Evidence map›Paper›PMID 39288409›Full record

ArticleJournal of medical Internet research2024

Public Maternal Health Dashboards in the United States: Descriptive Assessment.

Jennifer A Callaghan-Koru, Paige Newman Chargois, Tanvangi Tiwari, Clare C Brown, William Greenfield, Güneş Koru

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Jennifer A Callaghan-KoruFay W Boozman College of Public Health, University of Arkansas for Medical Sciences, Springdale, AR, United States.ORCID 0000-0002-3007-1702
Paige Newman ChargoisFay W Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States.ORCID 0009-0002-8316-0378
Tanvangi TiwariFay W Boozman College of Public Health, University of Arkansas for Medical Sciences, Springdale, AR, United States.ORCID 0009-0004-8076-7056
Clare C BrownFay W Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States.ORCID 0000-0001-7149-4739
William GreenfieldDepartment of Obstetrics & Gynecology, University of Arkansas for Medical Sciences, Little Rock, AR, United States.ORCID 0000-0002-0339-4493
Güneş KoruFay W Boozman College of Public Health, University of Arkansas for Medical Sciences, Springdale, AR, United States.ORCID 0000-0003-1693-4986

Funding

Expanding Translational Research in ArkansasUL1TR003107 · NCATS · UNIV OF ARKANSAS FOR MED SCIS · PI JAMES, LAURA P · 2019 to 2023
$21.6M
Algorithmic fairness in predictive models to eliminate disparities in adverse infant outcomes: A case for raceK01MD018072 · NIMHD · UNIV OF ARKANSAS FOR MED SCIS · PI BROWN, CLARE C · 2022 to 2025
$496k
NCATS NIH HHS UL1 TR003107NIMHD NIH HHS K01 MD018072
6 · The paper itself

Abstract

backgroundData dashboards have become more widely used for the public communication of health-related data, including in maternal health.

objectiveWe aimed to evaluate the content and features of existing publicly available maternal health dashboards in the United States.

methodsThrough systematic searches, we identified 80 publicly available, interactive dashboards presenting US maternal health data. We abstracted and descriptively analyzed the technical features and content of identified dashboards across four areas: (1) scope and origins, (2) technical capabilities, (3) data sources and indicators, and (4) disaggregation capabilities. Where present, we abstracted and qualitatively analyzed dashboard text describing the purpose and intended audience.

resultsMost reviewed dashboards reported state-level data (58/80, 72%) and were hosted on a state health department website (48/80, 60%). Most dashboards reported data from only 1 (33/80, 41%) or 2 (23/80, 29%) data sources. Key indicators, such as the maternal mortality rate (10/80, 12%) and severe maternal morbidity rate (12/80, 15%), were absent from most dashboards. Included dashboards used a range of data visualizations, and most allowed some disaggregation by time (65/80, 81%), geography (65/80, 81%), and race or ethnicity (55/80, 69%). Among dashboards that identified their audience (30/80, 38%), legislators or policy makers and public health agencies or organizations were the most common audiences.

conclusionsWhile maternal health dashboards have proliferated, their designs and features are not standard. This assessment of maternal health dashboards in the United States found substantial variation among dashboards, including inconsistent data sources, health indicators, and disaggregation capabilities. Opportunities to strengthen dashboards include integrating a greater number of data sources, increasing disaggregation capabilities, and considering end-user needs in dashboard design.

Indexed as

Maternal HealthDashboard SystemsFemaleHumansPregnancyPublic HealthUnited Statesdashboarddata communicationdata visualizationmaternal healthperinatal health

Identifiers

PMID39288409
PMCPMC11445621

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.