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Research ArticleOriginal Research

Building a Foundation to Reduce Health Inequities: Routine Collection of Sociodemographic Data in Primary Care

Andrew D. Pinto, Gabriela Glattstein-Young, Anthony Mohamed, Gary Bloch, Fok-Han Leung and Richard H. Glazier
The Journal of the American Board of Family Medicine May 2016, 29 (3) 348-355; DOI: https://doi.org/10.3122/jabfm.2016.03.150280
Andrew D. Pinto
From the Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Centre for Research on Inner City Health, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada (ADP, RHG); the Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (ADP, RHG); the Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (GG-Y); Inner City Health, St. Michael's Hospital, Toronto, Ontario, Canada (AM); and the Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (RHG).
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Gabriela Glattstein-Young
From the Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Centre for Research on Inner City Health, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada (ADP, RHG); the Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (ADP, RHG); the Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (GG-Y); Inner City Health, St. Michael's Hospital, Toronto, Ontario, Canada (AM); and the Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (RHG).
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Anthony Mohamed
From the Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Centre for Research on Inner City Health, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada (ADP, RHG); the Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (ADP, RHG); the Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (GG-Y); Inner City Health, St. Michael's Hospital, Toronto, Ontario, Canada (AM); and the Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (RHG).
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Gary Bloch
From the Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Centre for Research on Inner City Health, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada (ADP, RHG); the Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (ADP, RHG); the Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (GG-Y); Inner City Health, St. Michael's Hospital, Toronto, Ontario, Canada (AM); and the Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (RHG).
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Fok-Han Leung
From the Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Centre for Research on Inner City Health, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada (ADP, RHG); the Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (ADP, RHG); the Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (GG-Y); Inner City Health, St. Michael's Hospital, Toronto, Ontario, Canada (AM); and the Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (RHG).
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Richard H. Glazier
From the Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (ADP, GB, F-HL, RHG); the Centre for Research on Inner City Health, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada (ADP, RHG); the Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (ADP, RHG); the Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (GG-Y); Inner City Health, St. Michael's Hospital, Toronto, Ontario, Canada (AM); and the Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (RHG).
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Abstract

Introduction: Detailed data on social determinants of health can facilitate the identification of inequities in access to health care. We report on a sociodemographic data collection tool used in a family medicine clinic.

Methods: Four major health organizations in Toronto collaborated to identify a set of 14 questions that covered a range of social determinants of health. These were translated into 13 languages. This survey was self-administered using an electronic tablet to a convenience sample of 407 patients in the waiting room of a primary care clinic. Data were uploaded directly to the electronic medical record.

Results: The rate of valid responses provided for each question was high, ranging from 84% to 100%. The questions with the highest number of patients selecting “do not know” and “prefer not to answer” pertained to disabilities and income. Patients reported finding the process acceptable. In subsequent implementation across 5 clinics, 10,536 patients have been surveyed; only 724 (6.9%) declined to participate.

Conclusion: Collecting data on social determinants of health through a self-administered survey, and linking them to a patient's chart, is feasible and acceptable. A modified survey is now administered to all patients. Such data are already being used to identify health inequities, develop novel interventions, and evaluate their impact on health outcomes.

  • Epidemiology
  • Health Care Disparities
  • Social Determinants of Health
  • Social Problems
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The Journal of the American Board of Family     Medicine: 29 (3)
The Journal of the American Board of Family Medicine
Vol. 29, Issue 3
May-June 2016
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Building a Foundation to Reduce Health Inequities: Routine Collection of Sociodemographic Data in Primary Care
Andrew D. Pinto, Gabriela Glattstein-Young, Anthony Mohamed, Gary Bloch, Fok-Han Leung, Richard H. Glazier
The Journal of the American Board of Family Medicine May 2016, 29 (3) 348-355; DOI: 10.3122/jabfm.2016.03.150280

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Building a Foundation to Reduce Health Inequities: Routine Collection of Sociodemographic Data in Primary Care
Andrew D. Pinto, Gabriela Glattstein-Young, Anthony Mohamed, Gary Bloch, Fok-Han Leung, Richard H. Glazier
The Journal of the American Board of Family Medicine May 2016, 29 (3) 348-355; DOI: 10.3122/jabfm.2016.03.150280
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Keywords

  • Epidemiology
  • Health Care Disparities
  • Social Determinants of Health
  • Social Problems

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