An observational, sequential analysis of the relationship between local economic distress and inequities in health outcomes, clinical care, health behaviors, and social determinants of health.

BACKGROUND: Socioeconomic status has long been associated with population health and health outcomes. While ameliorating social determinants of health may improve health, identifying and targeting areas where feasible interventions are most needed would help improve health equity. We sought to identify inequities in health and social determinants of health (SDOH) associated with local economic distress at the county-level. METHODS: For 3,131 counties in the 50 US states and Washington, DC (wherein approximately 325,711,203 people lived in 2019), we conducted a retrospective analysis of county-level data collected from County Health Rankings in two periods (centering around 2015 and 2019). We used ANOVA to compare thirty-three measures across five health and SDOH domains (Health Outcomes, Clinical Care, Health Behaviors, Physical Environment, and Social and Economic Factors) that were available in both periods, changes in measures between periods, and ratios of measures for the least to most prosperous counties across county-level prosperity quintiles, based on the Economic Innovation Group's 2015-2019 Distressed Community Index Scores. RESULTS: With seven exceptions, in both periods, we found a worsening of values with each progression from more to less prosperous counties, with least prosperous counties having the worst values (ANOVA p < 0.001 for all measures). Between 2015 and 2019, all except six measures progressively worsened when comparing higher to lower prosperity quintiles, and gaps between the least and most prosperous counties generally widened. CONCLUSIONS: In the late 2010s, the least prosperous US counties overwhelmingly had worse values in measures of Health Outcomes, Clinical Care, Health Behaviors, the Physical Environment, and Social and Economic Factors than more prosperous counties. Between 2015 and 2019, for most measures, inequities between the least and most prosperous counties widened. Our findings suggest that local economic prosperity may serve as a proxy for health and SDOH status of the community. Policymakers and leaders in public and private sectors might use long-term, targeted economic stimuli in low prosperity counties to generate local, community health benefits for vulnerable populations. Doing so could sustainably improve health; not doing so will continue to generate poor health outcomes and ever-widening economic disparities.

Trends in Characteristics of Adults Enrolled in Traditional Fee-for-Service Medicare and Medicare Advantage, 2011-2019.

BACKGROUND: While overall Medicare Part C (Medicare Advantage) enrollment has grown more rapidly than fee-for-service Medicare enrollment, changes in the growth and characteristics of different enrollee populations have not been examined. OBJECTIVES: For 2011-2019, to compare changes in the growth and characteristics of younger (age younger than 65) and older (age 65 and older) Medicare beneficiaries enrolled in Medicare Part A only, Medicare Parts A & B, and Medicare Part C. RESEARCH DESIGN: This was a retrospective, observational study. SUBJECTS: Medicare beneficiaries who were alive and enrolled in Medicare Part A only, Medicare Parts A & B, or Medicare Part C on June 30 of each year and in no other plan that year. MEASURES: For each plan type and age group the numbers and mean ages of enrollees and the proportion of enrollees who were: black, female, concurrently enrolled in Medicaid, and (for older enrollees), whose initial reason for eligibility was old age and survivors' benefits. RESULTS: Between 2011 and 2019, Medicare Part C experienced rapid expansions of 85.0% among older and 109.5% among younger enrollees. Part C enrollees were increasingly likely to be dually enrolled in Medicaid, Black and, among younger enrollees, female. CONCLUSIONS: Trends in demographic characteristics and changes in policy and growth in employer group plan offerings will likely continue to impact health care service utilization and costs in the Medicare population. Particularly as Medicare expansion to younger age groups is considered, future research should explore disparities in risk scores and care equity, quality, and costs across different Medicare enrollment options.

Episode-of-Care Characteristics and Costs for Hip and Knee Replacement Surgery in Hospitals Belonging to the High Value Healthcare Collaborative Compared With Similar Hospitals in the Same Health Care Markets.

BACKGROUND: To inform consumers and restrain health care cost growth, efforts to promote transparency and to reimburse for care episodes are accelerating in the United States. OBJECTIVE: To compare characteristics and costs of 30-day episode of care for hip and knee replacement occurring in High Value Healthcare Collaborative (HVHC)-member hospitals to those occurring in like non-HVHC-member hospitals in the same 15 health care markets before interventions by HVHC members to improve health care value for those interventions. RESEARCH DESIGN: This is a retrospective analysis of fee-for-service Medicare data from 2012 and 2013. SUBJECTS: For hip arthroplasty, 4030 HVHC-member and 7572 non-HVHC-member, and for knee arthroplasty, 6542 HVHC-member and 13,900 non-HVHC-member fee-for-service Medicare patients aged 65 and older. MEASURES: Volumes, patient demographics, hospital stay characteristics, and acute and postacute care standardized costs for a 30-day episode of care. RESULTS: HVHC-member hospitals differed from similar non-HVHC-member hospitals in the same health care markets when considering volumes of surgeries, patient demographics, Charlson scores, and patient distance to care during the index admission. There was little variation in acute care costs of hip or knee replacement surgery across health care markets; however, there was substantial variation in postacute care costs across those same markets. We saw less variation in postacute care costs within markets than across markets. Regression analyses showed that HVHC-member status was not associated with shorter lengths of stay, different complication rates, or lower total or postacute care costs for hip or knee replacement. CONCLUSIONS: Health care regions appear to be a more important predictor of episode costs of care than HVHC status.

The High Value Healthcare Collaborative: Observational Analyses of Care Episodes for Hip and Knee Arthroplasty Surgery.

BACKGROUND: Broader use of value-based reimbursement models will require providers to transparently demonstrate health care value. We sought to determine and report cost and quality data for episodes of hip and knee arthroplasty surgery among 13 members of the High Value Healthcare Collaborative (HVHC), a consortium of health care systems interested in improving health care value. METHODS: We conducted a retrospective, cross-sectional observational cohort study of 30-day episodes of care for hip and knee arthroplasty in fee-for-service Medicare beneficiaries aged 65 or older who had hip or knee osteoarthritis and used 1 of 13 HVHC member systems for uncomplicated primary hip arthroplasty (N = 8853) or knee arthroplasty (N = 16,434), respectively, in 2012 or 2013. At the system level, we calculated: per-capita utilization rates; postoperative complication rates; standardized total, acute, and postacute care Medicare expenditures for 30-day episodes of care; and the modeled impact of reducing episode expenditures or per-capita utilization rates. RESULTS: Adjusted per-capita utilization rates varied across HVHC systems and postacute care reimbursements varied more than 3-fold for both types of arthroplasty in both years. Regression analysis confirmed that total episode and postacute care reimbursements significantly differed across HVHC members after considering patient demographic differences. Potential Medicare cost savings were greatest for knee arthroplasty surgery and when lower total reimbursement targets were achieved. CONCLUSION: The substantial variation that we found offers opportunities for learning and collaboration to collectively improve outcomes, reduce costs, and enhance value. Ceteris paribus, reducing per-episode reimbursements would achieve greater Medicare cost savings than reducing per-capita rates.

Using Publicly Available Data to Construct a Transparent Measure of Health Care Value: A Method and Initial Results.

POLICY POINTS: Using publicly available Hospital Compare and Medicare data, we found a substantial range of hospital-level performance on quality, expenditure, and value measures for 4 common reasons for admission. Hospitals' ability to consistently deliver high-quality, low-cost care varied across the different reasons for admission. With the exception of coronary artery bypass grafting, hospitals that provided the highest-value care had more beds and a larger average daily census than those providing the lowest-value care. Transparent data like those we present can empower patients to compare hospital performance, make better-informed treatment decisions, and decide where to obtain care for particular health care problems. CONTEXT: In the United States, the transition from volume to value dominates discussions of health care reform. While shared decision making might help patients determine whether to get care, transparency in procedure- and hospital-specific value measures would help them determine where to get care. METHODS: Using Hospital Compare and Medicare expenditure data, we constructed a hospital-level measure of value from a numerator composed of quality-of-care measures (satisfaction, use of timely and effective care, and avoidance of harms) and a denominator composed of risk-adjusted 30-day episode-of-care expenditures for acute myocardial infarction (1,900 hospitals), coronary artery bypass grafting (884 hospitals), colectomy (1,252 hospitals), and hip replacement surgery (1,243 hospitals). FINDINGS: We found substantial variation in aggregate measures of quality, cost, and value at the hospital level. Value calculation provided additional richness when compared to assessment based on quality or cost alone: about 50% of hospitals in an extreme quality- (and about 65% more in an extreme cost-) quintile were in the same extreme value quintile. With the exception of coronary artery bypass grafting, higher-value hospitals were larger and had a higher average daily census than lower-value hospitals, but were no more likely to be accredited by the Joint Commission or to have a residency program accredited by the American Council of Graduate Medical Education. CONCLUSIONS: While future efforts to compose value measures will certainly be modified and expanded to examine other reasons for admission, the construct that we present could allow patients to transparently compare procedure- and hospital-specific quality, spending, and value and empower them to decide where to obtain care.

The SPORT value compass: do the extra costs of undergoing spine surgery produce better health benefits?

BACKGROUND: The Spine Patient Outcomes Research Trial aimed to determine the comparative effectiveness of surgical care versus nonoperative care by measuring longitudinal values: outcomes, satisfaction, and costs. METHODS: This paper aims to summarize available evidence from the Spine Patient Outcomes Research Trial by addressing 2 important questions about outcomes and costs for 3 types of spine problem: (1) how do outcomes and costs of spine patients differ depending on whether they are treated surgically compared with nonoperative care? (2) What is the incremental cost per quality adjusted life year for surgical care over nonoperative care? RESULTS: After 4 years of follow-up, patients with 3 spine conditions that may be treated surgically or nonoperatively have systematic differences in value endpoints. The average surgical patient enjoys better health outcomes and higher treatment satisfaction but incurs higher costs. CONCLUSIONS: Spine care is preference sensitive and because outcomes, satisfaction, and costs vary over time and between patients, data on value can help patients make better-informed decisions and help payers know what their dollars are buying.

The unintended consequences of bundled payments.

Consensus is building that episode-based bundled payments can produce substantial Medicare savings, and the Center for Medicare & Medicaid Innovation's Bundled Payment Initiative endorses this concept. The program generates potential cost savings by reducing the historic cost of time-defined episodes of care, provided through a discount. Although bundled payments can reduce waste primarily in the postacute care setting, concerns arise that, in an effort to maintain income levels that are necessary to cover fixed costs, providers may change their behaviors to increase the volume of episodes. Such actions would mitigate the savings that Medicare might have accrued and may perpetuate the fee-for-service payment mechanism, with episodes of care becoming the new service. Although bundled payments have some advantages over the current reimbursement system, true cost-savings to Medicare will be realized only when the federal government addresses the use issue that underlies much of the waste inherent in the system and provides ample incentives to eliminate capacity and move toward capitation.

Rural-urban disparities in health outcomes, clinical care, health behaviors, and social determinants of health and an action-oriented, dynamic tool for visualizing them.

While rural-urban disparities in health and health outcomes have been demonstrated, because of their impact on (and intervenability to improve) health and health outcomes, we sought to examine cross-sectional and longitudinal inequities in health, clinical care, health behaviors, and social determinants of health (SDOH) between rural and non-rural counties in the pre-pandemic era (2015 to 2019), and to present a Health Equity Dashboard that can be used by policymakers and researchers to facilitate examining such disparities. Therefore, using data obtained from 2015-2022 County Health Rankings datasets, we used analysis of variance to examine differences in 33 county level attributes between rural and non-rural counties, calculated the change in values for each measure between 2015 and 2019, determined whether rural-urban disparities had widened, and used those data to create a Health Equity Dashboard that displays county-level individual measures or compilations of them. We followed STROBE guidelines in writing the manuscript. We found that rural counties overwhelmingly had worse measures of SDOH at the county level. With few exceptions, the measures we examined were getting worse between 2015 and 2019 in all counties, relatively more so in rural counties, resulting in the widening of rural-urban disparities in these measures. When rural-urban gaps narrowed, it tended to be in measures wherein rural counties were outperforming urban ones in the earlier period. In conclusion, our findings highlight the need for policymakers to prioritize rural settings for interventions designed to improve health outcomes, likely through improving health behaviors, clinical care, social and environmental factors, and physical environment attributes. Visualization tools can help guide policymakers and researchers with grounded information, communicate necessary data to engage relevant stakeholders, and track SDOH changes and health outcomes over time.