Evidence map›Paper›PMID 38546731›Full record

ArticleJMIR public health and surveillance2024

Data Sharing in a Decentralized Public Health System: Lessons From COVID-19 Syndromic Surveillance.

Ryan C Rigby, Alva O Ferdinand, Hye-Chung Kum, Cason Schmit

Abstract 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Observational
  2. 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.

Ryan C RigbyProgram in Health, Law, and Policy, Department of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States.ORCID 0009-0008-3416-9732
Alva O FerdinandDepartment of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States.ORCID 0000-0002-5941-8335
Hye-Chung KumPopulation Informatics Lab, Department of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States.ORCID 0000-0002-6882-8053
Cason SchmitProgram in Health, Law, and Policy, Department of Health Policy and Management, Texas A&M University School of Public Health, College Station, TX, United States.ORCID 0000-0002-7929-7198

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic revealed that data sharing challenges persist across public health information systems. We examine the specific challenges in sharing syndromic surveillance data between state, local, and federal partners. These challenges are complicated by US federalism, which decentralizes public health response and creates friction between different government units. The current policies restrict federal access to state and local syndromic surveillance data without each jurisdiction's consent. These policies frustrate legitimate federal governmental interests and are contrary to ethical guidelines for public health data sharing. Nevertheless, state and local public health agencies must continue to play a central role as there are important risks in interpreting syndromic surveillance data without understanding local contexts. Policies establishing a collaborative framework will be needed to support data sharing between federal, state, and local partners. A collaborative framework would be enhanced by a governance group with robust state and local involvement and policy guardrails to ensure the use of data is appropriate. These policy and relational challenges must be addressed to actualize a truly national public health information system.

Indexed as

COVID-19HumansInformation DisseminationPandemicsPublic HealthSentinel Surveillancecollaborative frameworkCOVID-19COVID-19 pandemicdata sharingdecentralizeddigital healthdigital technologyethical guidelinesfederalismhealth datahealth informationhealth policyinfodemiologyinformation systeminnovationpublic healthrisk scoreSARS-CoV-2surveillancesyndromic surveillancetechnologyUnited States

Identifiers

PMID38546731
PMCPMC11009847

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.