Evidence map›Paper›PMID 38656780›Full record

ArticleJMIR public health and surveillance2024

Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response.

Jian-Sin Lee, Allison R B Tyler, Tiffany Christine Veinot, Elizabeth Yakel

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

8 citing papers in PubMed, 8 citations in OpenAlex.

  1. From Integrated Care to Learning Systems.Healthcare (Basel, Switzerland) · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Jian-Sin LeeSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID 0000-0002-8322-2981
Allison R B TylerUK Data Archive, University of Essex, Colchester, United Kingdom.ORCID 0000-0002-0350-7267
Tiffany Christine VeinotSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID 0000-0003-1200-9515
Elizabeth YakelSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID 0000-0002-8792-6900
University of Michigan · USUK Data Archive · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.

Indexed as

COVID-19Information DisseminationPublic HealthGovernmentHumansPandemicsCOVID-19crisis responsecross-sector collaborationdata infrastructuresdata sciencedata sharingpandemicpublic health

Identifiers

PMID38656780
PMCPMC11079773
OpenAlexW4392478835

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.