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

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          Abstract

          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.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13054-023-04565-9.

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          Most cited references22

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          Comorbidity measures for use with administrative data.

          This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets. The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death. A comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. The comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
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            Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index.

            We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomes: in-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly used in research as an adjustment factor to control for severity of illness.
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              Area deprivation and widening inequalities in US mortality, 1969-1998.

              G. Singh (2003)
              This study examined age-, sex-, and race-specific gradients in US mortality by area deprivation between 1969 and 1998. A census-based area deprivation index was linked to county mortality data. Area deprivation gradients in US mortality increased substantially during 1969 through 1998. The gradients were steepest for men and women aged 25 to 44 years and those younger than 25 years, with higher mortality rates observed in more deprived areas. Although area gradients were less pronounced for women in each age group, they rose sharply for women aged 25 to 44 and 45 to 64 years. Areal inequalities in mortality widened because of slower mortality declines in more deprived areas. Future research needs to examine population-level social, behavioral, and medical care factors that may account for the increasing gradient.
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                Author and article information

                Contributors
                brad.hammill@duke.edu
                Journal
                Crit Care
                Critical Care
                BioMed Central (London )
                1364-8535
                1466-609X
                15 July 2023
                15 July 2023
                2023
                : 27
                : 287
                Affiliations
                [1 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke University School of Medicine, ; Durham, NC USA
                [2 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke University Fuqua School of Business, ; Durham, NC USA
                [3 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke University Department of Population Health Sciences, ; 215 Morris St, Durham, NC 27701 USA
                [4 ]GRID grid.412100.6, ISNI 0000 0001 0667 3730, Duke University Health System, ; Durham, NC USA
                [5 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke University Department of Medicine, ; Durham, NC USA
                Article
                4565
                10.1186/s13054-023-04565-9
                10349422
                37454127
                93e475e3-b903-45c1-91ad-4c65ef8996e6
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 5 April 2023
                : 6 July 2023
                Funding
                Funded by: Duke University Health System
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Emergency medicine & Trauma
                neighborhood deprivation,sepsis,socioeconomic disparities,area deprivation index

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