Predictive Statistical Modeling of Unemployment-Related Lack of Health Insurance Coverage among US Adults

Emmanuel Thompson; Mohamed Abu Sheha; Seidu Sofo

doi:10.56741/IISTR.jphs.001951

Authors

Emmanuel Thompson
ethompson@semo.edu
Southeast Missouri State University https://orcid.org/0009-0002-1094-0656
Mohamed Abu Sheha Southeast Missouri State University
Seidu Sofo Southeast Missouri State University

Keywords:

Binary Logistics, Health Insurance, Predictive Modeling, Risk Factors, Unemployment

Abstract

The study aimed to develop an accurate predictive model to identify individuals without health insurance due to unemployment. The study participants comprised 2,496 individuals who lacked general health insurance coverage in the United States for the period 2020-2023. Microdata records from the National Health Interview Survey available in the Integrated Public Use Microdata Series (IPUMS) served as the data source. The response variable was a binary variable indicating whether an individual had no health insurance (uninsured) due to unemployment. Statistical analyses involved descriptive measures: frequencies, percentages, and quartiles and inferential tests: Pearson’s chi-squared test, Wilcoxon rank-sum test, and Goodman and Kruskal’s measures. For predictive analysis, data were partitioned into training and testing sets, with class imbalance addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Binary logistic regression and probit models were applied, and model performance was assessed using accuracy, precision, recall, and F1 score. Most participants reported being in good, very good, or excellent health. The demographic and health-related factors in this study had limited standalone predictive values. However, Probit and Logit models identified obesity, age, lower education, and lack of healthcare access as critical risk factors. Furthermore, the prediction performance metrics for both Logit and Probit models were identical.

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Author Biographies

Emmanuel Thompson, Southeast Missouri State University

is a professor of actuarial science and statistics in the Department of Mathematics at Southeast Missouri State University, USA. His academic preparation is in Statistics and Actuarial Science. His research interest is mainly inspired by applications, especially those from the financial, insurance, and health services industries. His research has been on actuarial risk and mortality modeling with their applications. Currently, his research interests are centered on the applications of modern statistical methods and data science techniques to high-dimensional messy and massive data sets. (email: ethompson@semo.edu).

Mohamed Abu Sheha, Southeast Missouri State University

Mohamed Abu Sheha (PhD) is an Assistant Professor of Statistics in the Department of Mathematics at Southeast Missouri State University. His academic background is in Statistics, with expertise in Statistical Learning, Causal Inference, and Bayesian Methods. He develops advanced analytical and AI-driven models to address critical challenges in areas like health science and climate. Currently, he focuses on modeling the cognitive and brain changes associated with aging and Alzheimer's disease. His broader research interests span data science applications across health, environmental science, and engineering.

Seidu Sofo, Southeast Missouri State University

Seidu Sofo is a professor of physical education pedagogy in the Department of Allied Health, Kinesiology, and Sport Sciences at Southeast Missouri State University, USA. His academic preparation is in Physical Education Pedagogy concentration, with cognates in Health Education and Educational Research. His research interests are in early childhood/elementary (primary) physical education, physical activity, perceptual-motor development, and decolonizing pedagogy.

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