PRESCRIPTION, OUTCOMES, AND RISK ASSESSMENT OF WHEELCHAIRS FOR AGING POPULATION

Older adults are the largest and fastest growing users of wheeled mobility devices (wheelchairs). Research in the areas of: utilization; and outcomes is very limited. Lack of evidence based research often results in the provision of lower quality of wheelchairs to aging adults. This problem is more predominant for those living in Nursing Homes (NH) or other institutional settings. The goal of this dissertation work was to present a continuum of research studies, conducted with older adults that emphasized on: the development of a methodology (utilization review); measurement of outcomes; and identification of problems associated with use of wheelchairs that may pose a threat to the health, and safety of older adults. We anticipate that the overall outcomes of this work will help rehabilitation professionals to move towards performing utilization reviews through appropriate use of clinical environment. Such research will help in both the development of standard of care guidelines, and proving effectiveness and efficiency of service provision. We also expect to see this work influencing the outcomes research for older adults using wheelchairs. This will help in needs assessment of potential users (of wheelchairs) and will also help to evaluate: quality of services (devices) provided, and impact of types of wheelchairs on mobility and safety of users. Finally, we anticipate to see application use of the Wheelchair Assessment Checklist (WAC) by clinicians in detection of problems associated with wheelchairs and prevention of component failures, which in turn, will control (to some extent) the occurrence of unintentional (acute and chronic) injuries

Healthcare Utilization and Costs Among High-Need and Frail Mexican American Medicare Beneficiaries

Objectives To examine Medicare health care spending and health services utilization among high-need population segments in older Mexican Americans, and to examine the association of frailty on health care spending and utilization. Methods Retrospective cohort study of the innovative linkage of Medicare data with the Hispanic Established Populations for the Epidemiologic Study of the Elderly (H-EPESE) were used. There were 863 participants, which contributed 1,629 person years of information. Frailty, cognition, and social risk factors were identified from the H-EPESE, and chronic conditions were identified from the Medicare file. The Cost and Use file was used to calculate four categories of Medicare spending on: hospital services, physician services, post-acute care services, and other services. Generalized estimating equations (GEE) with a log link gamma distribution and first order autoregressive, correlation matrix was used to estimate cost ratios (CR) of population segments, and GEE with a logit link binomial distribution was applied to estimate odds ratios (OR) of healthcare use. Results Participants in the major complex chronic illness segment who were also pre-frail or frail had higher total costs and utilization compared to the healthy segment. The CR for total Medicare spending was 3.05 (95% CI, 2.48–3.75). Similarly, this group had higher odds of being classified in the high-cost category 5.86 (95% CI, 3.35–10.25), nursing home care utilization 11.32 (95% CI, 3.88–33.02), hospitalizations 4.12 (95% CI, 2.88–5.90) and emergency room admissions 4.24 (95% CI, 3.04–5.91). Discussion Our findings highlight that frailty assessment is an important consideration when identifying high-need and high-cost patients

Comparison of methods to identify long term care nursing home residence with administrative data

Abstract Background To compare different methods for identifying a long term care (LTC) nursing home stay, distinct from stays in skilled nursing facilities (SNFs), to the method currently used by the Center for Medicare and Medicaid Services (CMS). We used national and Texas Medicare claims, Minimum Data Set (MDS), and Texas Medicaid data from 2011-2013. Methods We used Medicare Part A and B and MDS data either alone or in combination to identify LTC nursing home stays by three methods. One method used Medicare Part A and B data; one method used Medicare Part A and MDS data; and the current CMS method used MDS data alone. We validated each method against Texas 2011 Medicare-Medicaid linked data for those with dual eligibility. Results Using Medicaid data as a gold standard, all three methods had sensitivities > 92% to identify LTC nursing home stays of more than 100 days in duration. The positive predictive value (PPV) of the method that used both MDS and Medicare Part A data was 84.65% compared to 78.71% for the CMS method and 66.45% for the method using Part A and B Medicare. When the patient population was limited to those who also had a SNF stay, the PPV for identifying LTC nursing home was highest for the method using Medicare plus MDS data (88.1%). Conclusions Using both Medicare and MDS data to identify LTC stays will lead to more accurate attribution of CMS nursing home quality indicators

Pilot study for quantifying driving characteristics during power wheelchair soccer

This study determined the driving characteristics of wheelchair users during power wheelchair soccer games. Data for this study were collected at the 28th and 29th National Veterans Wheelchair Games. Nineteen veterans who were 18 years or older and power wheelchair soccer players completed a brief demographic survey and provided information about their power wheelchairs. A customized data-logging device was placed on each participant's wheelchair before power soccer game participation. The data logger was removed at the end of the final game for each participant. The average distance traveled during the games was 899.5 +/- 592.5 m, and the average maximum continuous distance traveled was 256.0 +/- 209.4 m. The average wheelchair speed was 0.8 +/- 0.2 m/s, and the average duration of driving time was 17.6 +/- 8.3 min. Average proportion of time spent at a speed >1 m/s was 30.7% +/- 33.8%, between 0.5 and 1 m/s was 16.2% +/- 34.4%, and <0.5 m/s was 21.4% +/- 24.3%. The information from this descriptive study provides insight for future research in the field of adapted sports for people with high levels of impairments who use power wheelchairs for their mobility

A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care

© 2020 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Methods used to categorize functional status to predict health outcomes across post-acute care settings vary significantly. Objectives We compared three methods that categorize functional status to predict 30-day and 90-day hospital readmission across inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF) and home health agencies (HHA). Research design Retrospective analysis of 2013–2014 Medicare claims data (N = 740,530). Data were randomly split into two subsets using a 1:1 ratio. We used half of the cohort (development subset) to develop functional status categories for three methods, and then used the rest (testing subset) to compare outcome prediction. Three methods to generate functional categories were labeled as: Method I, percentile based on proportional distribution; Method II, percentile based on change score distribution; and Method III, functional staging categories based on Rasch person strata. We used six differentiation and classification statistics to determine the optimal method of generating functional categories. Setting IRF, SNF and HHA. Subjects We included 130,670 (17.7%) Medicare beneficiaries with stroke, 498,576 (67.3%) with lower extremity joint replacement and 111,284 (15.0%) with hip and femur fracture. Measures Unplanned 30-day and 90-day hospital readmission. Results For all impairment conditions, Method III best predicted 30-day and 90-day hospital readmission. However, we observed overlapping confidence intervals among some comparisons of three methods. The bootstrapping of 30-day and 90-day hospital readmission predictive models showed the area under curve for Method III was statistically significantly higher than both Method I and Method II (all paired-comparisons, p\u3c.001), using the testing sample. Conclusions Overall, functional staging was the optimal method to generate functional status categories to predict 30-day and 90-day hospital readmission. To facilitate clinical and scientific use, we suggest the most appropriate method to categorize functional status should be based on the strengths and weaknesses of each method

A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care.

BackgroundMethods used to categorize functional status to predict health outcomes across post-acute care settings vary significantly.ObjectivesWe compared three methods that categorize functional status to predict 30-day and 90-day hospital readmission across inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF) and home health agencies (HHA).Research designRetrospective analysis of 2013-2014 Medicare claims data (N = 740,530). Data were randomly split into two subsets using a 1:1 ratio. We used half of the cohort (development subset) to develop functional status categories for three methods, and then used the rest (testing subset) to compare outcome prediction. Three methods to generate functional categories were labeled as: Method I, percentile based on proportional distribution; Method II, percentile based on change score distribution; and Method III, functional staging categories based on Rasch person strata. We used six differentiation and classification statistics to determine the optimal method of generating functional categories.SettingIRF, SNF and HHA.SubjectsWe included 130,670 (17.7%) Medicare beneficiaries with stroke, 498,576 (67.3%) with lower extremity joint replacement and 111,284 (15.0%) with hip and femur fracture.MeasuresUnplanned 30-day and 90-day hospital readmission.ResultsFor all impairment conditions, Method III best predicted 30-day and 90-day hospital readmission. However, we observed overlapping confidence intervals among some comparisons of three methods. The bootstrapping of 30-day and 90-day hospital readmission predictive models showed the area under curve for Method III was statistically significantly higher than both Method I and Method II (all paired-comparisons, pConclusionsOverall, functional staging was the optimal method to generate functional status categories to predict 30-day and 90-day hospital readmission. To facilitate clinical and scientific use, we suggest the most appropriate method to categorize functional status should be based on the strengths and weaknesses of each method

Additional file 1: Table S1A. of Comparison of methods to identify long term care nursing home residence with administrative data

Validation of three methods of identifying a LTC nursing home stay using Medicaid charges for LTC services as the gold standard, in patients dually eligible for Medicare and Medicaid, stratified by age. Table S1B. Validation of three methods of identifying a LTC nursing home stay using Medicaid charges for LTC services as the gold standard in patients dually eligible for Medicare and Medicaid, stratified by race. Table S1C. Validation of three methods of identifying a LTC nursing home stay using Medicaid charges for LTC services, as the gold standard in patients dually eligible for Medicare and Medicaid, stratified by hospitalization or SNF stay in that year. Table S1D. Validation of three methods of identifying a LTC nursing home stay using Medicaid charges for LTC services as the gold standard in patients dually eligible for Medicare and Medicaid, stratified by gender and location. (DOCX 16 kb