Primary care payment models and avoidable hospitalizations in Ontario, Canada: A multivalued treatment effects analysis.

Improving access to primary care physicians' services may help reduce hospitalizations due to Ambulatory Care Sensitive Conditions (ACSCs). Ontario, Canada's most populous province, introduced blended payment models for primary care physicians in the early- to mid-2000s to increase access to primary care, preventive care, and better chronic disease management. We study the impact of payment models on avoidable hospitalizations due to two incentivized ACSCs (diabetes and congestive heart failure) and two non-incentivized ACSCs (angina and asthma).

Self-reported mental health during the COVID-19 pandemic and its association with alcohol and cannabis use: a latent class analysis.

Background: Mental health problems and substance use co-morbidities during and after the COVID-19 pandemic are a public health priority. Identifying individuals at high-risk of developing mental health problems and potential sequela can inform mitigating strategies. We aimed to identify distinct groups of individuals (i.

Alcohol and cannabis use during the COVID-19 pandemic among transgender, gender-diverse, and cisgender adults in Canada.

Background: This study examined whether heavy episodic drinking (HED), cannabis use, and subjective changes in alcohol and cannabis use during the COVID-19 pandemic differ between transgender and gender-diverse (TGD) and cisgender adults.

Methods: Successive waves of web-based cross-sectional surveys.

Setting: Canada, May 2020 to March 2021.

Machine Learning-Based Predictive Modeling of Anxiety and Depressive Symptoms During 8 Months of the COVID-19 Global Pandemic: Repeated Cross-sectional Survey Study.

Background: The COVID-19 global pandemic has increased the burden of mental illness on Canadian adults. However, the complex combination of demographic, economic, and lifestyle factors and perceived health risks contributing to patterns of anxiety and depression has not been explored.

Objective: The aim of this study is to harness flexible machine learning methods to identify constellations of factors related to symptoms of mental illness and to understand their changes over time during the COVID-19 pandemic.

Mental Health Services Provision in Primary Care and Emergency Department Settings: Analysis of Blended Fee-for-Service and Blended Capitation Models in Ontario, Canada.

Treating mental illnesses in primary care is increasingly emphasized to improve access to mental health services. Although family physicians (FPs) or general practitioners are in an ideal position to provide the bulk of mental health care, it is unclear how best to remunerate FPs for the adequate provision of mental health services. We examined the quantity of mental health services provided in Ontario's blended fee-for-service and blended capitation models.

Stirring the pot: Switching from blended fee-for-service to blended capitation models of physician remuneration.

In Canada's most populous province, Ontario, family physicians may choose between the blended fee-for-service (Family Health Group [FHG]) and blended capitation (Family Health Organization [FHO] payment models). Both models incentivize physicians to provide after-hours (AH) and comprehensive care, but FHO physicians receive a capitation payment per enrolled patient adjusted for age and sex, plus a reduced fee-for-service while FHG physicians are paid by fee-for-service. We develop a theoretical model of physician labor supply with multitasking to predict their behavior under FHG and FHO, and estimable equations are derived to test the predictions empirically.