Evidence map›Paper›PMID 36750286›Full record

ArticleBMJ open2023

Canadians' opinions towards COVID-19 data-sharing: a national cross-sectional survey.

Sarah A Savic Kallesoe, Tian Rabbani, Erin E Gill, Fiona Brinkman, Emma J Griffiths, Ma'n Zawati, Hanshi Liu, Nicole Palmour, Yann Joly, William W L Hsiao

Abstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. 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

10 authors.

Sarah A Savic KallesoeSimon Fraser University Faculty of Health Sciences, Burnaby, British Columbia, Canada.ORCID 0000-0003-1329-8275
Tian RabbaniSimon Fraser University Faculty of Health Sciences, Burnaby, British Columbia, Canada.ORCID 0000-0002-9661-4749
Erin E GillDepartment of Molecular Biology and Biochemistry, Simon Fraser University Faculty of Sciences, Burnaby, British Columbia, Canada.
Fiona BrinkmanDepartment of Molecular Biology and Biochemistry, Simon Fraser University Faculty of Sciences, Burnaby, British Columbia, Canada.
Emma J GriffithsSimon Fraser University Faculty of Health Sciences, Burnaby, British Columbia, Canada.
Ma'n ZawatiDepartment of Human Genetics, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada.
Hanshi LiuDepartment of Human Genetics, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada.
Nicole PalmourDepartment of Human Genetics, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada.
Yann JolyDepartment of Human Genetics, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada.ORCID 0000-0002-8775-2322
William W L HsiaoSimon Fraser University Faculty of Health Sciences, Burnaby, British Columbia, Canada wwhsiao@sfu.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesCOVID-19 research has significantly contributed to pandemic response and the enhancement of public health capacity. COVID-19 data collected by provincial/territorial health authorities in Canada are valuable for research advancement yet not readily available to the public, including researchers. To inform developments in public health data-sharing in Canada, we explored Canadians' opinions of public health authorities sharing deidentified individual-level COVID-19 data publicly. DESIGN/SETTING/INTERVENTIONS/OUTCOMES: A national cross-sectional survey was administered in Canada in March 2022, assessing Canadians' opinions on publicly sharing COVID-19 datatypes. Market research firm Léger was employed for recruitment and data collection.

participantsAnyone greater than or equal to 18 years and currently living in Canada.

results4981 participants completed the survey with a 92.3% response rate. 79.7% were supportive of provincial/territorial authorities publicly sharing deidentified COVID-19 data, while 20.3% were hesitant/averse/unsure. Datatypes most supported for being shared publicly were symptoms (83.0% in support), geographical region (82.6%) and COVID-19 vaccination status (81.7%). Datatypes with the most aversion were employment sector (27.4% averse), postal area (26.7%) and international travel history (19.7%). Generally supportive Canadians were characterised as being ≥50 years, with higher education, and being vaccinated against COVID-19 at least once. Vaccination status was the most influential predictor of data-sharing opinion, with respondents who were ever vaccinated being 4.20 times more likely (95% CI 3.21 to 5.48, p=0.000) to be generally supportive of data-sharing than those unvaccinated.

conclusionsThese findings suggest that the Canadian public is generally favourable to deidentified data-sharing. Identifying factors that are likely to improve attitudes towards data-sharing are useful to stakeholders involved in data-sharing initiatives, such as public health agencies, in informing the development of public health communication and data-sharing policies. As Canada progresses through the COVID-19 pandemic, and with limited testing and reporting of COVID-19 data, it is essential to improve deidentified data-sharing given the public's general support for these efforts.

Indexed as

COVID-19CanadaCOVID-19 VaccinesCross-Sectional StudiesHumansPandemicsPublic OpinionCOVID-19 VaccinesCOVID-19EpidemiologyGENETICSHealth policyPublic health

Identifiers

PMID36750286
PMCPMC9905784

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.