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Zhong X, Palin V, Ashcroft DM, Goldacre B, MacKenna B, Mehrkar A, Bacon SCJ, Massey J, Inglesby P, Hand K, Pate A, van Staa TP. Risk of emergency hospital admission related to adverse events after antibiotic treatment in adults with a common infection: impact of COVID-19 and derivation and validation of risk prediction models. BMC Med 2024; 22:277. [PMID: 38956603 PMCID: PMC11220965 DOI: 10.1186/s12916-024-03480-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/12/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND With the global challenge of antimicrobial resistance intensified during the COVID-19 pandemic, evaluating adverse events (AEs) post-antibiotic treatment for common infections is crucial. This study aims to examines the changes in incidence rates of AEs during the COVID-19 pandemic and predict AE risk following antibiotic prescriptions for common infections, considering their previous antibiotic exposure and other long-term clinical conditions. METHODS With the approval of NHS England, we used OpenSAFELY platform and analysed electronic health records from patients aged 18-110, prescribed antibiotics for urinary tract infection (UTI), lower respiratory tract infections (LRTI), upper respiratory tract infections (URTI), sinusitis, otitis externa, and otitis media between January 2019 and June 2023. We evaluated the temporal trends in the incidence rate of AEs for each infection, analysing monthly changes over time. The survival probability of emergency AE hospitalisation was estimated in each COVID-19 period (period 1: 1 January 2019 to 25 March 2020, period 2: 26 March 2020 to 8 March 2021, period 3: 9 March 2021 to 30 June 2023) using the Kaplan-Meier approach. Prognostic models, using Cox proportional hazards regression, were developed and validated to predict AE risk within 30 days post-prescription using the records in Period 1. RESULTS Out of 9.4 million patients who received antibiotics, 0.6% of UTI, 0.3% of URTI, and 0.5% of LRTI patients experienced AEs. UTI and LRTI patients demonstrated a higher risk of AEs, with a noted increase in AE incidence during the COVID-19 pandemic. Higher comorbidity and recent antibiotic use emerged as significant AE predictors. The developed models exhibited good calibration and discrimination, especially for UTIs and LRTIs, with a C-statistic above 0.70. CONCLUSIONS The study reveals a variable incidence of AEs post-antibiotic treatment for common infections, with UTI and LRTI patients facing higher risks. AE risks varied between infections and COVID-19 periods. These findings underscore the necessity for cautious antibiotic prescribing and call for further exploration into the intricate dynamics between antibiotic use, AEs, and the pandemic.
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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, Manchester, M13 9PL, UK.
- Applied Health Research Unit, Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK.
| | - Victoria Palin
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, Manchester, M13 9PL, UK
- Maternal and Fetal Research Centre, Division of Developmental Biology and Medicine, the University of Manchester, St Marys Hospital, Oxford Road, Manchester, M13 9WL, UK
| | - Darren M Ashcroft
- Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
- NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, 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, Oxford, OX2 6GG, UK
| | - Sebastian C J Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Jon Massey
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
| | - Kieran Hand
- Pharmacy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
- NHS England, Wellington House, Waterloo Road, London, SE1 8UG, UK
| | - Alexander Pate
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, Manchester, M13 9PL, UK
| | - Tjeerd Pieter van Staa
- Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, Manchester, M13 9PL, UK
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Xu L, Ceolotto N, Jagadeesan K, Standerwick R, Robertson M, Barden R, Kasprzyk-Hordern B. Antimicrobials and antimicrobial resistance genes in the shadow of COVID-19 pandemic: A wastewater-based epidemiology perspective. WATER RESEARCH 2024; 257:121665. [PMID: 38692256 DOI: 10.1016/j.watres.2024.121665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 03/21/2024] [Accepted: 04/21/2024] [Indexed: 05/03/2024]
Abstract
Higher usage of antimicrobial agents in both healthcare facilities and the communities has resulted in an increased spread of resistant bacteria. However, the improved infection prevention and control practices may also contribute to decreasing antimicrobial resistance (AMR). In the present study, wastewater-based epidemiology (WBE) approach was applied to explore the link between COVID-19 and the community usage of antimicrobials, as well as the prevalence of resistance genes. Longitudinal study has been conducted to monitor the levels of 50 antimicrobial agents (AAs), 24 metabolites, 5 antibiotic resistance genes (ARGs) and class 1 integrons (intI 1) in wastewater influents in 4 towns/cities over two years (April 2020 - March 2022) in the South-West of England (a total of 1,180 samples collected with 87,320 individual AA measurements and 8,148 ARG measurements). Results suggested higher loads of AAs and ARGs in 2021-22 than 2020-21, with beta-lactams, quinolones, macrolides and most ARGs showing statistical differences. In particular, the intI 1 gene (a proxy of environmental ARG pollution) showed a significant increase after the ease of the third national lockdown in England. Positive correlations for all quantifiable parent AAs and metabolites were observed, and consumption vs direct disposal of unused AAs has been identified via WBE. This work can help establish baselines for AMR status in communities, providing community-wide surveillance and evidence for informing public health interventions. Overall, studies focused on AMR from the start of the pandemic to the present, especially in the context of environmental settings, are of great importance to further understand the long-term impact of the pandemic on AMR.
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Affiliation(s)
- Like Xu
- Department of Chemistry, University of Bath, Bath BA2 7AY, UK
| | - Nicola Ceolotto
- Department of Chemistry, University of Bath, Bath BA2 7AY, UK; Institute for Sustainability, University of Bath, Bath BA2 7AY, UK
| | | | | | | | - Ruth Barden
- Wessex Water Service Ltd., Claverton Down, Bath BA2 7WW, UK
| | - Barbara Kasprzyk-Hordern
- Department of Chemistry, University of Bath, Bath BA2 7AY, UK; Institute for Sustainability, University of Bath, Bath BA2 7AY, UK.
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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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