Multivariate Regression Modeling of Health Inequities: The Role of Social Determinants of Health

Background: Health inequities remain a worldwide public health problem, and the populations that are affected disproportionately are the vulnerable populations, so it is related to income disparities, education disparities, employment disparities, housing disparities, and healthcare disparities. These differences are substantially engraved in the social determinants of health and cannot be solved without some thoughtful data-oriented means to study these effects on the variety of groups of people and in different nations. Objective: to evaluate the impact of various social factors on health outcomes under a multivariate regression model and seek to find common global trends that cause health inequity. Subjects and Methods: A merged cross-section was made with publicly available data on health, covering various countries, whose national health surveys and databases consisted of the World Health Organization, the World Bank, and so on. Among the main variables to be analyzed, the researchers focused on the household income, educational level, employment, living conditions (quality of housing), and accessibility to healthcare services. Linear and logistic regression techniques (in a multivariate framework) were used to determine the relationship with health outcomes, including self-reported health status and prevalence of non-communicable diseases. Adjustment was done on age, gender, and urban-rural classification. Statistical significance was determined by p-values, adjusted R2, and Akaike Information Criterion. Results: The results demonstrated a similar trend in every country: low-income earners and lowly educated people registered dismal health conditions and a high prevalence of chronic diseases. These disparities were further propagated by poor access to medical treatment and poor housing. The multivariate model accounted to 64.8 percent of the variation in health outcomes at a global level with the factors that exerted the greatest influence being education (0.38, p < 0.001) and access to healthcare (0.31, p = 0.003). Conclusions: This global comparison highlights the profound impact of social determinants on populations worldwide. Multivariate statistical modeling proves highly valuable in identifying and quantifying these associations, providing evidence that can inform international health policies aimed at reducing inequities and promoting health equity across all nations.