Using unsupervised clustering approaches to identify common mental health profiles and associated mental health-care service-use patterns in Ontario, Canada.

Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress, and well-being. In this study, we aimed to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service-use patterns. The data source was the 2012 Canadian Community Health Survey-Mental Health, linked to administrative health-care data; all Ontario, Canada, adult respondents were included. We used a partitioning around medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them according to their sociodemographic and service-use characteristics. We identified 4 groups with distinct mental health profiles, including 1 group that met the clinical threshold for a depressive diagnosis, with the remaining 3 groups expressing differences in positive mental health, life stress, and self-rated mental health. The 4 groups had different age, employment, and income profiles and exhibited differential access to mental health-care services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified. This article is part of a Special Collection on Mental Health.

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.

The impact of different imputation methods on estimates and model performance: an example using a risk prediction model for premature mortality.

OBJECTIVE: To compare how different imputation methods affect the estimates and performance of a prediction model for premature mortality. STUDY DESIGN AND SETTING: Sex-specific Weibull accelerated failure time survival models were run on four separate datasets using complete case, mode, single and multiple imputation to impute missing values. Six performance measures were compared to access predictive accuracy (Nagelkerke R2, integrated brier score), discrimination (Harrell's c-index, discrimination slope) and calibration (calibration in the large, calibration slope). RESULTS: The highest proportion of missingness for a single variable was 10.86% for the female model and 8.24% for the male model. Comparing the performance measures for complete case, mode, single and multiple imputation: the Nagelkerke R2 values for the female model was 0.1084, 0.1116, 0.1120 and 0.111-0.1120 with the male model exhibited similar variation of 0.1050, 0.1078, 0.1078 and 0.1078-0.1081. Harrell's c-index also demonstrated small variation with values of 0.8666, 0.8719, 0.8719 and 0.8711-0.8719 for the female model and 0.8549, 0.8548, 0.8550 and 0.8550-0.8553 for the male model. CONCLUSION: In the scenarios examined in this study, mode imputation performed well when using a population health survey compared to single and multiple imputation when predictive performance measures is the main model goal. To generate unbiased hazard ratios, multiple imputation methods were superior. This study shows the need to consider the best imputation approach for a predictive model development given the conditions of missing data and the goals of the analysis.

Using community-based participatory research methods to build the foundation for an equitable integrated health data system within a Canadian urban context.

Health inequalities amplified by the COVID-19 pandemic have disproportionately affected racialized and equity-deserving communities across Canada. In the Municipality of Peel, existing data, while limited, illustrates that individuals from racialized and equity-deserving communities continue to suffer, receive delayed care, and die prematurely. In response to these troubling statistics, grassroots community advocacy has called on health systems leaders in Peel to work with community and non-profit organizations to address the critical data and infrastructure gaps that hinder addressing the social determinants of health in the region. To support these advocacy efforts, we used a community-based participatory research approach to understand how we might build a data collection ecosystem across sectors, alongside community residents and service providers, to accurately capture the data about the social determinants of health. This approach involved developing a community engagement council, defining the problem with the community, mapping what data is actively collected and what is excluded, and understanding experiences of sociodemographic data collection from community members and service providers. Guided by community voices, our study focused on sociodemographic data collection in the primary care context and identified which service providers use and collect these data, how data are used in their work, the facilitators and barriers to data use and collection. Additionally, we gained insight into how sociodemographic data collection could be respectful, safe, and properly governed from the perspectives of community members. From this study, we identify a set of eight recommendations for sociodemographic data collection and highlight limitations. This foundational community-based work will inform future research in establishing data governance in partnership with diverse and equity-deserving communities.

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.

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.

"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.

Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review.

BACKGROUND: Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)-based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment. OBJECTIVE: The objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images. METHODS: We conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not address SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using tables. Employment of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines was evaluated and PROBAST (Prediction Model Risk of Bias Assessment Tool) was used to assess risk of bias (RoB). RESULTS: In total, 10 of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly color images, and the volume of images used ranged from under 50 to thousands. Further, 10 TRIPOD items were reported in at least 4 studies, though 15 items were reported in fewer than 4 studies. PROBAST assessment led to 9 studies being identified as having an overall high RoB, with 1 study having overall unclear RoB. CONCLUSIONS: Research on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future.

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.

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.

[Epidemiological signatures and surveillance bias in cancer]. / Signatures épidémiologiques et biais de surveillance du cancer.

Surveillance bias occurs when variations in cancer incidence are the result of changes in screening or diagnostic practices rather than increases in the true occurrence of cancer. This bias is linked to the issue of overdiagnosis and can be apprehended by looking at epidemiological signatures of cancer. We explain the concept of epidemiological signatures using the examples of melanoma and of lung and prostate cancer. Accounting for surveillance bias is particularly important for assessing the true burden of cancer and for accurately communicating cancer information to the population and decision-makers.

Le biais de surveillance se produit lorsque les variations d'incidence d'un cancer sont le résultat d'un changement dans les pratiques de dépistage ou de diagnostic plutôt que d'une augmentation de la fréquence réelle de ce cancer. Ce biais est lié au concept du surdiagnostic et peut être appréhendé en examinant les signatures épidémiologiques des cancers. Nous expliquons le concept de signature épidémiologique à l'aide des exemples du mélanome et des cancers du poumon et de la prostate. La prise en compte des biais de surveillance est particulièrement importante pour évaluer le fardeau réel du cancer et communiquer avec précision l'information sur le cancer à la population et aux décideurs.

Evaluating the Impact of Statin Use on Influenza Vaccine Effectiveness and Influenza Infection in Older Adults.

BACKGROUND: Older adults are recommended to receive influenza vaccination annually, and many use statins. Statins have immunomodulatory properties that might modify influenza vaccine effectiveness (VE) and alter influenza infection risk. METHODS: Using the test-negative design and linked laboratory and health administrative databases in Ontario, Canada, we estimated VE against laboratory-confirmed influenza among community-dwelling statin users and nonusers aged ≥66 years during the 2010-2011 to 2018-2019 influenza seasons. We also estimated the odds ratio for influenza infection comparing statin users and nonusers by vaccination status. RESULTS: Among persons tested for influenza across the 9 seasons, 54 243 had continuous statin exposure before testing and 48 469 were deemed unexposed. The VE against laboratory-confirmed influenza was similar between statin users and nonusers (17% [95% confidence interval, 13%-20%] and 17% [13%-21%] respectively; test for interaction, P = .87). In both vaccinated and unvaccinated persons, statin users had higher odds of laboratory-confirmed influenza than nonusers (odds ratios for vaccinated and unvaccinated persons 1.15 [95% confidence interval, 1.10-1.21] and 1.15 [1.10-1.20], respectively). These findings were consistent by mean daily dose and statin type. VE did not differ between users and nonusers of other cardiovascular drugs, except for ß-blockers. We did not observe that vaccinated and unvaccinated users of these drugs had increased odds of influenza, except for unvaccinated ß-blocker users. CONCLUSIONS: Influenza VE did not differ between statin users and nonusers. Statin use was associated with increased odds of laboratory-confirmed influenza in vaccinated and unvaccinated persons, but these associations might be affected by residual confounding.

Risk Factors for Major Cardiovascular Events in Adult Sepsis Survivors: A Population-Based Cohort Study.

OBJECTIVES: To describe risk factors for major cardiovascular events in adults following hospital discharge after sepsis. DESIGN: Population-based cohort study. SETTING: Ontario, Canada (2008-2017). PATIENTS: Adult patients (age 18 yr or older) who survived a first sepsis hospitalization without preexisting cardiovascular disease. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary composite outcome was myocardial infarction, stroke, or cardiovascular death up to 5 years following hospital discharge. We used cause-specific Cox proportional hazards models that accounted for the competing risk of noncardiovascular death to describe factors associated with major cardiovascular events. We identified 268,259 adult patients without cardiovascular disease (median age, 72 yr), of whom 10.4% experienced a major cardiovascular event during a median follow-up of 3 years. After multivariable adjustment, age (hazard ratio [HR], 1.53 for every 10 yr; 95% CI, 1.51-1.54), male sex (HR, 1.23; 95% CI, 1.20-1.26), diabetes mellitus (HR, 1.24; 95% CI, 1.21-1.27), hypertension (HR, 1.34; 95% CI, 1.30-1.38), prevalent atrial fibrillation (HR, 1.46; 95% CI, 1.40-1.52), and chronic kidney disease (HR, 1.11; 95% CI, 1.06-1.16) were associated with major cardiovascular events during long-term follow-up. Sepsis characteristics such as site of infection (pneumonia vs other: HR, 1.09; 95% CI, 1.05-1.12), septic shock (HR, 1.08; 95% CI, 1.05-1.11), and renal replacement therapy (HR, 1.51; 95% CI, 1.38-1.64) were also associated with subsequent cardiovascular events. In an analysis restricting to patients with troponin values measured during the hospitalization (26,400 patients), an elevated troponin was also associated with subsequent cardiovascular events (HR, 1.23; 95% CI, 1.13-1.33). CONCLUSIONS: Classic cardiovascular risk factors, comorbid conditions, and characteristics of the sepsis episode were associated with a higher hazard of major cardiovascular events in adult sepsis survivors. These findings may inform enrichment strategies for future studies.

In utero Exposure to Maternal Diabetes and the Risk of Cerebral Palsy: A Population-based Cohort Study.

BACKGROUND: Evidence on the effects of in utero exposure to maternal diabetes on cerebral palsy (CP) in offspring is limited. We aimed to examine the effects of pregestational (PGDM) and gestational diabetes (GDM) separately on CP risk and the mediating role of increased fetal size. METHODS: In a population-based study, we included all live births in Ontario, Canada, between 2002 and 2017 followed up through 2018 (n = 2,110,177). Using administrative health data, we estimated crude and adjusted associations between PGDM or GDM and CP using Cox proportional hazards models to account for unequal follow-up in children. For the mediation analysis, we used marginal structural models to estimate the controlled direct effect of PGDM (and GDM) on the risk of CP not mediated by large-for-gestational age (LGA). RESULTS: During the study period, 5,317 children were diagnosed with CP (187 exposed to PGDM and 171 exposed to GDM). Children of mothers with PGDM showed an increased risk (hazard ratio [HR]: 1.84 [95% confidence interval (CI): 1.59, 2.14]) after adjusting for maternal sociodemographic and clinical factors. We found no associations between GDM and CP (adjusted HR: 0.91 [0.77, 1.06]). Our mediation analysis estimated that LGA explained 14% of the PDGM-CP association. CONCLUSIONS: In this population-based birth cohort study, maternal pregestational diabetes was associated with increased risk of CP, and the increased risk was not substantially mediated by the increased fetal size.

Variation and Correlation of Potential Unintended Consequences of Antipsychotic Reduction in Ontario Nursing Homes Over Time.

BACKGROUND: Potentially inappropriate antipsychotic use has declined in nursing homes over the past decade; however, increases in the documentation of relevant clinical indications (eg, delusions) and the use of other psychotropic medications have raised concerns about diagnosis upcoding and medication substitution. Few studies have examined how these trends over time vary across and within nursing homes, information that may help to support antipsychotic reduction efforts. OBJECTIVE: To jointly model facility-level time trends in potentially inappropriate antipsychotic use, antidepressant use, and the indications used to define appropriate antipsychotic use. RESEARCH DESIGN: We conducted a repeated cross-sectional study of all nursing homes in Ontario, Canada between April 1, 2010 and December 31, 2019 using linked health administrative data (N=649). Each nursing home's quarterly prevalence of potentially inappropriate antipsychotic use, antidepressant use, and relevant indications were measured as outcome variables. With time as the independent variable, multivariate random effects models jointly estimated time trends for each outcome across nursing homes and the correlations between time trends within nursing homes. RESULTS: We observed notable variations in the time trends for each outcome across nursing homes, especially for the relevant indications. Within facilities, we found no correlation between time trends for potentially inappropriate antipsychotic and antidepressant use ( r =-0.0160), but a strong negative correlation between time trends for potentially inappropriate antipsychotic use and relevant indications ( r =-0.5036). CONCLUSIONS: Nursing homes with greater reductions in potentially inappropriate antipsychotics tended to show greater increases in the indications used to define appropriate antipsychotic use-possibly leading to unmonitored use of antipsychotics.

The association between offspring birthweight and future risk of maternal diabetes: A population-based study.

AIMS: As an indicator of maternal cardiometabolic health, newborn birthweight may be an important predictor of maternal type 2 diabetes mellitus (diabetes). We evaluated the relation between offspring birthweight and onset of maternal diabetes after pregnancy. METHODS: This retrospective cohort study used linked population-based health databases from Ontario, Canada. We included women aged 16-50 years without pre-pregnancy diabetes, and who had a live birth between 2006 and 2014. We used Cox proportional hazard regression to evaluate the association between age- and sex-standardized offspring birthweight percentile categories and incident maternal diabetes, while adjusting for maternal age, parity, year, ethnicity, gestational diabetes (GDM) and hypertensive disorders of pregnancy (HDP). Results were further stratified by the presence of GDM in the index pregnancy. RESULTS: Of 893,777 eligible participants, 14,329 (1.6%) women were diagnosed with diabetes over a median (IQR) of 4.4 (1.5-7.4) years of follow-up. There was a continuous positive relation between newborn birthweight above the 75th percentile and maternal diabetes. Relative to a birthweight between the 50th and 74.9th percentiles, women whose newborn had a birthweight between the 97th and 100th percentiles had an adjusted hazards ratio (aHR) of diabetes of 2.30 (95% CI 2.16-2.46), including an aHR of 2.01 (95% CI 1.83-2.21) among those with GDM, and 2.59 (2.36-2.84) in those without GDM. CONCLUSIONS: A higher offspring birthweight signals an increased risk of maternal diabetes, offering another potentially useful way to identify women especially predisposed to diabetes.

The association between immigration status and the development of type 2 diabetes in women with a prior diagnosis of gestational diabetes: A population-based study.

AIMS: The aim of this study was to examine the influence of immigration status and region of origin on the risk of type 2 diabetes in women with prior gestational diabetes (GDM). METHODS: This retrospective population-based cohort study included women with gestational diabetes (GDM) aged 16 to 50 years in Ontario, Canada, who gave birth between 2006 and 2014. We compared the incidence of type 2 diabetes after delivery between long-term residents and immigrants-overall, by time since immigration and by region of-using Cox regression adjusted for age, year, neighbourhood income, rurality, infant birth weight and presence of hypertensive disorders of pregnancy (HDP). RESULTS: Among 38,515 women with prior GDM (42% immigrants), immigrants had a significantly higher risk of type 2 diabetes compared with long-term residents (adjusted hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.13-1.26), with no meaningful difference based on time since immigration. The highest adjusted relative risks of type 2 diabetes compared with long-term residents were found for immigrants from Sub-Saharan Africa (HR 1.63, 95% CI 1.40-1.90), Latin America/Caribbean (HR 1.44, 95% CI 1.28-1.62) and South Asia (HR 1.34, 95% CI 1.25-1.44). CONCLUSIONS: Immigration is associated with a significantly higher risk of type 2 diabetes after GDM, particularly for women from certain low- and middle-income countries. Diabetes prevention strategies will need to consider the unique needs of immigrants from these regions.

Changes in the Initiation of Antipsychotics and Trazodone Over Time: A Cohort Study of New Admissions to Nursing Homes in Ontario, Canada.

OBJECTIVES: To investigate whether trazodone is being initiated in lieu of antipsychotics following antipsychotic reduction efforts, this study described changes in medication initiation over time. METHODS: We conducted a retrospective cohort study of new admissions to nursing homes in Ontario, Canada between April 2010 and December 2019 using health administrative data (N = 61,068). The initiation of antipsychotic and trazodone use was compared by year of admission using discrete time survival analysis and stratified by history of dementia. RESULTS: Relative to residents admitted in 2014, antipsychotic initiation significantly decreased in later years (e.g., 2017 admission year hazard odds ratio [HOR2017]=0.72 [95% confidence interval (95%CI)=0.62-0.82]) while trazodone initiation modestly increased (e.g., HOR2017=1.09 [95%CI=0.98-1.21]). The relative increase in trazodone initiation was larger among residents with dementia (e.g., HOR2017Dem =1.22 [95%CI=1.07-1.39]). CONCLUSIONS: Differences in which medications were started following nursing home admission were observed and suggest trazodone may be initiated in lieu of antipsychotics.

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.