Evidence map›Paper›PMID 37464368›Full record

ArticleBMC public health2023

Immediate and long-term changes in infectious diseases in China at the "First-level-response", "Normalized-control" and "Dynamic-COVID-zero" stages from 2020 to 2022: a multistage interrupted-time-series-analysis.

Tianshan Shi, Xiaoshu Zhang, Lei Meng, Donghua Li, Na Jin, Xin Zhao, Hongmiao Zheng, Tingrong Wang, Rui Li, Xiaowei Ren

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.8field-weighted citation impact, top 17% 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

9 citing papers in PubMed, 8 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Tianshan Shi *Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
Xiaoshu Zhang *Gansu Provincial Center for Disease Control and Prevention, Lanzhou, Gansu, China.
Lei MengGansu Provincial Center for Disease Control and Prevention, Lanzhou, Gansu, China.
Donghua LiInstitute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
Na JinGansu Provincial Center for Disease Control and Prevention, Lanzhou, Gansu, China.
Xin ZhaoInstitute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
Hongmiao ZhengInstitute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
Tingrong WangInstitute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
Rui LiInstitute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
Xiaowei RenInstitute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China. renxw@lzu.edu.cn.
Lanzhou University · CNGansu Provincial Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFrom January 2020 to December 2022, China implemented "First-level-response", "Normalized-control" and "Dynamic-COVID-zero" to block the COVID-19 epidemic; however, the immediate and long-term impact of three strategies on other infectious diseases and the difference in their impact is currently unknown. We aim to provide a more comprehensive understanding of the impact of non-pharmacological interventions (NPIs) on infectious diseases in China.

methodsWe collected data on the monthly case count of infectious diseases in China from January 2015 to July 2022. After considering long-term trends using the Cox-Stuart test, we performed the two ratio Z tests to preliminary analyze the impact of three strategies on infectious diseases. Next, we used a multistage interrupted-time-series analysis fitted by the Poisson regression to evaluate and compare the immediate and long-term impact of three strategies on infectious diseases in China.

resultsCompared to before COVID-19, the incidence of almost all infectious diseases decreased immediately at stages 1, 2, and 3; meanwhile, the slope in the incidence of many infectious diseases also decreased at the three stages. However, the slope in the incidence of all sexually transmitted diseases increased at stage 1, the slope in the incidence of all gastrointestinal infectious diseases increased at stage 2, and the slope in the incidence of some diseases such as pertussis, influenza, and brucellosis increased at stage 3. The immediate and long-term limiting effects of "Normalized-control" on respiratory-transmitted diseases were weaker than "First-level-response" and the long-term limiting effects of "Dynamic-COVID-zero" on pertussis, influenza, and hydatid disease were weaker than "Normalized-control".

conclusionsThree COVID-19 control strategies in China have immediate and long-term limiting effects on many infectious diseases, but there are differences in their limiting effects. Evidence from this study shows that pertussis, influenza, brucellosis, and hydatid disease began to recover at stage 3, and relaxation of NPIs may lead to the resurgence of respiratory-transmitted diseases and vector-borne diseases.

Indexed as

BrucellosisCommunicable DiseasesCOVID-19EchinococcosisInfluenza, HumanRespiration DisordersRespiratory Tract DiseasesWhooping CoughChinaHumansCOVID-19Dynamic-COVID-zeroInfectious diseaseInterrupted time series analysisNon-pharmaceutical intervention

Identifiers

PMID37464368
PMCPMC10354895
OpenAlexW4384664956

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.