TY - JOUR
T1 - Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections
T2 - Risk prediction models to tackle antimicrobial resistance in primary care
AU - OpenSAFELY collaborative
AU - Fahmi, Ali
AU - Palin, Victoria
AU - Zhong, Xiaomin
AU - Yang, Ya-Ting
AU - Watts, Simon
AU - Ashcroft, Darren M
AU - Goldacre, Ben
AU - MacKenna, Brian
AU - Fisher, Louis
AU - Massey, Jon
AU - Mehrkar, Amir
AU - Bacon, Seb
AU - Hand, Kieran
AU - van Staa, Tjeerd Pieter
N1 - Copyright: © 2024 Fahmi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/12/31
Y1 - 2024/12/31
N2 - BACKGROUND: Antimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The objectives of this study were to evaluate the impact of the COVID-19 pandemic on treatment of common infections, develop risk prediction models and examine the effects of antibiotics on infection-related hospital admissions.METHODS: With the approval of NHS England, we accessed electronic health records from The Phoenix Partnership (TPP) through OpenSAFELY platform. We included adult patients with primary care diagnosis of common infections, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI), from 1 January 2019 to 31 August 2022. We excluded patients with a COVID-19 record in the 90 days before to 30 days after the infection diagnosis. Risk prediction models using Cox proportional-hazard regression were developed for infection-related hospital admission in the 30 days after the common infection diagnosis.RESULTS: We found 12,745,165 infection diagnoses from 1 January 2019 to 31 August 2022. Of them, 80,395 (2.05%) cases were admitted to the hospital during follow-up. Counts of hospital admission for infections dropped during COVID-19, for example LRTI from 3,950 in December 2019 to 520 in April 2020. Comparing those prescribed an antibiotic to those without, reduction in risk of hospital admission were largest with LRTI (adjusted hazard ratio (aHR) of 0.35; 95% confidence interval (CI), 0.35-0.36) and UTI (aHR 0.45; 95% CI, 0.44-0.46), compared to URTI (aHR 1.04; 95% CI, 1.03-1.06).CONCLUSIONS: A substantial variation in hospital admission risks between infections and patient groups was found. Antibiotics appeared more effective in preventing infection-related complications with LRTI and UTI, but not URTI. While this study has several limitations, the results indicate that a focus on risk-based antibiotic prescribing could help tackle AMR in primary care.
AB - BACKGROUND: Antimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The objectives of this study were to evaluate the impact of the COVID-19 pandemic on treatment of common infections, develop risk prediction models and examine the effects of antibiotics on infection-related hospital admissions.METHODS: With the approval of NHS England, we accessed electronic health records from The Phoenix Partnership (TPP) through OpenSAFELY platform. We included adult patients with primary care diagnosis of common infections, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI), from 1 January 2019 to 31 August 2022. We excluded patients with a COVID-19 record in the 90 days before to 30 days after the infection diagnosis. Risk prediction models using Cox proportional-hazard regression were developed for infection-related hospital admission in the 30 days after the common infection diagnosis.RESULTS: We found 12,745,165 infection diagnoses from 1 January 2019 to 31 August 2022. Of them, 80,395 (2.05%) cases were admitted to the hospital during follow-up. Counts of hospital admission for infections dropped during COVID-19, for example LRTI from 3,950 in December 2019 to 520 in April 2020. Comparing those prescribed an antibiotic to those without, reduction in risk of hospital admission were largest with LRTI (adjusted hazard ratio (aHR) of 0.35; 95% confidence interval (CI), 0.35-0.36) and UTI (aHR 0.45; 95% CI, 0.44-0.46), compared to URTI (aHR 1.04; 95% CI, 1.03-1.06).CONCLUSIONS: A substantial variation in hospital admission risks between infections and patient groups was found. Antibiotics appeared more effective in preventing infection-related complications with LRTI and UTI, but not URTI. While this study has several limitations, the results indicate that a focus on risk-based antibiotic prescribing could help tackle AMR in primary care.
KW - Humans
KW - COVID-19/epidemiology
KW - Primary Health Care
KW - Female
KW - Male
KW - Hospitalization
KW - Middle Aged
KW - Aged
KW - Anti-Bacterial Agents/therapeutic use
KW - Adult
KW - SARS-CoV-2/isolation & purification
KW - Pandemics
KW - Urinary Tract Infections/epidemiology
KW - Respiratory Tract Infections/drug therapy
KW - England/epidemiology
KW - Proportional Hazards Models
KW - Aged, 80 and over
U2 - 10.1371/journal.pone.0311515
DO - 10.1371/journal.pone.0311515
M3 - Article
C2 - 39739781
SN - 1932-6203
VL - 19
JO - PLoS ONE
JF - PLoS ONE
IS - 12
M1 - e0311515
ER -