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The Family Caregiver Activation in Transitions (FCAT) tool in its current, non-scalar form is not pragmatic for clinical use as each item is scored and intended to be interpreted individually. The purpose of the current study was to create a scalar version of the FCAT to facilitate better care communications between hospital staff and family caregivers. We also assessed the scale's validity by comparing the scalar version of the measure against patient health measures. Data were collected from 463 family caregiver–patient dyads from January 2016 to July 2019. An exploratory factor analysis was performed on the 10-item FCAT, resulting in a statistically homogeneous six-item scale focused on current caregiving activation factors. The measure was then compared against patient health measures, with no significant biases found. The six-item scalar FCAT can provide hospital staff insight into the level of caregiver activation occurring in the patient's health care and help tailor care transition needs for family caregiver–patient dyads. [Journal of Gerontological Nursing, 48(12), 35–42.], Family caregivers are characteristically defined as individuals who assist a care recipient in completing activities of daily living (ADLs), such as bathing, toileting, taking medications, and managing appointments (Coleman, Ground, [...]

Objective: Medicare's Hospital Readmissions Reduction Program (HRRP) does not account for social risk factors in risk adjustment, and this may lead the program to unfairly penalize safety-net hospitals. Our objective was to determine the impact of adjusting for social risk factors on HRRP penalties. Study Design: Retrospective cohort study. Data Sources/Study Setting: Claims data for 2 952 605 fee-for-service Medicare beneficiaries with acute myocardial infarction (AMI), congestive heart failure (CHF) or pneumonia from December 2012 to November 2015. Principal Findings: Poverty, disability, housing instability, residence in a disadvantaged neighborhood, and hospital population from a disadvantaged neighborhood were associated with higher readmission rates. Under current program specifications, safety-net hospitals had higher readmission ratios (AMI, 1.020 vs 0.986 for the most affluent hospitals; pneumonia, 1.031 vs 0.984; and CHF, 1.037 vs 0.977). Adding social factors to risk adjustment cut these differences in half. Over half the safety-net hospitals saw their penalty decline; 4-7.5 percent went from having a penalty to having no penalty. These changes translated into a $17 million reduction in penalties to safety-net hospitals. Conclusions: Accounting for social risk can have a major financial impact on safety-net hospitals. Adjustment for these factors could reduce negative unintended consequences of the HRRP. KEYWORDS Medicare, readmission, 1 | INTRODUCTION Since its implementation in 2012, Medicare's Hospital Readmissions Reduction Program (HRRP) has been the source of significant controversy. One major source of that controversy has been the [...]

Objective: To assess the relationship between a composite measure of neighborhood disadvantage, the Area Deprivation Index (ADI), and control of blood pressure, diabetes, and cholesterol in the Medicare Advantage (MA) population. Data Sources: Secondary analysis of 2013 Medicare Healthcare Effectiveness Data and Information Set, Medicare enrollment data, and a neighborhood disadvantage indicator. Study Design: We tested the association of neighborhood disadvantage with intermediate health outcomes. Generalized estimating equations were used to adjust for geographic and individual factors including region, sex, race/ethnicity, dual eligibility, disability, and rurality. Data Collection: Data were linked by ZIP+4, representing compact geographic areas that can be linked to Census block groups. Principal Findings: Compared with enrollees residing in the least disadvantaged neighborhoods, enrollees in the most disadvantaged neighborhoods were 5 percentage points (P < 0.05) less likely to have controlled blood pressure, 6.9 percentage points (P < 0.05) less likely to have controlled diabetes, and 9.9 percentage points (P < 0.05) less likely to have controlled cholesterol. Adjustment attenuated this relationship, but the association remained. Conclusions: The ADI is a strong, independent predictor of diabetes and cholesterol control, a moderate predictor of blood pressure control, and could be used to track neighborhood-level disparities and to target disparities-focused interventions in the MA population. KEYWORDS social determinants of health, geographic/spatial factors/small area variations, Medicare, 1 | INTRODUCTION Persons living in disadvantaged neighborhoods have increased risk of worse health outcomes, including uncontrolled blood pressure, blood sugar, and cholesterol. (1,2) Most health insurers in the United [...]

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1532-5415.2008.01693.x Byline: Amy J. H. Kind (*[dagger][double dagger]), Maureen A. Smith (*), Jinn-Ing Liou (*), Nancy Pandhi (*s.), Jennifer R. Frytak ([parallel]), Michael D. Finch (#) Keywords: stroke; patient discharge; survival; health insurance reimbursement; patient readmission Abstract: OBJECTIVES: To examine 1-year mortality and healthcare payments of stroke patients experiencing zero, one and two or more bounce-backs within 30 days of discharge. DESIGN: Retrospective analysis of administrative data. SETTING: Four hundred twenty-two hospitals in the southern and eastern United States. PARTICIPANTS: Eleven thousand seven hundred twenty-nine Medicare beneficiaries aged 65 and older surviving at least 30 days with acute ischemic stroke in 2000. MEASUREMENTS: One-year mortality and predicted total healthcare payments were calculated using log-normal parametric survival analysis and quantile regression, respectively. Models included sociodemographics, prior medical history, stroke severity, length of stay, and discharge site. RESULTS: Crude survival at 1 year for the zero, one and two or more bounce-back groups was 83%, 67%, and 55%, respectively. The one bounce-back group had 49% shorter (time ratio (TR)=0.51, 95% confidence interval (CI)=0.46-0.56) and the two or more bounce-backs group had 68% shorter (TR=0.32, 95% CI=0.27-0.38) adjusted 1-year survival time than the zero bounce-back group. For high- and low-cost patients, adjusted predicted payments were greater with each additional bounce-back experienced. CONCLUSION: Acute stroke patients experiencing bounce-backs within 30 days have strikingly poorer survival and higher healthcare payments over the subsequent year than their counterparts with no bounce-backs. Bounce-backs may serve as a simple predictor for identifying stroke patients at extremely high risk for poor outcomes. Author Affiliation: (*)Population Health Sciences ([double dagger])Medicine, Geriatrics Section (s.)Family Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin ([dagger])William S. Middleton Hospital, Department of Veterans Affairs, Geriatric Research Education and Clinical Center, Madison, Wisconsin ([parallel])i3 Innovus, Eden Prairie, Minnesota (#)Center for Health Care Policy and Evaluation, Eden Prairie, Minnesota. Article note: Address correspondence to Dr. Amy J. H. Kind, William S. Middleton VA Hospital-GRECC, 2500 Overlook Terrace, Madison, WI 53705. E-mail: ajk@medicine.wisc.edu

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1532-5415.2007.01091.x Byline: Amy J. H. Kind (*[dagger][double dagger]s.), Maureen A. Smith (*), Jennifer R. Frytak ([parallel]i3), Michael D. Finch ([paragraph]) Keywords: transition; stroke; hospital discharge; risk factors Abstract: OBJECTIVES: To identify predictors of complicated transitions within 30 days after discharge from hospitalization for acute stroke. DESIGN: Retrospective analysis of administrative data. SETTING: Four hundred twenty-two hospitals in the southern and eastern United States. PARTICIPANTS: Thirty-nine thousand three hundred eighty-four Medicare beneficiaries aged 65 and older discharged after acute ischemic stroke from 1998 to 2000. MEASUREMENTS: Complicated transition, defined as movement from less- to more-intensive care setting after hospital discharge, with hospital being most intensive and home without home health care being least intensive. RESULTS: Twenty percent of patients experienced at least one complicated transition; 16% of those experienced more than one complicated transition. After adjustment using logistic regression, factors predicting any complicated transition included older age, African-American race, Medicaid enrollment, prior hospitalization, gastrostomy tube, chronic disease, length of stay, and discharge site. Patients with multiple complicated transitions were more likely to be African American (odds ratio (OR)=1.38, 95% confidence interval (CI)=1.13-1.68), be male (OR=1.21, 95% CI=1.04-1.40), have a prior diagnosis of fluid and electrolyte disorder (e.g., dehydration) (OR=1.23, 95% CI=1.07-1.43), have a prior hospitalization (OR=1.18, 95% CI=1.01-1.36), and be initially discharged to a skilled-nursing facility or long-term care (OR=1.22, 95% CI=1.04-1.44) than patients with only one complicated transition. They were less likely to be initially discharged to a rehabilitation center (OR=0.71, 95% CI=0.57-0.89). CONCLUSION: Significant numbers of stroke patients experience complicated transitions soon after hospital discharge. Sociodemographic factors and initial discharge site distinguish patients with multiple complicated transitions. These factors may enable prospective identification and targeting of stroke patients at risk for 'bouncing back.' Author Affiliation: (*)Department of Population Health Sciences ([dagger])Department of Medicine, Division of Geriatrics ([double dagger])Center for Women's Health Research, University of Wisconsin, Madison, Wisconsin (s.)William S. Middleton Hospital, Geriatric Research Education and Clinical Center, Department of Veterans Affairs, Madison, Wisconsin ([parallel]i3)Magnifi, Eden Prairie, Minnesota ([paragraph])Center for Health Care Policy and Evaluation, Eden Prairie, Minnesota Article note: Address correspondence to Dr. Amy J. H. Kind, William S. Middleton VA Hospital-GRECC, 2500 Overlook Terrace, Madison, WI 53705. E-mail: ajk@medicine.wisc.edu