OBJECTIVE: To evaluate the impact of patient socioeconomic status (SES) on operative mortality within the context of associated factors. SUMMARY OF BACKGROUND DATA: Outcomes disparities among surgical patients are a significant concern. Previous studies have suggested that the correlation between SES and outcomes is attributable to other patient- or hospital-level explanatory factors such as race or hospital wealth. These studies have typically focused on a single explanation for the existence of these inequalities. METHODS: Analyzing more than 1 million records of the Nationwide Inpatient Sample, we used multimodel inference to evaluate the effects of socioeconomic predictors on surgical mortality. RESULTS: Using univariate and multivariate logistic regression, we find that patient's SES is a strong predictor of operative mortality. Multivariate regressions incorporated many additional hospital- and patient-level covariates. A single-level increase in patient SES results in a mean decrease in operative mortality risk of 7.1%. CONCLUSIONS: SES at the level of the individual patient has a statistically significant effect on operative mortality. Mortality is greatest among patients in the lowest socioeconomic strata. The effect of patient SES on mortality is not mitigated by other explanatory hospital- or patient-level factors.
OBJECTIVE: To evaluate the impact of patient socioeconomic status (SES) on operative mortality within the context of associated factors. SUMMARY OF BACKGROUND DATA: Outcomes disparities among surgical patients are a significant concern. Previous studies have suggested that the correlation between SES and outcomes is attributable to other patient- or hospital-level explanatory factors such as race or hospital wealth. These studies have typically focused on a single explanation for the existence of these inequalities. METHODS: Analyzing more than 1 million records of the Nationwide Inpatient Sample, we used multimodel inference to evaluate the effects of socioeconomic predictors on surgical mortality. RESULTS: Using univariate and multivariate logistic regression, we find that patient's SES is a strong predictor of operative mortality. Multivariate regressions incorporated many additional hospital- and patient-level covariates. A single-level increase in patient SES results in a mean decrease in operative mortality risk of 7.1%. CONCLUSIONS: SES at the level of the individual patient has a statistically significant effect on operative mortality. Mortality is greatest among patients in the lowest socioeconomic strata. The effect of patient SES on mortality is not mitigated by other explanatory hospital- or patient-level factors.
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