Evidence map›Paper›PMID 35124544›Full record

ArticleSocial science & medicine (1982)2022

Government-sponsored disinformation and the severity of respiratory infection epidemics including COVID-19: A global analysis, 2001-2020.

Thung-Hong Lin, Min-Chiao Chang, Chun-Chih Chang, Ya-Hsuan Chou

Abstract read
In one paragraph

Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025
    Pooled it
  2. Article
  3. Article
  4. Infodemic: Challenges and solutions in topic discovery and data process.Archives of public health = Archives belges de sante publique · 2023
    Article
  5. Article
  6. Article
  7. Article
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

4 authors.

Thung-Hong LinResearch Fellow, Institute of Sociology, Academia Sinica, Taiwan. Electronic address: zoo42@gate.sinica.edu.tw.
Min-Chiao ChangMackay Memorial Hospital, Post graduate year program, Taiwan.
Chun-Chih ChangAssociate Professor, Department of Political Science, Xiamen University, China.
Ya-Hsuan ChouMA, National Taiwan University, College of Social Sciences, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Internet misinformation and government-sponsored disinformation campaigns have been criticized for their presumed/hypothesized role in worsening the coronavirus disease 2019 (COVID-19) pandemic. We hypothesize that these government-sponsored disinformation campaigns have been positively associated with infectious disease epidemics, including COVID-19, over the last two decades. By integrating global surveys from the Digital Society Project, Global Burden of Disease, and other data sources across 149 countries for the period 2001-2019, we examined the association between government-sponsored disinformation and the spread of respiratory infections before the COVID-19 outbreak. Then, building on those results, we applied a negative binomial regression model to estimate the associations between government-sponsored disinformation and the confirmed cases and deaths related to COVID-19 during the first 300 days of the outbreak in each country and before vaccination began. After controlling for climatic, public health, socioeconomic, and political factors, we found that government-sponsored disinformation was significantly associated with the incidence and prevalence percentages of respiratory infections in susceptible populations during the period 2001-2019. The results also show that disinformation is significantly associated with the incidence rate ratio (IRR) of cases of COVID-19. The findings imply that governments may contain the damage associated with pandemics by ending their sponsorship of disinformation campaigns.

Indexed as

COVID-19DisinformationGovernmentHumansPandemicsSARS-CoV-2COVID-19DisinformationGovernment-sponsored disinformationInternet censorshipPandemicRespiratory infections

Identifiers

PMID35124544
PMCPMC8789386

What OpenQuestion holds

Textmetadata
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.