HealthyPlan.City: A Web Tool to Support Urban Environmental Equity and Public Health in Canadian Communities.

Urban environmental factors such as air quality, heat islands, and access to greenspaces and community amenities impact public health. Some vulnerable populations such as low-income groups, children, older adults, new immigrants, and visible minorities live in areas with fewer beneficial conditions, and therefore, face greater health risks. Planning and advocating for equitable healthy urban environments requires systematic analysis of reliable spatial data to identify where vulnerable populations intersect with positive or negative urban/environmental characteristics. To facilitate this effort in Canada, we developed HealthyPlan.City ( https://healthyplan.city/ ), a freely available web mapping platform for users to visualize the spatial patterns of built environment indicators, vulnerable populations, and environmental inequity within over 125 Canadian cities. This tool helps users identify areas within Canadian cities where relatively higher proportions of vulnerable populations experience lower than average levels of beneficial environmental conditions, which we refer to as Equity priority areas. Using nationally standardized environmental data from satellite imagery and other large geospatial databases and demographic data from the Canadian Census, HealthyPlan.City provides a block-by-block snapshot of environmental inequities in Canadian cities. The tool aims to support urban planners, public health professionals, policy makers, and community organizers to identify neighborhoods where targeted investments and improvements to the local environment would simultaneously help communities address environmental inequities, promote public health, and adapt to climate change. In this paper, we report on the key considerations that informed our approach to developing this tool and describe the current web-based application.

The equitable impact of sugary drink taxation structures on sugary drink consumption among Canadians: a modelling study using the 2015 Canadian Community Health Survey-Nutrition.

OBJECTIVE: Estimate the impact of 20 % flat-rate and tiered sugary drink tax structures on the consumption of sugary drinks, sugar-sweetened beverages and 100 % juice by age, sex and socio-economic position. DESIGN: We modelled the impact of price changes - for each tax structure - on the demand for sugary drinks by applying own- and cross-price elasticities to self-report sugary drink consumption measured using single-day 24-h dietary recalls from the cross-sectional, nationally representative 2015 Canadian Community Health Survey-Nutrition. For both 20 % flat-rate and tiered sugary drink tax scenarios, we used linear regression to estimate differences in mean energy intake and proportion of energy intake from sugary drinks by age, sex, education, food security and income. SETTING: Canada. PARTICIPANTS: 19 742 respondents aged 2 and over. RESULTS: In the 20 % flat-rate scenario, we estimated mean energy intake and proportion of daily energy intake from sugary drinks on a given day would be reduced by 29 kcal/d (95 % UI: 18, 41) and 1·3 % (95 % UI: 0·8, 1·8), respectively. Similarly, in the tiered tax scenario, additional small, but meaningful reductions were estimated in mean energy intake (40 kcal/d, 95 % UI: 24, 55) and proportion of daily energy intake (1·8 %, 95 % UI: 1·1, 2·5). Both tax structures reduced, but did not eliminate, inequities in mean energy intake from sugary drinks despite larger consumption reductions in children/adolescents, males and individuals with lower education, food security and income. CONCLUSIONS: Sugary drink taxation, including the additional benefit of taxing 100 % juice, could reduce overall and inequities in mean energy intake from sugary drinks in Canada.

Exploring the use of experimental small area estimates to examine the relationship between individual-level and area-level community belonging and self-rated health.

Background: Small area estimation refers to statistical modelling procedures that leverage information or "borrow strength" from other sources or variables. This is done to enhance the reliability of estimates of characteristics or outcomes for areas that do not contain sufficient sample sizes to provide disaggregated estimates of adequate precision and reliability. There is growing interest in secondary research applications for small area estimates (SAEs). However, it is crucial to assess the analytic value of these estimates when used as proxies for individual-level characteristics or as distinct measures that offer insights at the area level. This study assessed novel area-level community belonging measures derived using small area estimation and examined associations with individual-level measures of community belonging and self-rated health. Data and methods: SAEs of community belonging within census tracts produced from the 2016-2019 cycles of the Canadian Community Health Survey (CCHS) were merged with respondent data from the 2020 CCHS. Multinomial logistic regression models were run between area-level SAEs, individual-level sense of community belonging, and self-rated health on the study sample of people aged 18 years and older. Results: Area-level community belonging was associated with individual-level community belonging, even after adjusting for individual-level sociodemographic characteristics, despite limited agreement between individual- and area-level measures. Living in a neighbourhood with low community belonging was associated with higher odds of reporting being in fair or poor health, versus being in very good or excellent health (odds ratio: 1.53; 95% confidence interval: 1.22, 1.91), even after adjusting for other factors such as individual-level sense of community belonging, which was also associated with self-rated health. Interpretation: Area-level and individual-level sense of community belonging were independently associated with self-rated health. The novel SAEs of community belonging can be used as distinct measures of neighbourhood-level community belonging and should be understood as complementary to, rather than proxies for, individual-level measures of community belonging.

Characterizing high-cost healthcare users among adults with back pain in Ontario, Canada: a population-based cohort study.

ABSTRACT: Some patients with back pain contribute disproportionately to high healthcare costs; however, characteristics of high-cost users with back pain are not well defined. We described high-cost healthcare users based on total costs among a population-based cohort of adults with back pain within the Ontario government's single-payer health system across sociodemographic, health, and behavioural characteristics. We conducted a population-based cohort study of Ontario adult (aged 18 years or older) respondents of the Canadian Community Health Survey (CCHS) with back pain (2003-2012), linked to administrative data (n = 36,605; weighted n = 2,076,937, representative of Ontario). Respondents were ranked based on gradients of total healthcare costs (top 1%, top 2%-5%, top 6%-50%, and bottom 50%) for 1 year following the CCHS survey, with high-cost users as top 5%. We used multinomial logistic regression to investigate characteristics associated with the 4 cost groups. Top 5% of cost users accounted for 49% ($4 billion CAD) of total healthcare spending, with inpatient hospital care as the largest contributing service type (approximately 40% of costs). Top 5% high-cost users were more likely aged 65 years or older (ORtop1% = 16.6; ORtop2-5% = 44.2), with lower income (ORtop1% = 3.6; ORtop 2-5% = 1.8), chronic disease(s) (ORtop1% = 3.8; ORtop2-5% = 1.6), Aggregated Diagnosis Groups measuring comorbidities (ORtop1% = 25.4; ORtop2-5% = 13.9), and fair/poor self-rated general health (ORtop1% = 6.7; ORtop2-5% = 4.6) compared with bottom 50% users. High-cost users tended to be current/former smokers, obese, and report fair/poor mental health. High-cost users (based on total costs) among adults with back pain account for nearly half of all healthcare spending over a 1-year period and are associated with older age, lower income, comorbidities, and fair/poor general health. Findings identify characteristics associated with a high-risk group for back pain to inform healthcare and public health strategies that target upstream determinants.

External validation and application of the Diabetes Population Risk Tool (DPoRT) for prediction of type 2 diabetes onset in the US population.

INTRODUCTION: Characterizing diabetes risk in the population is important for population health assessment and diabetes prevention planning. We aimed to externally validate an existing 10-year population risk model for type 2 diabetes in the USA and model the population benefit of diabetes prevention approaches using population survey data. RESEARCH DESIGN AND METHODS: The Diabetes Population Risk Tool (DPoRT), originally derived and validated in Canada, was applied to an external validation cohort of 23 477 adults from the 2009 National Health Interview Survey (NHIS). We assessed predictive performance for discrimination (C-statistic) and calibration plots against observed incident diabetes cases identified from the NHIS 2009-2018 cycles. We applied DPoRT to the 2018 NHIS cohort (n=21 187) to generate 10-year risk prediction estimates and characterize the preventive benefit of three diabetes prevention scenarios: (1) community-wide strategy; (2) high-risk strategy and (3) combined approach. RESULTS: DPoRT demonstrated good discrimination (C-statistic=0.778 (males); 0.787 (females)) and good calibration across the range of risk. We predicted a baseline risk of 10.2% and 21 076 000 new cases of diabetes in the USA from 2018 to 2028. The community-wide strategy and high-risk strategy estimated diabetes risk reductions of 0.2% and 0.3%, respectively. The combined approach estimated a 0.4% risk reduction and 843 000 diabetes cases averted in 10 years. CONCLUSIONS: DPoRT has transportability for predicting population-level diabetes risk in the USA using routinely collected survey data. We demonstrate the model's applicability for population health assessment and diabetes prevention planning. Our modeling predicted that the combination of community-wide and targeted prevention approaches for those at highest risk are needed to reduce diabetes burden in the USA.

Risk of premature mortality due to smoking, alcohol use, obesity and physical activity varies by income: A population-based cohort study.

Background: Premature deaths are a strong population health indicator. There is a persistent and widening pattern of income inequities for premature mortality. We sought to understand the combined effect of health behaviours and income on premature mortality in a large population-based cohort. Methods: We analyzed a cohort of 121,197 adults in the 2005-2014 Canadian Community Health Surveys, linked to vital statistics data to ascertain deaths for up to 5 years following baseline. Information on household income quintile and mortality-relevant risk factors (smoking status, alcohol use, body mass index (BMI), and physical activity) was captured from the survey. Hazard ratios (HR) for combined income-risk factor groups were estimated using Cox proportional hazards models. Stratified Cox models were used to identify quintile-specific HR for each risk factor. Results: For each risk factor, HR of premature mortality was highest in the lowest-income, highest-risk group. Additionally, an income gradient was seen for premature mortality HR for every exposure level of each risk factor. In the stratified models, risk factor HRs did not vary meaningfully between income groups. All findings were consistent in the unadjusted and adjusted models. Conclusion: These findings highlight the need for targeted strategies to reduce health inequities and more careful attention to how policies and interventions are distributed at the population level. This includes targeting and tailoring resources to those in lower income groups who disproportionately experience premature mortality risk to prevent further widening health inequities.

Sex-based trajectories of health system use in lonely and not lonely older people: A population-based cohort study.

BACKGROUND: There is growing interest in understanding the care needs of lonely people but studies are limited and examine healthcare settings separately. We estimated and compared healthcare trajectories in lonely and not lonely older female and male respondents to a national health survey. METHODS: We conducted a retrospective cohort study of community-dwelling, Ontario respondents (65+ years) to the 2008/2009 Canadian Community Health Survey-Healthy Aging. Respondents were classified at baseline as not lonely, moderately lonely, or severely lonely using the Three-Item Loneliness Scale and then linked with health administrative data to assess healthcare transitions over a 12 -year observation period. Annual risks of moving from the community to inpatient, long-stay home care, long-term care settings-and death-were estimated across loneliness levels using sex-stratified multistate models. RESULTS: Of 2684 respondents (58.8% female sex; mean age 77 years [standard deviation: 8]), 635 (23.7%) experienced moderate loneliness and 420 (15.6%) severe loneliness. Fewer lonely respondents remained in the community with no transitions (not lonely, 20.3%; moderately lonely, 17.5%; and severely lonely, 12.6%). Annual transition risks from the community to home care and long-term care were higher in female respondents and increased with loneliness severity for both sexes (e.g., 2-year home care risk: 6.1% [95% CI 5.5-6.6], 8.4% [95% CI 7.4-9.5] and 9.4% [95% CI 8.2-10.9] in female respondents, and 3.5% [95% CI 3.1-3.9], 5.0% [95% CI 4.0-6.0], and 5.4% [95% CI 4.0-6.8] in male respondents; 5-year long-term care risk: 9.2% [95% CI 8.0-10.8], 11.1% [95% CI 9.3-13.6] and 12.2% [95% CI 9.9-15.3] [female], and 5.3% [95% CI 4.2-6.7], 9.1% [95% CI 6.8-12.5], and 10.9% [95% CI 7.9-16.3] [male]). CONCLUSIONS: Lonely older female and male respondents were more likely to need home care and long-term care, with severely lonely female respondents having the highest probability of moving to these settings.

Modeling chronic disease risk across equity factors using a population-based prediction model: the Chronic Disease Population Risk Tool (CDPoRT).

BACKGROUND: Predicting chronic disease incidence at a population level can help inform overall future chronic disease burden and opportunities for prevention. This study aimed to estimate the future burden of chronic disease in Ontario, Canada, using a population-level risk prediction algorithm and model interventions for equity-deserving groups who experience barriers to services and resources due to disadvantages and discrimination. METHODS: The validated Chronic Disease Population Risk Tool (CDPoRT) estimates the 10-year risk and incidence of major chronic diseases. CDPoRT was applied to data from the 2017/2018 Canadian Community Health Survey to predict baseline 10-year chronic disease estimates to 2027/2028 in the adult population of Ontario, Canada, and among equity-deserving groups. CDPoRT was used to model prevention scenarios of 2% and 5% risk reductions over 10 years targeting high-risk equity-deserving groups. RESULTS: Baseline chronic disease risk was highest among those with less than secondary school education (37.5%), severe food insecurity (19.5%), low income (21.2%) and extreme workplace stress (15.0%). CDPoRT predicted 1.42 million new chronic disease cases in Ontario from 2017/2018 to 2027/2028. Reducing chronic disease risk by 5% prevented 1500 cases among those with less than secondary school education, prevented 14 900 cases among those with low household income and prevented 2800 cases among food-insecure populations. Large reductions of 57 100 cases were found by applying a 5% risk reduction in individuals with quite a bit workplace stress. CONCLUSION: Considerable reduction in chronic disease cases was predicted across equity-defined scenarios, suggesting the need for prevention strategies that consider upstream determinants affecting chronic disease risk.

Measuring waning protection from seasonal influenza vaccination during nine influenza seasons, Ontario, Canada, 2010/11 to 2018/19.

BackgroundWaning immunity from seasonal influenza vaccination can cause suboptimal protection during peak influenza activity. However, vaccine effectiveness studies assessing waning immunity using vaccinated and unvaccinated individuals are subject to biases.AimWe examined the association between time since vaccination and laboratory-confirmed influenza to assess the change in influenza vaccine protection over time.MethodsUsing linked laboratory and health administrative databases in Ontario, Canada, we identified community-dwelling individuals aged ≥ 6 months who received an influenza vaccine before being tested for influenza by RT-PCR during the 2010/11 to 2018/19 influenza seasons. We estimated the adjusted odds ratio (aOR) for laboratory-confirmed influenza by time since vaccination (categorised into intervals) and for every 28 days.ResultsThere were 53,065 individuals who were vaccinated before testing for influenza, with 10,264 (19%) influenza-positive cases. The odds of influenza increased from 1.05 (95% CI: 0.91-1.22) at 42-69 days after vaccination and peaked at 1.27 (95% CI: 1.04-1.55) at 126-153 days when compared with the reference interval (14-41 days). This corresponded to 1.09-times increased odds of influenza every 28 days (aOR = 1.09; 95% CI: 1.04-1.15). Individuals aged 18-64 years showed the greatest decline in protection against influenza A(H1N1) (aORper 28 days = 1.26; 95% CI: 0.97-1.64), whereas for individuals aged ≥ 65 years, it was against influenza A(H3N2) (aORper 28 days = 1.20; 95% CI: 1.08-1.33). We did not observe evidence of waning vaccine protection for individuals aged < 18 years.ConclusionsInfluenza vaccine protection wanes during an influenza season. Understanding the optimal timing of vaccination could ensure robust protection during seasonal influenza activity.

A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT).

INTRODUCTION: Avoidable hospitalizations are considered preventable given effective and timely primary care management and are an important indicator of health system performance. The ability to predict avoidable hospitalizations at the population level represents a significant advantage for health system decision-makers that could facilitate proactive intervention for ambulatory care-sensitive conditions (ACSCs). The aim of this study is to develop and validate the Avoidable Hospitalization Population Risk Tool (AvHPoRT) that will predict the 5-year risk of first avoidable hospitalization for seven ACSCs using self-reported, routinely collected population health survey data. METHODS AND ANALYSIS: The derivation cohort will consist of respondents to the first 3 cycles (2000/01, 2003/04, 2005/06) of the Canadian Community Health Survey (CCHS) who are 18-74 years of age at survey administration and a hold-out data set will be used for external validation. Outcome information on avoidable hospitalizations for 5 years following the CCHS interview will be assessed through data linkage to the Discharge Abstract Database (1999/2000-2017/2018) for an estimated sample size of 394,600. Candidate predictor variables will include demographic characteristics, socioeconomic status, self-perceived health measures, health behaviors, chronic conditions, and area-based measures. Sex-specific algorithms will be developed using Weibull accelerated failure time survival models. The model will be validated both using split set cross-validation and external temporal validation split using cycles 2000-2006 compared to 2007-2012. We will assess measures of overall predictive performance (Nagelkerke R2), calibration (calibration plots), and discrimination (Harrell's concordance statistic). Development of the model will be informed by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement. ETHICS AND DISSEMINATION: This study was approved by the University of Toronto Research Ethics Board. The predictive algorithm and findings from this work will be disseminated at scientific meetings and in peer-reviewed publications.

A national cohort study of community belonging and its influence on premature mortality.

BACKGROUND: Community belonging, an important constituent of subjective well-being, is an important target for improving population health. Ageing involves transitioning across different social conditions thus, community belonging on health may vary across the life course. Using a nationally representative cohort, this study estimates the life stage-specific impact of community belonging on premature mortality. METHODS: Six cycles of the Canadian Community Health Survey (2000-2012) were combined and linked to the Canadian Vital Statistics Database (2000-2017). Respondents were followed for up to 5 years. Multivariable-adjusted modified Poisson regression models were used to estimate the relative risk of premature mortality for three life stages: early adulthood (18-35 years), middle adulthood (36-55 years) and late adulthood (56-70 years). RESULTS: The final analytical sample included 477 100 respondents. Most reported a 'somewhat strong' sense of belonging (45.9%). Compared with their 'somewhat strong' counterparts, young adults reporting a 'somewhat weak' sense of belonging exhibited an increased relative risk (RR) of 1.76 (95% CI 1.27 to 2.43) for premature mortality, whereas middle-aged adults reporting the same exhibited a decreased RR of 0.82 (95% CI 0.69, 0.98). Among older adults, groups reporting a 'very strong' (RR 1.10, 95% CI 1.01, 1.21) or a 'very weak' sense (RR 1.14, 95% CI 1.01, 1.28) of belonging exhibited higher RRs for premature mortality. CONCLUSION: The results demonstrate how community belonging relates to premature mortality differs across age groups underscoring the importance of considering life stage-specific perspectives when researching and developing approaches to strengthen belonging.

"Whatever journey you want to take, I'll support you through": a mixed methods evaluation of a peer worker program in the hospital emergency department.

BACKGROUND: People who are unhoused, use substances (drugs and/or alcohol), and who have mental health conditions experience barriers to care access and are frequently confronted with discrimination and stigma in health care settings. The role of Peer Workers in addressing these gaps in a hospital-based context is not well characterized. The aim of this evaluation was to 1) outline the role of Peer Workers in the care of a marginalized populations in the emergency department; 2) characterize the impact of Peer Workers on patient care, and 3) to describe how being employed as a Peer Worker impacts the Peer. METHODS: Through a concurrent mixed methods evaluation, we explore the role of Peer Workers in the care of marginalized populations in the emergency department at two urban hospitals in Toronto, Ontario Canada. We describe the demographic characteristics of patients (n = 555) and the type of supports provided to patients collected through a survey between February and June 2022. Semi-structured, in-depth interviews were completed with Peer Workers (n = 7). Interviews were thematically analyzed using a deductive approach, complemented by an inductive approach to allow new themes to emerge from the data. RESULTS: Support provided to patients primarily consisted of friendly conversations (91.4%), discharge planning (59.6%), tactics to help the patient navigate their emotions/mental wellbeing (57.8%) and sharing their lived experience (50.1%). In over one third (38.9%) of all patient interactions, Peer Workers shared new information about the patient with the health care team (e.g., obtaining patient identification). Five major themes emerged from our interviews with Peer Workers which include: (1) Establishing empathy and building trust between the patient and their care team through self-disclosure; (2) Facilitating a person-centered approach to patient care through trauma-informed listening and accessible language; (3) Support for patient preferences on harm reduction; (4) Peer worker role facilitating self-acceptance and self-defined recovery; and (5) Importance of supports and resources to help Peer Workers navigate the emotional intensity of the emergency department. CONCLUSIONS: The findings add to the literature on Peer Worker programs and how such interventions are designed to best meet the needs of marginalized populations.

Patterns of multiple chronic conditions in pregnancy: Population-based study using latent class analysis.

BACKGROUND: Adults with multiple chronic conditions (MCC) are a heterogeneous population with elevated risk of future adverse health outcomes. Yet, despite the increasing prevalence of MCC globally, data about MCC in pregnancy are scarce. OBJECTIVES: To estimate the population prevalence of MCC in pregnancy and determine whether certain types of chronic conditions cluster together among pregnant women with MCC. METHODS: We conducted a population-based cohort study in Ontario, Canada, of all 15-55-year-old women with a recognised pregnancy, from 2007 to 2020. MCC was assessed from a list of 22 conditions, identified using validated algorithms. We estimated the prevalence of MCC. Next, we used latent class analysis to identify classes of co-occurring chronic conditions in women with MCC, with model selection based on parsimony, clinical interpretability and statistical fit. RESULTS: Among 2,014,508 pregnancies, 324,735 had MCC (161.2 per 1000, 95% confidence interval [CI] 160.6, 161.8). Latent class analysis resulted in a five-class solution. In four classes, mood and anxiety disorders were prominent and clustered with one additional condition, as follows: Class 1 (22.4% of women with MCC), osteoarthritis; Class 2 (23.7%), obesity; Class 3 (15.8%), substance use disorders; and Class 4 (22.1%), asthma. In Class 5 (16.1%), four physical conditions clustered together: obesity, asthma, chronic hypertension and diabetes mellitus. CONCLUSIONS: MCC is common in pregnancy, with sub-types dominated by co-occurring mental and physical health conditions. These data show the importance of preconception and perinatal interventions, particularly integrated care strategies, to optimise treatment and stabilisation of chronic conditions in women with MCC.

Towards a community-driven definition of community wellbeing: A qualitative study of residents.

BACKGROUND: Understanding what promotes or hinders a community's capacity to serve the priorities of its residents is essential for the alignment of citizen needs and governance. Participatory approaches that engage community residents on the topic of community wellbeing are useful methods for defining outcomes that reflect a community's goals and priorities. Using qualitative focus group methods, the aim of this study was to outline bottom-up definitions of community wellbeing from a diverse pool of community residents in Ontario, Canada. METHODS: Semi-structured, two-hour group interviews were conducted with adult (≥18 years) participants (N = 15) residing in four communities across Canada's largest province of Ontario. Participants were purposively selected from a pool of screening questionnaires to ensure diverse group compositions based on race, gender, age, and educational attainment. Interviews were thematically analysed using descriptive and interpretive methods to characterize resident conceptions of community wellbeing. RESULTS: Focus group participants were between 18 and 75 years of age and most had lived in their local community for 5 or more years. Four major themes emerged: (1) a sense of community belonging is cultivated through shared spaces, routines, support, and identities; (2) a community constitutes the amenities and social contexts that enable residents to thrive; (3) effective regional decision-making must be community-informed; and (4) the wellbeing of a community relies on equal opportunities for engagement and participation. CONCLUSIONS: Residents described their communities and their associated wellbeing as a combination of accessible amenities and opportunities to engage without marginalization. This study underscores the value of participatory approaches in community wellbeing research, where the viewpoint and life experience of residents is used to inform local decision-making and service delivery. Future research will capture more diverse perspectives towards community belonging, particularly from community newcomers, for the development of regionally appropriate indicators of community wellbeing.

Accounting for Competing Events When Evaluating Long-Term Outcomes in Survivors of Critical Illness.

The clinical trajectory of survivors of critical illness after hospital discharge can be complex and highly unpredictable. Assessing long-term outcomes after critical illness can be challenging because of possible competing events, such as all-cause death during follow-up (which precludes the occurrence of an event of particular interest). In this perspective, we explore challenges and methodological implications of competing events during the assessment of long-term outcomes in survivors of critical illness. In the absence of competing events, researchers evaluating long-term outcomes commonly use the Kaplan-Meier method and the Cox proportional hazards model to analyze time-to-event (survival) data. However, traditional analytical and modeling techniques can yield biased estimates in the presence of competing events. We present different estimands of interest and the use of different analytical approaches, including changes to the outcome of interest, Fine and Gray regression models, cause-specific Cox proportional hazards models, and generalized methods (such as inverse probability weighting). Finally, we provide code and a simulated dataset to exemplify the application of the different analytical strategies in addition to overall reporting recommendations.

Bridging ethics and epidemiology: Modelling ethical standards of health equity.

Health inequities are differences in health that are 'unjust'. Yet, despite competing ethical views about what counts as an 'unjust difference in health', theoretical insights from ethics have not been systematically integrated into epidemiological research. Using diabetes as an example, we explore the impact of adopting different ethical standards of health equity on population health outcomes. Specifically, we explore how the implementation of population-level weight-loss interventions using different ethical standards of equity impacts the intervention's implementation and resultant population health outcomes. We conducted a risk prediction modelling study using the nationally representative 2015-16 Canadian Community Health Survey (n = 75,044, 54% women). We used the Diabetes Population Risk Tool (DPoRT) to calculate individual-level 10-year diabetes risk. Hypothetical weight-loss interventions were modelled in individuals with overweight or obesity based on each ethical standard: 1) health sufficiency (reduce DPoRT risk below a high-risk threshold (16.5%); 2) health equality (equalize DPoRT risk to the low risk group (5%)); 3) social-health sufficiency (reduce DPoRT risk <16.5 in individuals with lower education); 4) social-health equality (equalize DPoRT risk to the level of individuals with high education). For each scenario, we calculated intervention impacts, diabetes cases prevented or delayed, and relative and absolute educational inequities in diabetes. Overall, we estimated that achieving health sufficiency (i.e., all individuals below the diabetes risk threshold) was more feasible than achieving health equality (i.e., diabetes risk equalized for all individuals), requiring smaller initial investments and fewer interventions; however, fewer diabetes cases were prevented or delayed. Further, targeting only diabetes inequalities related to education reduced the target population size and number of interventions required, but consequently resulted in even fewer diabetes cases prevented or delayed. Using diabetes as an example, we found that an explicit, ethically-informed definition of health equity is essential to guide population-level interventions that aim to reduce health inequities.

Modelling population-level and targeted interventions of weight loss on chronic disease prevention in the Canadian population.

Obesity is a known risk factor for major chronic diseases. Prevention of chronic disease is a top global priority. The study aimed to model scenarios of population-level and targeted weight loss interventions on 10-year projected risk of chronic disease in Canada using a population-level risk prediction algorithm. The validated Chronic Disease Population Risk Tool (CDPoRT) forecasts 10-year risk of chronic disease in the adult population. We applied CDPoRT to the 2013/14 Canadian Community Health Survey to generate prospective chronic disease estimates for adults 20 years and older in Canada (n = 83,220). CDPoRT was used to model the following scenarios: British Columbia's (BC) and Quebec's (QC) provincial population-level weight reduction targets, a population-level intervention that could achieve weight loss, targeted weight loss interventions for overweight and obese groups, and the combination of a population-level and targeted weight loss intervention. We estimated chronic disease risk reductions and number of cases prevented in each scenario compared with the baseline. At baseline, we predicted an 18.4% risk and 4,151,929 new cases of chronic disease in Canada over the 10-year period. Provincial weight loss targets applied to the Canadian population estimated chronic disease reductions of 0.6% (BC) and 0.1% (QC). The population-level intervention estimated a greater reduction in risk (0.2%), compared to the targeted interventions (0.1%). The combined approach estimated a 0.3% reduction in chronic disease risk. Our modelling predicted that population-level approaches that achieve weight loss in combination with targeted weight loss interventions can substantially decrease the chronic disease burden in Canada.

First-line opioid agonist treatment as prevention against assisting others in initiating injection drug use: A longitudinal cohort study of people who inject drugs in Vancouver, Canada.

Background: Among people who inject drugs, frequent injecting and experiencing withdrawal are associated with facilitating others' first injections. As these factors may reflect an underlying substance use disorder, we investigated whether first-line oral opioid agonist treatment (OAT; methadone or buprenorphine/naloxone) reduces the likelihood that people who inject drugs help others initiate injecting. Methods: We used questionnaire data from semi-annual visits between December 2014-May 2018 on 334 people who inject drugs with frequent non-medical opioid use in Vancouver, Canada. We estimated the effect of current first-line OAT on subsequent injection initiation assistance provision (i.e., helped someone initiate injecting in the following six months) using inverse-probability-weighted estimation of repeated measures marginal structural models to reduce confounding and informative censoring by time-fixed and time-varying covariates. Results: By follow-up visit, 54-64% of participants reported current first-line OAT whereas 3.4-6.9% provided subsequent injection initiation assistance. Per the primary weighted estimate (n = 1114 person-visits), participants currently on first-line OAT (versus no OAT) were 50% less likely, on average, to subsequently help someone initiate injecting (relative risk [RR]=0.50, 95% CI=0.23-1.11). First-line OAT was associated with reduced risk of subsequent injection initiation assistance provision in participants who, at baseline, injected opioids less than daily (RR=0.15, 95% CI=0.05-0.44) but not in those who injected opioids daily (RR=0.86, 95% CI=0.35-2.11). Conclusions: First-line OAT seemingly reduces the short-term likelihood that people who inject drugs facilitate first injections. However, the extent of this potential effect remains uncertain due to imprecise estimation and observed heterogeneity by baseline opioid injecting frequency.

Artificial intelligence and the work-health interface: A research agenda for a technologically transforming world of work.

The labor market is undergoing a rapid artificial intelligence (AI) revolution. There is currently limited empirical scholarship that focuses on how AI adoption affects employment opportunities and work environments in ways that shape worker health, safety, well-being and equity. In this article, we present an agenda to guide research examining the implications of AI on the intersection between work and health. To build the agenda, a full day meeting was organized and attended by 50 participants including researchers from diverse disciplines and applied stakeholders. Facilitated meeting discussions aimed to set research priorities related to workplace AI applications and its impact on the health of workers, including critical research questions, methodological approaches, data needs, and resource requirements. Discussions also aimed to identify groups of workers and working contexts that may benefit from AI adoption as well as those that may be disadvantaged by AI. Discussions were synthesized into four research agenda areas: (1) examining the impact of stronger AI on human workers; (2) advancing responsible and healthy AI; (3) informing AI policy for worker health, safety, well-being, and equitable employment; and (4) understanding and addressing worker and employer knowledge needs regarding AI applications. The agenda provides a roadmap for researchers to build a critical evidence base on the impact of AI on workers and workplaces, and will ensure that worker health, safety, well-being, and equity are at the forefront of workplace AI system design and adoption.