Work

Socioenvironmental Vulnerability Profiles and Health Expenditure in Mexican Households Using Data Science

Year: 2026

Type: article

Abstract: This study aimed to identify socioenvironmental vulnerability profiles among Mexican households and analyze their association with health expenditure. Data from 86,102 households included in the 2024 National Household Income and Expenditure Survey were analyzed. Socioenvironmental profiles were constructed using housing, basic services, sanitation, household energy, socioeconomic stratum, and overcrowding through factor analysis of mixed data and k-means. Internal validation, stability analyses, algorithm comparisons, and sensitivity analyses supported a three-profile solution representing low, intermediate, and high vulnerability. Health expenditure was examined using survey-weighted descriptive estimates, exploratory nonparametric comparisons, and a survey-adjusted two-part model. The intermediate vulnerability profile showed higher odds of reporting health expenditure than the low vulnerability profile (OR = 1.129, 95% CI: 1.048 to 1.217, p = 0.002), whereas the high vulnerability profile showed no significant difference. Among households with positive expenditure, high vulnerability was associated with lower logarithmic expenditure (β=−0.341, 95% CI: −0.490 to −0.192, p < 0.001), whereas the intermediate profile was not significantly different in the main model. The association for high vulnerability remained significant in sensitivity analyses, while results for the intermediate profile were more sensitive to model specification and income adjustment. Despite several statistically significant associations, effect sizes in the exploratory comparisons were small and the regression models explained a limited proportion of the variability in health expenditure. The findings therefore indicate modest associations between socioenvironmental vulnerability and health expenditure, with household economic resources and other unmeasured health-related factors likely contributing to the observed differences.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc10090284

Authors: Héctor Alejandro Acuña-Cid, Eduardo Ahumada-Tello, Cristina Almeida-Perales, Mónica Judith Chávez-Soto, Pablo Gerardo Guerrero-Herrera, José Eduardo Briceño-Muro

Institutions: Instituto Politécnico Nacional, Universidad Autónoma de Baja California, Universidad Autónoma de Zacatecas "Francisco García Salinas", Mexican Social Security Institute

Language: English

Topic: Public Health and Social Inequalities

Subfield: Health

Field: Social Sciences

Domain: Social Sciences

SDGs: Good health and well-being, Reduced inequalities, No poverty

Open Access status: gold

Estimated APC paid: $2,172