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Evaluating Usability and Feasibility of Implementing a Novel Cancer Mapping Tool
Preprint   Open access

Evaluating Usability and Feasibility of Implementing a Novel Cancer Mapping Tool

Erin Wissler Gerdes, Jinyi Cai, Carly Mahoney, Grant Brown, Jacob Clark, Mary Charlton, Caglar Koylu, Emily Roberts, Brittany McKelvey, Charles Wiggins, …
Research square
11/15/2024
DOI: 10.21203/rs.3.rs-5321299/v1
PMCID: PMC11601841
PMID: 39606473
url
https://doi.org/10.21203/rs.3.rs-5321299/v1View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Purpose: Cancer registries are often asked to present cancer data for small geographic areas to inform and facilitate targeted interventions and prevention programs. However, it is challenging to compute and visualize reliable cancer estimates for areas with small case counts and populations to support cancer control planning. Methods: We used a Bayesian hierarchical model that borrows strength from neighboring areas and over time to produce cancer estimates for small areas. We developed a visual analytics platform to present these estimates in interactive graphics that demonstrate risk in small areas. In a user-centered design process, development of the tool was informed by cancer registry and public health professionals through focus groups and surveys. Results: The Cancer Analytics and Maps for Small Areas tool (CAMSA) provides age-adjusted cancer incidence and mortality rates and risk probabilities for eight cancers at the county and ZIP-code tabulation area (ZCTA) levels. It allows the user to identify cancer hotpots, including among sub-groups defined by sex and race/ethnicity. Potential end users were enthusiastic about the opportunity to implement CAMSA within their practice, emphasizing the tool's potential for increasing collaborative opportunities at local and state levels. Suggestions for improvement included adding map overlays such as additional cancer risk variables and incorporating functionalities like exportable data tables. Conclusions: CAMSA presents cancer rate and risk estimates for small geographic areas where they may have previously been suppressed. Through our user-informed design process, we developed statistical models and data visualizations to support the needs of an array of potential end users.Purpose: Cancer registries are often asked to present cancer data for small geographic areas to inform and facilitate targeted interventions and prevention programs. However, it is challenging to compute and visualize reliable cancer estimates for areas with small case counts and populations to support cancer control planning. Methods: We used a Bayesian hierarchical model that borrows strength from neighboring areas and over time to produce cancer estimates for small areas. We developed a visual analytics platform to present these estimates in interactive graphics that demonstrate risk in small areas. In a user-centered design process, development of the tool was informed by cancer registry and public health professionals through focus groups and surveys. Results: The Cancer Analytics and Maps for Small Areas tool (CAMSA) provides age-adjusted cancer incidence and mortality rates and risk probabilities for eight cancers at the county and ZIP-code tabulation area (ZCTA) levels. It allows the user to identify cancer hotpots, including among sub-groups defined by sex and race/ethnicity. Potential end users were enthusiastic about the opportunity to implement CAMSA within their practice, emphasizing the tool's potential for increasing collaborative opportunities at local and state levels. Suggestions for improvement included adding map overlays such as additional cancer risk variables and incorporating functionalities like exportable data tables. Conclusions: CAMSA presents cancer rate and risk estimates for small geographic areas where they may have previously been suppressed. Through our user-informed design process, we developed statistical models and data visualizations to support the needs of an array of potential end users.

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