Publications

BACKGROUND: Hospitalizations fell precipitously among the general population during the COVID-19 pandemic. It remains unclear whether individuals experiencing homelessness experienced similar reductions.

OBJECTIVE: To examine how overall and cause-specific hospitalizations changed among individuals with a recent history of homelessness (IRHH) and their housed counterparts during the first wave of the COVID-19 pandemic, using corresponding weeks in 2019 as a historical control.

DESIGN: Population-based cohort study conducted in Ontario, Canada, between September 30, 2018, and September 26, 2020.

PARTICIPANTS: In total, 38,617 IRHH, 15,022,368 housed individuals, and 186,858 low-income housed individuals matched on age, sex, rurality, and comorbidity burden.

MAIN MEASURES: Primary outcomes included medical-surgical, non-elective (overall and cause-specific), elective surgical, and psychiatric hospital admissions.

KEY RESULTS: Average rates of medical-surgical (rate ratio: 3.8, 95% CI: 3.7-3.8), non-elective (10.3, 95% CI: 10.1-10.4), and psychiatric admissions (128.1, 95% CI: 126.1-130.1) between January and September 2020 were substantially higher among IRHH compared to housed individuals. During the peak period (March 17 to June 16, 2020), rates of medical-surgical (0.47, 95% CI: 0.47-0.47), non-elective (0.80, 95% CI: 0.79-0.80), and psychiatric admissions (0.86, 95% CI: 0.84-0.88) were significantly lower among housed individuals relative to equivalent weeks in 2019. No significant changes were observed among IRHH. During the re-opening period (June 17-September 26, 2020), rates of non-elective hospitalizations for liver disease (1.41, 95% CI: 1.23-1.69), kidney disease (1.29, 95% CI: 1.14-1.47), and trauma (1.19, 95% CI: 1.07-1.32) increased substantially among IRHH but not housed individuals. Distinct hospitalization patterns were observed among IRHH even in comparison with more medically and socially vulnerable matched housed individuals.

CONCLUSIONS: Persistence in overall hospital admissions and increases in non-elective hospitalizations for liver disease, kidney disease, and trauma indicate that the COVID-19 pandemic presented unique challenges for recently homeless individuals. Health systems must better address the needs of this population during public health crises.

The Medicare Hospital Readmissions Reduction Program (HRRP) financially penalizes hospitals with high readmission rates. In fiscal year 2019 the program was changed to account for the association between social risk and high readmission rates. The new approach stratifies hospitals into five groups by hospitals' proportion of patients dually enrolled in Medicare and Medicaid, and it evaluates performance within each stratum instead of within the national cohort. Its impact on hospitals caring for vulnerable populations has not been studied. We calculated the change in average annual penalty percentage, before and after stratification, for safety-net hospitals, rural hospitals, and hospitals caring for a high share of Black and Hispanic or Latino patients. We found that stratification by proportion of dual enrollees was associated with a decrease in penalties by -0.09 percentage points at hospitals with the highest proportion of dual enrollees, -0.08 percentage points at rural hospitals, and -0.06 percentage points at hospitals with a large share of Black and Hispanic or Latino patients. Fully adjusted analyses suggest that these patterns were driven by penalty reductions at rural hospitals and hospitals disproportionately serving Black and Hispanic or Latino patients. Given the allocation of fewer penalties to these hospitals, we conclude that the stratification mandate was a modest step toward equity within the HRRP.

BACKGROUND: Limited English proficiency (LEP) heart failure (HF) patients experience worse HF outcomes, including higher readmission rates and emergency department visits. To elucidate the challenges this population faces, the authors interviewed interpreters to identify gaps in care quality and ways to improve care for LEP HF patients.

OBJECTIVES: The authors sought to understand the challenges facing HF patients with LEP using medical interpreters' perspectives.

METHODS: The authors conducted a qualitative study using semistructured interviews with interpreters working at an academic medical center. All interpreters employed by the medical center were eligible to participate. Interviews were analyzed using thematic analysis.

RESULTS: The authors interviewed 20 interpreters from 9 languages (mean age: 48 ± 14.3 years; mean experience: 16.3 ± 10.6 years). Two themes regarding the challenges of care delivery to LEP HF patients emerged: 1) LEP patients often had a limited understanding of HF etiology, prognosis, and treatment options, and interpreters cited difficulty explaining HF given the complexity of the subject; and 2) practical steps to improve the discharge process for LEP HF patients. Integrating interpreters into both the inpatient and outpatient HF teams was a strongly supported intervention. Additionally, conducting pre-encounter huddles, providing the interpreter service phone number at the time of discharge, involving family members when appropriate, and considering nutrition referrals were all important steps highlighted by interpreters.

CONCLUSIONS: This study illuminates challenges that LEP HF patients face and provides potential solutions to improve care for this vulnerable group. Integrating interpreters as part of the HF team and designing practical discharge plans for LEP HF patients could reduce current disparities.

AIMS: To examine how weekly rates of emergency department (ED) visits for drug overdoses changed among individuals with a recent history of homelessness (IRHH) and their housed counterparts during the pre-pandemic, peak, and re-opening periods of the first wave of the COVID-19 pandemic, using corresponding weeks in 2019 as a historical control.

DESIGN: Population-based retrospective cohort study conducted between September 30, 2018 and September 26, 2020.

SETTING: Ontario, Canada.

PARTICIPANTS: A total of 38 617 IRHH, 15 022 369 housed individuals, and 186 858 low-income housed individuals matched on age, sex, rurality, and comorbidity burden.

MEASUREMENTS: ED visits for drug overdoses of accidental and undetermined intent.

FINDINGS: Average rates of ED visits for drug overdoses between January and September 2020 were higher among IRHH compared with housed individuals (rate ratio [RR], 148.0; 95% CI, 142.7-153.5) and matched housed individuals (RR, 22.3; 95% CI, 20.7-24.0). ED visits for drug overdoses decreased across all groups by  20% during the peak period (March 17 to June 16, 2020) compared with corresponding weeks in 2019. During the re-opening period (June 17 to September 26, 2020), rates of ED visits for drug overdoses were significantly higher among IRHH (RR, 1.56; 95% CI, 1.44-1.69), matched housed individuals (RR, 1.25; 95% CI, 1.08-1.46), and housed individuals relative to equivalent weeks in 2019 (RR, 1.07; 95% CI, 1.02-1.11). The relative increase in drug overdose ED visits among IRHH was larger compared with both matched housed individuals (P = 0.01 for interaction between group and year) and housed individuals (P < 0.001) during this period.

CONCLUSIONS: Recently homeless individuals in Ontario, Canada experienced disproportionate increases in ED visits for drug overdoses during the re-opening period of the COVID-19 pandemic compared with housed people.

BACKGROUND: U.S. policy efforts have focused on reducing rural-urban health inequities. However, it is unclear whether gaps in care and outcomes remain among older adults with acute cardiovascular conditions.

OBJECTIVES: This study aims to evaluate rural-urban differences in procedural care and mortality for acute myocardial infarction (AMI), heart failure (HF), and ischemic stroke.

METHODS: This is a retrospective cross-sectional study of Medicare fee-for-service beneficiaries aged ≥65 years with acute cardiovascular conditions from 2016 to 2018. Cox proportional hazards models with random hospital intercepts were fit to examine the association of presenting to a rural (vs urban) hospital and 30- and 90-day patient-level mortality.

RESULTS: There were 2,182,903 Medicare patients hospitalized with AMI, HF, or ischemic stroke from 2016 to 2018. Patients with AMI were less likely to undergo cardiac catherization (49.7% vs 63.6%, P < 0.001), percutaneous coronary intervention (42.1% vs 45.7%, P < 0.001) or coronary artery bypass graft (9.0% vs 10.2%, P < 0.001) within 30 days at rural versus urban hospitals. Thrombolysis rates (3.1% vs 10.1%, P < 0.001) and endovascular therapy (1.8% vs 3.6%, P < 0.001) for ischemic stroke were lower at rural hospitals. After adjustment for demographics and clinical comorbidities, the 30-day mortality HR was significantly higher among patients presenting to rural hospitals for AMI (HR: 1.10, 95% CI: 1.08 to 1.12), HF (HR: 1.15; 95% CI: 1.13 to 1.16), and ischemic stroke (HR: 1.20; 95% CI: 1.18 to 1.22), with similar patterns at 90 days. These differences were most pronounced for the subset of critical access hospitals that serve remote, rural areas.

CONCLUSIONS: Clinical, public health, and policy efforts are needed to improve rural-urban gaps in care and outcomes for acute cardiovascular conditions.

BACKGROUND: The Centers for Medicare and Medicaid Services' Hospital Value-Based Purchasing program uses 30-day mortality rates for acute myocardial infarction, heart failure, and pneumonia to evaluate US hospitals, but does not account for neighborhood socioeconomic disadvantage when comparing their performance.

OBJECTIVE: To determine if neighborhood socioeconomic disadvantage is associated with worse 30-day mortality rates after a hospitalization for acute myocardial infarction (AMI), heart failure (HF), or pneumonia in the USA, as well as within the subset of counties with a high proportion of Black individuals.

DESIGN AND PARTICIPANTS: This retrospective, population-based study included all Medicare fee-for-service beneficiaries aged 65 years or older hospitalized for acute myocardial infarction, heart failure, or pneumonia between 2012 and 2015.

EXPOSURE: Residence in most socioeconomically disadvantaged vs. less socioeconomically disadvantaged neighborhoods as measured by the area deprivation index (ADI).

MAIN MEASURE(S): All-cause mortality within 30 days of admission.

KEY RESULTS: The study included 3,471,592 Medicare patients. Of these patients, 333,472 resided in most disadvantaged neighborhoods and 3,138,120 in less disadvantaged neighborhoods. Patients living in the most disadvantaged neighborhoods were younger (78.4 vs. 80.0 years) and more likely to be Black adults (24.6% vs. 7.5%) and dually enrolled in Medicaid (39.4% vs. 21.8%). After adjustment for demographics (age, sex, race/ethnicity), poverty, and clinical comorbidities, 30-day mortality was higher among beneficiaries residing in most disadvantaged neighborhoods for AMI (adjusted odds ratio 1.08, 95% CI 1.06-1.11) and pneumonia (aOR 1.05, 1.03-1.07), but not for HF (aOR 1.02, 1.00-1.04). These patterns were similar within the subset of US counties with a high proportion of Black adults (AMI, aOR 1.07, 1.03-1.11; HF 1.02, 0.99-1.05; pneumonia 1.03, 1.00-1.07).

CONCLUSIONS: Neighborhood socioeconomic disadvantage is associated with higher 30-day mortality for some conditions targeted by value-based programs, even after accounting for individual-level demographics, clinical comorbidities, and poverty. These findings may have implications as policymakers weigh strategies to advance health equity under value-based programs.

BACKGROUND: International Classification of Disease (ICD)-10 coding of type 1 myocardial infarction (MI) is used for reimbursement, value-based programs, and clinical research.

OBJECTIVES: This study sought to determine whether the introduction of ICD-10 codes for type 2 and types 3-5 MI was associated with changes in hospitalizations for ICD-10 codes now attributed to type 1 MI.

METHODS: Using the Nationwide Readmissions Database, we identified patients with ICD-10 codes now attributed to type 1 MI between January 2016 and December 2018. Patients were stratified according to the timing of their event in relation to the introduction of the type 2 and types 3-5 MI codes on October 1, 2017.

RESULTS: There were 2,680,323 hospitalizations for ICD-10 codes now attributed to type 1 MI; after adjustment for seasonality, there was a 13.7% decline in hospitalizations after the introduction of the new subtype codes. Patients with ICD-10 codes now attributed to type 1 MI after the coding change were less likely to be female, had lower prevalence of several cardiovascular and noncardiovascular comorbidities, and had higher rates of coronary angiography and revascularization. After introduction of the new codes, there was a positive deflection in the slope of risk-adjusted in-hospital mortality (0.007%; P <0.001) and a negative deflection in risk-adjusted 30-day readmission (-0.002%; P = 0.05) for patients with ICD-10 codes now attributed to type 1 MI.

CONCLUSIONS: The introduction of ICD-10 codes for type 2 and types 3-5 MI was associated with a decrease in hospitalizations for ICD-10 codes now attributed to type 1 MI and changes in the observed characteristics and treatment patterns of these patients.

BACKGROUND In January 2011, Centers for Medicare and Medicaid Services expanded the number of inpatient diagnosis codes from 9 to 25, which may influence comorbidity counts and risk-adjusted outcome rates for studies spanning January 2011. This study examines the association between (1) limiting versus not limiting diagnosis codes after 2011, (2) using inpatient-only versus inpatient and outpatient data, and (3) using logistic regression versus the Centers for Medicare and Medicaid Services risk-standardized methodology and changes in risk-adjusted outcomes. METHODS AND RESULTS Using 100% Medicare inpatient and outpatient files between January 2009 and December 2013, we created 2 cohorts of fee-for-service beneficiaries aged ≥65 years. The acute myocardial infarction cohort and the heart failure cohort had 578 728 and 1 595 069 hospitalizations, respectively. We calculate comorbidities using (1) inpatient-only limited diagnoses, (2) inpatient-only unlimited diagnoses, (3) inpatient and outpatient limited diagnoses, and (4) inpatient and outpatient unlimited diagnoses. Across both cohorts, International Classification of Diseases, Ninth Revision (ICD-9) diagnoses and hierarchical condition categories increased after 2011. When outpatient data were included, there were no significant differences in risk-adjusted readmission rates using logistic regression or the Centers for Medicare and Medicaid Services risk standardization. A difference-in-differences analysis of risk-adjusted readmission trends before versus after 2011 found that no significant differences between limited and unlimited models for either cohort. CONCLUSIONS For studies that span 2011, researchers should consider limiting the number of inpatient diagnosis codes to 9 and/or including outpatient data to minimize the impact of the code expansion on comorbidity counts. However, the 2011 code expansion does not appear to significantly affect risk-adjusted readmission rate estimates using either logistic or risk-standardization models or when using or excluding outpatient data.