Disentangling socioeconomic inequalities of type 2 diabetes mellitus in Chile: A population-based analysis.

• Main Authors: Manuel S Ortiz, Baltica Cabieses, Marcela Oyarte, Paula Repetto
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0238534

<h4>Introduction</h4>Chile experiences a growing prevalence of DM2 in its adult population over time. The country has prioritised the diagnosis and treatment of DM2 through a universal health care package, largely focused on the clinical dimensions of the disease. We analysed the significance of socioeconomic variables in the prevalence of DM2, as well as its related dimensions of presence of complications (diabetic foot and ophthalmologic complications), attendance to health checks and acquisition of recommended lifestyle changes due to this condition.<h4>Methods</h4>Secondary analysis of the national health survey (ENS) 2016-2017 (n = 6,233 respondents). Crude and income-adjusted odds of reporting DM2 was estimated, as well as the relationship between complications due to diabetes and a number of clinical and sociodemographic variables using weighted log-linear multiple regression models.<h4>Results</h4>We found a clear social gradient of the prevalence of DM2 by household income quintiles and educational level in the adult population. Income quintile and educational level gradients remained significantly associated with the presence of complications and attendance to health checks. We found no significant association, however; between income quintile and reported lifestyle change. The association between complications due to DM2 and socioeconomic variables, particularly income, remained relevant even after adjusting for all sociodemographic variables.<h4>Conclusion</h4>This is the first study to analyse the association between DM2 and socioeconomic variables in Chile, useful for monitoring and policy planning. Income was strongly associated with DM2 prevalence and with related clinical variables (complications and attendance to health checks). Age, health care provision and educational level were also relevant factors, but lost significance in the fully adjusted model.