Arjumand Siddiqi

Addressing unhealthy movement behaviors in adolescence could reduce socioeconomic inequalities in adult cardiovascular disease (CVD) risk. Among 6608 adolescents enrolled in the Add Health cohort (1994-1995) and followed into adulthood (2016-2018), movement behaviors (physical activity, screen time, sleep) were classified as healthy or unhealthy based on adherence to the 24-Hour Movement Guidelines. Parental education and family financial hardship captured socioeconomic position (SEP). The outcome was 30-year CVD risk in adulthood. G-formula-based causal decomposition quantified changes in socioeconomic inequalities under 2 interventional scenarios: (1) eliminating unhealthy movement behaviors across the population and (2) equalizing the low-SEP group’s movement behavior distributions to match the high-SEP group. Adolescents whose parents did not graduate from college/university had a higher adult CVD risk than those whose parents did (risk difference [RD]: 6.9%, relative risk [RR]: 1.73). Eliminating all unhealthy movement behaviors reduced the RD to 4.9% (–2.0% absolute change), while the RR remained similar (1.76). Eliminating unhealthy levels of physical activity produced the largest absolute reduction (–1.7%), with negligible reductions for screen time (–0.2%) and sleep (–0.2%). The equalization scenario had minimal impact, and results were similar for family financial hardship. Whole-population interventions targeting unhealthy adolescent movement behaviors, particularly physical activity, may reduce absolute socioeconomic inequalities in adult CVD risk.

Multimorbidity follows a strong social gradient, yet how the co-occurrence of social determinants shapes risk remains poorly understood. Most research examines individual determinants in isolation, potentially obscuring socially patterned subgroups relevant to multimorbidity risk. This study examines the association between multidimensional social exposure profiles (derived from individual- and area-level determinants) and multimorbidity risk. Profiles were derived through unsupervised clustering applied to population-based cohort of Ontario respondents to the Canadian Community Health Survey (2001-2011) and linked area-level Census measures. Chronic disease and subsequent multimorbidity status were identified through linkage to administrative health data until 2022. Sex-stratified Weibull models were used to estimate hazard ratios for the association between multidimensional social exposure and incident multimorbidity. Unsupervised clustering identified six distinct social exposure profiles, with multimorbidity onset differing across profiles and elevated risk observed across multiple patterns of social disadvantage, rather than following a simple graded gradient. In sex-stratified analyses, disadvantage social exposure profiles were associated with an earlier multimorbidity onset among females (adjusted HR: 1.70, 95%CI, 1.18-2.53) and males (adjusted HR: 1.59, 95%CI, 1.20-2.05). These multidimensional social exposure profiles capture population-level social patterning associated with differential multimorbidity risk. These findings have implications for upstream policy aimed at reducing inequities in multimorbidity.

Empirical evidence on the indirect herd benefits of COVID-19 vaccination and/or prior infection is limited. We examined how area-level immunity interacts with individual-level immunity to affect COVID-19 diagnoses and deaths. Ontario residents aged ≥18 years were followed from August 1, 2021, to January 30, 2022. Individual-level immunity was defined as receipt of a primary series of COVID-19 vaccines or a positive SARS-CoV-2 test in the past 165 days. Area-level immunity was based on the proportion of immune individuals in an individual's residing area. Logistic regression and cause-specific hazard models were used to examine the relationship between immunity and COVID-19 diagnosis, and between immunity and COVID-19 death, with an interaction term between individual-level and area-level immunity. Of 11 122 816 adults, 7 518 015 (67.6%) were classified as having individual-level immunity at baseline. After accounting for potential confounders, area-level immunity (highest vs. lowest quintiles) was associated with lower odds of COVID-19 diagnosis. Higher area-level immunity (highest vs. lowest quintiles) was also associated with lower hazard of COVID-19 death among non-immune individuals (hazard ratio, 0.77 [0.60, 1.00]). Findings provide evidence supporting the herd benefits of vaccination or prior infection on COVID-19 diagnosis and deaths, and provide insights for interpreting vaccine effectiveness estimates in the context of herd immunity.

Background: Bias evaluations of machine learning (ML) models often focus on performance in research settings, with limited assessment of downstream bias following clinical deployment. The objective of this study was to evaluate whether CHARTwatch, a real-time ML early warning system for inpatient deterioration, demonstrated algorithmic bias in model performance, or produced disparities in care processes, and outcomes across patient sociodemographic groups. Methods: We evaluated CHARTwatch implementation on the internal medicine service at a large academic hospital. Patient outcomes during the intervention period (November 1, 2020–June 1, 2022) were compared to the control period (November 1, 2016–December 31, 2019) using propensity score overlap weighting. We evaluated differences across key sociodemographic subgroups, including age, sex, homelessness, and neighborhood-level socioeconomic and racialized composition. Outcomes included model performance (sensitivity and specificity), processes of care, and patient outcomes (non-palliative in-hospital death). Results: Among 12 877 patients (9079 control, 3798 intervention), 13.3% were experiencing homelessness and 36.9% lived in the quintile with the highest neighborhood racialized and newcomer populations. Model sensitivity was 70.1% overall, with no significant variation across subgroups. Model specificity varied by age, 80 years: 84% (95%CI 79-88%), P < .001, but not other subgroups. CHARTwatch implementation was associated with an increase in code status documentation among patients experiencing homelessness, without significant differences in other care processes or outcomes. Conclusion: CHARTwatch model performance and impact were generally consistent across measured sociodemographic subgroups. ML-based clinical decision support tools, and associated standardization of care, may reduce existing inequities, as was observed for code status orders among patients experiencing homelessness. This evaluation provides a framework for future bias assessments of deployed ML-CDS tools.

Background Research has shown that workers in non-standard (eg, temporary and part-time) employment experience poorer health outcomes than their permanent, full-time counterparts. However, previous studies have overlooked important differences in the quality of non-standard employment. To address this gap, we examined associations between a diverse typology of employment quality and mortality in Canada. Methods The 2006 Canadian Health and Environment Cohort (n=2 805 550) was linked to death records from 2006 to 2019. Employment quality was assessed according to an empirical typology describing five distinct employment arrangements: standard (secure and gainful), portfolio (demanding but gainful), marginal (limited hours and earnings), intermittent (sporadic and unstable) and precarious (insecure and low paying). Poisson regression models estimated covariate-adjusted associations between employment quality, all-cause and cause-specific (cancer, cardiovascular and unintentional injury) mortality, by sex/gender. Results We observed a graded association between employment quality and mortality. Mortality rates were lowest among workers in standard and portfolio employment. Mortality rates were highest among workers in precarious employment, with workers in marginal and intermittent employment occupying intermediate positions along the risk gradient. Associations varied by sex/gender, with larger absolute and relative mortality inequalities among men. Conclusions Our findings reinforce the need to move away from a binary view of jobs as either ‘standard’ or ‘precarious’, encouraging a more nuanced understanding of contemporary employment arrangements and their health-related consequences. Policy interventions that promote access to high-quality jobs and protect workers exposed to precarious employment may yield substantial improvements in population health, including longevity.

Canada is undergoing a crisis in mental health, and the federal government has established a strategy that is attentive to mental health among Black people

Knowledge of patterns in COVID-19 deaths by area-level income over time and the mediating role of vaccination in inequality patterns remains limited. We used data from a population-based retrospective cohort of 11 248 572 adults in Ontario, Canada. Cause-specific hazard models were used examine the relationship between income (2016 Census at the dissemination area level) and COVID-19 deaths between March 1, 2020 and January 30, 2022, stratified by wave. We used regression-based causal mediation analyses to examine the mediating role of vaccination in the relationship between income and COVID-19 deaths during waves 4 and 5. After accounting for demographics, baseline health, and other social determinants of health, inequalities in COVID-19 deaths by income persisted over time (HR [95% CI] comparing lowest income vs highest income quintiles were 1.37 [0.98-1.92] for wave 1, 1.21 [0.99-1.48] for wave 2, 1.55 [1.22-1.96] for wave 3, and 1.57 [1.15-2.15] for waves 4 and 5). By the start of wave 4, 7 534 259 (67.7%) of those alive were vaccinated, with lower odds of vaccination in the lowest income vs highest income quintiles (0.71 [0.70-0.71]). This inequality in vaccination accounted for 56.9% [22.5%-91.3%] of inequalities in COVID-19 deaths between individuals in the lowest income vs highest income quintiles. Efforts are needed to address vaccination gaps and residual heightened risks associated with lower income to improve health equity in COVID-19 outcomes.

Background Lower-quality employment, characterised by excessive or part-time hours, irregular schedules and inadequate earnings, is a key social determinant of health among adults. Research examining parental employment quality in relation to the mental health and school performance of children is lacking. The study objective was to measure the associations between parental employment quality and child mental health symptoms and school performance. Methods We conducted a secondary analysis of the cross-sectional 2014 Ontario Child Health Study. Dependent variables were parent-reported child mental health symptoms and school performance. We used latent class analysis (LCA) to characterise employment status, hours, scheduling and earnings of parents. We used linear and multinomial regression to model the associations between parental employment quality, mental health symptoms, and school performance. Results Our study sample consisted of 9,927 children. The LCA of dual-parent households yielded three classes of parental employment quality, which we labelled ‘Dual Parent, High Quality’, ‘Dual Parent, Primary Earner Model’ and ‘Dual Parent, Precarious’. The LCA of single-parent households yielded two further classes, which we labelled ‘Single Parent, High Quality’ and ‘Single Parent, Precarious’. Compared with children in the ‘Dual Parent, High Quality’ group, children in all other groups had higher-level mental health symptoms and lower school performance. Children with ‘precarious’ parental employment in both groups showed the least favourable outcomes. Conclusions Lower-quality parental employment was associated with increased mental health symptoms and poorer school performance among children. A clearer understanding of these relationships and their underlying mechanisms can help inform relevant policies and interventions.

Suicide, drug poisoning, and alcohol-attributable mortality (SDAM)—often labeled “deaths of despair”—are increasing among working-aged individuals in many high-income countries. We examined the association between employment quality and SDAM in Canada. Census records from the 2006 Canadian Census Health and Environment Cohort (n = 2 805 550) were linked to mortality data from 2006 to 2019. Latent class analysis identified 5 employment quality types: standard (secure and rewarding), portfolio (rewarding but demanding), marginal (limited hours and earnings), intermittent (sporadic and unstable), and precarious (insecure and unrewarding). Poisson regression models estimated sex/gender-stratified associations between employment quality type and SDAM separately. We observed a consistent mortality gradient across employment quality groups, with lower-quality employment—and precarious employment in particular—associated with increased rates of SDAM relative to higher-quality (ie, standard) employment. For example, precarious employment was associated with a more than 3-fold rate of drug poisoning deaths among women (rate ratio [RR] = 3.58; 95% CI, 3.21-4.00) and a more than 2-fold rate of alcohol-attributable death among men (RR = 2.22; 95% CI, 2.07-2.38). Employment quality is an important determinant of SDAM, with varying associations by sex/gender. Improvements in employment conditions may help reduce the burden of premature deaths attributable to suicide and substance use.