评估2019冠状病毒病大流行对与常见感染相关的住院情况的影响：应对初级保健中抗菌药物耐药性的风险预测模型

Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections: Risk prediction models to tackle antimicrobial resistance in primary care.

作者信息

Fahmi Ali, Palin Victoria, Zhong Xiaomin, Yang Ya-Ting, Watts Simon, Ashcroft Darren M, Goldacre Ben, MacKenna Brian, Fisher Louis, Massey Jon, Mehrkar Amir, Bacon Seb, Hand Kieran, van Staa Tjeerd Pieter

机构信息

Centre for Health Informatics, Faculty of Biology, Medicine and Health, School of Health Sciences, the University of Manchester, Manchester, United Kingdom.

Maternal and Fetal Health Research Centre, Division of Developmental Biology and Medicine, the University of Manchester, Manchester, United Kingdom.

出版信息

PLoS One. 2024 Dec 31;19(12):e0311515. doi: 10.1371/journal.pone.0311515. eCollection 2024.

DOI:10.1371/journal.pone.0311515

PMID:39739781

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11687718/

Abstract

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.

摘要

背景

抗菌药物耐药性（AMR）是一个多方面的全球性挑战，部分原因是抗生素处方不当。本研究的目的是评估新冠疫情对常见感染治疗的影响，开发风险预测模型，并研究抗生素对感染相关住院情况的影响。

方法

在英国国民健康服务体系（NHS）英格兰地区的批准下，我们通过OpenSAFELY平台访问了凤凰医疗合作伙伴（TPP）的电子健康记录。我们纳入了2019年1月1日至2022年8月31日期间在初级医疗中被诊断为常见感染的成年患者，包括下呼吸道感染（LRTI）、上呼吸道感染（URTI）和下尿路感染（UTI）。我们排除了在感染诊断前90天至诊断后30天内有新冠记录的患者。使用Cox比例风险回归开发了常见感染诊断后30天内感染相关住院的风险预测模型。

结果

我们在2019年1月1日至2022年8月31日期间发现了12,745,165例感染诊断。其中，80,395例（2.05%）在随访期间住院。新冠疫情期间感染相关的住院人数下降，例如，下呼吸道感染从2019年12月的3950例降至2020年4月的520例。与未使用抗生素的患者相比，使用抗生素的患者中，下呼吸道感染住院风险降低幅度最大（调整后风险比（aHR）为0.35；95%置信区间（CI），0.35 - 0.36），下尿路感染也是如此（aHR 0.45；95% CI，0.44 - 0.46），而上呼吸道感染则不然（aHR 1.04；95% CI，1.03 - 1.06）。