ArticleCancer causes & control : CCC2022
An informatics infrastructure to catalyze cancer control research and practice.
Article in Cancer causes & control : CCC, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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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.
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Who cites it
3 citing papers in PubMed.
- Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.Journal of biomedical informatics · 2026Article
- Gaps in artificial intelligence research for rural health in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Prostate-specific antigen testing patterns and prostate cancer stage at diagnosis in older Ohio cancer patients.Cancer causes & control : CCC · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
purposeA disconnect often exists between those with the expertise to manage and analyze complex, multi-source data sets, and the clinical, social services, advocacy, and public health professionals who can pose the most relevant questions and best apply the answers. We describe development and implementation of a cancer informatics infrastructure aimed at broadening the usability of community cancer data to inform cancer control research and practice; and we share lessons learned.
methodsWe built a multi-level database known as The Ohio Cancer Assessment and Surveillance Engine (OH-CASE) to link data from Ohio's cancer registry with community data from the U.S. Census and other sources. Space-and place-based characteristics were assigned to individuals according to residential address. Stakeholder input informed development of an interface for generating queries based on geographic, demographic, and disease inputs and for outputting results aggregated at the state, county, municipality, or zip code levels.
resultsOH-CASE contains data on 791,786 cancer cases diagnosed from 1/1/2006 to 12/31/2018 across 88 Ohio counties containing 1215 municipalities and 1197 zip codes. Stakeholder feedback from cancer center community outreach teams, advocacy organizations, public health, and researchers suggests a broad range of uses of such multi-level data resources accessible via a user interface.
conclusionOH-CASE represents a prototype of a transportable model for curating and synthesizing data to understand cancer burden across communities. Beyond supporting collaborative research, this infrastructure can serve the clinical, social services, public health, and advocacy communities by enabling targeting of outreach, funding, and interventions to narrow cancer disparities.
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Registered trials
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