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Oud L, Garza J. Trajectories of State-Level Sepsis-Related Mortality by Race and Ethnicity Group in the United States. J Clin Med 2024; 13:2848. [PMID: 38792390 PMCID: PMC11122657 DOI: 10.3390/jcm13102848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/01/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Background: Recent reports on the national temporal trends of sepsis-related mortality in the United States (US) suggested improvement of outcomes in several race and ethnicity groups. However, it is unknown whether national data reflect state-level trajectories. Methods: We used the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research Multiple Cause of Death data set to identify all decedents with sepsis in the US during 2010-2019. Negative binomial regression models were fit to estimate national and state-level trends of age-adjusted sepsis-related mortality rates within race and ethnicity groups. Results: There were 1,852,610 sepsis-related deaths in the US during 2010-2019. Nationally, sepsis-related mortality rates decreased among Blacks and Asians, were unchanged among Hispanics and Native Americans, and rose among Whites. The percent of states with similar trends were 30.0% among Blacks, 32.1% among Asians, 74.3% among Hispanics, 75.0% among Native Americans, and 66.7%% among Whites, while trending in opposite direction from 3.6% among Asians to 15.0% among Blacks. Conclusions: National trends in sepsis-related mortality in the US did not represent state-level trajectories in race ethnicity groups. Gains in sepsis outcomes among race and ethnicity groups at the national level were not shared equitably at the state level.
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Affiliation(s)
- Lavi Oud
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Texas Tech University Health Sciences Center at the Permian Basin, 701 W. 5th Street, Odessa, TX 79763, USA
| | - John Garza
- Texas Tech University Health Sciences Center at the Permian Basin, 701 W. 5th Street, Odessa, TX 79763, USA;
- Department of Mathematics, The University of Texas of the Permian Basin, 4901 E. University Blvd, Odessa, TX 79762, USA
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Zhong X, Ashiru-Oredope D, Pate A, Martin GP, Sharma A, Dark P, Felton T, Lake C, MacKenna B, Mehrkar A, Bacon SC, Massey J, Inglesby P, Goldacre B, Hand K, Bladon S, Cunningham N, Gilham E, Brown CS, Mirfenderesky M, Palin V, van Staa TP. Clinical and health inequality risk factors for non-COVID-related sepsis during the global COVID-19 pandemic: a national case-control and cohort study. EClinicalMedicine 2023; 66:102321. [PMID: 38192590 PMCID: PMC10772239 DOI: 10.1016/j.eclinm.2023.102321] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 01/10/2024] Open
Abstract
Background Sepsis, characterised by significant morbidity and mortality, is intricately linked to socioeconomic disparities and pre-admission clinical histories. This study aspires to elucidate the association between non-COVID-19 related sepsis and health inequality risk factors amidst the pandemic in England, with a secondary focus on their association with 30-day sepsis mortality. Methods With the approval of NHS England, we harnessed the OpenSAFELY platform to execute a cohort study and a 1:6 matched case-control study. A sepsis diagnosis was identified from the incident hospital admissions record using ICD-10 codes. This encompassed 248,767 cases with non-COVID-19 sepsis from a cohort of 22.0 million individuals spanning January 1, 2019, to June 31, 2022. Socioeconomic deprivation was gauged using the Index of Multiple Deprivation score, reflecting indicators like income, employment, and education. Hospitalisation-related sepsis diagnoses were categorised as community-acquired or hospital-acquired. Cases were matched to controls who had no recorded diagnosis of sepsis, based on age (stepwise), sex, and calendar month. The eligibility criteria for controls were established primarily on the absence of a recorded sepsis diagnosis. Associations between potential predictors and odds of developing non-COVID-19 sepsis underwent assessment through conditional logistic regression models, with multivariable regression determining odds ratios (ORs) for 30-day mortality. Findings The study included 224,361 (10.2%) cases with non-COVID-19 sepsis and 1,346,166 matched controls. The most socioeconomic deprived quintile was associated with higher odds of developing non-COVID-19 sepsis than the least deprived quintile (crude OR 1.80 [95% CI 1.77-1.83]). Other risk factors (after adjusting comorbidities) such as learning disability (adjusted OR 3.53 [3.35-3.73]), chronic liver disease (adjusted OR 3.08 [2.97-3.19]), chronic kidney disease (stage 4: adjusted OR 2.62 [2.55-2.70], stage 5: adjusted OR 6.23 [5.81-6.69]), cancer, neurological disease, immunosuppressive conditions were also associated with developing non-COVID-19 sepsis. The incidence rate of non-COVID-19 sepsis decreased during the COVID-19 pandemic and rebounded to pre-pandemic levels (April 2021) after national lockdowns had been lifted. The 30-day mortality risk in cases with non-COVID-19 sepsis was higher for the most deprived quintile across all periods. Interpretation Socioeconomic deprivation, comorbidity and learning disabilities were associated with an increased odds of developing non-COVID-19 related sepsis and 30-day mortality in England. This study highlights the need to improve the prevention of sepsis, including more precise targeting of antimicrobials to higher-risk patients. Funding The UK Health Security Agency, Health Data Research UK, and National Institute for Health Research.
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Affiliation(s)
- Xiaomin Zhong
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, M13 9PL, UK
| | - Diane Ashiru-Oredope
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London SW1P 3JR, UK
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK
| | - Alexander Pate
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, M13 9PL, UK
| | - Glen P. Martin
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, M13 9PL, UK
| | - Anita Sharma
- Chadderton South Health Centre, Eaves Lane, Chadderton, Oldham OL9 8RG, UK
| | - Paul Dark
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Tim Felton
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Intensive Care Unit, Manchester University NHS Foundation Trust, Wythenshawe Hospital, Manchester, UK
| | - Claire Lake
- Maples Medical Centre, 2 Scout Dr, Baguley, Manchester M23 2SY, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
- NHS England, Wellington House, Waterloo Road, London SE1 8UG, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Sebastian C.J. Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Jon Massey
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
| | - Kieran Hand
- Pharmacy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Sian Bladon
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, M13 9PL, UK
| | - Neil Cunningham
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London SW1P 3JR, UK
| | - Ellie Gilham
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London SW1P 3JR, UK
| | - Colin S. Brown
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London SW1P 3JR, UK
- NIHR Health Protection Unit in Healthcare-Associated Infection & Antimicrobial Resistance, Imperial College London, London, UK
| | - Mariyam Mirfenderesky
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London SW1P 3JR, UK
| | - Victoria Palin
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, M13 9PL, UK
- Division of Developmental Biology and Medicine, Maternal and Fetal Research Centre, The University of Manchester, St Marys Hospital, Oxford Road, Manchester M13 9WL, UK
| | - Tjeerd Pieter van Staa
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, M13 9PL, UK
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