Overview

Abstract (English)

Introduction High-cost users (HCUs) account for a small proportion of the population but use a disproportionately large share of healthcare resources. Although HCUs exist in all healthcare types, acute care is the most expensive type of service and the most significant contributor to expenditures among HCUs. This study aims to determine demographic, socioeconomic and clinical factors associated with being HCUs in adult patients (-18 years) receiving acute care in Canada. Methods and analysis This is a population-based analysis using a national linked dataset. Adult patients who had at least one interaction with acute care facilities each year from 2011 to 2014 were captured in the dataset, and those living in institutions or other collective residences were not covered. The primary outcome is HCU of acute care (yes/no), which is defined as whether a patient is within the top 10% of the highest acute care cost users in his/her province. Multilevel logistic regression will be used to identify factors associated with HCU and to examine the provincial variations of these identified risk factors. Sensitivity analyses investigating the influences of different high user definitions and missing data on the study results will also be performed. Ethics and dissemination All researchers will follow the codes and rules set by Statistics Canada and the Research Data Centre and give priority to the confidentiality of the data during and after this study. The study findings will be published in peer-review journals and disseminated at academic conferences. Strengths and limitations of this study * The linked dataset used in our study allows us to analyse a broad range of demographic, socioeco-nomic and clinical factors that are potentially asso-ciated with high use of healthcare resources. * This study will be conducted using national data, al-lowing us to compare similarities and differences of the acute care high-cost user characteristics across different provinces in Canada. * The variations of patient characteristics across dif-ferent definitions on high system users will be inves-tigated through sensitivity analyses. * Populations who did not participate in the surveys (eg, people living in institutions or other collective residences) are not included in our analysis and thus might be under-represented. * The information for each patient is retrieved from different databases with different data collection time, and there is a time lag between the demo-graphic and socioeconomic information collected within the Census 2006/National Household Survey 2011 and the status of patients being high system users or non-high system users from 2011 to 2014.

Abstract (French)

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