Accepted for/Published in: JMIR Cancer
Date Submitted: Nov 30, 2022
Open Peer Review Period: Nov 29, 2022 - Jan 24, 2023
Date Accepted: Mar 7, 2023
(closed for review but you can still tweet)
Exploring Cancer Incidence, Risk Factors and Mortality in the Lleida Region: An Interactive open-source R Shiny Application for cancer data analysis.
ABSTRACT
Background:
The cancer incidence rate is essential in public health surveillance. The analysis of this information allows the authorities to know the cancer situation in their regions.
Objective:
This study aimed to present the design and implementation of a shiny app to assist cancer registers in conducting rapid descriptive and predictive analytics in a user-friendly, intuitive, portable and scalable way. Moreover, we want to describe the design and implementation roadmap to inspire other population registers to exploit their datasets and develop similar tools and models.
Methods:
The first step is to consolidate the data into the population register cancer database. This data is cross-validated by ASEDAT software, checked later, and reviewed by experts. Next, we developed an online tool to visualise data and generate reports to assist decision-making under the R Shiny framework. Currently, the app can generate descriptive analytics using population variables, such as age, sex and cancer type; cancer incidence in region-level geographical heatmaps; line plots to visualise temporal trends and typical risk factor plots.
Results:
The results provide a successful case study where the tool was applied to the cancer register of the Lleida region. The study illustrates how researchers and cancer registers can use the app to analyse cancer databases. Furthermore, the results highlight the analytics related to risk factors and second tumours.
Conclusions:
This paper aims to show a successful methodology for exploiting the data in the population cancers register and propose guidelines for other similar records to develop similar tools. We intend to inspire other entities to build an app that can help decision-making and also make data more accessible and transparent for the community of users.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.