Kwame McKenzie

Machine learning (ML), a subset of artificial intelligence, uses large datasets to identify patterns between potential predictors and outcomes. ML involves iterative learning from data and is increasingly used in population and public health. Examples include early warning of infectious disease outbreaks, predicting the future burden of noncommunicable diseases, and assessing public health interventions. However, ML can inadvertently produce biased outputs related to the quality and quantity of data, who is engaged and helping direct the analysis, and how findings are interpreted. Specific guidelines for using ML in population and public health have not yet been created. We assembled a diverse team of experts in computer science, statistical modeling, clinical and population health epidemiology, health economics, ethics, sociology, and public health. Drawing on literature reviews and a modified Delphi process, we identified five key recommendations: (1) prioritize partnerships and interventions to support communities considered structurally disadvantaged; (2) use ML for dynamic situations, such as public health emergencies, while adhering to ethical standards; (3) conduct risk assessments and bias mitigation strategies aligned with identified risks; (4) ensure technical transparency and reproducibility by publicly sharing data sources and methodologies; and (5) foster multidisciplinary dialogue to discuss the potential harms of ML-related bias and raise awareness among the public and public health community. The proposed guidelines provide operational steps for stakeholders, ensuring that ML tools are not only effective but also ethically grounded and feasible in real-world scenarios.

Summary: Recent changes to US research funding are having far-reaching consequences that imperil the integrity of science and the provision of care to vulnerable populations. Resisting these changes, the BJPsych Portfolio reaffirms its commitment to publishing mental science and advancing psychiatric knowledge that improves the mental health of one and all.

Youth homelessness represents a persistent and significant challenge for service sectors with limited best practice guidance. Housing supports, in particular, are widely deployed, with the practice-oriented literature providing little detail regarding service design beyond broad domains such as employment support and life skills coaching. The present multiple case study investigation was designed to develop a preliminary understanding of the guiding principles that attend the development of interventions that support youth exiting homelessness in the Canadian context. These case studies were conducted with a diverse group of five organizations recognized as sector leaders, with findings considered in light of practice standards from the better-established adult housing literature. Key findings with respect to service models included the strategies used to provide flexible, culturally responsive, tailored services with an emphasis on specialist support. Implementation factors included the navigation of strategic partnerships, the use of data in capacity-building, and the benefits and drawbacks of larger, centralized service environments versus smaller, dispersed environments. Youth-specific considerations in housing stabilization models are highlighted. This work contributes to a growing body of literature that seeks to articulate best practices in the effort to address and prevent youth homelessness.

There is an increasing commitment to “Personal Recovery” as the desired outcome for mental health rehabilitation, yet there is little agreement about how to measure it. The purpose of this study was to develop a method of measuring recovery in community-dwelling people with mental health concerns. We describe a four-phase process, informed by guidelines for (patient-reported outcome measure) PROM development described by the Scientific Advisory Committee of the Medical Outcomes Trust, the Food and Drug Administration, and the International Society for Quality of Life Research, on how to quantify recovery in mental health care. The approach includes an iterative mixed methods process, guided by Classical Test Theory (CTT) and Rasch Measurement Theory (RMT), to develop the Canadian Personal Recovery Outcome Measure (C-PROM) for adults receiving mental health services. In Phases 1 and 2, 40 new items were generated by people with schizophrenia. In Phase 3, psychometric analysis and cognitive interviewing suggested that the item set be reduced to 30 items. Phase 4 (n = 575) showed good overall fit of the C-PROM items to the Rasch model (χ ² = 163, df = 130, p = .05), no item misfit, high reliability ( r _p = 0.92), an ordered response scale structure, high correlation between logit and scale scores (0.92) and no item bias for gender, age, or diagnosis. This study provided evidence for the C-PROM as a measure of personal recovery for people with mental health concerns. The measurement model underpinning this set of items has potential to support clinical relevance of scale scores, advancing an evidence-based approach to mental health rehabilitation practice and outcomes.

During the COVID-19 pandemic, mental health services rapidly transitioned to virtual care. Although such services can improve access for underserved populations, they may also present unique challenges, especially for refugee newcomers. This study examined the multidimensional nature of access to virtual mental health (VMH) care for refugee newcomers during the COVID-19 pandemic, using Levesque et al.’s Client-Centered Framework for Assessing Access to Health Care. One hundred and eight structured and semi structured interviews were conducted in four Canadian provinces (8 community leaders, 37 newcomer clients, 63 mental health or service providers or managers). Deductive qualitative analysis, based on the Client-Centered Framework, identified several overarching themes: challenges due to the cost and complexity of using technology; comfort for VMH outside clinical settings; sustainability post-COVID-19; and communication and the therapeutic alliance. Mental health organizations, community organizations, and service providers can improve access to (virtual) mental health care for refugee newcomers by addressing cultural and structural barriers, tailoring services, and offering choice and flexibility to newcomers.

Background: Studies have shown mixed results regarding social capital and the risk of developing a psychotic disorder, and this has yet to be studied in North America. We sought to examine the relationship between neighbourhood-level marginalisation, social capital, and the incidence of schizophrenia and schizoaffective disorder in Toronto, Canada.Methods: We used a retrospective population-based cohort to identify incident cases of schizophrenia and schizoaffective disorder over a 10 year period and accounted for neighbourhood-level marginalisation and a proxy indicator of neighbourhood social capital. Mixed Poisson regression models were used to estimate adjusted incidence rate ratios (aIRRs).Results: In the cohort (n= 649 020) we identified 4841 incident cases of schizophrenia and schizoaffective disorder. A 27% variation in incidence was observed between neighbourhoods. All marginalisation dimensions, other than ethnic concentration, were associated with incidence. Compared to areas with low social capital, areas with intermediate social capital in the second [aIRR = 1.17, 95% confidence interval (CI) 1.03–1.33] and third (aIRR = 1.23, 95% CI 1.08–1.40) quintiles had elevated incidence rates after accounting for marginalisation. There was a higher risk associated with the intermediate levels of social capital (aIRR = 1.18, 95% CI 1.00–1.39) when analysed in only the females in the cohort, but the CI includes the possibility of a null effect.Conclusions: The risk of developing schizophrenia and schizoaffective disorder in Toronto varies by neighbourhood and is associated with socioenvironmental exposures. Social capital was not linearly associated with risk, and risk differs by sex and social capital quintile. Future research should examine these relationships with different forms of social capital and examine how known individual-level risk factors impact these findings.

This systematic review of reviews was conducted to examine housing precarity and homelessness in relation to climate change and weather extremes internationally. In a thematic analysis of 15 reviews (5 systematic and 10 non-systematic), the following themes emerged: risk factors for homelessness/housing precarity, temperature extremes, health concerns, structural factors, natural disasters, and housing. First, an increased risk of homelessness has been found for people who are vulnerably housed and populations in lower socio-economic positions due to energy insecurity and climate change-induced natural hazards. Second, homeless/vulnerably-housed populations are disproportionately exposed to climatic events (temperature extremes and natural disasters). Third, the physical and mental health of homeless/vulnerably-housed populations is projected to be impacted by weather extremes and climate change. Fourth, while green infrastructure may have positive effects for homeless/vulnerably-housed populations, housing remains a major concern in urban environments. Finally, structural changes must be implemented. Recommendations for addressing the impact of climate change on homelessness and housing precarity were generated, including interventions focusing on homelessness/housing precarity and reducing the effects of weather extremes, improved housing and urban planning, and further research on homelessness/housing precarity and climate change. To further enhance the impact of these initiatives, we suggest employing the Human Rights-Based Approach (HRBA).

Background: In schizophrenia spectrum populations, adherence to treatment is poor, community-based supports are limited, and efforts to foster illness self-management have had limited success. These challenges contribute to frequent, lengthy, and costly hospital readmissions and poor functional outcomes. Digital health strategies, in turn, hold considerable promise in the effort to address these problems. Objective: This feasibility trial will examine a digital health platform called App4Independence (A4i), which was designed to enhance illness self-management and treatment engagement for individuals with schizophrenia. Methods: Feasibility metrics in this single-blind, randomized trial include study recruitment and retention, rate of technology use, safety, and utility in clinical interactions. Other outcome metrics include symptomatology, treatment adherence, patient-provider alliance, and quality of life. In this trial, 160 study participants with schizophrenia spectrum diagnoses will be randomized to either treatment or control conditions, with pretest-posttest outcomes measured over a 6-month period. Results: This study was funded by the Canadian Institutes of Health Research in January 2020 and received Institutional Review Board approval on August 13, 2020. This study plans to begin recruiting in January 2021 and will be completed within 3 years. Data collection is projected to begin in January 2021. Conclusions: This research will provide critical information for the development of this new technology in the larger effort to address a key problem in the schizophrenia field—how to leverage technology to enhance illness self-management and care engagement in resource-limited service contexts. International Registered Report Identifier (IRRID): PRR1-10.2196/24736