Evidence map›Paper›PMID 36895021›Full record

SynthesisInfectious diseases of poverty2023

Non-pharmaceutical interventions for COVID-19 reduced the incidence of infectious diseases: a controlled interrupted time-series study.

Wenyi Zhang, Yao Wu, Bo Wen, Yongming Zhang, Yong Wang, Wenwu Yin, Shanhua Sun, Xianyu Wei, Hailong Sun, Zhijie Zhang and 2 more

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Infectious diseases of poverty, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
7.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 33 citations in OpenAlex.

  1. Article
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  7. Effects of the COVID-19 Pandemic on Tuberculosis Outcomes in the United States: A Bayesian Analysis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
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  13. Etiological characteristics of Mycoplasma pneumoniae infection and macrolide resistance in children in beijing: a retrospective study from 2018 to 2024.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 6 institutions in 2 countries.

Wenyi Zhang *Division of Disease Surveillance, Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China.
Yao Wu *School of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia.ORCID http://orcid.org/0000-0002-8105-0522
Bo Wen *School of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia.
Yongming Zhang *Department of Respiratory and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, 100029, China.
Yong WangDivision of Disease Surveillance, Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China.
Wenwu YinDivision of Infectious Diseases, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Shanhua SunBeijing Center for Disease Prevention and Control, Beijing, 100013, China.
Xianyu WeiDivision of Disease Surveillance, Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China.
Hailong SunDivision of Disease Surveillance, Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China.
Zhijie ZhangDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Shanshan LiSchool of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia. shanshan.li@monash.edu.
Yuming GuoSchool of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia. yuming.guo@monash.edu.
Chinese Center For Disease Control and Prevention · CNMonash Health · AUMonash University · AUBeijing Center for Disease Prevention and Control · CNChina-Japan Friendship Hospital · CNFudan University · CN

Funding

Australian Research Council DP210102076China Scholarship Council 202006010043China Scholarship Council 202006010044Chinese Major Grant for the Prevention and Control of Infectious Diseases 2018ZX10713003National Health and Medical Research Council GNT1163693National Health and Medical Research Council GNT2000581National Health and Medical Research Council GNT2008813National Health and Medical Research Council GNT2009866
6 · The paper itself

Abstract

backgroundNon-pharmaceutical interventions (NPIs) have been implemented worldwide to suppress the spread of coronavirus disease 2019 (COVID-19). However, few studies have evaluated the effect of NPIs on other infectious diseases and none has assessed the avoided disease burden associated with NPIs. We aimed to assess the effect of NPIs on the incidence of infectious diseases during the COVID-19 pandemic in 2020 and evaluate the health economic benefits related to the reduction in the incidence of infectious diseases.

methodsData on 10 notifiable infectious diseases across China during 2010-2020 were extracted from the China Information System for Disease Control and Prevention. A two-stage controlled interrupted time-series design with a quasi-Poisson regression model was used to examine the impact of NPIs on the incidence of infectious diseases. The analysis was first performed at the provincial-level administrative divisions (PLADs) level in China, then the PLAD-specific estimates were pooled using a random-effect meta-analysis.

resultsA total of 61,393,737 cases of 10 infectious diseases were identified. The implementation of NPIs was associated with 5.13 million (95% confidence interval [CI] 3.45‒7.42) avoided cases and USD 1.77 billion (95% CI 1.18‒2.57) avoided hospital expenditures in 2020. There were 4.52 million (95% CI 3.00‒6.63) avoided cases for children and adolescents, corresponding to 88.2% of total avoided cases. The top leading cause of avoided burden attributable to NPIs was influenza [avoided percentage (AP): 89.3%; 95% CI 84.5‒92.6]. Socioeconomic status and population density were effect modifiers.

conclusionsNPIs for COVID-19 could effectively control the prevalence of infectious diseases, with patterns of risk varying by socioeconomic status. These findings have important implications for informing targeted strategies to prevent infectious diseases.

Indexed as

Communicable DiseasesCOVID-19AdolescentChildHumansIncidencePandemicsSARS-CoV-2COVID-19Infectious diseasesNon-pharmaceutical interventionPrevalence

Identifiers

PMID36895021
PMCPMC9996566
OpenAlexW4323656542

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.