Evidence map›Paper›PMID 39224554›Full record

Observational studyFrontiers in public health2024

Multi-dimensional epidemiology and informatics data on COVID-19 wave at the end of zero COVID policy in China.

Xin-Sheng Yu, Shaoying Tan, Wanting Tang, Fang-Fang Zhao, Jie Ji, Jianwei Lin, Han-Jie He, Youxin Gu, Jia-Jian Liang, Meng Wang and 21 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

31 authors.

Xin-Sheng Yu *Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Shaoying Tan *School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Wanting Tang *Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Fang-Fang Zhao *Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Jie JiNetwork & Information Centre, Shantou University, Shantou, China.
Jianwei LinJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Han-Jie HeJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Youxin GuQueensland University of Technology, Brisbane, QLD, Australia.
Jia-Jian LiangJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Meng WangJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Yequn ChenThe First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Jiancheng YangShantou Healthcare Security Administration Center, Shantou, China.
Longxu XieHybribio Medical Laboratory Group Ltd., Chaozhou, China.
Qian WangYulin First Hospital, Yulin, China.
Mengyu LiuShantou University Medical College, Shantou, China.
Yang HeHybribio Medical Laboratory Group Ltd., Chaozhou, China.
Lan ChenJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Ya Xing WangBeijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Zhaoxiong WuJinping District People's Hospital of Shantou, Shantou, China.
Gang ZhaoZhengzhou Second Hospital, Henan, China.
Yi LiuZhengzhou Second Hospital, Henan, China.
Yun WangJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Dongning HaoYulin First Hospital, Yulin, China.
Jingyun CenShaoguan University Medical College, Shaoguan, China.
Shi-Qi YaoJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Dan ZhangJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
Lifang LiuJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.
David Chien LyeNational Centre for Infectious Diseases, Singapore, Singapore.
Zhifeng HaoCollege of Mathematics and Computer Science, Shantou University, Shantou, China.
Tien Yin WongTsinghua Medicine, Tsinghua University, Beijing, China.
Ling-Ping CenJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks. Methods: Retrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny. Results: Various diseases exhibited a notable upsurge in the BDI after the policy change, consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r > 0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% post-policy transition, with myocarditis escalating by 1,708% and pneumonia by 1,332%. In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the 2 months subsequent to the policy relaxation. Conclusion: This investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19's disease spectrum, advocating for the prospective utility of internet-derived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks.

Indexed as

COVID-19SARS-CoV-2Bayes TheoremChinaHealth PolicyHospitalizationHumansPandemicsRetrospective StudiesBaidu search indexBayesian structural time seriesCOVID-19Granger causality testzero-COVID policy

Identifiers

PMID39224554
PMCPMC11366567

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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.