Hospital segregation, critical care strain, and inpatient mortality during the COVID-19 pandemic in New York City.

BACKGROUND: Hospital segregation by race, ethnicity, and health insurance coverage is prevalent, with some hospitals providing a disproportionate share of undercompensated care. We assessed whether New York City (NYC) hospitals serving a higher proportion of Medicaid and uninsured patients pre-pandemic experienced greater critical care strain during the first wave of the COVID-19 pandemic, and whether this greater strain was associated with higher rates of in-hospital mortality. METHODS: In a retrospective analysis of all-payer NYC hospital discharge data, we examined changes in admissions, stratified by use of intensive care unit (ICU), from the baseline period in early 2020 to the first COVID-19 wave across hospital quartiles (265,329 admissions), and crude and risk-adjusted inpatient mortality rates, also stratified by ICU use, in the first COVID wave across hospital quartiles (23,032 inpatient deaths), based on the proportion of Medicaid or uninsured admissions from 2017-2019 (quartile 1 lowest to 4 highest). Logistic regressions were used to assess the cross-sectional association between ICU strain, defined as ICU volume in excess of the baseline average, and patient-level mortality. RESULTS: ICU admissions in the first COVID-19 wave were 84%, 97%, 108%, and 123% of the baseline levels by hospital quartile 1-4, respectively. The risk-adjusted mortality rates for ICU admissions were 36.4 (CI = 34.7,38.2), 43.6 (CI = 41.5,45.8), 45.9 (CI = 43.8,48.1), and 45.7 (CI = 43.6,48.0) per 100 admissions, and those for non-ICU admissions were 8.6 (CI = 8.3,9.0), 10.9 (CI = 10.6,11.3), 12.6 (CI = 12.1,13.0), and 12.1 (CI = 11.6,12.7) per 100 admissions by hospital quartile 1-4, respectively. Compared with the reference group of 100% or less of the baseline weekly average, ICU admissions on a day for which the ICU volume was 101-150%, 151-200%, and > 200% of the baseline weekly average had odds ratios of 1.17 (95% CI = 1.10, 1.26), 2.63 (95% CI = 2.31, 3.00), and 3.26 (95% CI = 2.82, 3.78) for inpatient mortality, and non-ICU admissions on a day for which the ICU volume was 101-150%, 151-200%, and > 200% of the baseline weekly average had odds ratios of 1.28 (95% CI = 1.22, 1.34), 2.60 (95% CI = 2.40, 2.82), and 3.44 (95% CI = 3.11, 3.63) for inpatient mortality. CONCLUSIONS: Our findings are consistent with hospital segregation as a potential driver of COVID-related mortality inequities and highlight the need to desegregate health care to address structural racism, advance health equity, and improve pandemic resiliency.

Medication Nonuse and Hospital Utilization: Medicaid Participants With Type 2 Diabetes in New York City.

INTRODUCTION: This study assesses the proportion of New York City Medicaid participants diagnosed with type 2 diabetes who did not have any claims for diabetes medication for an entire year and the association between nonuse of diabetes medication and subsequent hospitalizations. METHODS: The 2014‒2016 New York State Medicaid claims data were used for this cohort study. Two types of hospitalizations were examined: all-cause hospitalizations and preventable diabetes hospitalizations. A potential association between medication nonuse and the number of hospitalizations in the following year was assessed using the negative binomial regression model, adjusting for individual- and neighborhood-level factors. The study was conducted in 2019‒2020. RESULTS: Among the 117,183 individuals included in this study, 27.5% did not use any diabetes medication for an entire year. Compared with individuals using oral hypoglycemic medication only, the crude rate of all-cause hospitalizations among individuals who used no medication was approximately twice as high (37,111 vs 19,209 per 100,000 population), and the crude rate of preventable diabetes hospitalizations was almost 3 times as high (1,488 vs 537 per 100,000 population). Adjusting for individual- and neighborhood-level characteristics, medication nonuse was still associated with higher levels of all-cause hospitalizations (incidence rate ratio=1.26; 95% CI=1.21, 1.31) and preventable diabetes hospitalizations (incidence rate ratio=1.66; 95% CI=1.39, 1.99). CONCLUSIONS: Medication use and adherence are important for managing diabetes. However, almost 30% of New York City Medicaid participants with type 2 diabetes had no claims for diabetes medication for an entire year. Significantly higher hospitalization rates among this group warrant attention from providers and policy makers.

Social Determinants Matter For Hospital Readmission Policy: Insights From New York City.

This study assessed the impact of individual social risk factor variables and social determinants of health (SDOH) measures on hospital readmission rates and penalties used in the Centers for Medicare and Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP). Using 2012-16 hospital discharge data from New York City, we projected HRRP penalties by augmenting CMS's readmission model for heart attack, heart failure, and pneumonia with SDOH scores constructed at each of four geographic levels and a measure of individual-level social risk. Including additional SDOH scores in the model, especially those constructed with the most granular geographic data, along with social risk factor variables substantially affects projected penalties for hospitals treating the highest proportion of patients with high SDOH scores. Improved performance occurred even after we included peer-group stratification in the HRRP model pursuant to the 21st Century Cures Act. Small improvements in model accuracy were associated with substantial shifts in projected performance. Our results suggest that CMS's continued omission of relevant patient and geographic data from the HRRP readmission model misallocates penalties attributable to SDOH and social risk factor effects to hospitals with the largest share of high-risk patients.

Evaluating Race and Ethnicity Reported in Hospital Discharge Data and Its Impact on the Assessment of Health Disparities.

BACKGROUND: Improving the collection and quality of race and ethnicity reported in hospital data is a key step in identifying disparities in health service utilization and outcomes and opportunities for quality improvement. OBJECTIVE: The objective of this study was to assess the quality of race/ethnicity reported in hospital discharge data and examine the impact on the identification of disparities in select health outcomes in New York City. RESEARCH DESIGN: Using the birth certificate as a gold standard, we examined the quality of hospital discharge race/ethnicity and estimated the impact of misclassification on racial/ethnic disparities in severe maternal morbidity and preventable hospitalizations. SUBJECTS: Delivery hospitalizations from the New York State hospital discharge data (Statewide Planning and Research Cooperative System) linked with 2015 New York City birth certificates. MEASURES: Sensitivity and positive predictive value (PPV). RESULTS: The non-Hispanic white and black race had relatively high sensitivity and PPV. Hispanic ethnicity and Asian race had moderate sensitivity and high PPV, but were often misclassified as "Other." As a result, health disparities may be underestimated for those of Hispanic ethnicity and Asian race, particularly for indicators that use population denominators drawn from another source. CONCLUSIONS: The quality of hospital discharge data varies by race/ethnicity and may underestimate disparities in some groups. Future research should validate findings with other data sources, identify driving factors, and evaluate progress over time.

Potentially Avertable Premature Deaths Associated with Jail Incarceration in New York City.

This study assessed neighborhood-level association between jail incarceration and premature mortality and estimated the number of potentially avertable premature deaths associated with jail incarceration in NYC. The study outcome was premature mortality rate and the main predictor of interest was jail incarceration rate. Variables associated with premature mortality in bivariate analysis were considered for inclusion in the multivariable ordinary least squares model and in the multivariable linear mixed effects model accounting for spatial correlation. Numbers of potentially avertable premature deaths were calculated by substituting the citywide incarceration rate for the neighborhoods with incarceration rates higher than the citywide rate in the final regression model. There were large disparities in both jail incarceration and premature mortality rates. Incarceration was strongly associated with premature mortality. The number of potentially avertable premature deaths associated with jail incarceration from 2011 to 2015 was approximately 6000, representing 10% of all predicted premature deaths in NYC. This study indicates that incarceration is closely correlated with premature mortality rates, which may contribute to health inequities among low-income NYC neighborhoods with predominantly black and Latino residents.

Disparities in Preventable Hospitalizations Among Public Housing Developments.

INTRODUCTION: This study assesses preventable hospitalization rates among New York City residents living in public housing developments compared with all New York City residents and residents in low-income areas. Additionally, preventable hospitalization rates by development (one or multiple buildings in close proximity and served by the same management office) were determined. METHODS: The 2010-2014 New York City hospital discharge data were geocoded and linked with New York City Housing Authority records using building-level identifiers. Preventable hospitalizations resulting from ambulatory care-sensitive conditions were identified for public housing residents, citywide, and residents of low-income areas. Age-adjusted overall and ambulatory care-sensitive, condition-specific preventable hospitalization rates (11 outcomes) were determined and compared across groups to assess potential disparities. Additionally, rates were ranked and compared among public housing developments by quartiles. The analysis was conducted in 2016 and 2017. RESULTS: The age-adjusted rate of preventable hospitalization was significantly higher among public housing residents than citywide (rate ratio [RR]=2.67, 95% CI=2.65, 2.69), with the greatest disparities in hospitalizations related to diabetes (RR=3.12, 95% CI=3.07, 3.18) and asthma (RR=4.14, 95% CI=4.07, 4.21). The preventable hospitalization rate was also higher among residents of public housing than low-income areas (RR=1.33, 95% CI=1.31, 1.35). There were large differences between developments ranked in the top and bottom quartiles of preventable hospitalization (RR=1.81, 95% CI=1.76, 1.85) with the largest difference related to chronic obstructive pulmonary disease (RR=3.38, 95% CI=3.08, 3.70). CONCLUSIONS: Preventable hospitalization rates are high among public housing residents, and vary significantly by development and condition. By providing geographically granular information, geocoded hospital discharge data can serve as a valuable tool for health assessment and engagement of the healthcare sector and other stakeholders in interventions that address health inequities.

Estimating the Hospital Delivery Costs Associated With Severe Maternal Morbidity in New York City, 2008-2012.

OBJECTIVE: To quantify the average and total hospital delivery costs associated with severe maternal morbidity in excess of nonsevere maternal morbidity deliveries over a 5-year period in New York City adjusting for other sociodemographic and clinical factors. METHODS: We conducted a population-based cross-sectional study using linked birth certificates and hospital discharge data for New York City deliveries from 2008 to 2012. Severe maternal morbidity was defined using a published algorithm of International Classification of Diseases, 9 Revision, Clinical Modification disease and procedure codes. Hospital costs were estimated by converting hospital charges using factors specific to each year and hospital and to each diagnosis. These estimates approximate what it costs the hospital to provide services (excluding professional fees) and were used in all subsequent analyses. To estimate adjusted mean costs associated with severe maternal morbidity, we used multivariable regression models with a log link, gamma distribution, robust standard errors, and hospital fixed effects, controlling for age, race and ethnicity, neighborhood poverty, primary payer, number of deliveries, method of delivery, comorbidities, and year. We used the adjusted mean cost to determine the average and total hospital delivery costs associated with severe maternal morbidity in excess of nonsevere maternal morbidity deliveries from 2008 to 2012. RESULTS: Approximately 2.3% (n=13,502) of all New York City delivery hospitalizations were complicated by severe maternal morbidity. Compared with nonsevere maternal morbidity deliveries, these hospitalizations were clinically complicated, required more and intensive clinical services, and had a longer stay in the hospital. The average cost of delivery with severe maternal morbidity was $14,442 (95% CI $14,128-14,756), compared with $7,289 (95% CI $7,276-7,302) among deliveries without severe maternal morbidity. After adjusting for other factors, the difference between deliveries with and without severe maternal morbidity remained high ($6,126). Over 5 years, this difference resulted in approximately $83 million in total excess costs (13,502×$6,126). CONCLUSION: Severe maternal morbidity nearly doubled the cost of delivery above and beyond other drivers of cost, resulting in tens of millions of excess dollars spent in the health care system in New York City. These findings can be used to demonstrate the burden of severe maternal morbidity and evaluate the cost-effectiveness of interventions to improve maternal health.

Estimating Potential Reductions in Premature Mortality in New York City From Raising the Minimum Wage to $15.

OBJECTIVES: To assess potential reductions in premature mortality that could have been achieved in 2008 to 2012 if the minimum wage had been $15 per hour in New York City. METHODS: Using the 2008 to 2012 American Community Survey, we performed simulations to assess how the proportion of low-income residents in each neighborhood might change with a hypothetical $15 minimum wage under alternative assumptions of labor market dynamics. We developed an ecological model of premature death to determine the differences between the levels of premature mortality as predicted by the actual proportions of low-income residents in 2008 to 2012 and the levels predicted by the proportions of low-income residents under a hypothetical $15 minimum wage. RESULTS: A $15 minimum wage could have averted 2800 to 5500 premature deaths between 2008 and 2012 in New York City, representing 4% to 8% of total premature deaths in that period. Most of these avertable deaths would be realized in lower-income communities, in which residents are predominantly people of color. CONCLUSIONS: A higher minimum wage may have substantial positive effects on health and should be considered as an instrument to address health disparities.