Understanding contributors to racial and ethnic inequities in COVID-19 incidence and mortality rates.

BACKGROUND: Racial inequities in Coronavirus 2019 (COVID-19) have been reported over the course of the pandemic, with Black, Hispanic/Latinx, and Native American individuals suffering higher case rates and more fatalities than their White counterparts. METHODS: We used a unique statewide dataset of confirmed COVID-19 cases across Missouri, linked with historical statewide hospital data. We examined differences by race and ethnicity in raw population-based case and mortality rates. We used patient-level regression analyses to calculate the odds of mortality based on race and ethnicity, controlling for comorbidities and other risk factors. RESULTS: As of September 10, 2020 there were 73,635 confirmed COVID-19 cases in the State of Missouri. Among the 64,526 case records (87.7% of all cases) that merged with prior demographic and health care utilization data, 12,946 (20.1%) were Non-Hispanic (NH) Black, 44,550 (69.0%) were NH White, 3,822 (5.9%) were NH Other/Unknown race, and 3,208 (5.0%) were Hispanic. Raw cumulative case rates for NH Black individuals were 1,713 per 100,000 population, compared with 2,095 for NH Other/Unknown, 903 for NH White, and 1,218 for Hispanic. Cumulative COVID-19-related death rates for NH Black individuals were 58.3 per 100,000 population, compared with 38.9 for NH Other/Unknown, 19.4 for NH White, and 14.8 for Hispanic. In a model that included insurance source, history of a social determinant billing code in the patient's claims, census block travel change, population density, Area Deprivation Index, and clinical comorbidities, NH Black race (OR 1.75, 1.51-2.04, p<0.001) and NH Other/Unknown race (OR 1.83, 1.36-2.46, p<0.001) remained strongly associated with mortality. CONCLUSIONS: In Missouri, COVID-19 case rates and mortality rates were markedly higher among NH Black and NH Other/Unknown race than among NH White residents, even after accounting for social and clinical risk, population density, and travel patterns during COVID-19.

Utilization of Social Determinants of Health ICD-10 Z-Codes Among Hospitalized Patients in the United States, 2016-2017.

BACKGROUND: The inclusion of Z-codes for social determinants of health (SDOH) in the 10th revision of the International Classification of Diseases (ICD-10) may offer an opportunity to improve data collection of SDOH, but no characterization of their utilization exists on a national all-payer level. OBJECTIVE: To examine the prevalence of SDOH Z-codes and compare characteristics of patients with and without Z-codes and hospitals that do and do not use Z-codes. RESEARCH DESIGN: Retrospective cohort study using 2016 and 2017 National Inpatient Sample. PARTICIPANTS: Total of 14,289,644 inpatient hospitalizations. MEASURES: Prevalence of SDOH Z-codes (codes Z55-Z65) and descriptive statistics of patients and hospitals. RESULTS: Of admissions, 269,929 (1.9%) included SDOH Z-codes. Average monthly SDOH Z-code use increased across the study period by 0.01% per month (P<0.001). The cumulative number and proportion of hospitals that had ever used an SDOH Z-code also increased, from 1895 hospitals (41%) in January 2016 to 3210 hospitals (70%) in December 2017. Hospitals that coded at least 1 SDOH Z-code were larger, private not-for-profit, and urban teaching hospitals. Compared with admissions without an SDOH Z-code, admissions with them were for patients who were younger, more often male, Medicaid recipients or uninsured. A higher proportion of admissions with SDOH Z-codes were for mental health (44.0% vs. 3.3%, P<0.001) and alcohol and substance use disorders (9.6% vs. 1.1%, P<0.001) compared with those without. CONCLUSIONS: The uptake of SDOH Z-codes has been slow, and current coding is likely poorly reflective of the actual burden of social needs experienced by hospitalized patients.

Association of Stratification by Dual Enrollment Status With Financial Penalties in the Hospital Readmissions Reduction Program.

Importance: Beginning in fiscal year 2019, Medicare's Hospital Readmissions Reduction Program (HRRP) stratifies hospitals into 5 peer groups based on the proportion of each hospital's patient population that is dually enrolled in Medicare and Medicaid. The effect of this policy change is largely unknown. Objective: To identify hospital and state characteristics associated with changes in HRRP-related performance and penalties after stratification. Design, Setting, and Participants: A cross-sectional analysis was performed of all 3049 hospitals participating in the HRRP in fiscal years 2018 and 2019, using publicly available data on hospital penalties, merged with information on hospital characteristics and state Medicaid eligibility cutoffs. Exposures: The HRRP, under the 2018 traditional method and the 2019 stratification method. Main Outcomes and Measures: Performance on readmissions, as measured by the excess readmissions ratio, and penalties under the HRRP both in relative percentage change and in absolute dollars. Results: The study sample included 3049 hospitals. The mean proportion of dually enrolled beneficiaries ranged from 9.5% in the lowest quintile to 44.7% in the highest quintile. At the hospital level, changes in penalties ranged from an increase of $225 000 to a decrease of more than $436 000 after stratification. In total, hospitals in the lowest quintile of dual enrollment saw an increase of $12 330 157 in penalties, while those in the highest quintile of dual enrollment saw a decrease of $22 445 644. Teaching hospitals (odds ratio [OR], 2.13; 95% CI, 1.76-2.57; P < .001) and large hospitals (OR, 1.51; 95% CI, 1.22-1.86; P < .001) had higher odds of receiving a reduced penalty. Not-for-profit hospitals (OR, 0.64; 95% CI, 0.52-0.80; P < .001) were less likely to have a penalty reduction than for-profit hospitals, and hospitals in the Midwest (OR, 0.44; 95% CI, 0.34-0.57; P < .001) and South (OR, 0.42; 95% CI, 0.30-0.57; P < .001) were less likely to do so than hospitals in the Northeast. Hospitals with patients from the most disadvantaged neighborhoods (OR, 2.62; 95% CI, 2.03-3.38; P < .001) and those with the highest proportion of beneficiaries with disabilities (OR, 3.12; 95% CI, 2.50-3.90; P < .001) were markedly more likely to see a reduction in penalties, as were hospitals in states with the highest Medicaid eligibility cutoffs (OR, 1.79; 95% CI, 1.50-2.14; P < .001). Conclusions and Relevance: Stratification of the hospitals under the HRRP was associated with a significant shift in penalties for excess readmissions. Policymakers should monitor the association of this change with readmission rates as well as hospital financial performance as the policy is fully implemented.

Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program.

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.

Adding socioeconomic data to hospital readmissions calculations may produce more useful results.

To better understand the degree to which risk-standardized thirty-day readmission rates may be influenced by social factors, we compared results for hospitals in Missouri under two types of models. The first type of model is currently used by the Centers for Medicare and Medicaid Services for public reporting of condition-specific hospital readmission rates of Medicare patients. The second type of model is an "enriched" version of the first type of model with census tract-level socioeconomic data, such as poverty rate, educational attainment, and housing vacancy rate. We found that the inclusion of these factors had a pronounced effect on calculated hospital readmission rates for patients admitted with acute myocardial infarction, heart failure, and pneumonia. Specifically, the models including socioeconomic data narrowed the range of observed variation in readmission rates for the above conditions, in percentage points, from 6.5 to 1.8, 14.0 to 7.4, and 7.4 to 3.7, respectively. Interestingly, the average readmission rates for the three conditions did not change significantly between the two types of models. The results of our exploratory analysis suggest that further work to characterize and report the effects of socioeconomic factors on standardized readmission measures may assist efforts to improve care quality and deliver more equitable care on the part of hospitals, payers, and other stakeholders.