Neighborhood socioeconomic deprivation, healthcare access, and 30-day mortality and readmission after sepsis or critical illness: findings from a nationwide study.

BACKGROUND: To determine if neighborhood socioeconomic deprivation independently predicts 30-day mortality and readmission for patients with sepsis or critical illness after adjusting for individual poverty, demographics, comorbidity burden, access to healthcare, and characteristics of treating healthcare facilities. METHODS: We performed a nationwide study of United States Medicare beneficiaries from 2017 to 2019. We identified hospitalized patients with severe sepsis and patients requiring prolonged mechanical ventilation, tracheostomy, or extracorporeal membrane oxygenation (ECMO) through Diagnosis Related Groups (DRGs). We estimated the association between neighborhood socioeconomic deprivation, measured by the Area Deprivation Index (ADI), and 30-day mortality and unplanned readmission using logistic regression models with restricted cubic splines. We sequentially adjusted for demographics, individual poverty, and medical comorbidities, access to healthcare services; and characteristics of treating healthcare facilities. RESULTS: A total of 1,526,405 admissions were included in the mortality analysis and 1,354,548 were included in the readmission analysis. After full adjustment, 30-day mortality for patients was higher for those from most-deprived neighborhoods (ADI 100) compared to least deprived neighborhoods (ADI 1) for patients with severe sepsis (OR 1.35 95% [CI 1.29-1.42]) or with prolonged mechanical ventilation with or without sepsis (OR 1.42 [95% CI 1.31, 1.54]). This association was linear and dose dependent. However, neighborhood socioeconomic deprivation was not associated with 30-day unplanned readmission for patients with severe sepsis and was inversely associated with readmission for patients requiring prolonged mechanical ventilation with or without sepsis. CONCLUSIONS: A strong association between neighborhood socioeconomic deprivation and 30-day mortality for critically ill patients is not explained by differences in individual poverty, demographics, measured baseline medical risk, access to healthcare resources, or characteristics of treating hospitals.

Neighborhood Socioeconomic Disadvantage, Healthcare Access, and Outcomes of Hospitalizations for Common Pulmonary Conditions: A National Study of Medicare Beneficiaries.

Rationale: Understanding how systemic forces and environmental exposures impact patient outcomes is critical to advancing health equity and improving population health for patients with pulmonary disease. This relationship has not yet been assessed at the population level nationally. Objectives: To determine whether neighborhood socioeconomic deprivation is independently associated with 30-day mortality and readmission for hospitalized patients with pulmonary conditions, after controlling for demographics, access to healthcare resources, and characteristics of admitting healthcare facilities. Methods: This was a retrospective, population-level cohort study of 100% of United States nationwide Medicare inpatient and outpatient claims from 2016-2019. Patients were admitted for one of four pulmonary conditions (pulmonary infections, chronic lower respiratory disease, pulmonary embolism, and pleural and interstitial lung diseases), defined by diagnosis-related group. The primary exposure was neighborhood socioeconomic deprivation, measured by the area deprivation index. The main outcomes were 30-day mortality and 30-day unplanned readmission, defined by Centers for Medicare and Medicaid Services methodologies. Generalized estimating equations were used to estimate logistic regression models for the primary outcomes, addressing clustering by hospital. A sequential adjustment strategy was first adjusted for age, legal sex, Medicare-Medicaid dual eligibility, and comorbidity burden, then adjusted for metrics of access to healthcare resources, and finally adjusted for characteristics of the admitting healthcare facility. Results: After full adjustment, patients from low socioeconomic status neighborhoods had greater 30-day mortality after admission for pulmonary embolism (odds ratio [OR], 1.26; 95% confidence interval [CI], 1.13-1.40), respiratory infections (OR, 1.20; 95% CI, 1.16-1.25), chronic lower respiratory disease (OR, 1.31; 95% CI, 1.22-1.41), and interstitial lung disease (OR, 1.15; 95% CI, 1.04-1.27) when compared to patients from the highest SES neighborhoods. Low neighborhood socioeconomic status was also associated with 30-day readmission for all groups except the interstitial lung disease group. Conclusions: Neighborhood socioeconomic deprivation may be a key factor driving poor health outcomes for patients with pulmonary diseases.

Association Between Neighborhood Socioeconomic Status and 30-Day Mortality and Readmission for Patients With Common Neurologic Conditions.

BACKGROUND AND OBJECTIVES: Patients of low individual socioeconomic status (SES) are at a greater risk of unfavorable health outcomes. However, the association between neighborhood socioeconomic deprivation and health outcomes for patients with neurologic disorders has not been studied at the population level. Our objective was to determine the association between neighborhood socioeconomic deprivation and 30-day mortality and readmission after hospitalization for various neurologic conditions. METHODS: This was a retrospective study of nationwide Medicare claims from 2017 to 2019. We included patients older than 65 years hospitalized for the following broad categories based on diagnosis-related groups (DRGs): multiple sclerosis and cerebellar ataxia (DRG 058-060); stroke (061-072); degenerative nervous system disorders (056-057); epilepsy (100-101); traumatic coma (082-087), and nontraumatic coma (080-081). The exposure of interest was neighborhood SES, measured by the area deprivation index (ADI), which uses socioeconomic indicators, such as educational attainment, unemployment, infrastructure access, and income, to estimate area-level socioeconomic deprivation at the level of census block groups. Patients were grouped into high, middle, and low neighborhood-level SES based on ADI percentiles. Adjustment covariates included age, comorbidity burden, race/ethnicity, individual SES, and sex. RESULTS: After exclusions, 905,784 patients were included in the mortality analysis and 915,993 were included in the readmission analysis. After adjustment for age, sex, race/ethnicity, comorbidity burden, and individual SES, patients from low SES neighborhoods had higher 30-day mortality rates compared with patients from high SES neighborhoods for all disease categories except for multiple sclerosis: magnitudes of the effect ranged from an adjusted odds ratio of 2.46 (95% CI 1.60-3.78) for the nontraumatic coma group to 1.23 (95% CI 1.19-1.28) for the stroke group. After adjustment, no significant differences in readmission rates were observed for any of the groups. DISCUSSION: Neighborhood SES is strongly associated with 30-day mortality for many common neurologic conditions even after accounting for baseline comorbidity burden and individual SES. Strategies to improve health equity should explicitly consider the effect of neighborhood environments on health outcomes.

Life expectancy and voting patterns in the 2020 U.S. presidential election.

INTRODUCTION: In the 2016 U.S. Presidential election, voters in communities with recent stagnation or decline in life expectancy were more likely to vote for the Republican candidate than in prior Presidential elections. We aimed to assess the association between change in life expectancy and voting patterns in the 2020 Presidential election. METHODS: With data on county-level life expectancy from the Institute for Health Metrics and Evaluation and voting data from a GitHub repository of results scraped from news outlets, we used weighted multivariable linear regression to estimate the association between the change in life expectancy from 1980 to 2014 and the proportion of votes for the Republican candidate and change in the proportion of votes cast for the Republican candidate in the 2020 Presidential election. RESULTS: Among 3110 U.S counties and Washington, D.C., change in life expectancy at the county level was negatively associated with Republican share of the vote in the 2020 Presidential election (parameter estimate -7.2, 95% confidence interval, -7.8 to -6.6). With the inclusion of state, sociodemographic, and economic variables in the model, the association was attenuated (parameter estimate -0.8; 95% CI, -1.5 to -0.2). County-level change in life expectancy was positively associated with change in Republican vote share 0.29 percentage points (95% CI, 0.23 to 0.36). The association was attenuated when state, sociodemographic, and economic variables were added (parameter estimate 0.24; 95% CI, 0.15 to 0.33). CONCLUSION: Counties with a less positive trajectory in life expectancy were more likely to vote for the Republican candidate in the 2020 U.S. Presidential election, but the Republican candidate's share improved in some counties that experienced marked gains in life expectancy. Associations were moderated by demographic, social and economic factors.