Evidence map›Paper›PMID 40241913›Full record

ArticlePreventive oncology & epidemiology2024

Developing Catchment Area Data Dashboards for Cancer Centers: A Stakeholder-engaged Approach.

Kalyani Sonawane, Ketki N Borse, Melanie Jefferson, Haluk Damgacioglu, Matthew J Carpenter, John L Pearce, Besim Ogretmen, Sophie Paczesny, John P O'Bryan, Jihad S Obeid and 2 more

Abstract read
In one paragraph

Article in Preventive oncology & epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Kalyani SonawaneMUSC Hollings Cancer Center, Charleston, SC, USA.
Ketki N BorseMUSC Hollings Cancer Center, Charleston, SC, USA.
Melanie JeffersonMUSC Hollings Cancer Center, Charleston, SC, USA.
Haluk DamgaciogluMUSC Hollings Cancer Center, Charleston, SC, USA.
Matthew J CarpenterMUSC Hollings Cancer Center, Charleston, SC, USA.
John L PearceMUSC Hollings Cancer Center, Charleston, SC, USA.
Besim OgretmenMUSC Hollings Cancer Center, Charleston, SC, USA.
Sophie PaczesnyMUSC Hollings Cancer Center, Charleston, SC, USA.
John P O'BryanMUSC Hollings Cancer Center, Charleston, SC, USA.
Jihad S ObeidMUSC Hollings Cancer Center, Charleston, SC, USA.
Marvella E FordMUSC Hollings Cancer Center, Charleston, SC, USA.
Ashish A DeshmukhMUSC Hollings Cancer Center, Charleston, SC, USA.

Funding

Translational Science Laboratory Shared ResourceP30CA138313 · NCI · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI John J Lemasters · 2009 to 2026
$42.7M
Harnessing Big Data to Identify Geographic Clusters of Low-income children with Poor HPV Vaccination RatesK01MD016440 · NIMHD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI SONAWANE, KALYANI · 2021 to 2024
$464k
A Health Disparities Visualization Tool for Informing Cancer Prevention and Control EffortsG08LM014413 · NLM · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Kalyani Sonawane · 2024 to 2026
$418k
NCI NIH HHS P30 CA138313NIMHD NIH HHS K01 MD016440NLM NIH HHS G08 LM014413
6 · The paper itself

Abstract

Background: Data dashboards that can communicate complex and diverse catchment area data effectively can transform cancer prevention and care delivery and strengthen community engagement efforts. Engaging stakeholders in data dashboard development, by seeking their inputs and collecting feedback, has the potential to maximize user-centeredness. Objective: To describe a systematic, stakeholder-driven, and theory-based approach for developing catchment area data visualization tools for cancer centers. Methods: Cancer-relevant catchment area data were identified from national- and state-level data sources (including cancer registries, national surveys, and administrative claims databases). A prototype tool for data visualization was designed, developed, and tested based on the Results: We developed a visualization dashboard that illustrates cancer incidence and mortality, risk factor prevalence, healthcare access, and social determinants of health for the Hollings Cancer Center catchment area. Color-coded maps, time-series plots, and graphs illustrate these catchment area data. A total of 21 participants representing key stakeholders [general audience (n=4), community advisory board members and other representatives (n=7), and researchers (n=10)] were identified. The understandability and actionability scores exceeded the minimum (80%) threshold. Themes that emerged from qualitative data suggest that additional changes to the tool such as a warm color palette, data source transparency, and the addition of analytical features (data overlaying and area-resolution selection) would further enhance the tool. Integration of communication efforts and messages within a broader context is in progress. Discussion: A catchment area data resource developed through a systematic, stakeholder-driven, and theory-based approach can meet (and surpass) benchmarks for understandability and actionability, and lead to an overall positive response from stakeholders. Creating channels for advocacy and forming community partnerships will be the next step necessary to promote policies and programs for improving cancer outcomes in the catchment areas.

Identifiers

PMID40241913
PMCPMC12002557

What Socratic holds

Textmetadata
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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 Socratic graph.