Place of Death Among Assisted Living Residents as a Factor of Hospice Regulations

Our objective was to examine the likelihood of dying in RC/AL among a national cohort of fee-for-service Medicare beneficiaries who died in 2018 (N=31,414) as a factor regulations allowing hospice care. We estimated multivariable logistic regression models to examine the association between RC/AL as place of death and supportive hospice regulations, controlling for demographic characteristics, dual Medicare/Medicaid eligibility, years in AL, and hospital referral region (HRR) to control for hospice practice patterns. A majority of beneficiaries in our cohort died in RC/AL; more than half while receiving hospice services. In unadjusted models, the odds of remaining in RC/AL communities until death were significantly higher in the presence of regulations supportive of hospice care. This relationship was no longer significant once adjusting for covariates and an HRR fixed effect, suggesting important variation in end-of-life experiences for AL residents not explained by hospice regulations.

Determinants of total end-of-life healthcare costs of Medicare beneficiaries: A quantile regression forests analysis

Background To identify and rank the importance of key determinants of end-of-life (EOL) healthcare costs, and to understand how the key factors impact different percentiles of the distribution of healthcare costs. Methods We applied a principled, machine learning based variable selection algorithm, using Quantile Regression Forests, to identify key determinants for predicting the 10 th (low), 50 th (median) and 90 th (high) quantiles of EOL healthcare costs, including costs paid for by Medicare, Medicaid, Medicare Health Maintenance Organizations (HMO), private HMO, and patient’s out-of-pocket expenditures. Results Our sample included 7,539 Medicare beneficiaries who died between 2002 and 2017. The 10 th, 50 th and 90 th quantiles of EOL healthcare cost are $5,244, $35,466 and $87,241 respectively. Regional characteristics, specifically, the EOL-expenditure index, a measure for regional variation in Medicare spending driven by physician practice, and the number of total specialists in the hospital referral region, were the top two influential determinants for predicting the 50 th and 90 th quantiles of EOL costs, but were not determinants of the 10 th quantile. Black race and Hispanic ethnicity were associated with lower EOL healthcare costs among decedents with lower total EOL healthcare costs but were associated with higher costs among decedents with the highest total EOL healthcare costs. Conclusions Factors associated with EOL healthcare costs varied across different percentiles of the cost distribution. Regional characteristics and decedent race/ethnicity exemplified factors that did not impact EOL costs uniformly across its distribution, suggesting the need to use a “higher-resolution” analysis for examining the association between risk factors and healthcare costs.

Care Deficiencies and Super-Organization of American Nursing Homes in Hospital Referral Region

Super-organization has been associated with worse care quality in nursing homes. Previous research on the chain ownership of American nursing homes excluded government facilities in public-private partnerships, and focused on corporate entities. This longitudinal study proposes a novel method of demarcating the latent ownership networks of for-profit, government and non-profit nursing homes in the United States through use of open data and social network analysis. Facility characteristics and care quality measures were analyzed from an ecological cohort of 9,001 American nursing homes that had a registered organization for owner, and were reimbursed through Medicare or Medicaid. Information was obtained from the Nursing Home Compare open datasets at five semi-annual processing dates from March 2016 to March 2018. Ownership networks of American nursing homes were constructed using the exact legal name of registered organizations. As hospital discharge is a routine admission source of nursing home residents, hospital referral region was actualized to demarcate focal area. Utilizing Bayesian hierarchical models, the association between nursing home super-organization in hospital referral region (inferred by degree-based centrality and Herfindahl-Hirschman Index) to scope of cited care deficiencies (denoted by Total Weighted Health Survey Score) was explored. The percentage of nursing homes having super-organization increased from 56.8 to 56.9% over the 2-year period. During this interval, the mean size of nursing home ownership group in hospital referral region increased from 3.11 to 3.23 facilities. Overall, super-organization in hospital referral region was not associated with care deficiencies in American nursing homes. However, being part of an ownership group with more facilities was beneficial for care quality among nursing homes with super-organization.

Geographic variation in neurologist density and neurologic care in the United States

Objective:To describe geographic variation in neurologist density, neurologic conditions, and neurologist involvement in neurologic care.Methods:We used 20% 2015 Medicare data to summarize variation by Hospital Referral Region (HRR). Neurologic care was defined as office-based evaluation/management visits with a primary diagnosis of a neurologic condition.Results:Mean density of neurologists varied nearly 4-fold from the lowest to the highest density quintile (9.7 [95%CI: 9.2-10.2] vs. 43.1 [95%CI: 37.6-48.5] per 100,000 Medicare beneficiaries). The mean prevalence of patients with neurologic conditions did not substantially differ across neurologist density quintile regions (293 vs. 311 per 1,000 beneficiaries in the lowest vs. highest quintiles, respectively). Of patients with a neurologic condition, 23.5% were seen by a neurologist, ranging from 20.6% in the lowest quintile regions to 27.0% in the highest quintile regions (6.4% absolute difference). Most of the difference was comprised of dementia, pain, and stroke conditions seen by neurologists. In contrast, very little of the difference was comprised of Parkinson’s disease and multiple sclerosis both of which had a very high proportion (>80%) of neurologist involvement even in the lowest quintile regions.Conclusions:The supply of neurologists varies substantially by region, but the prevalence of neurologic conditions does not. As neurologist supply increases, access to neurologist care for certain neurologic conditions (dementia, pain, and stroke) increases much more than for others (Parkinson’s disease and multiple sclerosis). These data provide insight for policy makers when considering strategies in matching the demand for neurologic care with the appropriate supply of neurologists.

Abstract 14968: Geographic Variation in the Early Diffusion and of Sacubitril/Valsartan

Introduction: In the PARADIGM-HF trial, sacubitril/valsartan demonstrated a 20% reduction in mortality and heart failure (HF) hospitalization compared to enalapril. Despite this success, diffusion has been slower than anticipated. To date, high co-payments have been identified as a key barrier. The aim of this study is to examine the role of geographic prescribing patterns in the early diffusion of sacubitril/valsartan, after controlling for cost. Hypothesis: There will be significant geographic variation in the use of sacubitril/valsartan, after controlling for cost. Methods: We used 100% Medicare Parts A and B and a random 40% sample of Part D to create a cohort of beneficiaries with heart failure with reduced ejection fraction (HFrEF) between 2016 and 2018. We calculated age, sex, race adjusted rates of sacubitril/valsartan use by hospital referral region (HRR) and used multivariable logistic regression to determine the association between geographic region and sacubitril/valsartan use, controlling for cost. Results: Early use rates of sacubitril/valsartan have been low: 1.9% in 2016, 3.3% in 2017 and 4% in 2018. There has been substantial geographic variation with most concentrated in the South and Northeast ( Figure ). After controlling for cost, HFrEF patients in the Northeast were 1.32 times (95% CI 1.26-1.29, p<0.001)) more likely, those in the South were 1.24 times more likely (95% CI 1.20-1.29, p<0.001)) and those in the West were 1.10 times more likely (95% CI 1.05-1.15, p<0.001) to receive sacubitril/valsartan compared to HFrEF patients in the Midwest. Conclusions: There has been substantial variation in the early diffusion of sacubitril/valsartan. In addition to cost, geographic prescribing patterns play a major role in early drug diffusion.

Medical costs of keeping the US economy open during COVID-19

We use an individual based model and national level epidemic simulations to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios. We model an unmitigated scenario and 12 mitigation scenarios which differ in compliance behavior to social distancing strategies and in the duration of the stay-home order. Under each scenario we estimate the number of people who are likely to get infected and require medical attention, hospitalization, and ventilators. Given the per capita medical cost for each of these health states, we compute the total medical costs for each scenario and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order. We also consider the hospital bed capacity of each Hospital Referral Region (HRR) in the US to estimate the deficit in beds each HRR will likely encounter given the demand for hospital beds. We consider a case where HRRs share hospital beds among the neighboring HRRs during a surge in demand beyond the available beds and the impact it has in controlling additional deaths.

Medical Costs of Keeping the US Economy Open During COVID-19

We use an individual based model and national level epidemic simulations to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios. We model an unmitigated scenario and 12 mitigation scenarios which differ in compliance behavior to social distancing strategies and to the duration of the stay-home order. Under each scenario we estimate the number of people who are likely to get infected and require medical attention, hospitalization, and ventilators. Given the per capita medical cost for each of these health states, we compute the total medical costs for each scenario and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order. We also consider the hospital bed capacity of each Hospital Referral Region (HRR) in the US to estimate the deficit in beds each HRR will likely encounter given the demand for hospital beds. We consider a case where HRRs share hospital beds among the neighboring HRRs during a surge in demand beyond the available beds and the impact it has in controlling additional deaths.