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End-of-life (EOL) costs constitute a substantial portion of healthcare spending in the USA and have been increasing. ACOs may offer an opportunity to improve quality and curtail EOL spending.To examine whether practices that became ACOs altered spending and utilization at the EOL.Retrospective analysis of Medicare claims.We assigned patients who died in 2012 and 2015 to an ACO or non-ACO practice. Practices that converted to ACOs in 2013 or 2014 were matched to non-ACOs in the same region. A total of 23,643 ACO patients were matched to 23,643 non-ACO patients.Using a difference-in-differences model, we examined changes in EOL spending and care utilization after ACO implementation.The introduction of ACOs did not significantly impact overall spending for patients in the last 6 months of life (difference-in-difference (DID) = $192, 95%CI -$841 to $1125, P = 0.72). Changes in spending did not differ between ACO and non-ACO patients across spending categories (inpatient, outpatient, physician services, skilled nursing, home health, hospice). No differences were seen between ACO and non-ACO patients in rates of ED visits, inpatient admissions, ICU admission, mean healthy days at home, and mean hospice days at 180 and 30 days prior to death. However, non-ACO patients had a significantly greater increase in hospice utilization compared to ACO patients at 180 days (DID P-value = 0.02) and 30 days (DID P-value = 0.01) prior to death.With the exception of hospice care utilization, spending and utilization were not different between ACOs and non-ACO patients at the EOL. Longer follow-up may be necessary to evaluate the impact of ACOs on EOL spending and care.

Background: After the Centers for Medicare and Medicaid Services modified reimbursement rates for outpatient peripheral vascular intervention in 2008 with the intent of improving access to care, providers began to increasingly perform peripheral vascular interventions in privately owned office-based clinics. Little is known about the characteristics of patients treated in this setting and their long-term outcomes as compared with those treated in hospital-based centers. Methods: In this retrospective cohort study, Medicare beneficiaries ≥66 years undergoing outpatient femoropopliteal peripheral vascular interventions in office-based clinics and hospital-based centers from 2015 to 2017 were identified. Sociodemographics, comorbidities, and institutional characteristics were compared across sites. Multivariable Cox proportional hazards models were used to estimate the adjusted associations between practice site location and outcomes. The primary outcome was the composite of major amputation or death analyzed through the end of follow-up. Results: Among 134 869 patients, 29.9% were treated in office-based clinics and 70.1% in hospital-based centers. Patients treated in office-based clinics were more often Black (16.9% versus 11.9%), dually enrolled in Medicaid (26.3% versus 19.6%), and residents of lower-resourced regions (32.6% versus 25.6%). Over a median follow-up time of 800 days (interquartile range, 531–1119 days), patients treated in office-based clinics had reduced risks of major amputation or death compared with outpatients treated in hospital-based centers (hazard ratio, 0.92 [95% CI, 0.89–0.95]). They also had lower adjusted all-cause mortality (hazard ratio, 0.93 [95% CI, 0.90–0.96]), major lower extremity amputation (hazard ratio, 0.84 [95% CI, 0.79–0.89]), and all-cause hospitalization (hazard ratio, 0.86 [95% CI, 0.84–0.88]). These findings persisted after stratification by critical limb ischemia, race, dual enrollment, and regional socioeconomic status, as well as among operators treating patients in both clinical settings. Conclusions: In this large nationwide analysis of Medicare beneficiaries, office-based clinics treated a more socioeconomically disadvantaged population compared with hospital-based centers. Long-term outcomes were comparable between locations. As such, these clinics appear to be selecting lower-risk patients for outpatient peripheral vascular interventions, although there remains the possibility of unmeasured confounding.

Background: The impact of the COVID-19 pandemic on participation in and availability of cardiac rehabilitation (CR) is unknown. Methods: Among eligible Medicare fee-for-service beneficiaries, we evaluated, by month, the number of CR sessions attended per 100 000 beneficiaries, individuals eligible to initiate CR, and centers offering in-person CR between January 2019 and December 2021. We compared these outcomes between 2 periods: December 1, 2019 through February 28, 2020 (period 1, before declaration of the pandemic-related national emergency) and October 1, 2021 through December 31, 2021 (period 2, the latest period for which data are currently available). Results: In period 1, Medicare beneficiaries participated in (mean±SD) 895±84 CR sessions per 100 000 beneficiaries each month. After the national emergency was declared, CR participation sharply declined to 56 CR sessions per 100 000 beneficiaries in April 2020. CR participation recovered gradually through December 2021 but remained lower than prepandemic levels (period 2: 698±29 CR sessions per month per 100 000 beneficiaries, P =0.02). Declines in CR participation were most marked among dual Medicare and Medicaid enrollees and patients residing in rural areas or socially vulnerable communities. There was no statistically significant change in CR eligibility between the 2 periods. Compared with 2618±5 CR centers in period 1, there were 2464±7 in period 2 ( P Conclusions: The COVID-19 pandemic was associated with a persistent decline in CR participation and the closure of CR centers, which disproportionately affected rural and low-income patients and the most socially vulnerable communities. Innovation in CR financing and delivery is urgently needed to equitably enhance CR participation among Medicare beneficiaries.

BACKGROUND The long-term impact of affordable care organizations (ACOs) on cancer spending remains unknown. The authors examined whether practices that became ACOs altered their spending for patients with cancer in the first 4 years after ACO implementation. METHODS By using national Medicare data from 2011 to 2017, a random sample of 20% of fee-for-service Medicare beneficiaries aged 65 years and older with cancer was obtained (n = 866,532), and each patient was assigned to a practice. Practices that became ACOs in the Medicare Shared Savings Program were matched to non-ACO practices. Total, cancer-specific, and service category-specific yearly spending per patient was calculated. A difference-in-differences model was used to determine spending changes associated with ACO status for patients with cancer in the 4 years after ACO implementation. RESULTS The introduction of ACOs did not have a significant impact on overall spending for patients with cancer in the 2 years after ACO implementation (difference, -$38; 95% CI, -$268, $191; P = .74). Changes in spending also did not differ between ACO and non-ACO patients within service categories or among the 11 cancer types examined. The lack of difference in spending for patients with cancer in ACO and non-ACO practices persisted in the third and fourth years after ACO implementation (difference, -$120; 95% CI, -$284, $525; P = .56). CONCLUSIONS ACOs did not significantly change spending for patients with cancer in the first 4 years after their implementation compared with non-ACOs. This prompts a reevaluation of the current efficacy of ACOs in reducing spending for cancer care and may encourage policymakers to reconsider the incentive structures of ACOs. LAY SUMMARY Accountable care organizations (ACOs) were developed to curtail health care spending and improve quality, but their effects on cancer spending in their first 2 years have been minimal. The long-term impact of ACOs on cancer spending remains unknown. By using data from 866,532 Medicare beneficiaries with cancer, the authors observed that the association of a practice with an ACO did not significantly change total yearly spending per patient in the first 4 years after ACO implementation. This finding prompts a reevaluation of the current efficacy of ACOs in reducing spending for cancer care.

Objective To identify and explore differences in spending and utilization of key health services at the end of life among hip fracture patients across seven developed countries. Data Sources Individual-level claims data from the inpatient and outpatient health care sectors compiled by the International Collaborative on Costs, Outcomes, and Needs in Care (ICCONIC). Study Design We retrospectively analyzed utilization and spending from acute hospital care, emergency department, outpatient primary care and specialty physician visits, and outpatient drugs. Patterns of spending and utilization were compared in the last 30, 90, and 180 days across Australia, Canada, England, Germany, New Zealand, Spain, and the United States. We employed linear regression models to measure age- and sex-specific effects within and across countries. In addition, we analyzed hospital-centricity, that is, the days spent in hospital and site of death. Data Collection/Extraction Methods We identified patients who sustained a hip fracture in 2016 and died within 12 months from date of admission. Principal Findings Resource use, costs, and the proportion of deaths in hospital showed large variability being high in England and Spain, while low in New Zealand. Days in hospital significantly decreased with increasing age in Canada, Germany, Spain, and the United States. Hospital spending near date of death was significantly lower for women in Canada, Germany, and the United States. The age gradient and the sex effect were less pronounced in utilization and spending of emergency care, outpatient care, and drugs. Conclusions Across seven countries, we find important variations in end-of-life care for patients who sustained a hip fracture, with some differences explained by sex and age. Our work sheds important insights that may help ongoing health policy discussions on equity, efficiency, and reimbursement in health care systems.

Supplemental Digital Content is available in the text., Background: Cardiovascular deaths increased during the early phase of the COVID-19 pandemic in the United States. However, it is unclear whether diverse racial/ethnic populations have experienced a disproportionate rise in heart disease and cerebrovascular disease deaths. Methods: We used the National Center for Health Statistics to identify heart disease and cerebrovascular disease deaths for non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and Hispanic individuals from March to August 2020 (pandemic period), as well as for the corresponding months in 2019 (historical control). We determined the age- and sex-standardized deaths per million by race/ethnicity for each year. We then fit a modified Poisson model with robust SEs to compare change in deaths by race/ethnicity for each condition in 2020 versus 2019. Results: There were a total of 339 076 heart disease and 76 767 cerebrovascular disease deaths from March through August 2020, compared with 321 218 and 72 190 deaths during the same months in 2019. Heart disease deaths increased during the pandemic in 2020, compared with the corresponding period in 2019, for non-Hispanic White (age–sex standardized deaths per million, 1234.2 versus 1208.7; risk ratio for death [RR], 1.02 [95% CI, 1.02–1.03]), non-Hispanic Black (1783.7 versus 1503.8; RR, 1.19 [95% CI, 1.17–1.20]), non-Hispanic Asian (685.7 versus 577.4; RR, 1.19 [95% CI, 1.15–1.22]), and Hispanic (968.5 versus 820.4; RR, 1.18 [95% CI, 1.16–1.20]) populations. Cerebrovascular disease deaths also increased for non-Hispanic White (268.7 versus 258.2; RR, 1.04 [95% CI, 1.03–1.05]), non-Hispanic Black (430.7 versus 379.7; RR, 1.13 [95% CI, 1.10–1.17]), non-Hispanic Asian (236.5 versus 207.4; RR, 1.15 [95% CI, 1.09–1.21]), and Hispanic (264.4 versus 235.9; RR, 1.12 [95% CI, 1.08–1.16]) populations. For both heart disease and cerebrovascular disease deaths, Black, Asian, and Hispanic populations experienced a larger relative increase in deaths than the non-Hispanic White population (interaction term, P

ImportanceLittle is known about changes in obstetric outcomes during the COVID-19 pandemic.ObjectiveTo assess whether obstetric outcomes and pregnancy-related complications changed during the COVID-19 pandemic.Design, Setting, and ParticipantsThis retrospective cohort study included pregnant patients receiving care at 463 US hospitals whose information appeared in the PINC AI Healthcare Database. The relative differences in birth outcomes, pregnancy-related complications, and length of stay (LOS) during the pandemic period (March 1, 2020, to April 30, 2021) were compared with the prepandemic period (January 1, 2019, to February 28, 2020) using logistic and Poisson models, adjusting for patients’ characteristics, and comorbidities and with month and hospital fixed effects.ExposuresCOVID-19 pandemic period.Main Outcomes and MeasuresThe 3 primary outcomes were the relative change in preterm vs term births, mortality outcomes, and mode of delivery. Secondary outcomes included the relative change in pregnancy-related complications and LOS.ResultsThere were 849 544 and 805 324 pregnant patients in the prepandemic and COVID-19 pandemic periods, respectively, and there were no significant differences in patient characteristics between periods, including age (≥35 years: 153 606 [18.1%] vs 148 274 [18.4%]), race and ethnicity (eg, Hispanic patients: 145 475 [17.1%] vs 143 905 [17.9%]; White patients: 456 014 [53.7%] vs 433 668 [53.9%]), insurance type (Medicaid: 366 233 [43.1%] vs 346 331 [43.0%]), and comorbidities (all standardized mean differences Conclusions and RelevanceDuring the COVID-19 pandemic, there were increased odds of maternal death during delivery hospitalization, cardiovascular disorders, and obstetric hemorrhage. Further efforts are needed to ensure risks potentially associated with the COVID-19 pandemic do not persist beyond the current state of the pandemic.

One of the most pressing challenges facing most health care systems is rising costs. As the population ages and the demand for health care services grows, there is a growing need to understand the drivers of these costs across systems. This paper attempts to address this gap by examining utilization and spending of the course of a year for two specific high-need high-cost patient types: a frail older person with a hip fracture and an older person with congestive heart failure and diabetes. Data on utilization and expenditure is collected across five health care settings (hospital, post-acute rehabilitation, primary care, outpatient specialty and drugs), in six countries (Canada (Ontario), France, Germany, Spain (Aragon), Sweden and the United States (fee for service Medicare) and used to construct treatment episode Purchasing Power Parities (PPPs) that compare prices using baskets of goods from the different care settings. The treatment episode PPPs suggest other countries have more similar volumes of care to the US as compared to other standardization approaches, suggesting that US prices account for more of the differential in US health care expenditures. The US also differs with regards to the share of expenditures across care settings, with post-acute rehab and outpatient speciality expenditures accounting for a larger share of the total relative to comparators.

ImportancePayers are increasingly using approaches to risk adjustment that incorporate community-level measures of social risk with the goal of better aligning value-based payment models with improvements in health equity.ObjectiveTo examine the association between community-level social risk and health care spending and explore how incorporating community-level social risk influences risk adjustment for Medicare beneficiaries.Design, Setting, and ParticipantsUsing data from a Medicare Advantage plan linked with survey data on self-reported social needs, this cross-sectional study estimated health care spending health care spending was estimated as a function of demographics and clinical characteristics, with and without the inclusion of Area Deprivation Index (ADI), a measure of community-level social risk. The study period was January to December 2019. All analyses were conducted from December 2021 to August 2022.ExposuresCensus block group–level ADI.Main Outcomes and MeasuresRegression models estimated total health care spending in 2019 and approximated different approaches to social risk adjustment. Model performance was assessed with overall model calibration (adjusted R2) and predictive accuracy (ratio of predicted to actual spending) for subgroups of potentially vulnerable beneficiaries.ResultsAmong a final study population of 61 469 beneficiaries (mean [SD] age, 70.7 [8.9] years; 35 801 [58.2%] female; 48 514 [78.9%] White; 6680 [10.9%] with Medicare-Medicaid dual eligibility; median [IQR] ADI, 61 [42-79]), ADI was weakly correlated with self-reported social needs (r = 0.16) and explained only 0.02% of the observed variation in spending. Conditional on demographic and clinical characteristics, every percentile increase in the ADI (ie, more disadvantage) was associated with a $11.08 decrease in annual spending. Directly incorporating ADI into a risk-adjustment model that used demographics and clinical characteristics did not meaningfully improve model calibration (adjusted R2 = 7.90% vs 7.93%) and did not significantly reduce payment inequities for rural beneficiaries and those with a high burden of self-reported social needs. A postestimation adjustment of predicted spending for dual-eligible beneficiaries residing in high ADI areas also did not significantly reduce payment inequities for rural beneficiaries or beneficiaries with self-reported social needs.Conclusions and RelevanceIn this cross-sectional study of Medicare beneficiaries, the ADI explained little variation in health care spending, was negatively correlated with spending conditional on demographic and clinical characteristics, and was poorly correlated with self-reported social risk factors. This prompts caution and nuance when using community-level measures of social risk such as the ADI for social risk adjustment within Medicare value-based payment programs.

There is growing concern about the health of older US adults who live in rural areas, but little is known about how mortality has changed over time for low-income Medicare beneficiaries residing in...

Massachusetts has one of the highest cumulative incidence rates of coronavirus disease 2019 (COVID-19) cases in the US. Understanding which specific demographic, economic, and occupational factors have contributed to disparities in COVID-19 incidence rates across the state is critical to informing public health strategies. We performed a cross-sectional study of 351 Massachusetts cities and towns from January 1 to May 6, 2020, and found that a 10-percentage-point increase in the Black non-Latino population was associated with an increase of 312.3 COVID-19 cases per 100,000 population, whereas a 10-percentage-point increase in the Latino population was associated with an increase of 258.2 cases per 100,000. Independent predictors of higher COVID-19 rates included the proportion of foreign-born noncitizens living in a community, mean household size, and share of food service workers. After adjustment for these variables, the association between the Latino population and COVID-19 rates was attenuated. In contrast, the association between the Black population and COVID-19 rates persisted but may be explained by other systemic inequities. Public health and policy efforts that improve care for foreign-born noncitizens, address crowded housing, and protect food service workers may help mitigate the spread of COVID-19 among minority communities.

Rapid growth of Medicare Advantage (MA) plans has the potential to change clinical practice for both MA and fee-for-service (FFS) beneficiaries, particularly for high-need, high-cost beneficiaries with multiple chronic conditions or a costly single condition. We assessed whether MA growth from 2010 to 2017 spilled over to county-level per capita spending, emergency department visits, and readmission rates among FFS beneficiaries, and how much this varied by the comorbidity burden of the beneficiary. We also examined whether the association between MA growth and per capita spending in FFS varied in beneficiaries with specific chronic conditions. MA growth was associated with decreased FFS spending and emergency department visits only among beneficiaries with six or more chronic conditions. MA growth was associated with decreased FFS spending among beneficiaries with 11 of the 20 chronic conditions. This suggests that MA growth may drive improvements in efficiency of health care delivery for high-need, high-cost beneficiaries.

Racial disparities in hospitalization rates for ambulatory care-sensitive conditions are concerning and may signal differential access to high-quality ambulatory care. Whether racial disparities are improving as a result of better ambulatory care versus artificially narrowing because of increased use of observation status is unclear. Using Medicare data for 2011-15, we sought to determine whether black-white disparities in avoidable hospitalizations were improving and evaluated the degree to which changes in observations for ambulatory care-sensitive conditions may be contributing to changes in these gaps. We found that while the racial gap in avoidable hospitalizations due to such conditions has decreased, that seems to be explained by a concomitant increase in the gap of avoidable observation stays. This suggests that changes from inpatient admissions to observation status seem to be driving the reduction in racial disparities in avoidable hospitalizations, rather than changes in the ambulatory setting.

ImportanceMedicare Advantage plans have strong incentives to reduce potentially wasteful health care, including costly acute care visits for ambulatory care−sensitive conditions (ACSCs). However, it remains unknown whether Medicare Advantage plans lower acute care use compared with traditional Medicare, or if it shifts patients from hospitalization to observation stays and emergency department (ED) direct discharges.ObjectiveTo determine whether Medicare Advantage is associated with differential utilization of hospitalizations, observations, and ED direct discharges for ACSCs compared with traditional Medicare.Design, Setting, and ParticipantsCross-sectional study of US Medicare Advantage vs traditional Medicare beneficiaries from January 1 to December 31, 2018. Poisson regression models were used to compare risk-adjusted rates of Medicare Advantage vs traditional Medicare, controlling for patient demographic characteristics and clinical risk and including county fixed-effects. Data were analyzed between April 2021 and November 2022.Main Outcomes and MeasuresHospitalizations, observation stays, and ED direct discharges for ACSCs.ResultsThe study sample comprised 2 665 340 Medicare Advantage patients (mean [SD] age, 72.7 [9.8] years; 1 504 519 [56.4%] women; 1 859 067 [69.7%] White individuals) and 7 981 547 traditional Medicare patients (mean [SD] age, 71.2 [11.8] years; 4 232 201 [53.0%] women; 6 176 239 [77.4%] White individuals). Medicare Advantage patients had lower risk of hospitalization for ACSCs compared with traditional Medicare patients (relative risk [RR], 0.94; 95% CI, 0.93-0.95), primarily owing to fewer hospitalizations for acute conditions (eg, pneumonia). Medicare Advantage patients had a higher risk of ED direct discharges (RR, 1.44; 95% CI, 1.43-1.45) and observation stays (RR, 2.38; 95% CI, 2.34-2.41) for ACSCs vs traditional Medicare patients. Overall, Medicare Advantage patients were at higher risk of needing care for an ACSC (hospitalization, ED direct discharge, or observation stay) than traditional Medicare patients (RR, 1.30; 95% CI, 1.30-1.31). Within the Medicare Advantage population, patients in health maintenance organizations (HMOs) were at lower risk of ACSC-related hospitalization compared with patients in its preferred provider organizations (RR, 0.96; 95% CI, 0.95-0.98); however, those in the HMOs had a higher risk of ED direct discharge (RR, 1.08; 95% CI, 1.07-1.09) and observation stay (overall RR, 1.10; 95% CI, 1.02-1.12).Conclusions and RelevanceThe findings of this cross-sectional study of Medicare Advantage and traditional Medicare patients with ACSCs indicate that apparent gains in lowering rates of potentially avoidable acute care have been associated with shifting inpatient care to settings such as ED direct discharges and observation stays.

ImportanceStudies suggest that academic medical centers (AMCs) have better outcomes than nonteaching hospitals. However, whether AMCs have spillover benefits for patients treated at neighboring community hospitals is unknown.ObjectiveTo examine whether market-level AMC presence is associated with outcomes for patients treated at nonteaching hospitals within the same markets.Design, Setting, and ParticipantsThis retrospective, population-based cohort study assessed traditional Medicare beneficiaries aged 65 years and older discharged from US acute care hospitals between 2015 and 2017 (100% sample). Data were analyzed from August 2021 to December 2022.ExposuresThe primary exposure was market-level AMC presence. Health care markets (ie, hospital referral regions) were categorized by AMC presence (percentage of hospitalizations at AMCs) as follows: no presence (0%), low presence (>0% to 20%), moderate presence (>20% to 35%), and high presence (>35%).Main Outcomes and MeasuresThe primary outcomes were 30-day and 90-day mortality and healthy days at home (HDAH), a composite outcome reflecting mortality and time spent in facility-based health care settings.ResultsThere were 22 509 824 total hospitalizations, with 18 865 229 (83.8%) at non-AMCs. The median (IQR) age of patients was 78 (71-85) years, and 12 568 230 hospitalizations (55.8%) were among women. Of 306 hospital referral regions, 191 (62.4%) had no AMCs, 61 (19.9%) had 1 AMC, and 55 (17.6%) had 2 or more AMCs. Markets characteristics differed significantly by category of AMC presence, including mean population, median income, proportion of White residents, and physicians per population. Compared with markets with no AMC presence, receiving care at a non-AMC in a market with greater AMC presence was associated with lower 30-day mortality (9.5% vs 10.1%; absolute difference, −0.7%; 95% CI, −1.0% to −0.4%; P P P = .005) and 90 days (61.08 vs 59.83 HDAH; absolute difference, 1.25 HDAH; 95% CI, 0.58 to 1.92 HDAH; P Conclusions and RelevanceAMCs may have spillover effects on outcomes for patients treated at non-AMCs, suggesting that they have a broader impact than is traditionally recognized. These associations are greatest in markets with the highest AMC presence and persist to 90 days.

ObjectiveTo compare within-country variation of health care utilization and spending of patients with chronic heart failure (CHF) and diabetes across countries.Data sourcesPatient-level linked data sources compiled by the International Collaborative on Costs, Outcomes, and Needs in Care across nine countries: Australia, Canada, England, France, Germany, New Zealand, Spain, Switzerland, and the United States.Data collection methodsPatients were identified in routine hospital data with a primary diagnosis of CHF and a secondary diagnosis of diabetes in 2015/2016.Study designWe calculated the care consumption of patients after a hospital admission over a year across the care pathway-ranging from primary care to home health nursing care. To compare the distribution of care consumption in each country, we use Gini coefficients, Lorenz curves, and female-male ratios for eight utilization and spending measures.Principal findingsIn all countries, rehabilitation and home nursing care were highly concentrated in the top decile of patients, while the number of drug prescriptions were more uniformly distributed. On average, the Gini coefficient for drug consumption is about 0.30 (95% confidence interval (CI): 0.27-0.36), while it is, 0.50 (0.45-0.56) for primary care visits, and more than 0.75 (0.81-0.92) for rehabilitation use and nurse visits at home (0.78; 0.62-0.9). Variations in spending were more pronounced than in utilization. Compared to men, women spend more days at initial hospital admission (+5%, 1.01-1.06), have a higher number of prescriptions (+7%, 1.05-1.09), and substantially more rehabilitation and home care (+20% to 35%, 0.79-1.6, 0.99-1.64), but have fewer visits to specialists (-10%; 0.84-0.97).ConclusionsDistribution of health care consumption in different settings varies within countries, but there are also some common treatment patterns across all countries. Clinicians and policy makers need to look into these differences in care utilization by sex and care setting to determine whether they are justified or indicate suboptimal care.

Among a group of primary care accountable care organizations, patients with hypertension were 50% less likely to have a blood pressure recorded in April compared with February.

Introduction: Variation exists in outcomes following out-of-hospital cardiac arrest (OHCA), but whether racial/ethnic disparities exist in post-arrest provision of therapeutic hypothermia (TH) is unknown. Hypothesis: Racial/ethnic disparities exist in the utilization of guideline-recommended TH following OHCA. Methods: We performed a retrospective analysis of a cohort of 96,695 patients who survived to hospital admission following OHCA from the Cardiac Arrest Registry to Enhance Survival, whose catchment area represents ~40% of the United States, from 2013 through 2019. Our primary exposure was race/ethnicity, and the primary outcome was utilization of TH. We performed a secondary analysis to assess for racial/ethnic disparities in the reasons why TH was not used (supplemental data element data available since 2016). Results: Among 96,695 patients [mean (SD) age 61.4 (16.3) years, 24.6% Black, 8.0% Hispanic/Latino, 63.4% White] that survived to hospital admission following OHCA, 54,687 (56.6%) did not receive TH. Using a mixed-effects model that adjusted for patient, arrest, neighborhood, and hospital factors with state of arrest modeled as a random intercept to account for clustering, we found that Hispanics/Latinos were less likely to receive TH than Whites (Odds Ratio [OR] 0.79, 95 % Confidence Interval [CI] 0.75-0.83). When the clustering variable was changed from the state of arrest to the admitting hospital, Hispanics/Latinos were more likely to receive TH (OR 1.07, 95% CI 1.00 to 1.14). In the 22,896 patients with data regarding why they did not receive TH, a higher percentage of Hispanics/Latinos compared to Blacks and Whites did not receive TH due to lack of a TH program at the hospital (4.0% vs. 2.5 % vs 1.8%, p < .001). No disparity in TH utilization was found for Black patients. Conclusion: We found disparities in access to TH for Hispanics/Latinos following OHCA. Reassuringly, we did not find any disparity in TH utilization for Black patients.

Background Variation exists in outcomes following out‐of‐hospital cardiac arrest (OHCA), but whether racial and ethnic disparities exist in post‐arrest provision of targeted temperature management (TTM) is unknown. Methods and Results We performed a retrospective analysis of a prospectively collected cohort of patients who survived to admission following OHCA from the Cardiac Arrest Registry to Enhance Survival, whose catchment area represents ~50% of the United States from 2013‐2019. Our primary exposure was race/ethnicity and primary outcome was utilization of TTM. We built a mixed‐effects model with both state of arrest and admitting hospital modeled as random intercepts to account for clustering. Among 96,695 patients (24.6% Black, 8.0% Hispanic/Latino, 63.4% White), a smaller percentage of Hispanic/Latino patients received TTM than Black or White patients (37.5% vs. 45.0 % vs 43.3%, P < .001) following OHCA. In the mixed‐effects model, Black patients (Odds Ratio [OR] 1.153, 95% Confidence Interval [CI] 1.102‐1.207, P < .001) and Hispanic/Latino patients (OR 1.086, 95% CI 1.017‐1.159, P < .001) were slightly more likely to receive TTM compared to White patients, perhaps due to worse admission neurological status. We did find community level disparity as Hispanic/Latino‐serving hospitals (defined as the top decile of hospitals that cared for the highest proportion of Hispanic/Latino patients) provided less TTM (OR 0.587, 95% CI 0.474 to 0.742, P < .001). Conclusions Reassuringly, we did not find evidence of intrahospital or interpersonal racial or ethnic disparity in the provision of TTM. However, we did find inter‐hospital, community level disparity. Hispanic/Latino‐serving hospitals provided less guideline‐recommended TTM after OHCA.

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): internally funded by Diagnostic Robotics Inc.

IMPORTANCE: Despite the growing number of physicians who reduce clinical time owing to research, administrative work, and family responsibilities, the quality of care provided by these physicians remains unclear. OBJECTIVE: To examine the association between the number of days worked clinically per year by physicians and patient mortality. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional analysis was completed on a 20% random sample of Medicare fee-for-service beneficiaries 65 years and older who were admitted to the hospital with an emergency medical condition and treated by a hospitalist in 2011 through 2016. Because hospitalists typically work in shifts, hospitalists’ patients are plausibly quasirandomized to hospitalists based on the hospitalists’ work schedules (natural experiment). The associations between hospitalists’ number of days worked clinically per year and 30-day patient mortality and readmission rates were examined, adjusting for patient and physician characteristics and hospital fixed effects (effectively comparing physicians within the same hospital). Data analysis was conducted from July 1, 2020, to July 2, 2021. EXPOSURES: Physicians’ number of days worked clinically per year. MAIN OUTCOMES AND MEASURES: The primary outcome was 30-day patient mortality, and the secondary outcome was 30-day patient readmission. RESULTS: Among 392 797 hospitalizations of patients treated by 19 170 hospitalists (7482 female [39.0%], 11 688 male [61.0%]; mean [SD] age, 41.1 [8.8] years), patients treated by physicians with more days worked clinically exhibited lower mortality. Adjusted 30-day mortality rates were 10.5% (reference), 10.0% (adjusted risk difference [aRD], −0.5%; 95% CI, −0.8% to −0.2%; P = .002), 9.5% (aRD, −0.9%; 95% CI, −1.2% to −0.6%; P

While the impact of the COVID-19 recession on the economy is clear, there is limited evidence on how the COVID-19 pandemic-related job losses among low-income people may have affected their access to health care.To determine the association of job loss during the pandemic with insurance coverage and access to and affordability of health care among low-income adults.Using a random digit dialing telephone survey from October 2020 to December 2020 of low-income adults in 4 states-Arkansas, Kentucky, Louisiana, and Texas-we conducted a series of multivariable logistic regression analyses, adjusting for demographics, chronic conditions, and state of residence.US citizens aged 19-64 with a family income less than 138% of the federal poverty line who became newly unemployed during pandemic, remained employed during pandemic, or were chronically unemployed before and during the pandemic.Rates of insurance, type of insurance coverage, measures of access to/affordability of care, and food/housing security KEY RESULTS: Of 1,794 respondents, 14.5% were newly unemployed, 49.6% were chronically unemployed, and 35.7% were employed. The newly unemployed were slightly younger and more likely Black or Latino. The newly unemployed were more likely to report uninsurance compared to the employed (+16.4 percentage points, 95% CI 6.0-26.9), and the chronically unemployed (+26.4 percentage points, 95% CI 16.2-36.6), mostly driven by Texas' populations. The newly unemployed also reported lower rates of access to care and higher rates of financial barriers to care. They were also more likely to report food and housing insecurity compared to others.In a survey of 4 Southern States during pandemic, the newly unemployed had higher rates of uninsurance and worse access to care-largely due to financial barriers-and reported more housing and food insecurity than other groups. Our study highlights the vulnerability of low-income populations who experienced a job loss, especially in Texas, which did not expand Medicaid.

This survey study uses 2020 American Hospital Association data to assess strategies of US hospitals serving vulnerable populations in addressing social needs during the COVID-19 pandemic.

ObjectiveTo determine changes in industry marketing payments to physicians due to the covid-19 pandemic.DesignQuasi experimental, difference-in-difference study.Data sourceUS nationwide database of licensed physicians, the National Plan and Provider Enumeration System, which was linked to a database of industry marketing payments made to physicians, Open Payments.PopulationAll licensed US physicians from 2018 to 2020 and those who received payments from industry.Main outcome measuresChanges in the value and the number of monthly industry payments physician received before (January-February 2020) and during the pandemic (April-December 2020) were assessed, adjusting for physicians’ characteristics (gender and specialty). As the control, data for the same months in 2019 were used. Industry payments by type of payments (eg, meals, travel, consulting fees, speaker compensation, honorariums), were also examined.ResultsAmong 880 589 US physicians included in this study, 267 463 (30.4%) physicians received a total of 4 117 482 non-research payments with $626 million ($710 per physician; £610; €708) in 2020 (40-44% decrease from $1047m in 2018 and $1115m in 2019). Industry payments decreased significantly in the months of the covid-19 pandemic (adjusted change in the value of −48.4%; 95% confidence interval −50.6 to −46.2; PConclusionsIndustry payments to physicians, particularly those involving physical interactions such as meals and travel, substantially decreased during the pandemic. How such changes affect prescription practices and the quality of clinical practice in the long term should be investigated.

Item does not contain fulltext OBJECTIVE: The objectives of this study are to compare the relative use of different postacute care settings in different countries and to compare three important outcomes as follows: total expenditure, total days of care in different care settings, and overall longevity over a 1-year period following a hip fracture. DATA SOURCES: We used administrative data from hospitals, institutional and home-based long-term care (LTC), physician visits, and medications compiled by the International Collaborative on Costs, Outcomes, and Needs in Care (ICCONIC) from five countries as follows: Canada, France, Germany, the Netherlands, and Sweden. DATA EXTRACTION METHODS: Data were extracted from existing administrative data systems in each participating country. STUDY DESIGN: This is a retrospective cohort study of all individuals admitted to acute care for hip fracture. Descriptive comparisons were used to examine aggregate institutional and home-based postacute care. Care trajectories were created to track sequential care settings after acute-care discharge through institutional and community-based care in three countries where detailed information allowed. Comparisons in patient characteristics, utilization, and costs were made across these trajectories and countries. PRINCIPAL FINDINGS: Across five countries with complete LTC data, we found notable variations with Germany having the highest days of home-based services with relatively low costs, while Sweden incurred the highest overall expenditures. Comparisons of trajectories found that France had the highest use of inpatient rehabilitation. Germany was most likely to discharge hip fracture patients to home. Over 365 days, France averaged the highest number of days in institution with 104, Canada followed at 94, and Germany had just 87 days of institutional care on average. CONCLUSION: In this comparison of LTC services following a hip fracture, we found international differences in total use of institutional and noninstitutional care, longevity, and total expenditures. There exist opportunities to organize postacute care differently to maximize independence and mitigate costs.

Contains fulltext : 245434.pdf (Publisher’s version ) (Open Access) OBJECTIVE: This study explores differences in spending and utilization of health care services for an older person with frailty before and after a hip fracture. DATA SOURCES: We used individual-level patient data from five care settings. STUDY DESIGN: We compared utilization and spending of an older person aged older than 65 years for 365 days before and after a hip fracture across 11 countries and five domains of care as follows: acute hospital care, primary care, outpatient specialty care, post-acute rehabilitative care, and outpatient drugs. Utilization and spending were age and sex standardized.. DATA COLLECTION/EXTRACTION METHODS: The data were compiled by the International Collaborative on Costs, Outcomes, and Needs in Care (ICCONIC) across 11 countries as follows: Australia, Canada, England, France, Germany, the Netherlands, New Zealand, Spain, Sweden, Switzerland, and the United States. PRINCIPAL FINDINGS: The sample ranged from 1859 patients in Spain to 42,849 in France. Mean age ranged from 81.2 in Switzerland to 84.7 in Australia. The majority of patients across countries were female. Relative to other countries, the United States had the lowest inpatient length of stay (11.3), but the highest number of days were spent in post-acute care rehab (100.7) and, on average, had more visits to specialist providers (6.8 per year) than primary care providers (4.0 per year). Across almost all sectors, the United States spent more per person than other countries per unit ($13,622 per hospitalization, $233 per primary care visit, $386 per MD specialist visit). Patients also had high expenditures in the year prior to the hip fracture, mostly concentrated in the inpatient setting. CONCLUSION: Across 11 high-income countries, there is substantial variation in health care spending and utilization for an older person with frailty, both before and after a hip fracture. The United States is the most expensive country due to high prices and above average utilization of post-acute rehab care.

Medicare Advantage (MA) plans often establish restrictive networks of covered providers. Some policy makers have raised concerns that networks may have become excessively restrictive over time, potentially interfering with patients' access to providers. Because of data limitations, little is known about the breadth of MA networks. Taking a novel approach, we used Medicare Part D claims data for 2011-15 to examine how primary care physician networks have changed over time and what demographic and plan characteristics are associated with varying levels of network breadth. Our findings indicate that the share of MA plans with broad networks increased from 80.1 percent in 2011 to 82.5 percent in 2015. Enrollment in broad-network plans grew from 54.1 percent to 64.9 percent over the same period. In an adjusted analysis, we detected no significant time trend. In addition, narrow networks were associated with urbanicity, higher income, higher physician density, and more competition among plans. Health maintenance organizations had narrower networks than did point-of-service plans, whose networks were narrower than those of preferred provider organizations.

There is increased focus on identifying and addressing health-related social needs (HRSNs). Understanding how different HRSNs relate to different health outcomes can inform targeted, evidence-based policies, investments, and innovations to address HRSNs.To examine the association between self-reported HRSNs and acute care utilization among older adults enrolled in Medicare Advantage.This cross-sectional study used data from a large, national survey of Medicare Advantage beneficiaries to identify the presence of HRSNs. Survey data were linked to medical claims, and regression models were used to estimate the association between HRSNs and rates of acute care utilization from January 1, 2019, through December 31, 2019.Self-reported HRSNs, including food insecurity, financial strain, loneliness, unreliable transportation, utility insecurity, housing insecurity, and poor housing quality.All-cause hospital stays (inpatient admissions and observation stays), avoidable hospital stays, all-cause emergency department (ED) visits, avoidable ED visits, and 30-day readmissions.Among a final study population of 56 155 Medicare Advantage beneficiaries (mean [SD] age, 74.0 [5.8] years; 32 779 [58.4%] women; 44 278 [78.8%] White; and 7634 [13.6%] dual eligible for Medicaid), 27 676 (49.3%) reported 1 or more HRSNs. Health-related social needs were associated with statistically significantly higher rates of all utilization measures, with the largest association observed for avoidable hospital stays (incident rate ratio for any HRSN, 1.53; 95% CI, 1.35-1.74;In this cross-sectional study of older adults enrolled in Medicare Advantage, self-reported HRSNs were common and associated with statistically significantly increased rates of acute care utilization, with variation in which HRSNs were associated with different utilization measures. These findings provide evidence of the unique association between certain HRSNs and different types of acute care utilization, which could help refine the development and targeting of efforts to address HRSNs.

The COVID-19 pandemic threatens the health and well-being of older adults with multiple chronic conditions. To date, limited information exists about how Accountable Care Organizations (ACOs) are adapting to manage these patients. We surveyed 78 Medicare ACOs about their concerns for these patients during the pandemic and strategies they are employing to address them. ACOs expressed major concerns about disruptions to necessary care for this population, including the accessibility of social services and long-term care services. While certain strategies like virtual primary and specialty care visits were being used by nearly all ACOs, other services such as virtual social services, home medication delivery, and remote lab monitoring were far less commonly accessible. ACOs expressed that support for telehealth services, investment in remote monitoring capabilities, and funding for new, targeted care innovation initiatives would help them better care for vulnerable patients during this pandemic.

Purpose Health care practices across the United States have been consolidating in response to various market forces. The degree of practice consolidation varies widely across specialties but has not been well studied within radiation oncology. This study used Medicare data to characterize the extent of practice consolidation among radiation oncologists and to investigate associated market factors. Methods and Materials We utilized Medicare Provider Enrollment, Chain, and Ownership System data to assess the practice size and billing patterns of U.S. radiation oncologists in 2013 and again in 2017. Individual practices were categorized by the number of radiation oncologists practicing together: solo practices had 1 radiation oncologist, small practices 2 to 10, and large practices 11 or more. Market consolidation within each hospital referral region (HRR) across the country was quantified using the Herfindahl-Hirschman Index. Hospital and market level data were obtained for each HRR, and factors associated with the growth of radiation oncology practices over time were calculated via multivariable linear regression. Results Across the United States, radiation oncology practices appear to be highly consolidated. The mean Herfindahl-Hirschman Index was 0.4711 in 2013—indicating high levels of consolidation at baseline—and increased further to 0.4865 by 2017. Between 2013 and 2017, the number of practices with radiation oncologists in the United States decreased 3.8%, from 1679 to 1615, whereas the number of practicing radiation oncologists increased 9.4%, from 4948 to 5415. Over the study period, the number of solo practices fell 11% (from 708 in 2013 to 627 in 2017), whereas the number of large practices (those with 11 or more radiation oncologists) increased 50% (from 60 to 90). Large practices likewise grew to employ a greater share of all radiation oncologists (23.9%-32.4%) and accounted for a larger proportion of total Medicare billing (21%-26%). Two market factors were predictive for increases in the mean radiation oncology practice size. HRRs with greater hospital market consolidation and those with lower levels of baseline radiation oncology consolidation were more likely to experience higher levels of growth over the study period. Conclusions Radiation oncologists are increasingly working in larger practices. By 2017, nearly one-third of all practicing radiation oncologists in the United States were employed by just the 90 largest practices. Radiation oncology, as a field, is highly concentrated, and represents one of the most consolidated specialties across the country. The implications of practice consolidation among radiation oncologists warrants further investigation.

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions.

Contains fulltext : 245458.pdf (Publisher’s version ) (Open Access) OBJECTIVE: The objective of this study was to explore cross-country differences in spending and utilization across different domains of care for a multimorbid persona with heart failure and diabetes. DATA SOURCES: We used individual-level administrative claims or registry data from inpatient and outpatient health care sectors compiled by the International Collaborative on Costs, Outcomes, and Needs in Care (ICCONIC) across 11 countries: Australia, Canada, England, France, Germany, the Netherlands, New Zealand, Spain, Sweden, Switzerland, and the United States (US). DATA COLLECTION/EXTRACTION METHODS: Data collected by ICCONIC partners. STUDY DESIGN: We retrospectively analyzed age-sex standardized utilization and spending of an older person (65-90 years) hospitalized with a heart failure exacerbation and a secondary diagnosis of diabetes across five domains of care: hospital care, primary care, outpatient specialty care, post-acute rehabilitative care, and outpatient drugs. PRINCIPAL FINDINGS: Sample sizes ranged from n = 1270 in Spain to n = 21,803 in the United States. Mean age (standard deviation [SD]) ranged from 76.2 (5.6) in the Netherlands to 80.3 (6.8) in Sweden. We observed substantial variation in spending and utilization across care settings. On average, England spent $10,956 per person in hospital care while the United States spent $30,877. The United States had a shorter length of stay over the year (18.9 days) compared to France (32.9) and Germany (33.4). The United States spent more days in facility-based rehabilitative care than other countries. Australia spent $421 per person in primary care, while Spain (Aragon) spent $1557. The United States and Canada had proportionately more visits to specialist providers than primary care providers. Across almost all sectors, the United States spent more than other countries, suggesting higher prices per unit. CONCLUSION: Across 11 countries, there is substantial variation in health care spending and utilization for a complex multimorbid persona with heart failure and diabetes. Drivers of spending vary across countries, with the United States being the most expensive country due to high prices and higher use of facility-based rehabilitative care.