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Background: New case-mix tools from the Canadian Institute for Health Information offer a novel way of exploring the prevalence of chronic disease and multimorbidity using diagnostic data. We took a comprehensive approach to determine whether the prevalence of chronic disease and multimorbidity has been rising in Ontario, Canada.Methods: In this observational study, we applied case-mix methodology to a population-based cohort. We used 10 years of patient-level data (fiscal years 2008/09 to 2017/18) from multiple care settings to compute the rolling 5-year prevalence of 85 chronic diseases and multimorbidity (i.e., the co-occurrence of 2 or more diagnoses). Diseases were further classified based on type and severity. We report both crude and age- and sex-standardized trends.Results: The number of patients with chronic disease increased by 11.0% over the 10-year study period to 9.8 million in 2017/18, and the number with multimorbidity increased 12.2% to 6.5 million. Overall increases from 2008/09 to 2017/18 in the crude prevalence of chronic conditions and multimorbidity were driven by population aging. After adjustments for age and sex, the prevalence of patients with ≥ 1 chronic conditions decreased from 70.2% to 69.1%, and the prevalence of multimorbidity decreased from 47.1% to 45.6%. This downward trend was concentrated in minor and moderate diseases, whereas the prevalence of many major chronic diseases rose, along with instances of extreme multimorbidity (≥ 8 conditions). Age- and sex-standardized resource intensity weights, which reflect relative expected costs associated with patient diagnostic profiles, increased 4.6%.Interpretation: Evidence of an upward trend in the prevalence of chronic disease was mixed. However, the change in case mix toward more serious conditions, along with increasing patient resource intensity weights overall, may portend a future need for population health management and increased health system spending above that predicted by population aging. [ABSTRACT FROM AUTHOR]

Background: Prior research has consistently shown that the heaviest users account for a disproportionate share of health care costs. As such, predicting high-cost users may be a precondition for cost containment. We evaluated the ability of a new health risk predictive modelling tool, which was developed by the Canadian Institute for Health Information (CIHI), to identify future high-cost cases.Methods: We ran the CIHI model using administrative health care data for Ontario (fiscal years 2014/15 and 2015/16) to predict the risk, for each individual in the study population, of being a high-cost user 1 year in the future. We also estimated actual costs for the prediction period. We evaluated model performance for selected percentiles of cost based on the discrimination and calibration of the model.Results: A total of 11 684 427 individuals were included in the analysis. Overall, 10% of this population had annual costs exceeding $3050 per person in fiscal year 2016/17, accounting for 71.6% of total expenditures; 5% had costs above $6374 (58.2% of total expenditures); and 1% exceeded $22 995 (30.5% of total expenditures). Model performance increased with higher cost thresholds. The c-statistic was 0.78 (reasonable), 0.81 (strong) and 0.86 (very strong) at the 10%, 5% and 1% cost thresholds, respectively.Interpretation: The CIHI Population Grouping Methodology was designed to predict the average user of health care services, yet performed adequately for predicting high-cost users. Although we recommend the development of a purpose-designed tool to improve model performance, the existing CIHI Population Grouping Methodology may be used - as is or in concert with additional information - for many applications requiring prediction of future high-cost users. [ABSTRACT FROM AUTHOR]

Background: Studies have estimated that a large backlog of procedures was generated by emergency measures implemented in Ontario, Canada, at the onset of the COVID-19 pandemic, when nonessential and scheduled procedures were postponed. Understanding the impact of the COVID-19 pandemic on the time needed to perform a procedure may help to determine the resources needed to tackle the substantial backlog caused by the deferral of cases. The purpose of this study was to examine the duration of operating room (OR) procedures before and after the onset of the COVID-19 pandemic to inform planning around changes in required resources., Methods: A population-based, retrospective cohort study was conducted using Ontario Health Insurance Plan claims data and other administrative health care data from Apr. 1, 2019, to Sept. 30, 2020. Statistical analysis was conducted using multivariate regression, with procedure duration as the outcome variable., Results: Results showed that the average duration of nonelective procedures increased by 34 minutes during the COVID-19 period and by 19 minutes after the resumption of scheduled procedures. Controlling for physician, patient and hospital characteristics, and the procedure code submitted, procedure duration increased by 12 minutes in the nonelective COVID-19 period and by 5 minutes when scheduled procedures resumed, compared with the pre-COVID-19 period., Conclusion: Procedures may take longer in the COVID-19 period. This will affect wait times, which had already increased because of the deferral of procedures at the beginning of the pandemic, and will have an impact on Ontario's ability to provide patients with timely care., Competing Interests: Competing interests: C. Vinden is chair of the Section of General Surgery, Ontario Medical Association. S. Hill is a past president and board member of the Ontario Medical Association. No other competing interests were declared., (© 2022 CMA Impact Inc. or its licensors.)

Objectives: To estimate the impact of the SARS-CoV-2 (COVID-19) pandemic on levels of burnout among physicians in Ontario, Canada, and to understand physician perceptions of the contributors and solutions to burnout., Design: Repeated cross-sectional survey., Setting: Active and retired physicians, residents and medical students in Canada's largest province were invited to participate in an online survey via an email newsletter., Participants: In the first survey wave (March 2020), 1400 members responded (representing 76.3% of those who could be confirmed to have received the survey and 3.1% of total membership). In the second wave (March 2021), 2638 responded (75.9% of confirmed survey recipients and 5.8% of membership)., Key Outcome Measure: Level of burnout was assessed using a validated, single-item, self-defined burnout measure where options ranged from 1 (no symptoms of burnout) to 5 (completely burned out)., Results: The overall rate of high levels of burnout (self-reported levels 4-5) increased from 28.0% in 2020 (99% CI: 24.3% to 31.7%) to 34.7% in 2021 (99% CI: 31.8% to 37.7%), a 1-year increase of 6.8 percentage points (p<0.01). After a full year of practising during the COVID-19 pandemic, respondents ranked 'patient expectations/patient accountability', 'reporting and administrative obligations' and 'practice environment' as the three factors that contributed most to burnout. Respondents ranked 'streamline and reduce required documentation/administrative work', 'provide fair compensation' and 'improve work-life balance' as the three most important solutions., Conclusions: During the first 12 months of the COVID-19 pandemic in Ontario, prevalence of high levels of burnout had significantly increased. The contributors and solutions ranked highest by physicians were system-level or organisational in nature., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Applications of behavioral economics targeted at optimizing laboratory utilization among physicians have been implemented in Ontario through different types of nonfinancial interventions. Strict policy interventions restrict Ontario Health Insurance Plan (OHIP) payment for tests to patients with specific conditions or limit ordering to particular physician specialties, while soft policy interventions involve modifications to the laboratory requisition form. This study evaluates the effectiveness of these interventions in terms of changing physician ordering behavior for eight tests that were subject to a strict or soft policy intervention during the study period. We use a Bayesian structural time series model applied to Ontario laboratory claims data for FY2006 through FY2017. Results show a 16-75% reduction in laboratory services with a strict policy intervention and an 8-36% reduction in laboratory services with a soft policy intervention. Although the overall magnitude of change was smaller for soft policy interventions, interventions designed with soft or strict policy mechanisms addressing laboratory utilization management are effective at influencing physicians' test ordering behavior., Competing Interests: Declaration of Competing Interest The authors report no declarations of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Prior research has consistently shown that the heaviest users account for a disproportionate share of health care costs. As such, predicting high-cost users may be a precondition for cost containment. We evaluated the ability of a new health risk predictive modelling tool, which was developed by the Canadian Institute for Health Information (CIHI), to identify future high-cost cases., Methods: We ran the CIHI model using administrative health care data for Ontario (fiscal years 2014/15 and 2015/16) to predict the risk, for each individual in the study population, of being a high-cost user 1 year in the future. We also estimated actual costs for the prediction period. We evaluated model performance for selected percentiles of cost based on the discrimination and calibration of the model., Results: A total of 11 684 427 individuals were included in the analysis. Overall, 10% of this population had annual costs exceeding $3050 per person in fiscal year 2016/17, accounting for 71.6% of total expenditures; 5% had costs above $6374 (58.2% of total expenditures); and 1% exceeded $22 995 (30.5% of total expenditures). Model performance increased with higher cost thresholds. The c-statistic was 0.78 (reasonable), 0.81 (strong) and 0.86 (very strong) at the 10%, 5% and 1% cost thresholds, respectively., Interpretation: The CIHI Population Grouping Methodology was designed to predict the average user of health care services, yet performed adequately for predicting high-cost users. Although we recommend the development of a purpose-designed tool to improve model performance, the existing CIHI Population Grouping Methodology may be used - as is or in concert with additional information - for many applications requiring prediction of future high-cost users., Competing Interests: Competing interests: None declared., (© 2020 Joule Inc. or its licensors.)