Dataset Information

Predicting Disability among Community-Dwelling Medicare Beneficiaries Using Claims-Based Indicators.

ABSTRACT: To assess the feasibility of using existing claims-based algorithms to identify community-dwelling Medicare beneficiaries with disability based solely on the conditions for which they are being treated, and improving on these algorithms by combining them in predictive models.Data on 12,415 community-dwelling fee-for-service Medicare beneficiaries who first responded to the Medicare Current Beneficiary Survey (MCBS) in 2003-2006.Logistic regression models in which six claims-based disability indicators are used to predict self-reported disability. Receiver operating characteristic (ROC) curves were used to assess the performance of the predictive models.The predictive performance of the regression-based models is better than that of the individual claims-based indicators. At a predicted probability threshold chosen to maximize the sum of sensitivity and specificity, sensitivity is 0.72 for beneficiaries age 65 or older and specificity is 0.65. For those under 65, sensitivity is 0.54 and specificity is 0.67. The findings also suggest ways to improve predictive performance for specific disability populations of interest to researchers.Predictive models that incorporate multiple claims-based indicators provide an improved tool for researchers seeking to identify people with disabilities in claims data.

SUBMITTER: Ben-Shalom Y

PROVIDER: S-EPMC4722209 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Predicting Disability among Community-Dwelling Medicare Beneficiaries Using Claims-Based Indicators.

Ben-Shalom Yonatan Y Stapleton David C DC

Health services research 20150525 1

<h4>Objectives</h4>To assess the feasibility of using existing claims-based algorithms to identify community-dwelling Medicare beneficiaries with disability based solely on the conditions for which they are being treated, and improving on these algorithms by combining them in predictive models.<h4>Data source</h4>Data on 12,415 community-dwelling fee-for-service Medicare beneficiaries who first responded to the Medicare Current Beneficiary Survey (MCBS) in 2003-2006.<h4>Study design</h4>Logistic ...[more]

PMID: 26015332

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Project description:BackgroundDisability in Activities of Daily Living (ADL) is an adverse outcome of frailty that places a burden on frail elderly people, care providers and the care system. Knowing which physical frailty indicators predict ADL disability is useful in identifying elderly people who might benefit from an intervention that prevents disability or increases functioning in daily life. The objective of this study was to systematically review the literature on the predictive value of physical frailty indicators on ADL disability in community-dwelling elderly people.MethodsA systematic search was performed in 3 databases (PubMed, CINAHL, EMBASE) from January 1975 until April 2010. Prospective, longitudinal studies that assessed the predictive value of individual physical frailty indicators on ADL disability in community-dwelling elderly people aged 65 years and older were eligible for inclusion. Articles were reviewed by two independent reviewers who also assessed the quality of the included studies.ResultsAfter initial screening of 3081 titles, 360 abstracts were scrutinized, leaving 64 full text articles for final review. Eventually, 28 studies were included in the review. The methodological quality of these studies was rated by both reviewers on a scale from 0 to 27. All included studies were of high quality with a mean quality score of 22.5 (SD 1.6). Findings indicated that individual physical frailty indicators, such as weight loss, gait speed, grip strength, physical activity, balance, and lower extremity function are predictors of future ADL disability in community-dwelling elderly people.ConclusionsThis review shows that physical frailty indicators can predict ADL disability in community-dwelling elderly people. Slow gait speed and low physical activity/exercise seem to be the most powerful predictors followed by weight loss, lower extremity function, balance, muscle strength, and other indicators. These findings should be interpreted with caution because the data of the different studies could not be pooled due to large variations in operationalization of the indicators and ADL disability across the included studies. Nevertheless, our study suggests that monitoring physical frailty indicators in community-dwelling elderly people might be useful to identify elderly people who could benefit from disability prevention programs.

Project description:Hospital readmission within thirty days is common among Medicare beneficiaries, but the relationship between rehospitalization and subsequent mortality in older adults is not known.To compare one-year mortality rates among community-dwelling elderly hospitalized Medicare beneficiaries who did and did not experience early hospital readmission (within 30 days), and to estimate the odds of one-year mortality associated with early hospital readmission and with other patient characteristics.A cohort study of 2133 hospitalized community-dwelling Medicare beneficiaries older than 64 years, who participated in the nationally representative Cost and Use Medicare Current Beneficiary Survey between 2001 and 2004, with follow-up through 2006.One-year mortality after index hospitalization discharge.Three hundred and four (13.7 %) hospitalized beneficiaries had an early hospital readmission. Those with early readmission had higher one-year mortality (38.7 %) than patients who were not readmitted (12.1 %; p<0.001). Early readmission remained independently associated with mortality after adjustment for sociodemographic factors, health and functional status, medical comorbidity, and index hospitalization-related characteristics [HR (95 % CI) 2.97 (2.24-3.92)]. Other patient characteristics independently associated with mortality included age [1.03 (1.02-1.05) per year], low income [1.39 (1.04-1.86)], limited self-rated health [1.60 (1.20-2.14)], two or more recent hospitalizations [1.47 (1.01-2.15)], mobility difficulty [1.51 (1.03-2.20)], being underweight [1.62 (1.14-2.31)], and several comorbid conditions, including chronic lung disease, cancer, renal failure, and weight loss. Hospitalization-related factors independently associated with mortality included longer length of stay, discharge to a skilled nursing facility for post-acute care, and primary diagnoses of infections, cancer, acute myocardial infarction, and heart failure.Among community-dwelling older adults, early hospital readmission is a marker for notably increased risk of one-year mortality. Providers, patients, and families all might respond profitably to an early readmission by reviewing treatment plans and goals of care.

Project description:ImportanceImpairments in vision or hearing are common and have been independently linked to higher risk of dementia in older adults. There is a limited understanding of the prevalence of concurrent functional vision and hearing impairment (dual sensory impairment) and its contribution to dementia risk.ObjectiveTo examine the age-specific prevalence of functional dual sensory impairment among older adults, and to investigate the cross-sectional and 7-year longitudinal associations between functional dual sensory impairment and dementia.Design, setting, and participantsThis cohort study of 7562 older adults used data from the US National Health and Aging Trends Study (NHATS), a nationally representative cohort study of community-dwelling, Medicare beneficiaries aged 65 years and older in the US. Participants in the study with complete data on hearing, vision, and dementia were included in analysis. Data were collected between 2011 and 2018, and between March 2018 and May 2020.ExposuresSelf-reported functional sensory impairments (ie, no sensory impairment, functional vision impairment only, functional hearing impairment only, and functional dual sensory impairment).Main outcomes and measuresAge-specific prevalence of functional sensory impairments was calculated. Generalized linear regression with a complementary log-log link and a discrete time proportional hazards model with a complementary log-log link were used to assess the cross-sectional and 7-year longitudinal hazard of dementia.ResultsOf 7562 participants, 3073 (40.7%) were ages 80 years or older and 4411 (58.3%) were women. Overall, 5.4% (95% CI, 4.7%-6.1%) of participants reported functional vision impairment only, 18.9% (95% CI, 18.9%-17.8%) reported functional hearing impairment only, and 3.1% (95% CI, 2.7%-3.5%) reported functional dual sensory impairment (prevalence estimates are weighted). Participants reporting sensory impairments were older (no impairment: age ≥90 years, 2.12% [95% CI, 1.79%-2.46%] vs functional dual sensory impairment: age ≥90 years, 20.06% [95% CI, 16.02%-24.10%]), had lower education (no impairment: <high school, 19.05% [95% CI, 17.27%-20.83%] vs functional dual sensory impairment: <high school, 46.15% [95% CI, 38.38%-53.92%]), and greater disease burden (eg, heart disease: no impairment, 15.30% [95% CI, 14.04%-16.55%] vs functional dual sensory impairment, 25.49% [95% CI, 19.96%-31.02%]). Compared with no impairment, functional vision impairment (adjusted hazard ratio [aHR], 1.89; 95% CI, 1.57-2.28), functional hearing impairment (aHR, 1.14; 95% CI, 1.00-1.31), and functional dual sensory impairment (aHR, 2.00; 95% CI, 1.57-2.53) were associated with a higher cross-sectional hazard of dementia. Over 7 years, functional vision impairment (aHR, 1.40; 95% CI, 1.12-1.74), functional hearing impairment (aHR, 1.09; 95% CI, 0.95-1.24), and functional dual sensory impairment (aHR, 1.50; 95% CI, 1.12-2.02) were associated with a higher hazard of incident dementia compared with no impairment.Conclusions and relevanceIn this cohort study of US Medicare beneficiaries, dual sensory impairment was prevalent in older adults and associated with increased risk of dementia. These findings suggest that sensory rehabilitative interventions for multiple impairments may be an additional resource in efforts to reduce dementia risk.

Project description:OBJECTIVE:To use patient-level clinical variables to develop and validate a parsimonious model to predict hospital readmissions from home healthcare (HHC) in Medicare fee-for-service beneficiaries. DESIGN:Retrospective analysis using multivariable logistic regression and gradient boosting machine (GBM) learning to develop and validate a predictive model. SETTING/PARTICIPANTS/MEAUREMENTS:A 5% national sample of patients, aged 65?years or older, with Medicare fee-for-service who received skilled HHC services within 5 days of hospital discharge in 2012 (n = 43 407). Multiple data sets were merged, including Medicare Outcome and Assessment Information Set, Home Health Claims, Medicare Provider Analysis and Review, and Master Beneficiary Summary Files, to extract patient-level variables from the first HHC visit after discharge and measure 30-day readmission outcomes. RESULTS:Among 43 407 patients with inpatient hospitalizations followed by HHC, 14.7% were readmitted within 30?days. Of the 53 candidate variables, seven remained in the final model as individually predictive of outcome: Elixhauser comorbidity index, index hospital length of stay, urinary catheter presence, patient status (ie, fragile health with high risk of complications or serious progressive condition), two or more hospitalizations in prior year, pressure injury risk or presence, and surgical wound presence. Of interest, surgical wounds, either from a total hip or total knee arthroplasty procedure or another surgical procedure, were associated with fewer readmissions. The optimism-corrected c-statistics for the full model and parsimonious model were 0.67 and 0.66, respectively, indicating fair discrimination. The Brier score for both models was 0.120, indicating good calibration. The GBM model identified similar predictive variables. CONCLUSION:Variables available to HHC clinicians at the first postdischarge HHC visit can predict readmission risk and inform care plans in HHC. Future analyses incorporating measures of social determinants of health, such as housing instability or social support, have the potential to enhance prediction of this outcome. J Am Geriatr Soc 67:2505-2510, 2019.

Project description:Databases of medical claims can be valuable resources for cardiovascular research, such as comparative effectiveness and pharmacovigilance studies of cardiovascular medications. However, claims data do not include all of the factors used for risk stratification in clinical care. We sought to develop claims-based algorithms to identify individuals at high estimated risk for coronary heart disease (CHD) events, and to identify uncontrolled low-density lipoprotein (LDL) cholesterol among statin users at high risk for CHD events.We conducted a cross-sectional analysis of 6,615 participants ?66 years old using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study baseline visit in 2003-2007 linked to Medicare claims data. Using REGARDS data we defined high risk for CHD events as having a history of CHD, at least 1 risk equivalent, or Framingham CHD risk score >20%. Among statin users at high risk for CHD events we defined uncontrolled LDL cholesterol as LDL cholesterol ?100 mg/dL. Using Medicare claims-based variables for diagnoses, procedures, and healthcare utilization, we developed algorithms for high CHD event risk and uncontrolled LDL cholesterol.REGARDS data indicated that 49% of participants were at high risk for CHD events. A claims-based algorithm identified high risk for CHD events with a positive predictive value of 87% (95% CI: 85%, 88%), sensitivity of 69% (95% CI: 67%, 70%), and specificity of 90% (95% CI: 89%, 91%). Among statin users at high risk for CHD events, 30% had LDL cholesterol ?100 mg/dL. A claims-based algorithm identified LDL cholesterol ?100 mg/dL with a positive predictive value of 43% (95% CI: 38%, 49%), sensitivity of 19% (95% CI: 15%, 22%), and specificity of 89% (95% CI: 86%, 90%).Although the sensitivity was low, the high positive predictive value of our algorithm for high risk for CHD events supports the use of claims to identify Medicare beneficiaries at high risk for CHD events.

Dataset Information

Predicting Disability among Community-Dwelling Medicare Beneficiaries Using Claims-Based Indicators.

Publications

Predicting Disability among Community-Dwelling Medicare Beneficiaries Using Claims-Based Indicators.

Similar Datasets

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