Retrospective analysis of macroscopic health, socioeconomic, and demographic risk predictors for COVID-19 accumulated mortality ratio

Retrospective analysis of macroscopic health, socioeconomic, and demographic risk predictors for COVID-19 accumulated mortality ratio | 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 Retrospective analysis of macroscopic health, socioeconomic, and demographic risk predictors for COVID-19 accumulated mortality ratio Murat Razi, Manuel Grana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7293958/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 Background : COVID-19 pandemic resulted in stark disparities in mortality outcomes across countries, influenced by a complex interplay of demographic, economic, political, and health-related factors. This study investigates the macroscopic risk factors of COVID-19 mortality using stepwise multiple linear regression models on data from 174 countries. Methods : We carried out multiple regression modeling, with automated predictor selection. Two regression model-ing approaches were employed: one using only main effects (Modeling approach A) and another including pairwise interaction terms (Modeling approach B) to capture conditional effects, and another using for improved interpretability. Ten distinct analyses were conducted, covering comparisons across country development status levels (developed, developing, least developed) and between temporal phases (2020–2023), enabling both structural and dynamic assessments. Model-ing approach B was applied only when data availability was sufficient to avoid overfitting. Predictors considered per country are (1) the prevalences of health conditions (obesity, diabetes, hypertension), (2) socioeconomic indices (Gini index , GDP, democracy index), and demographics (age distribution). Results : The analysis identified age distribution, especially the proportion of individuals aged 65 and older, as the most consistent and statistically significant predictor of COVID-19 mortality. Obesity and income inequality (Gini index) were also found to be significant predictors across pandemic temporal phases, and country development categories. The study does not find any effect of the prevalence of respiratory conditions. The prevalence of diabetes is consistently found as 1 negatively correlated with mortality, while obesity prevalence is a positively correlated predictor of mortality. The effect of socioeconomic factors are significant at the global level, and it is more important in developed countries than in developing and under-developing counties. Conclusions : Aging population seems to be the strongest predictor for bad pandemic outcomes across all analyses carried out. The effect of the prevalence of medical conditions and socioeconomic factors seems conditioned to the stage of development of the countries and the pandemic phases. Negative correlation of diabetes prevalence and the absence of effect of respiratory disease prevalence are unexpected findings. COVID-19 mortality socioeconomic factors demographic clinical factors retrospective regression analysis 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7293958","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508205474,"identity":"7dfe3bc8-38e7-4349-83fa-08117ff82db5","order_by":0,"name":"Murat Razi","email":"","orcid":"","institution":"Universidad del Pais Vasco (UPV/EHU)","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"Razi","suffix":""},{"id":508205475,"identity":"a10ea899-14b4-40b8-b212-c868e9ae7591","order_by":1,"name":"Manuel Grana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYNCCAij9geEAsVoMIBTjDJK1MPMQo0W3/ewxiQ8GDHbb288e/GxTcSexgb3H+AM+LWZn8tIkZxgwJM85k5csnXPmWWIDzxkzCbxaDuSYSfMAtUgw5BhI57YdTmyQyDHD6zCz82/MpP+AtPC/Mf5t+Q+oRf4NAYfdANoC9L6dBNBwacYGkC08BvgdduONsWWPgUSChMQbM8ueY4eN23jSyvBrOZ9jeONHhY29BH+O8Y0fNYdl+9kPb8brMCiQSGyAMdmIUA4G9sQqHAWjYBSMghEIAC5ORqfegQvgAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad del Pais Vasco (UPV/EHU)","correspondingAuthor":true,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Grana","suffix":""}],"badges":[],"createdAt":"2025-08-04 18:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7293958/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7293958/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97948604,"identity":"0eb62dcc-0661-4b9e-b446-ebe8bdd019b9","added_by":"auto","created_at":"2025-12-11 06:24:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":297770,"visible":true,"origin":"","legend":"","description":"","filename":"submisionBMCpublichealth.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7293958/v1_covered_1365ab48-eab2-4406-b7c6-9dc4a2db7f81.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Retrospective analysis of macroscopic health, socioeconomic, and demographic risk predictors for COVID-19 accumulated mortality ratio","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19 mortality, socioeconomic factors, demographic, clinical factors, retrospective regression analysis","lastPublishedDoi":"10.21203/rs.3.rs-7293958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7293958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: COVID-19 pandemic resulted in stark disparities in mortality outcomes across countries, influenced by a complex interplay of demographic, economic, political, and health-related factors. This study investigates the macroscopic risk factors of COVID-19 mortality using stepwise multiple linear regression models on data from 174 countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We carried out multiple regression modeling, with automated predictor selection. Two regression model-ing approaches were employed: one using only main effects (Modeling approach A) and another including pairwise interaction terms (Modeling approach B) to capture conditional effects, and another using for improved interpretability. Ten distinct analyses were conducted, covering comparisons across country development status levels (developed, developing, least developed) and between temporal phases (2020–2023), enabling both structural and dynamic assessments. Model-ing approach B was applied only when data availability was sufficient to avoid overfitting. Predictors considered per country are (1) the prevalences of health conditions (obesity, diabetes, hypertension), (2) socioeconomic indices (Gini index , GDP, democracy index), and demographics (age distribution).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The analysis identified age distribution, especially the proportion of individuals aged 65 and older, as the most consistent and statistically significant predictor of COVID-19 mortality. Obesity and income inequality (Gini index) were also found to be significant predictors across pandemic temporal phases, and country development categories. The study does not find any effect of the prevalence of respiratory conditions. The prevalence of diabetes is consistently found as 1 negatively correlated with mortality, while obesity prevalence is a positively correlated predictor of mortality. The effect of socioeconomic factors are significant at the global level, and it is more important in developed countries than in developing and under-developing counties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Aging population seems to be the strongest predictor for bad pandemic outcomes across all analyses carried out. The effect of the prevalence of medical conditions and socioeconomic factors seems conditioned to the stage of development of the countries and the pandemic phases. 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