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Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada.

ABSTRACT: 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.

SUBMITTER: Richard L

PROVIDER: S-EPMC6797366 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada.

Richard Lucie L Hwang Stephen W SW Forchuk Cheryl C Nisenbaum Rosane R Clemens Kristin K Wiens Kathryn K Booth Richard R Azimaee Mahmoud M Shariff Salimah Z SZ

BMJ open 20191007 10

<h4>Objectives</h4>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.<h4>Design</h4>A population-based retrospective validation study.<h4>Setting</h4>Ontario, Canada, from 2007 to 2014 (validation) and 2007 to 2016 (estimation).<h4>Participants</h4>Our reference standard was the known housing status of a l ...[more]

PMID: 31594882

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Project description:ImportanceCeliac disease (CD) is a common pediatric illness, and awareness of gluten-related disorders including CD is growing. Health administrative data represents a unique opportunity to conduct population-based surveillance of this chronic condition and assess the impact of caring for children with CD on the health system.ObjectiveThe objective of the study was to validate an algorithm based on health administrative data diagnostic codes to accurately identify children with biopsy-proven CD. We also evaluated trends over time in the use of health services related to CD by children in Ontario, Canada.Study design and settingWe conducted a retrospective cohort study and validation study of population-based health administrative data in Ontario, Canada. All cases of biopsy-proven CD diagnosed 2005-2011 in Ottawa were identified through chart review from a large pediatric health care center, and linked to the Ontario health administrative data to serve as positive reference standard. All other children living within Ottawa served as the negative reference standard. Case-identifying algorithms based on outpatient physician visits with associated ICD-9 code for CD plus endoscopy billing code were constructed and tested. Sensitivity, specificity, PPV and NPV were tested for each algorithm (with 95% CI). Poisson regression, adjusting for sex and age at diagnosis, was used to explore the trend in outpatient visits associated with a CD diagnostic code from 1995-2011.ResultsThe best algorithm to identify CD consisted of an endoscopy billing claim follow by 1 or more adult or pediatric gastroenterologist encounters after the endoscopic procedure. The sensitivity, specificity, PPV, and NPV for the algorithm were: 70.4% (95% CI 61.1-78.4%), >99.9% (95% CI >99.9->99.9%), 53.3% (95% CI 45.1-61.4%) and >99.9% (95% CI >99.9->99.9%) respectively. It identified 1289 suspected CD cases from Ontario-wide administrative data. There was a 9% annual increase in the use of this combination of CD-associated diagnostic codes in physician billing data (RR 1.09, 95% CI 1.07-1.10, P<0.001).ConclusionsWith its current structure and variables Ontario health administrative data is not suitable in identifying incident pediatric CD cases. The tested algorithms suffer from poor sensitivity and/or poor PPV, which increase the risk of case misclassification that could lead to biased estimation of CD incidence rate. This study reinforced the importance of validating the codes used to identify cohorts or outcomes when conducting research using health administrative data.

Project description:Traumatic brain injury (TBI) is disproportionately prevalent among individuals experiencing homelessness. While rehabilitation is critical to facilitating recovery after TBI, there is currently limited information on the extent to which rehabilitation is provided to individuals experiencing homelessness and TBI. If unaddressed, this knowledge gap can perpetuate TBI-related challenges and contribute to a repetitive cycle of TBI and homelessness. This scoping review explored the extent to which rehabilitation, including the types of rehabilitation interventions, are available to, or used by, individuals experiencing homelessness and TBI. A systematic search of electronic databases (MEDLINE, Embase, Cochrane CENTRAL Register of Clinical Trials, CINAHL, APA PsycINFO, Applied Social Sciences Index and Abstracts, and Proquest Nursing and Allied Health) was conducted to identify peer-reviewed articles that met predetermined eligibility criteria. Gray literature and reference lists of eligible articles were also searched for relevant content. A descriptive numerical summary of extracted data was conducted, and qualitative analytic techniques were applied to analyze the data. Fifteen peer-reviewed articles and three gray literature reports were included, describing interventions for individuals experiencing homelessness and TBI (N = 4), rehabilitation for individuals experiencing homelessness without specific inclusion criteria for TBI (N = 11), and rehabilitation interventions that included individuals experiencing homelessness and TBI, without specific inclusion criteria for experiences of homelessness or TBI (N = 3). This review demonstrates that rehabilitation programs or interventions for this population already exist, and those that are focused on individuals experiencing homelessness are already serving individuals with TBI. Findings highlight opportunities to adapt existing rehabilitation for individuals who experience homelessness and TBI through screening for TBI, conducting cognitive and functional assessments, and tailoring interventions with multidisciplinary teams. Education and training for healthcare professionals working with individuals experiencing homelessness and TBI should be explored, including structured education and training, collaboration with a multidisciplinary team, and co-development of educational materials with service users. Research that considers the rehabilitation needs of diverse individuals experiencing homelessness and TBI is urgently needed.

Dataset Information

Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada.

Publications

Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada.

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