![]() In order to attain health equity, it is vital that the disadvantaged individuals are identified to quantify the problem and formulate targeted public health interventions. In the UK, reducing health inequities is a statutory requirement and is a common theme in the area of health improvement in the Public Health Outcome Framework. Health inequities not only exist between countries, but are apparent within a country. Health inequity is defined as unjust differences in health status amongst different social groups, and may be explained by the distribution of social determinants of health. AJ PhD studentship was funded by National Institute for Health Research (HPRU-2012-10096).Ĭompeting interests: The authors have declared that no competing interests exist. ![]() AJvH, JLW and SLT are supported by National Institute for Health Research (HPRU-2012-10096). LS is supported by a senior clinical fellowship from Wellcome Trust RM is supported by a Sir Henry Wellcome Postdoctoral Fellowship from the Wellcome Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or PHE. Researchers can apply for data access at and must have their study protocol approved by the Independent Scientific Advisory Committee for MHRA database research (details at The authors did not have any special access privileges to the data.įunding: The research was funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Immunisation at the London School of Hygiene and Tropical Medicine in partnership with Public Health England (PHE). CPRD data governance and our own license to use CPRD data do not allow us to distribute or make available patient data directly to other parties. CPRD is a research service that provides primary care and linked data for public health research. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The third-party data for this study were obtained from the Clinical Practice Research Datalink (CPRD). Received: Accepted: NovemPublished: November 30, 2017Ĭopyright: © 2017 Jain et al. ![]() PLoS ONE 12(11):Įditor: Gianluigi Forloni, Istituto Di Ricerche Farmacologiche Mario Negri, ITALY Applying these methodologies to routinely collected data could improve surveillance programmes and allow assessment of health equity in specific healthcare studies.Ĭitation: Jain A, van Hoek AJ, Walker JL, Mathur R, Smeeth L, Thomas SL (2017) Identifying social factors amongst older individuals in linked electronic health records: An assessment in a population based study. ![]() This work provides methods to identify social factors in EHR relevant to older individuals and shows that factors such as ethnicity, deprivation, not living alone, cohabitation and care home residence can be ascertained using these data. For time-varying variables such as residence and living alone, ~60% and ~35% respectively of those with available data, had this information recorded within the last 5 years of the index date. Data for ethnicity, deprivation, living arrangements and care home residence were comparable to the Census data. Linkages provided the deprivation data (available for 82% individuals) and improved completeness of ethnicity recording from 55% to 79% (when hospitalisation data were added). The completeness of recording varied from 1.6% for immigration status to ~80% for ethnicity. Data for 591,037 individuals from 389 practices from England were analysed. Each social factor was examined for: completeness of recording including improvements in completeness by using other linked EHR, timeliness of recording for factors that might change over time and their representativeness (compared with English 2011 Census data when available). Social factors included: religion, ethnicity, immigration status, small area-level deprivation, place of residence (including communal establishments such as care homes), marital status and living arrangements (e.g. Methodology was developed for ascertaining social factors recorded on or before a pre-specified index date () using primary care data from Clinical Practice Research Datalink (CPRD) linked to hospitalisation and deprivation data in a cross-sectional study. This study investigated the recording of social factors in linked electronic health records (EHR) of individuals aged ≥65 years, to assess the potential of these data to identify the social determinants of disease burden and uptake of healthcare interventions. Identification and quantification of health inequities amongst specific social groups is a pre-requisite for designing targeted healthcare interventions.
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