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van Staa TP, Pate A, Martin GP, Sharma A, Dark P, Felton T, Zhong X, Bladon S, Cunningham N, Gilham EL, Brown CS, Mirfenderesky M, Palin V, Ashiru-Oredope D. Sepsis and case fatality rates and associations with deprivation, ethnicity, and clinical characteristics: population-based case-control study with linked primary care and hospital data in England. Infection 2024; 52:1469-1479. [PMID: 38627354 PMCID: PMC11288984 DOI: 10.1007/s15010-024-02235-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/12/2024] [Indexed: 08/02/2024]
Abstract
PURPOSE Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. The purpose of the study was to measure the associations of specific exposures (deprivation, ethnicity, and clinical characteristics) with incident sepsis and case fatality. METHODS Two research databases in England were used including anonymized patient-level records from primary care linked to hospital admission, death certificate, and small-area deprivation. Sepsis cases aged 65-100 years were matched to up to six controls. Predictors for sepsis (including 60 clinical conditions) were evaluated using logistic and random forest models; case fatality rates were analyzed using logistic models. RESULTS 108,317 community-acquired sepsis cases were analyzed. Severe frailty was strongly associated with the risk of developing sepsis (crude odds ratio [OR] 14.93; 95% confidence interval [CI] 14.37-15.52). The quintile with most deprived patients showed an increased sepsis risk (crude OR 1.48; 95% CI 1.45-1.51) compared to least deprived quintile. Strong predictors for sepsis included antibiotic exposure in prior 2 months, being house bound, having cancer, learning disability, and diabetes mellitus. Severely frail patients had a case fatality rate of 42.0% compared to 24.0% in non-frail patients (adjusted OR 1.53; 95% CI 1.41-1.65). Sepsis cases with recent prior antibiotic exposure died less frequently compared to non-users (adjusted OR 0.7; 95% CI 0.72-0.76). Case fatality strongly decreased over calendar time. CONCLUSION Given the variety of predictors and their level of associations for developing sepsis, there is a need for prediction models for risk of developing sepsis that can help to target preventative antibiotic therapy.
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Affiliation(s)
- Tjeerd Pieter van Staa
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK.
| | - Alexander Pate
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
| | - Glen P Martin
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, 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
| | - Xiaomin Zhong
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
| | - Sian Bladon
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, 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 L 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 & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
- Maternal and Fetal Health Research Centre, Division of Developmental Biology and Medicine, The University of Manchester, Manchester, M13 9WL, 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
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Liu J, Ren J, Gao X, Zhang C, Deng G, Li J, Li R, Wang X, Wang G. A causal relationship between educational attainment and risk of infectious diseases: A Mendelian randomisation study. J Glob Health 2024; 14:04089. [PMID: 38665066 PMCID: PMC11046428 DOI: 10.7189/jogh.14.04089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
Background Previous observational studies have investigated the association between educational attainment and sepsis, pneumonia, and urinary tract infections (UTIs). However, their findings have been susceptible to reverse causality and confounding factors. Furthermore, no study has examined the effect of educational level on the risk of infections of the skin and subcutaneous tissue (SSTIs). Thus, we aimed to evaluate the causal relationships between educational level and the risk of four infectious diseases using Mendelian randomisation (MR) techniques. Methods We used univariable MR analysis to investigate the causal associations between educational attainment (years of schooling (n = 766 345) and holding college or university degree (n = 334 070)) and four infectious diseases (sepsis (n = 486 484), pneumonia (n = 486 484), UTIs (n = 463 010), and SSTIs (n = 218 792)). We included genetic instrumental variables with a genome-wide significance (P < 5 × 10-8) in the study. We used inverse variance-weighted estimation in the primary analysis and explored the stability of the results using multivariable MR analysis after adjusting for smoking, alcohol consumption, and body mass index. Results Genetically predicted years of schooling were associated with a reduced risk of sepsis (odds ratio (OR) = 0.763; 95% confidence interval (CI) = 0.668-0.870, P = 5.525 × 10-5), pneumonia (OR = 0.637; 95% CI = 0.577-0.702, P = 1.875 × 10-19), UTIs (OR = 0.995; 95% CI = 0.993-0.997, P = 1.229 × 10-5), and SSTIs (OR = 0.696; 95% CI = 0.605-0.801, P = 4.034 × 10-7). We observed consistent results for the correlation between qualifications and infectious diseases. These findings remained stable in the multivariable MR analyses. Conclusions Our findings suggest that increased educational attainment may be causally associated with a decreased risk of sepsis, pneumonia, UTIs, and SSTIs.
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Affiliation(s)
- Jueheng Liu
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Jiajia Ren
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Xiaoming Gao
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Chuchu Zhang
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Guorong Deng
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Jiamei Li
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Ruohan Li
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Xiaochuang Wang
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Gang Wang
- Department of Critical Care Medicine, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Key Laboratory of Surgical Critical Care and Life Support, Xi’an Jiaotong University, Ministry of Education, Xi’an, Shaanxi, China
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Lan Y, Chen L, Huang C, Wang X, Pu P. Associations of educational attainment with Sepsis mediated by metabolism traits and smoking: a Mendelian randomization study. Front Public Health 2024; 12:1330606. [PMID: 38362221 PMCID: PMC10867269 DOI: 10.3389/fpubh.2024.1330606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024] Open
Abstract
Objective Sepsis constitutes a significant global healthcare burden. Studies suggest a correlation between educational attainment and the likelihood of developing sepsis. Our goal was to utilize Mendelian randomization (MR) in order to examine the causal connection between educational achievement (EA) and sepsis, while measuring the mediating impacts of adjustable variables. Methods We collected statistical data summarizing educational achievement (EA), mediators, and sepsis from genome-wide association studies (GWAS). Employing a two-sample Mendelian randomization (MR) approach, we calculated the causal impact of education on sepsis. Following this, we performed multivariable MR analyses to assess the mediation proportions of various mediators, including body mass index (BMI), smoking, omega-3 fatty acids, and apolipoprotein A-I(ApoA-I). Results Genetic prediction of 1-SD (4.2 years) increase in educational attainment (EA) was negatively correlated with sepsis risk (OR = 0.83, 95% CI 0.71 to 0.96). Among the four identified mediators, ranked proportionally, they including BMI (38.8%), smoking (36.5%), ApoA-I (6.3%) and omega-3 (3.7%). These findings remained robust across a variety of sensitivity analyses. Conclusion The findings of this study provided evidence for the potential preventive impact of EA on sepsis, which may be influenced by factors including and metabolic traits and smoking. Enhancing interventions targeting these factors may contribute to reducing the burden of sepsis.
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Affiliation(s)
- Ying Lan
- Department of Critical Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, China
| | - Lvlin Chen
- Department of Critical Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, China
| | - Chao Huang
- Department of Critical Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, China
| | - Xiaoyan Wang
- Department of Clinical Nutrition, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Peng Pu
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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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: 4] [Impact Index Per Article: 4.0] [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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