Data on Machine Learning Reported by Researchers at University of Sao Paulo (Machine Learning and National Health Data To Improve Evidence: Finding Segmentation In Individuals Without Private Insurance): Machine Learning

2022 APR 22 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Sao Paulo, Brazil, by NewsRx journalists, research stated, “Individuals without private health insurance have less access to healthcare, therefore are more prone to experience poor health when compared to those who have. Segmentation is an approach to find homogenous groups of people with the purpose of tailoring services and products.”

The news reporters obtained a quote from the research from the University of Sao Paulo, “In public policy, segmentation might be used to identify characteristics and needs of specific groups and deliver targeted programs and spare costs. We aim to identify and describe segments within the uninsured population to aid targeted policy actions and improve health. We used secondary data collected from a representative, nationwide health survey (n = 18,204). For the purpose of our analysis, we included data from individuals who answered ‘no’ to the question: ‘Do you have private health insurance?’ (n = 12,134). Variables pertaining information on sociodemographic, health status, access and care were used. A multiple correspondence analysis was performed to find principal components followed by a hierarchical cluster. We found three clusters. The first (54.12% of our sample) composed by a group of young, middle aged and professionally active individuals without health problems. The second (36.70%), a cluster of aging individuals composed especially by elderly women, either retired or fulfilling domestic tasks, with a long-term health problem. The last (9.17%) composed by elder people, with long-term health problem and scoring low in mental health related questions. Our study found three clusters (profiles of individuals) among the uninsured.”

According to the news reporters, the research concluded: “Ultimately, our findings aim to support policy makers to deliver customized actions to improve health and provide cost-effective policies.”

This research has been peer-reviewed.

For more information on this research see: Machine Learning and National Health Data To Improve Evidence: Finding Segmentation In Individuals Without Private Insurance. Health Policy and Technology, 2021;10(1):79-86. Health Policy and Technology can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Health Policy and Technology - http://www.journals.elsevier.com/health-policy-and-technology/)

Our news correspondents report that additional information may be obtained by contacting Joana Raquel Raposo dos Santos, University of Sao Paulo, Faculty of Public Health, Dept. of Epidemiology, Sao Paulo, Brazil. Additional authors for this research include Alexandre Chiavegatto Filho and Carlos Matias Dias.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.hlpt.2020.11.002. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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