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). The data for our study came from health administrative data on practicing primary care physicians in Ontario between 2006 and 2015. We employ a two-stage estimation strategy on a balanced panel of 3710 primary care physicians (1158 blended-fee-for-service (FFS), 1388 blended-capitation models, and 1164 interprofessional team-based practices). First, we account for the differences in physician practices using a generalized propensity score based on a multinomial logit regression model, corresponding to three primary care payment models. Second, we use fractional regression models to estimate the average treatment effects on the treated outcome (i.e., avoidable hospitalizations). The capitation-based model sometimes increases avoidable hospitalizations due to angina (by 7 per 100,000 patients) and congestive heart failure (40 per 100,000) relative to the blended-FFS-based model. Switching capitation physicians into interprofessional teams mitigates this effect, reducing avoidable hospitalizations from congestive heart failure by 30 per 100,000 patients and suggesting better access to primary care and chronic disease management in team-based practices.

Computer-Based Training for Cognitive Behavioural Therapy for Substance Use Disorder: A Randomized Controlled Trial Including Quantitative and Qualitative Health and Economic Outcomes: Formation informatisée pour la thérapie cognitivo-comportementale pour les troubles liés à l'usage de substances : un essai randomisé contrôlé y compris les résultats quantitatifs et qualitatifs en matière de santé et d'économie.

OBJECTIVES: Heavy alcohol and drug use is reported by a substantial number of Canadians; yet, only a minority of those experiencing substance use difficulties access specialized services. Computer-Based Training for Cognitive Behavioural Therapy (CBT4CBT) offers a low-cost method to deliver accessible and high-quality CBT for substance use difficulties. To date, CBT4CBT has primarily been evaluated in terms of quantitative outcomes within substance use disorder (SUD) samples in the United States. A comparison between CBT4CBT versus standard care for SUDs in a Canadian sample is critical to evaluate its potential for health services in Canada. We conducted a randomized controlled trial of CBT4CBT versus standard care for SUD. METHODS: Adults seeking outpatient treatment for SUD (N = 50) were randomly assigned to receive either CBT4CBT or treatment-as-usual (TAU) for 8 weeks. Measures of substance use and associated harms and quality of life were completed before and after treatment and at 6-month follow-up. Qualitative interviews were administered after treatment and at follow-up, and healthcare utilization and costs were extracted for the entire study period. RESULTS: Participants exhibited improvements on the primary outcome as well as several secondary outcomes; however, there were no differences between groups. A cost-effectiveness analysis found lower healthcare costs in CBT4CBT versus TAU in a subsample analysis, but more days of substance use in CBT4CBT. Qualitative analyses highlighted the benefits and challenges of CBT4CBT. DISCUSSION: Findings supported an overall improvement in clinical outcomes. Further investigation is warranted to identify opportunities for implementation of CBT4CBT in tertiary care settings.Trial Registration: https://clinicaltrials.gov/ct2/show/NCT03767907.

Evaluating a digital intervention targeting substance use difficultiesPlain Language SummaryWhy was the study done?Heavy alcohol and drug use is frequent in the Canadian population, although very few people have access to treatment. The digital intervention, Computer-Based Training for Cognitive Behavioural Therapy (CBT4CBT), may provide a low-cost, high-quality, and easily accessible method of treatment for substance use difficulties. Limited research on this digital intervention has been conducted in Canadian populations, and few studies thus far have evaluated participants' subjective experience using the intervention, along with the cost on the Canadian healthcare system.What did the researchers do?The research team recruited participants and provided access to either CBT4CBT or to standard care at a mental health hospital for 8 weeks. Participants were asked questions about their substance use and related consequences, quality of life, and thoughts on the treatment they received. Information regarding healthcare use and the cost to the healthcare system was also gathered.What did the researchers find?Participants in both groups improved with regards to their substance use, some related consequences, and psychological quality of life. Participants provided insight on the benefits and challenges of both types of treatment. It was also found that the CBT4CBT intervention was less costly.What do these findings mean?These findings support that adults receiving CBT4CBT and standard care both improved to a similar degree in this sample. Participant feedback may inform future studies of how best to implement this intervention in clinical studies. Future studies with larger samples are needed to further examine whether CBT4CBT can increase access to supports and be beneficial in the Canadian healthcare system.

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.e., latent classes) based on patterns of self-reported mental health symptoms and investigate their associations with alcohol and cannabis use. METHODS: We used data from six successive waves of a web-based cross-sectional survey of adults aged 18 years and older living in Canada (6,021 participants). We applied latent class analysis to three domains of self-reported mental health most likely linked to effects of the pandemic: anxiety, depression, and loneliness. Logistic regression was used to characterize latent class membership, estimate the association of class membership with alcohol and cannabis use, and perform sex-based analyses. RESULTS: We identified two distinct classes: (1) individuals with low scores on all three mental health indicators (no/low-symptoms) and (2) those reporting high scores across the three measures (high-symptoms). Between 73.9 and 77.1% of participants were in the no/low-symptoms class and 22.9-26.1% of participants were in the high-symptom class. We consistently found across all six waves that individuals at greater risk of being in the high-symptom class were more likely to report worrying about getting COVID-19 with adjusted odds ratios (aORs) between 1.72 (95%CI:1.17-2.51) and 3.51 (95%CI:2.20-5.60). Those aged 60 + were less likely to be in this group with aORs (95%CI) between 0.26 (0.15-0.44) and 0.48 (0.29-0.77) across waves. We also found some factors associated with class membership varied at different time points. Individuals in the high-symptom class were more likely to use cannabis at least once a week (aOR = 2.28, 95%CI:1.92-2.70), drink alcohol heavily (aOR = 1.71, 95%CI:1.49-1.96); and increase the use of cannabis (aOR = 3.50, 95%CI:2.80-4.37) and alcohol (aOR = 2.37, 95%CI:2.06-2.74) during the pandemic. Women in the high-symptom class had lower odds of drinking more alcohol during the pandemic than men. CONCLUSIONS: We identified the determinants of experiencing high anxiety, depression, and loneliness symptoms and found a significant association with alcohol and cannabis consumption. This suggests that initiatives and supports are needed to address mental health and substance use multi-morbidities.

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. PARTICIPANTS: 6,016 adults (39 TGD, 2,980 cisgender men, 2,984 cisgender women, and 13 preferred not to answer), aged ≥18 years. MEASUREMENTS: Measures included self-reported HED (≥5 drinks on one or more occasions in the previous week for TGD and cisgender men and ≥4 for cisgender women) and any cannabis use in the previous week. Subjective changes in alcohol and cannabis use in the past week compared to before the pandemic were measured on a five-point Likert scale (1: much less to 5: much more). Binary and ordinal logistic regressions quantified differences between TGD and cisgender participants in alcohol and cannabis use, controlling for age, ethnoracial background, marital status, education, geographic location, and living arrangement. RESULTS: Compared to cisgender participants, TGD participants were more likely to use cannabis (adjusted odds ratio (aOR)=3.78, 95%CI: 1.89, 7.53) and to have reported subjective increases in alcohol (adjusted proportional odds ratios (aPOR)= 2.00, 95%CI: 1.01, 3.95) and cannabis use (aPOR=4.56, 95%CI: 2.13, 9.78) relative to before the pandemic. Compared to cisgender women, TGD participants were more likely to use cannabis (aOR=4.43, 95%CI: 2.21, 8.87) and increase their consumption of alcohol (aPOR=2.05, 95%CI: 1.03, 4.05) and cannabis (aPOR=4.71, 95%CI: 2.18, 10.13). Compared to cisgender men, TGD participants were more likely to use cannabis (aOR=3.20, 95%CI: 1.60, 6.41) and increase their use of cannabis (aPOR=4.40, 95%CI: 2.04, 9.49). There were no significant differences in HED between TGD and cisgender participants and in subjective change in alcohol between TGD and cisgender men; however, the odds ratios were greater than one as expected. CONCLUSIONS: Increased alcohol and cannabis use among TGD populations compared to before the pandemic may lead to increased health disparities. Accordingly, programs targeting the specific needs of TGD individuals should be prioritized.

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. We evaluated the impact of FPs switching from blended fee-for-service to blended capitation on the provision of mental health services in primary care and emergency department using longitudinal health administrative data from 2007 to 2016. We accounted for the differences between those who switched to blended capitation and non-switchers in the baseline using propensity score weighted fixed-effects regressions to compare remuneration models. We found that switching from blended fee-for-service to blended capitation was associated with a 14% decrease (95% CI 12-14%) in the number of mental health services and an 18% decrease (95% CI 15-20%) in the corresponding value of services. This result was driven by the decrease in services during regular-hours. During after-hours, the number of services increased by 20% (95% CI 10-32%) and the corresponding value increased by 35% (95% CI 17-54%). Switching was associated with a 4% (95% CI 1-8%) decrease in emergency department visits for mental health reasons. Blended capitation reduced provision of mental health services without increasing emergency department visits, suggesting potential efficiency gain in the blended capitation model in Ontario.

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. Using health administrative data from 2006 to 2014 and a two-stage estimation strategy, we study the impact of switching from FHG to FHO on the production of a capitated basket of services, after-hours services and nonincentivized services. Our results reveal that switching from the FHG to FHO reduces the production of capitated services to enrolled patients and services to nonenrolled patients by 15% and 5% per annum and increases the production of after-hours and nonincentivized services by 8% and 15% per annum.

Production of physician services under fee-for-service and blended fee-for-service: Evidence from Ontario, Canada.

We examine family physicians' responses to financial incentives for medical services in Ontario, Canada. We use administrative data covering 2003-2008, a period during which family physicians could choose between the traditional fee for service (FFS) and blended FFS known as the Family Health Group (FHG) model. Under FHG, FFS physicians are incentivized to provide comprehensive care and after-hours services. A two-stage estimation strategy teases out the impact of switching from FFS to FHG on service production. We account for the selection into FHG using a propensity score matching model, and then we use panel-data regression models to account for observed and unobserved heterogeneity. Our results reveal that switching from FFS to FHG increases comprehensive care, after-hours, and nonincentivized services by 3%, 15%, and 4% per annum. We also find that blended FFS physicians provide more services by working additional total days as well as the number of days during holidays and weekends. Our results are robust to a variety of specifications and alternative matching methods. We conclude that switching from FFS to blended FFS improves patients' access to after-hours care, but the incentive to nudge service production at the intensive margin is somewhat limited.

The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.

Background: Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature. Objective: Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified. Methods: A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed. Results: The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study. Conclusion: The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.

Physician remuneration schemes, psychiatric hospitalizations and follow-up care: Evidence from blended fee-for-service and capitation models.

Psychiatric hospitalizations could be reduced if mental illnesses were detected and treated earlier in the primary care setting, leading to the World Health Organization recommendation that mental health services be integrated into primary care. The mental health services provided in primary care settings may vary based on how physicians are incentivized. Little is known about the link between physician remuneration and psychiatric hospitalizations. We contribute to this literature by studying the relationship between physician remuneration and psychiatric hospitalizations in Canada's most populous province, Ontario. Specifically, we study family physicians (FPs) who switched from blended fee-for-service (FFS) to blended capitation remuneration model, relative to those who remained in the blended FFS model, on psychiatric hospitalizations. Outcomes included psychiatric hospitalizations by enrolled patients and the proportion of hospitalized patients who had a follow-up visit with the FP within 14 days of discharge. We used longitudinal health administrative data from a cohort of practicing physicians from 2006 through 2016. Because physicians practicing in these two models are likely to be different, we employed inverse probability weighting based on estimated propensity scores to ensure that switchers and non-switchers were comparable at the baseline. Using inverse probability weighted fixed-effects regressions controlling for relevant confounders, we found that switching from blended FFS to blended capitation was associated with a 6.2% decrease in the number of psychiatric hospitalizations and a 4.7% decrease in the number of patients with a psychiatric hospitalization. No significant effect of remuneration on follow-up visits within 14 days of discharge was observed. Our results suggest that the blended capitation model is associated with fewer psychiatric hospitalizations relative to blended FFS.

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. METHODS: Cross-sectional samples of Canadian adults (aged ≥18 years) completed web-based surveys in 6 waves from May to December 2020 (N=6021), and quota sampling strategies were used to match the English-speaking Canadian population in age, gender, and region. The surveys measured anxiety and depression symptoms, sociodemographic characteristics, substance use, and perceived COVID-19 risks and worries. First, principal component analysis was used to condense highly comorbid anxiety and depression symptoms into a single data-driven measure of emotional distress. Second, eXtreme Gradient Boosting (XGBoost), a machine learning algorithm that can model nonlinear and interactive relationships, was used to regress this measure on all included explanatory variables. Variable importance and effects across time were explored using SHapley Additive exPlanations (SHAP). RESULTS: Principal component analysis of responses to 9 anxiety and depression questions on an ordinal scale revealed a primary latent factor, termed "emotional distress," that explained 76% of the variation in all 9 measures. Our XGBoost model explained a substantial proportion of variance in emotional distress (r2=0.39). The 3 most important items predicting elevated emotional distress were increased worries about finances (SHAP=0.17), worries about getting COVID-19 (SHAP=0.17), and younger age (SHAP=0.13). Hopefulness was associated with emotional distress and moderated the impacts of several other factors. Predicted emotional distress exhibited a nonlinear pattern over time, with the highest predicted symptoms in May and November and the lowest in June. CONCLUSIONS: Our results highlight factors that may exacerbate emotional distress during the current pandemic and possible future pandemics, including a role of hopefulness in moderating distressing effects of other factors. The pandemic disproportionately affected emotional distress among younger adults and those economically impacted.