Fundamentals of Analysis of Health Data for Non-Physicians

Carlos Hernández-Nava (), Miguel-Félix Mata-Rivera and Sergio Flores-Hernández
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Carlos Hernández-Nava: Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas del, Gustavo A Madero, Ciudad de México 07340, Mexico
Miguel-Félix Mata-Rivera: Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas del, Gustavo A Madero, Ciudad de México 07340, Mexico
Sergio Flores-Hernández: Center for Health Systems Research, National Institute of Public Health, Av. Universidad 655, Col. Santa María Ahuacatitlán, Cuernavaca 62100, Morelos, Mexico

Data, 2024, vol. 9, issue 10, 1-14

Abstract: The increasing prevalence of diabetes worldwide, including in Mexico, presents significant challenges to healthcare systems. This has a notable impact on hospital admissions, as diabetes is considered an ambulatory care-sensitive condition, meaning that hospitalizations could be avoided. This is just one example of many challenges faced in the medical and public health fields. Traditional healthcare methods have been effective in managing diabetes and preventing complications. However, they often encounter limitations when it comes to analyzing large amounts of health data to effectively identify and address diseases. This paper aims to bridge this gap by outlining a comprehensive methodology for non-physicians, particularly data scientists, working in healthcare. As a case study, this paper utilizes hospital diabetes discharge records from 2010 to 2023, totaling 36,665,793 records from medical units under the Ministry of Health of Mexico. We aim to highlight the importance for data scientists to understand the problem and its implications. By doing so, insights can be generated to inform policy decisions and reduce the burden of avoidable hospitalizations. The approach primarily relies on stratification and standardization to uncover rates based on sex and age groups. This study provides a foundation for data scientists to approach health data in a new way.

Keywords: methodology; data science; public health; standardization; epidemiology; datasets; hospital discharges; adjusted rate (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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