Prognostic Gamma-Power Generalized Regression Modelling of Determinants Influencing Variations in Under-Five Mortality Rate

Prognostic Gamma-Power Generalized Regression Modelling of Determinants Influencing Variations in Under-Five Mortality Rate | 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 Prognostic Gamma-Power Generalized Regression Modelling of Determinants Influencing Variations in Under-Five Mortality Rate Joseph Adekunle Akinyemi, Matthew Iwada Ekum, Oluwatosin Jonadab Akinsola, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8948844/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract The under-five mortality rate is a serious health indicator in sub-Saharan Africa which has negative right skewed heteroscedastic nature that restricts the conventional regression models. This hypothesized the Gamma-Power-Log-Logistic (GPLL) prognostic framework to estimate determinants of under-five mortality rate in Lagos State, Nigeria, with the aid of primary data of 476 women of childbearing age (15-49 years) in five administrative divisions in 2025. The GPLL model incorporates a power-transformed distribution into a generalized linear model through a theoretically motivated transformation process, which makes it possible to perform likelihood-based inference that can deal with skewness, heavy tails and bounded support. Findings were that the new GPLL regression is significantly better than the gaussian linear and Gamma regression benchmarks, with maternal education and access to healthcare proved to be important protective factors and larger household size leads to higher risk of mortality. The framework effectively models the spatial heterogeneity and tail behaviour not in the other traditional models. The paper is concluded to be based on the evidence of flexible distributional modelling by showing reliable assessment of mortality rates and suggest specific intervention points that should focus on female education and access to health services as well as family planning to lower the under-five mortality rates in urban African environments. This study directly addresses the United Nations Sustainable Development Goal (SDG) 3.2 addressing preventable deaths of children under five years of age. Child survival Gamma-Power-Log-logistic distribution Maternal education Prognostic regression Spatial heterogeneity Under-five mortality rate Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 16 Mar, 2026 Submission checks completed at journal 15 Mar, 2026 First submitted to journal 15 Mar, 2026 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. 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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-8948844","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621394868,"identity":"1465fd79-8a1c-4625-bb25-1c76e0f95074","order_by":0,"name":"Joseph Adekunle Akinyemi","email":"","orcid":"","institution":"Lagos State University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"Adekunle","lastName":"Akinyemi","suffix":""},{"id":621394869,"identity":"37055313-5ced-4183-93f9-0897fa67457e","order_by":1,"name":"Matthew Iwada 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