Predicting Cancer Types Using Community factors. The Role of Deprivation, Race and Age

Predicting Cancer Types Using Community factors. The Role of Deprivation, Race and Age | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting Cancer Types Using Community factors. The Role of Deprivation, Race and Age Sotirios Raptis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6414005/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: 25 different cancer types are examined in Scotland as function of demographic factors that include their deprivation level. Findings can help poli-cymakers to design recovery plans per case and one used pure open demographic data from NHSS to link race to cancer type. Methods: Cancer’s progression is factored on four basic prevalent factors that may be linked to all types. These are (a) age bands, (b) race, (c) deprivation index, and (d) gender. The distributions of cancers per such attribute are taken and then three 3 different statistics measures are taken to address two questions: (1) are there economic factors that can capture cancer occurrence differences among these types ? (2) can some type prevail using them ?. The methods are: (a) the Kullback Leibler divergence transformation, (b) the significance test (p.value) of the similarity using the Chi-squared test, (c) the Entropy. These are compared. The raw data link these economy factors to all cancer types and can support the study of their joint distributions. The attributes that cause the most diverging distributions (frequencies of cancer types) among its values are taken as the more informative ones. All values for those attributes are examined that give quite different frequencies of cancer. This is an information-theoretic approach to the problem that accounts for common economy variables and reveals that diversity of age and race may be driver for cancer. Brief statement of primary results: The results show that among the four attributes studied age is the prevalent cause for having quite different occurrences of cancer given the data at hand. Conclusion: Policymakers can use such findings to roughly understand how cancer progression (types) can link to major demographics. Cancer factors cohorts probability significance tests Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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