Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, Canada

Introduction In most developed countries, a significant amount of mental health and addictions care occurs in community settings. Data reflecting populations served by community-based mental health and addictions providers and the types of services provided are not available, resulting in an incompl...

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Main Authors: Paul Kurdyak, Abigail Amartey, Julie Yang, Daniel Liadsky, Rachel Solomon, Stephanie Carter
Format: Article
Language:English
Published: Swansea University 2018-08-01
Series:International Journal of Population Data Science
Online Access:https://ijpds.org/article/view/669
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author Paul Kurdyak
Abigail Amartey
Julie Yang
Daniel Liadsky
Rachel Solomon
Stephanie Carter
author_facet Paul Kurdyak
Abigail Amartey
Julie Yang
Daniel Liadsky
Rachel Solomon
Stephanie Carter
author_sort Paul Kurdyak
collection DOAJ
description Introduction In most developed countries, a significant amount of mental health and addictions care occurs in community settings. Data reflecting populations served by community-based mental health and addictions providers and the types of services provided are not available, resulting in an incomplete reflection of the entire mental health and addictions system within existing administrative data. Objectives and Approach The Community Business Intelligence (CBI) initiative is a data collection project that captures information on adults receiving community-based mental health, addictions, and support services in Toronto Central Local Health Integration Network (LHIN), located in Ontario, Canada. Leveraging administrative health data and data linkage capacity at the Institute for Clinical Evaluative Sciences (ICES), along with engagement of external stakeholders knowledgeable of CBI and the community health sector, we linked the 2015/16 CBI dataset to administrative health data. Demographic characteristics, health-service utilization, primary care attachment, and 30-day emergency department (ED) revisits were calculated for individuals accessing community health services. Results There was an 80.8% linkage rate, of which 36.9% linked deterministically via health card number, while 43.9% linked probabilistically. After study exclusions, 37,688 individuals in the CBI dataset used community health services between April 2015 and March 2016. Compared to Toronto Central LHIN, a greater proportion in the CBI dataset were female, older than 65 years of age, and living in a low income neighbourhood. Furthermore, 95.5%of individuals had at least one outpatient physician visit, 51.3%had at least one ED visit, and 21.7%had at least one hospitalization in the past year. Few individuals in the CBI dataset were without primary care attachment (4.5%); however, a larger proportion had a 30-day ED revisit, particularly those receiving community addictions services (19%). Conclusion/Implications The availability of community health services data in the CBI dataset and its successful linkage to the administrative health data held at ICES identified health service intersections and outcomes that were previously unknown. This linkage project demonstrates a successful framework for sector-wide performance measurement to address a critical infrastructure gap.
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spelling doaj.art-623b92019ffc44109320108a88eeb0892023-12-02T17:15:04ZengSwansea UniversityInternational Journal of Population Data Science2399-49082018-08-013410.23889/ijpds.v3i4.669Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, CanadaPaul Kurdyak0Abigail Amartey1Julie Yang2Daniel Liadsky3Rachel Solomon4Stephanie Carter5Centre for Addiction and Mental HealthInstitute for Clinical Evaluative SciencesInstitute for Clinical Evaluative SciencesCanadian Urban InstituteCentre for Addiction and Mental HealthReconnect Community Health ServicesIntroduction In most developed countries, a significant amount of mental health and addictions care occurs in community settings. Data reflecting populations served by community-based mental health and addictions providers and the types of services provided are not available, resulting in an incomplete reflection of the entire mental health and addictions system within existing administrative data. Objectives and Approach The Community Business Intelligence (CBI) initiative is a data collection project that captures information on adults receiving community-based mental health, addictions, and support services in Toronto Central Local Health Integration Network (LHIN), located in Ontario, Canada. Leveraging administrative health data and data linkage capacity at the Institute for Clinical Evaluative Sciences (ICES), along with engagement of external stakeholders knowledgeable of CBI and the community health sector, we linked the 2015/16 CBI dataset to administrative health data. Demographic characteristics, health-service utilization, primary care attachment, and 30-day emergency department (ED) revisits were calculated for individuals accessing community health services. Results There was an 80.8% linkage rate, of which 36.9% linked deterministically via health card number, while 43.9% linked probabilistically. After study exclusions, 37,688 individuals in the CBI dataset used community health services between April 2015 and March 2016. Compared to Toronto Central LHIN, a greater proportion in the CBI dataset were female, older than 65 years of age, and living in a low income neighbourhood. Furthermore, 95.5%of individuals had at least one outpatient physician visit, 51.3%had at least one ED visit, and 21.7%had at least one hospitalization in the past year. Few individuals in the CBI dataset were without primary care attachment (4.5%); however, a larger proportion had a 30-day ED revisit, particularly those receiving community addictions services (19%). Conclusion/Implications The availability of community health services data in the CBI dataset and its successful linkage to the administrative health data held at ICES identified health service intersections and outcomes that were previously unknown. This linkage project demonstrates a successful framework for sector-wide performance measurement to address a critical infrastructure gap.https://ijpds.org/article/view/669
spellingShingle Paul Kurdyak
Abigail Amartey
Julie Yang
Daniel Liadsky
Rachel Solomon
Stephanie Carter
Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, Canada
International Journal of Population Data Science
title Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, Canada
title_full Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, Canada
title_fullStr Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, Canada
title_full_unstemmed Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, Canada
title_short Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, Canada
title_sort gaining knowledge of ontario s community mental health and addictions system linking community based health services data with administrative health data in toronto ontario canada
url https://ijpds.org/article/view/669
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