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Model-based estimation of individual-level social determinants of health and its applications in All of Us.
Kim, Bo Young; Anthopolos, Rebecca; Do, Hyungrok; Zhong, Judy.
Afiliación
  • Kim BY; Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States.
  • Anthopolos R; Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States.
  • Do H; Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States.
  • Zhong J; Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, United States.
J Am Med Inform Assoc ; 2024 Jul 14.
Article en En | MEDLINE | ID: mdl-39003521
ABSTRACT

OBJECTIVES:

We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program. MATERIALS AND

METHODS:

Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.

RESULTS:

Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900. DISCUSSION AND

CONCLUSION:

The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Am Med Inform Assoc Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos 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: J Am Med Inform Assoc Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido