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Understanding individual health-related social needs in the context of area-level social determinants of health: The case for granularity.
Telzak, Andrew; Levano, Samantha; Haughton, Jessica; Chambers, Earle C; Fiori, Kevin P.
Afiliação
  • Telzak A; Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Levano S; Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Haughton J; Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Chambers EC; Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Fiori KP; Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.
J Clin Transl Sci ; 8(1): e78, 2024.
Article em En | MEDLINE | ID: mdl-38745875
ABSTRACT

Introduction:

Screening for health-related social needs (HRSNs) within health systems is a widely accepted recommendation, however challenging to implement. Aggregate area-level metrics of social determinants of health (SDoH) are easily accessible and have been used as proxies in the interim. However, gaps remain in our understanding of the relationships between these measurement methodologies. This study assesses the relationships between three area-level SDoH measures, Area Deprivation Index (ADI), Social Deprivation Index (SDI) and Social Vulnerability Index (SVI), and individual HRSNs among patients within one large urban health system.

Methods:

Patients screened for HRSNs between 2018 and 2019 (N = 45,312) were included in the analysis. Multivariable logistic regression models assessed the association between area-level SDoH scores and individual HRSNs. Bivariate choropleth maps displayed the intersection of area-level SDoH and individual HRSNs, and the sensitivity, specificity, and positive and negative predictive values of the three area-level metrics were assessed in relation to individual HRSNs.

Results:

The SDI and SVI were significantly associated with HRSNs in areas with high SDoH scores, with strong specificity and positive predictive values (∼83% and ∼78%) but poor sensitivity and negative predictive values (∼54% and 62%). The strength of these associations and predictive values was poor in areas with low SDoH scores.

Conclusions:

While limitations exist in utilizing area-level SDoH metrics as proxies for individual social risk, understanding where and how these data can be useful in combination is critical both for meeting the immediate needs of individuals and for strengthening the advocacy platform needed for resource allocation across communities.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Clin Transl Sci Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Clin Transl Sci Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos
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