomes based on changes in c statistics for logistic and R(2) values for linear regression models. While ACG and DCG models performed similarly, the best models, based on DCGs, had a c statistic of .74 for 6-month mortality and .63 for 6-month rehospitalization, and an R(2) of .111 for change in FIM score., Conclusions: Comorbidities are important predictors of stroke rehabilitation outcomes. How they are classified has important implications for models that may be used in assessing quality of care.

Misclassification of race and ethnicity in administrative data may produce misleading results if it is overlooked or ignored. In this study, we examined the racial/ethnic classifications of 1,084 veterans with stroke in Florida who received inpatient and outpatient services within the Department of Veterans Affairs (VA) healthcare system and who were also eligible for Medicare between 2000 and 2001. We compared the reliability of racial/ethnic classifications between VA inpatient data, VA outpatient data, and Medicare data. Our results showed that (1) the rate of unknown racial/ethnic classification in VA outpatient and inpatient data was high, (2) minimizing the unknowns by substituting known values from other data when available would greatly enhance the overall and individual classification reliability, (3) black and white classifications in the VA data had stronger agreement with Medicare data, and (4) Medicare data may under-represent Hispanic patients.

A fundamental goal of the Rehabilitation Outcomes Research Center of Excellence is to improve care and outcomes for veterans with rehabilitation needs. To achieve this goal, the Center's primary objective is increasing research capacity. The Integrated Stroke Outcomes Database is a collection of Veterans Health Administration (VHA) clinical and administrative data containing patient information on a cohort of stroke patients found in the Functional Status Outcomes Database (FSOD), National Patient Care Database (NPCD), and other VHA sources. Clinical and administrative data were abstracted from several VHA data sources and linked to form an integrated outcomes database. A primary cohort of stroke patients treated during fiscal year (FY) 2001 was identified from the FSOD. Matching data from the NPCD, Decision Support System, Health Economics Resource Center, and the National Veterans Survey were obtained, merged, and reported in brief. This integrated database structure will provide valuable support to enhance the VHA capacity to perform stroke rehabilitation research.