Your browser doesn't support javascript.
loading
Social determinants of health and diabetes: using a nationally representative sample to determine which social determinant of health model best predicts diabetes risk.
Cooper, Zach W; Mowbray, Orion; Johnson, Leslie.
Afiliación
  • Cooper ZW; University of Georgia School of Social Work, 279 Williams Street, Athens, GA, 30602, Georgia. zach.cooper@uga.edu.
  • Mowbray O; University of Georgia School of Social Work, 279 Williams Street, Athens, GA, 30602, Georgia.
  • Johnson L; Department of Family and Preventative Medicine, School of Medicine, Emory University, Atlanta, Georgia.
Clin Diabetes Endocrinol ; 10(1): 4, 2024 Feb 25.
Article en En | MEDLINE | ID: mdl-38402223
ABSTRACT

OBJECTIVES:

Social determinants of health (SDOH) research demonstrates poverty, access to healthcare, discrimination, and environmental factors influence health outcomes. Several models are commonly used to assess SDOH, yet there is limited understanding of how these models differ regarding their ability to predict the influence of social determinants on diabetes risk. This study compares the utility of four SDOH models for predicting diabetes disparities. STUDY

DESIGN:

We utilized The National Longitudinal Study of Adolescent to Adulthood (Add Health) to compare SDOH models and their ability to predict risk of diabetes and obesity.

METHODS:

Previous literature has identified the World Health Organization (WHO), Healthy People, County Health Rankings, and Kaiser Family Foundation as the conventional SDOH models. We used these models to operationalize SDOH using the Add Health dataset. Add Health data were used to perform logistic regressions for HbA1c and linear regressions for body mass index (BMI).

RESULTS:

The Kaiser model accounted for the largest proportion of variance (19%) in BMI. Race/ethnicity was a consistent factor predicting BMI across models. Regarding HbA1c, the Kaiser model also accounted for the largest proportion of variance (17%). Race/ethnicity and wealth was a consistent factor predicting HbA1c across models.

CONCLUSION:

Policy and practice interventions should consider these factors when screening for and addressing the effects of SDOH on diabetes risk. Specific SDOH models can be constructed for diabetes based on which determinants have the largest predictive value.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Diabetes Endocrinol Año: 2024 Tipo del documento: Article País de afiliación: Georgia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Diabetes Endocrinol Año: 2024 Tipo del documento: Article País de afiliación: Georgia Pais de publicación: Reino Unido