John P Donnelly1,2, Sindhu Lakkur3, Suzanne E Judd3, Emily B Levitan2, Russell Griffin2, George Howard3, Monika M Safford4, Henry E Wang5. 1. Department of Emergency Medicine, University of Alabama School of Medicine. 2. Department of Epidemiology, School of Public Health, University of Alabama at Birmingham. 3. Department of Biostatistics, School of Public Health, University of Alabama at Birmingham. 4. Department of Medicine, Weill Cornell Medical College, New York, New York. 5. Department of Emergency Medicine, University of Texas Health Science Center at Houston.
Abstract
Background: Prior studies suggest disparities in sepsis risk and outcomes based on place of residence. We sought to examine the association between neighborhood socioeconomic status (nSES) and hospitalization for infection and sepsis. Methods: We conducted a prospective cohort study using data from 30239 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. nSES was defined using a score derived from census data and classified into quartiles. Infection and sepsis hospitalizations were identified over the period 2003-2012. We fit Cox proportional hazards models, reporting hazard ratios (HRs) with 95% confidence intervals (CIs) and examining mediation by participant characteristics. Results: Over a median follow-up of 6.5 years, there were 3054 hospitalizations for serious infection. Infection incidence was lower for participants in the highest nSES quartile compared with the lowest quartile (11.7 vs 15.6 per 1000 person-years). After adjustment for demographics, comorbidities, and functional status, infection hazards were also lower for the highest quartile (HR, 0.84 [95% CI, .73-.97]), with a linear trend (P = .011). However, there was no association between nSES and sepsis at presentation among those hospitalized with infection. Physical weakness, income, and diabetes had modest mediating effects on the association of nSES with infection. Conclusions: Our study shows that differential infection risk may explain nSES disparities in sepsis incidence, as higher nSES is associated with lower infection hospitalization rates, but there is no association with sepsis among those hospitalized. Mediation analysis showed that nSES may influence infection hospitalization risk at least partially through physical weakness, individual income, and comorbid diabetes.
Background: Prior studies suggest disparities in sepsis risk and outcomes based on place of residence. We sought to examine the association between neighborhood socioeconomic status (nSES) and hospitalization for infection and sepsis. Methods: We conducted a prospective cohort study using data from 30239 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. nSES was defined using a score derived from census data and classified into quartiles. Infection and sepsis hospitalizations were identified over the period 2003-2012. We fit Cox proportional hazards models, reporting hazard ratios (HRs) with 95% confidence intervals (CIs) and examining mediation by participant characteristics. Results: Over a median follow-up of 6.5 years, there were 3054 hospitalizations for serious infection. Infection incidence was lower for participants in the highest nSES quartile compared with the lowest quartile (11.7 vs 15.6 per 1000 person-years). After adjustment for demographics, comorbidities, and functional status, infection hazards were also lower for the highest quartile (HR, 0.84 [95% CI, .73-.97]), with a linear trend (P = .011). However, there was no association between nSES and sepsis at presentation among those hospitalized with infection. Physical weakness, income, and diabetes had modest mediating effects on the association of nSES with infection. Conclusions: Our study shows that differential infection risk may explain nSES disparities in sepsis incidence, as higher nSES is associated with lower infection hospitalization rates, but there is no association with sepsis among those hospitalized. Mediation analysis showed that nSES may influence infection hospitalization risk at least partially through physical weakness, individual income, and comorbid diabetes.
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