Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada
Objectives To validate case ascertainment algorithms for identifying individuals experiencing homelessness in health administrative databases between 2007 and 2014; and to estimate homelessness prevalence trends in Ontario, Canada, between 2007 and 2016.Design A population-based retrospective valida...
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| Format: | Article |
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BMJ Publishing Group
2019-10-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/9/10/e030221.full |
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| author | Lucie Richard Stephen W Hwang Cheryl Forchuk Rosane Nisenbaum Kristin Clemens Kathryn Wiens Richard Booth Mahmoud Azimaee Salimah Z Shariff |
| author_facet | Lucie Richard Stephen W Hwang Cheryl Forchuk Rosane Nisenbaum Kristin Clemens Kathryn Wiens Richard Booth Mahmoud Azimaee Salimah Z Shariff |
| author_sort | Lucie Richard |
| collection | DOAJ |
| description | Objectives To validate case ascertainment algorithms for identifying individuals experiencing homelessness in health administrative databases between 2007 and 2014; and to estimate homelessness prevalence trends in Ontario, Canada, between 2007 and 2016.Design A population-based retrospective validation study.Setting Ontario, Canada, from 2007 to 2014 (validation) and 2007 to 2016 (estimation).Participants Our reference standard was the known housing status of a longitudinal cohort of housed (n=137 200) and homeless or vulnerably housed (n=686) individuals. Two reference standard definitions of homelessness were adopted: the housing episode and the annual housing experience (any homelessness within a calendar year).Main outcome measures Sensitivity, specificity, positive and negative predictive values and positive likelihood ratios of 30 case ascertainment algorithms for detecting homelessness using up to eight health service databases.Results Sensitivity estimates ranged from 10.8% to 28.9% (housing episode definition) and 18.5% to 35.6% (annual housing experience definition). Specificities exceeded 99% and positive likelihood ratios were high using both definitions. The most optimal algorithm estimates that 59 974 (95% CI 55 231 to 65 208) Ontarians (0.53% of the adult population) experienced homelessness in 2016, a 67.3% increase from 2007.Conclusions In Ontario, case ascertainment algorithms for identifying homelessness had low sensitivity but very high specificity and positive likelihood ratio. The use of health administrative databases may offer opportunities to track individuals experiencing homelessness over time and inform efforts to improve housing and health status in this vulnerable population. |
| format | Article |
| id | doaj-art-dd1ab1a473bd4415978e90f048d8e8f7 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2019-10-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-dd1ab1a473bd4415978e90f048d8e8f72024-12-14T00:15:10ZengBMJ Publishing GroupBMJ Open2044-60552019-10-0191010.1136/bmjopen-2019-030221Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, CanadaLucie Richard0Stephen W Hwang1Cheryl Forchuk2Rosane Nisenbaum3Kristin Clemens4Kathryn Wiens5Richard Booth6Mahmoud Azimaee7Salimah Z Shariff8MAP Centre for Urban Health Solutions, Toronto, Ontario, Canada10 Division of General Internal Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, CanadaArthur Labatt Family School of Nursing, Western University, London, Ontario, CanadaApplied Health Research Centre, MAP Centre for Urban Health Solutions, St. Michael`s Hospital, Toronto, Ontario, CanadaSchulich School of Medicine and Dentistry, Western University, London, Ontario, CanadaDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, CanadaArthur Labatt Family School of Nursing, Western University, London, Ontario, CanadaICES, Toronto, Ontario, CanadaICES Western, Toronto, Ontario, CanadaObjectives To validate case ascertainment algorithms for identifying individuals experiencing homelessness in health administrative databases between 2007 and 2014; and to estimate homelessness prevalence trends in Ontario, Canada, between 2007 and 2016.Design A population-based retrospective validation study.Setting Ontario, Canada, from 2007 to 2014 (validation) and 2007 to 2016 (estimation).Participants Our reference standard was the known housing status of a longitudinal cohort of housed (n=137 200) and homeless or vulnerably housed (n=686) individuals. Two reference standard definitions of homelessness were adopted: the housing episode and the annual housing experience (any homelessness within a calendar year).Main outcome measures Sensitivity, specificity, positive and negative predictive values and positive likelihood ratios of 30 case ascertainment algorithms for detecting homelessness using up to eight health service databases.Results Sensitivity estimates ranged from 10.8% to 28.9% (housing episode definition) and 18.5% to 35.6% (annual housing experience definition). Specificities exceeded 99% and positive likelihood ratios were high using both definitions. The most optimal algorithm estimates that 59 974 (95% CI 55 231 to 65 208) Ontarians (0.53% of the adult population) experienced homelessness in 2016, a 67.3% increase from 2007.Conclusions In Ontario, case ascertainment algorithms for identifying homelessness had low sensitivity but very high specificity and positive likelihood ratio. The use of health administrative databases may offer opportunities to track individuals experiencing homelessness over time and inform efforts to improve housing and health status in this vulnerable population.https://bmjopen.bmj.com/content/9/10/e030221.full |
| spellingShingle | Lucie Richard Stephen W Hwang Cheryl Forchuk Rosane Nisenbaum Kristin Clemens Kathryn Wiens Richard Booth Mahmoud Azimaee Salimah Z Shariff Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada BMJ Open |
| title | Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada |
| title_full | Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada |
| title_fullStr | Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada |
| title_full_unstemmed | Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada |
| title_short | Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada |
| title_sort | validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in ontario canada |
| url | https://bmjopen.bmj.com/content/9/10/e030221.full |
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