Exploring the Relationship Between Doctors Availability and Mortality in Italy: A Machine Learning Approach using Multivariate Regression Trees

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Abstract We investigate how the percentages of medical specialists impact mortality rates and how they vary across different areas and time periods. The analysis is focused on Italian provinces during two different years, 2011 and 2019, to identify differences and similarities across geographical areas and time periods. We propose a new approach based on tree-based methodology that we will call \textit{Machine learning Enhanced Regression of ITAlian mortality rates (MERITA)}. It is based on a tree-based method called Multivariate RegressionTree (MRT), which is an extension of univariate regression trees. In this study, the MRT is used to identify the relationship between the percentage of active doctors by specialization per10,000 inhabitants and the Age-adjusted Mortality Rate (AMR) for specific disease and gender.We define nine models. Two focus on the overall AMR by gender. Seven are defined with specific disease-gender-AMR as response variables. All include the availability of active doctors and other socio-economics aspects as covariates. Our findings highlight that the percentages of medical specialists have a significant impact on the AMRs and how it changes across geographic areas and over time, providing insights on which specialists are most in need and where.
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Exploring the Relationship Between Doctors Availability and Mortality in Italy: A Machine Learning Approach using Multivariate Regression Trees | 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 Article Exploring the Relationship Between Doctors Availability and Mortality in Italy: A Machine Learning Approach using Multivariate Regression Trees giulia contu, marco ortu, Francesco Mola, Sara Pau This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8376433/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 We investigate how the percentages of medical specialists impact mortality rates and how they vary across different areas and time periods. The analysis is focused on Italian provinces during two different years, 2011 and 2019, to identify differences and similarities across geographical areas and time periods. We propose a new approach based on tree-based methodology that we will call \textit{Machine learning Enhanced Regression of ITAlian mortality rates (MERITA)}. It is based on a tree-based method called Multivariate RegressionTree (MRT), which is an extension of univariate regression trees. In this study, the MRT is used to identify the relationship between the percentage of active doctors by specialization per10,000 inhabitants and the Age-adjusted Mortality Rate (AMR) for specific disease and gender.We define nine models. Two focus on the overall AMR by gender. Seven are defined with specific disease-gender-AMR as response variables. All include the availability of active doctors and other socio-economics aspects as covariates. Our findings highlight that the percentages of medical specialists have a significant impact on the AMRs and how it changes across geographic areas and over time, providing insights on which specialists are most in need and where. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Health sciences/Risk factors 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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