Multimorbidity, social determinants and intersectionality in chronic patients. Results from the EpiChron Cohort

Moreno Juste, Aida ; Gimeno Miguel, Antonio ; Poblador Plou, Beatriz ; Calderón Larrañaga, Amaia ; Cano del Pozo, Mabel ; Forjaz, Maria João ; Prados Torres, Alexandra ; Gimeno Feliú, Luis A. (Universidad de Zaragoza)

Resumen: Background: Multimorbidity is influenced in an interconnected way, both in extent and nature, by the social determinants of health. We aimed at implementing an intersectional approach to analyse the association of multimorbidity with five important axes of social inequality (i.e. gender, age, ethnicity, residence area and socioeconomic class). Methods: We conducted a cross-sectional observational study of all individuals who presented with at least one chronic disease in 2019 (n = 1 086 948) from the EpiChron Cohort (Aragon, Spain). Applying intersectional analysis, the age-adjusted likelihood of multimorbidity was investigated across 36 intersectional strata defined by gender, ethnicity, residence area and socioeconomic class. We calculated odds ratios (OR) 95% confidence interval (CI) using high-income urban non-migrant men as the reference category. The area under the receiver operator characteristics curve (AUC) was calculated to evaluate the discriminatory accuracy of multimorbidity. Results: The prevalence of multimorbidity increased with age, female gender and low income. Young and middle-aged low-income individuals showed rates of multimorbidity equivalent to those of high-income people aged about 20 years older. The intersectional analysis showed that low-income migrant women living in urban areas for >15 years were particularly disadvantaged in terms of multimorbidity risk OR = 3.16 (95% CI = 2.79-3.57). Being a migrant was a protective factor for multimorbidity, and newly arrived migrants had lower multimorbidity rates than those with >15 years of stay in Aragon, and even non-migrants. Living in rural vs. urban areas was slightly protective against multimorbidity. All models had a large discriminatory accuracy (AUC = 0.7884-0.7895); the largest AUC was obtained for the model including all intersectional strata. Conclusions: Our intersectional approach uncovered the large differences in the prevalence of multimorbidity that arise due to the synergies between the different socioeconomic and demographic exposures, beyond their expected additive effects.

Idioma: Inglés

DOI: 10.7189/13.04014

Año: 2023

Publicado en: Journal of Global Health 13 (2023), 04014 [10 pp.]
ISSN: 2047-2978

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/B01-20R
Financiación: info:eu-repo/grantAgreement/ES/ISCIII-RICAPPS/RD21-0016-0019

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Area Medicina (Dpto. Medicina, Psiqu. y Derm.)