Search

Objective. To assess the issue of nonrepresentative sampling in Medicare Advantage (MA) risk adjustment.Data Sources. Medicare enrollment and claims data from 2008 to 2011.Data Extraction. Risk adjustment predictor variables were created from 2008 to 2010 Part A and B claims and the Medicare Beneficiary Summary File. Spending is based on 2009-2011 Part A and B, Durable Medical Equipment, and Home Health Agency claims files.Study Design. A propensity-score matched sample of Traditional Medicare (TM) beneficiaries who resembled MA enrollees was created. Risk adjustment formulas were estimated using multiple techniques, and performance was evaluated based on [R.sup.2], predictive ratios, and formula coefficients in the matched sample and a random sample of TM beneficiaries.Principal Findings. Matching improved balance on observables, but performance metrics were similar when comparing risk adjustment formula results fit on and evaluated in the matched sample versus fit on the random sample and evaluated in the matched sample.Conclusions. Fitting MA risk adjustment formulas on a random sample versus a matched sample yields little difference in MA plan payments. This does not rule out potential improvements via the matching method should reliable MA encounter data and additional variables become available for risk adjustment.Key Words. Risk adjustment, medicare, machine learning, regression, Missing data is a pervasive obstacle in health services research and may have particularly important implications for health plan payment policy. In some cases, variables may be systematically missing--unavailable for [...]

Objective. To compare the quality of care following admission to a nursing home (NH) with and without a dementia special care unit (SCU) for residents with dementia. Data Sources/Study Setting. National resident-level minimum dataset assessments (MDS) 2005-2010 merged with Medicare claims and provider-level data from the Online Survey, Certification, and Reporting database. Study Design. We employ an instrumental variable approach to address the endo-geneity of selection into an SCU facility controlling for a range of individual-level covariates. We use "differential distance" to a nursing home with and without an SCU as our instrument. Data Collection/Extraction Methods. Minimum dataset assessments performed at NH admission and every quarter thereafter. Principal Findings. Admission to a facility with an SCU led to a reduction in inappropriate antipsychotics (-9.7 percent), physical restraints (-9.6 percent), pressure ulcers (-3.3 percent), feeding tubes (-8.3 percent), and hospitalizations (-14.7 percent). We found no impact on the use of indwelling urinary catheters. Results held in sensitivity analyses that accounted for the share of SCU beds and the facilities' overall quality. Conclusions. Facilities with an SCU provide better quality of care as measured by several validated quality indicators. Given the aging population, policies to promote the expansion and use of dementia SCUs may be warranted. Key Words. Nursing home, quality, dementia, special care unit, On a given day, approximately 750,000 individuals residing in U.S. nursing homes have a diagnosis of dementia, constituting 50 percent of long-stay residents nationally (National Center for Health Statistics [U.S.] [...]

Objective. To forecast out-of-pocket health care spending among older adults. Long-term forecasts allow policy makers to explore potential impacts of policy scenarios, but existing microsimulations do not incorporate details of supplemental insurance coverage and income effects on health care spending. Data Sources. Dynamic microsimulation calibrated to survey and administrative data. Study Design. We augment Urban Institute's Dynamic Simulation of Income Model (DYNASIM) with modules that incorporate demand responses and economic equilibria, with dynamics driven by exogenous technological change. A lengthy technical appendix provides details of the microsimulation model and economic assumptions for readers interested in applying these techniques. Principal Findings. The model projects total out-of-pocket spending (point of care plus premiums) as a share of income for adults aged 65 and older. People with lower incomes and poor health fare worse, despite protections of Medicaid. Spending rises 40 percent from 2012 to 2035 (from 10 to 14 percent of income) for the median beneficiary, but it increases from 5 to 25 percent of income for low-income beneficiaries and from 23 to 29 percent for the near poor who are in fair/poor health. Conclusions. Despite Medicare coverage, near-poor seniors will face out-of-pocket spending that would render them, in practical terms, underinsured. Key Words. Health care costs, health insurance, Medicare beneficiaries, simulations, In spite of the recent spending slowdown, many analysts predict health care spending growth will rebound (Keehan et al. 2015). This presents a particular problem for those aged 65 and [...]