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External Validation of a Population-Based Prediction Model for High Healthcare Resource Use in Adults.
Rosella, Laura C; Kornas, Kathy; Sarkar, Joykrishna; Fransoo, Randy.
Afiliação
  • Rosella LC; Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.
  • Kornas K; ICES, Toronto, ON M4N 3M5, Canada.
  • Sarkar J; Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.
  • Fransoo R; Manitoba Centre for Health Policy, University of Manitoba, Winnipeg, MB R3E 3P5, Canada.
Healthcare (Basel) ; 8(4)2020 Dec 04.
Article em En | MEDLINE | ID: mdl-33291559
ABSTRACT
Predicting high healthcare resource users is important for informing prevention strategies and healthcare decision-making. We aimed to cross-provincially validate the High Resource User Population Risk Tool (HRUPoRT), a predictive model that uses population survey data to estimate 5 year risk of becoming a high healthcare resource user. The model, originally derived and validated in Ontario, Canada, was applied to an external validation cohort. HRUPoRT model predictors included chronic conditions, socio-demographics, and health behavioural risk factors. The cohort consisted of 10,504 adults (≥18 years old) from the Canadian Community Health Survey in Manitoba, Canada (cycles 2007/08 and 2009/10). A person-centred costing algorithm was applied to linked health administrative databases to determine respondents' healthcare utilization over 5 years. Model fit was assessed using the c-statistic for discrimination and calibration plots. In the external validation cohort, HRUPoRT demonstrated strong discrimination (c statistic = 0.83) and was well calibrated across the range of risk. HRUPoRT performed well in an external validation cohort, demonstrating transportability of the model in other jurisdictions. HRUPoRT's use of population survey data enables a health equity focus to assist with decision-making on prevention of high healthcare resource use.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article