Postacute stroke rehabilitation utilization: are there differences between rural-urban patients and taxonomies?

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.

Impact of comorbidities on stroke rehabilitation outcomes: does the method matter?

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.

A mixed integer programming model to locate traumatic brain injury treatment units in the Department of Veterans Affairs: a case study.

For the Department of Veterans Affairs (VA), traumatic brain injury (TBI) is a significant problem facing active duty military personnel, veterans, their families, and caregivers. The VA has designated TBI treatment as one of its physical medicine and rehabilitation special emphasis programs, thereby providing a comprehensive array of treatment services to those military personnel and veterans with TBI. Timely treatment of TBI is critical in achieving maximal recovery, and being in geographical proximity to a medical center with specialized TBI treatment services is a major determinant of whether such treatment is utilized. We present a mixed integer programming model for locating TBI treatment units in the VA. This model was developed for the VA Rehabilitation Strategic Healthcare Group to assist in locating new TBI treatment units. The optimization model assigns TBI treatment units to existing VA medical centers while minimizing the sum of patient treatment costs, patient lodging and travel costs, and the penalty costs associated with foregone treatment revenue and excess capacity utilization. We demonstrate our model with VA TBI admission data from one of the VA's integrated service networks, and discuss the expected service and cost implications for a range of TBI treatment unit location options.

Multiple system utilization and mortality for veterans with stroke.

BACKGROUND AND PURPOSE: Many Veteran Health Administration (VHA) enrollees receive health services outside the VHA system. However, limited information is available about poststroke utilization and mortality by veterans who used multiple sources of health care. This study assessed the likelihood of 12-month poststroke rehospitalization and mortality of veterans who used VHA only versus those who used multiple sources of care. METHODS: Our retrospective observational study examined veterans living in Florida and diagnosed with acute stroke. We categorized users into 4 groups: VHA-only, VHA-Medicare, VHA-Medicaid, and VHA-Medicare-Medicaid based on their use of each health care program. Logistic regression models were fitted for 12-month poststroke general rehospitalization, recurrent stroke readmission, and mortality, adjusting for sociodemographic and clinical factors. RESULTS: The sample consisted of 29% VHA-only users, 61% VHA-Medicare users, 3% VHA-Medicaid users, and 7% VHA-Medicare-Medicaid triple users. Compared with the VHA-only users, multiple system users were significantly more likely to be rehospitalized for any cause and for recurrent stroke 12-months postindex. Mortality outcomes depended on when the outcome was measured; at the index admission date, we found no significant difference in mortality across the user groups; at the index discharge date, the VHA-only users was less likely to die within the first 12 months than the users of the 2 dual groups (VHA-Medicare and VHA-Medicaid). CONCLUSIONS: Multiple health care source use was common among VHA enrollees with acute stroke in Florida. Multiple system users were more likely to be rehospitalized and the mortality outcomes were dependent on when the outcome was measured.

Race/ethnicity: who is counting what?

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.

Analyses of male residents in community nursing facilities: comparisons of Veterans Health Administration residents to other residents.

We compared Veterans Health Administration (VHA) residents in community nursing facilities to other residents. We used all admission assessments in the Minimum Data Set throughout the United States during 2000 to identify 7,296 male VHA residents and 159,203 other male residents in community nursing facilities. Male VHA residents were significantly more independent in the self-performance of activities of daily living and less physically disabled than other male residents, with minor differences in cognitive function as measured by a Cognitive Performance Scale. Male VHA residents were more likely to have comorbidities than other male residents. Significantly larger proportions of other male residents than VHA residents received special treatments and procedures, with especially large differences for various therapies (e.g., physical therapy). We found significant differences in the demographic and clinical characteristics of male VHA residents in community nursing facilities compared with other male residents. These differences in the delivery of services may have implications for the quality of care for veterans in this setting.

A primer and comparative review of major US mortality databases.

PURPOSE: Mortality data are important tools for research requiring vital status information. We reviewed the major mortality databases and mortality ascertainment services available in the United States, including the National Death Index (NDI), the Social Security Administration (SSA) files, and the Department of Veterans Affairs databases. METHODS: The content, reliability, and accuracy of mortality sources are described and compared. We also describe how investigators can gain access to these resources and provide further contact information. RESULTS: We reviewed the accuracy of major mortality sources. The sensitivity (i.e., the proportion of the true number of deaths) of the NDI ranged from 87.0% to 97.9%, whereas the sensitivity for the VA Beneficiary Identification and Records Locator System (BIRLS) ranged between 80.0% and 94.5%. The sensitivity of SSA files ranged between 83.0% and 83.6%. Sensitivity for the VA Patient Treatment File (PTF) was 33%. CONCLUSIONS: While several national mortality ascertainment services are available for vital status (i.e., death) analyses, the NDI information demonstrated the highest sensitivity and, currently, it is the only source at the national level with a cause of death field useful for research purposes. Researchers must consider methods used to ascertain vital status as well as the quality of the information in mortality databases.