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Consolidated Environmental and Social Data Facilitates Neighborhood-Level Health Studies in Philadelphia.
Christie, Colin; Xie, Sherrie; Diwadkar, Avantika R; Greenblatt, Rebecca E; Rizaldi, Alexandra; Himes, Blanca E.
Afiliación
  • Christie C; Department of Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Xie S; Department of Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Diwadkar AR; Department of Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Greenblatt RE; Department of Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Rizaldi A; Department of Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Himes BE; Department of Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
AMIA Annu Symp Proc ; 2021: 305-313, 2021.
Article en En | MEDLINE | ID: mdl-35308932
A wide range of datasets containing geographically distributed measures of the environment and social factors is currently available, and as low-cost sensors and other devices become increasingly used, the volume of these data will continue to grow. Because such factors influence many health outcomes, researchers with varied interests often repeat tasks related to gathering and preparing these data for studies. We created Sensor-based Analysis of Pollution in the Philadelphia Region with Information on Neighborhoods and the Environment (SAPPHIRINE), offered as a web application and R package, to integrate pollution, crime, social disadvantage, and traffic data relevant to investigators, citizen scientists, and policy makers in the Greater Philadelphia Area. SAPPHIRINE's capabilities include providing interactive maps and customizable data retrieval to aid in the visual identification of pollution and other factor hotspots, as well as hypothesis generation regarding relationships among these factors and health outcomes.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_desigualdade_iniquidade Asunto principal: Características de la Residencia Tipo de estudio: Prognostic_studies Aspecto: Determinantes_sociais_saude / Equity_inequality / Patient_preference Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: AMIA Annu Symp Proc Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_desigualdade_iniquidade Asunto principal: Características de la Residencia Tipo de estudio: Prognostic_studies Aspecto: Determinantes_sociais_saude / Equity_inequality / Patient_preference Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: AMIA Annu Symp Proc Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article
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