The Association between Limited English Proficiency and Sepsis Mortality
BACKGROUND: Limited English proficiency (LEP) has been implicated in poor health outcomes. Sepsis is a frequently fatal syndrome that is commonly encountered in hospital medicine. The impact of LEP on sepsis mortality is not currently known.
OBJECTIVE: To determine the association between LEP and sepsis mortality. DESIGN: Retrospective cohort study.
SETTING: 800-bed, tertiary care, academic medical center.
PATIENTS: Electronic health record data were obtained for adults admitted to the hospital with sepsis between June 1, 2012 and December 31, 2016.
MEASUREMENTS: The primary predictor was LEP. Patients were defined as having LEP if their self-reported primary language was anything other than English and interpreter services were required during hospitalization. The primary outcome was inpatient mortality. Mortality was compared across races stratified by LEP using chi-squared tests of significance. Bivariable and multivariable logistic regressions were performed to investigate the association between mortality, race, and LEP, adjusting for baseline characteristics, comorbidities, and illness severity.
RESULTS: Among 8,974 patients with sepsis, we found that 1 in 5 had LEP, 62% of whom were Asian. LEP was highly associated with death across all races except those identifying as Black and Latino. LEP was associated with a 31% increased odds of mortality after adjusting for illness severity, comorbidities, and other baseline characteristics, including race (OR 1.31, 95% CI 1.06-1.63, P = .02).
CONCLUSIONS: In a single-center study of patients hospitalized with sepsis, LEP was associated with mortality across nearly all races. This is a novel finding that will require further exploration into the causal nature of this association.
© 2019 Society of Hospital Medicine
Covariate Data Collection
Additional data were obtained from the demographics tables, including age, race, sex, and insurance status. Race and ethnicity were combined into a single five-category variable including White, Asian, Black, Latino, and Other. This approach has been suggested as the best way to operationalize these variables29 and has been utilized by similar studies in the literature.9,14,15 We considered the Asian race to include all people of East Asian, Southeast Asian, or South Asian descent, which is consistent with the United States Census Bureau definition.30 Patients identifying as Native Hawaiians/Pacific Islanders, Native Americans/Alaskan Natives, as well as those with unspecified race or ethnicity, were categorized as Other. Insurance status was categorized as Commercial, Medicare, Medicaid, or Other.
We estimated illness severity in several ways. First, the total qualifying SOFA score was calculated for each patient, which was defined as the total score achieved at the time that SOFA criteria were first met (≥2, within 48 hours). Second, we dichotomized patients based on whether they had received mechanical ventilation at any point during hospitalization. Finally, we used admission location as a surrogate marker for severity at the time of initial hospitalization.
To estimate the burden of baseline comorbidities, we calculated the van Walraven score (VWS),31 a validated modification of the Elixhauser Comorbidity Index.27 This score conveys an estimated risk of in hospital death based on ICD-9/10 diagnosis codes for preexisting conditions, which ranges from <1% for the minimum score of –19 to >99% for the maximum score of 89.
Statistical Analyses
All statistical analyses were performed using Stata software version 15 (StataCorp LLC, College Station, Texas). Baseline demographics and patient characteristics were stratified by LEP. These were compared using two-sample t-tests or chi-squared tests of significance. Wilcoxon rank-sum tests were used for non-normally distributed variables. Inpatient mortality was compared across all races stratified by LEP using chi-squared tests of significance.
We fit a series of multivariable logistic regression models to examine the association between race and inpatient mortality adjusting for LEP and other patient/clinical characteristics. We first examined the unadjusted association between mortality and race; then adjusted for LEP alone; and finally adjusted for all covariates of interest, including LEP, age, sex, insurance status, year, admission level of care, VWS, total qualifying SOFA score, need for mechanical ventilation, site of infection, and time to first IV antibiotic. A subgroup analysis was also performed using the fully adjusted model restricted to patients who were mechanically ventilated. This population was selected because the patients (1) have among the highest severity of illness and (2) share a common barrier to communication, regardless of English proficiency.
Several potential interactions between LEP with other covariates were explored, including age, race, ICU admission level of care, and need for mechanical ventilation. Lastly, a mediation analysis was performed based on Baron & Kenny’s four-step model32 in order to calculate the proportion of the association between race and mortality explained by the proposed mediator (LEP).
To evaluate for the likelihood of residual confounding, we calculated an E-value, which is defined as the minimum strength of association that an unmeasured confounder would need to have with both the predictor and outcome variables, above and beyond the measured covariates, in order to fully explain away an observed predictor-outcome association.33,34
