Introduction: Sodium glucose co-transporter 2 inhibitors (SGLT2i) and glucagon-like peptide 1 analogues (GLP1a) improve clinical outcomes (e.g., myocardial infarction, stroke, death from CV causes) for adults with type 2 diabetes mellitus. The majority of available data from clinical trials and observational studies are from outpatients. Data on their effectiveness among hospitalized patients, however, are lacking. Methods: We conducted a multicentre, retrospective, cohort study of adults aged 65 years and older with type 2 diabetes mellitus hospitalized between 2017 and 2023 in Ontario. We compared adults newly prescribed an SGLT2i or GLP1a in hospital to adults newly prescribed a DPP4i in hospital. In a sensitivity analysis, new use of sulfonylurea was the comparator. Our primary outcome was the 1-year risk of a composite of all-cause mortality, hospitalization with myocardial infarction, stroke, heart failure, or renal failure. Secondary outcomes included components of the composite, short-term (i.e., 30 day) risk of hypoglycemia, and the 1-year risk of DKA. Results: We identified 6,713 older adults with diabetes who were newly prescribed one of the following medications during an inpatient hospitalization: SGLT2i (N=1520), GLP1 (N=90), DPP4i (N=3726), sulfonylurea (N=1377). Because new use of GLP1 was rare in hospital, we updated our exposure group to SGLT2i alone. Adults who received an SGLT2i were typically younger, and more likely to have heart failure or coronary artery disease compared to adults who received a DPP4i or sulfonylurea. Among adults who received an SGLT2i, at 1-year the primary outcome occurred in 26% compared to 31% who newly received a DPP4i (adjusted hazard ratio [HR] 0.82 95% Confidence Interval [CI] 0.69,0.97). In our sensitivity analysis using sulfonylurea as the comparator group, the hazard ratio for the primary outcome was 0.97 (95% CI 0.80, 1.18). We did not identify an increased risk of DKA or hypoglycemia for SGLT2i compared to DPP4i, though patients receiving an SGLT2i did have a lower rate of 30-day readmission (HR 0.73, 95%CI 0.58-0.92). Conclusion: Among older adults with type 2 diabetes mellitus, newly prescribing an SGLT2i or GLP1 during a hospitalization was uncommon. New use of an SGLT2i was associated with improved outcomes compared to DPP4i but this finding was not robust when new use of a sulfonylurea was the comparator. Future larger studies are needed to provide more definitive results. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by PSI. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Research Ethics Board approval was obtained from St Michaels Hospital on behalf of all participating hospitals with a waiver of patient consent for this retrospective study using routinely collected health data I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon submission of a project proposal to ICES in Ontario, Canada.

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.

BACKGROUND:Current methods used to estimate surgical wait times in Ontario may be subject to inconsistencies and inaccuracies. In this population-level study, we aimed to estimate cataract surgery wait times in Ontario using a novel, objective and data-driven method.METHODS:We identified adults who underwent cataract surgery between 2005 and 2019 in Ontario, using administrative records. Wait time 1 represented the number of days from referral to initial visit with the surgeon, and wait time 2 represented the number of days from the decision for surgery until the first eye surgery date. In the primary analysis, a ranking method prioritized referrals from optometrists, followed by ophthalmologists and family physicians.RESULTS:The cohort consisted of 1 138 532 people with mostly female patients (57.4%) and those aged 65 years and older (79.0%). In the primary analysis, the median was 67 days for wait time 1 (interquartile range [IQR] 29-147). There was a median of 77 days for wait time 2 (IQR 37-155). Overall, the following proportions of patients waited less than 3, 6 and 12 months: 54.1%, 78.5% and 91.7%, respectively. For wait time 2, the proportions of patients who waited less than 3, 6 and 12 months were 49.5%, 77.1% and 93.3%, respectively. In total, 19.3% of patients did not meet the provincial target for wait time 1, 20.5% did not meet the target for wait time 2 and 35.0% did not meet the target for wait times 1 or 2.INTERPRETATION:Administrative health services data can be used to estimate cataract surgery wait times. With this method, 35.0% of patients in 2005-2019 did not receive initial consultation or surgery within the provincial wait time target.

Objective To investigate the prospective association between life satisfaction and future mental health service use in: (1) hospital/emergency department, and (2) outpatient settings. Design and setting Population-based cohort study of adults from Ontario, Canada. Baseline data were captured through pooled cycles of the Canadian Community Health Survey (CCHS 2005-2014) and linked to health administrative data for up to 5 years of follow-up. Participants 131 809 Ontarians aged 18 years and older. Main outcome measure The number of mental health-related visits in (1) hospitals/emergency department and (2) outpatient settings within 5 years of follow-up. Results Poisson regression models were used to estimate rate ratios in each setting, adjusting for sociodemographic measures, history of mental health-related visits, and health behaviours. In the hospital/emergency setting, compared to those most satisfied with life, those with the poorest satisfaction exhibited a rate ratio of 3.71 (95% CI 2.14 to 6.45) for future visits. In the outpatient setting, this same comparison group exhibited a rate ratio of 1.83 (95% CI 1.42 to 2.37). When the joint effects of household income were considered, compared with the highest income and most satisfied individuals, the least satisfied and lowest income individuals exhibited the highest rate ratio in the hospital/emergency setting at 11.25 (95% CI 5.32 to 23.80) whereas in the outpatient setting, the least satisfied and highest income individuals exhibited the highest rate ratio at 3.33 (95% CI 1.65 to 6.70). Conclusion The findings suggest that life satisfaction is a risk factor for future mental health visits. This study contributes to an evidence base connecting positive well-being with health system outcomes.

IntroductionData from population health surveys, administrative health records and environmental monitoring are increasingly being linked at the individual level. As these data become available to health researchers, there is an increasing need for methods which can make sense of large, noisy and heterogeneous data and can model complex relationships. Using these data, machine learning methods have the potential to produce population health risk algorithms with better performance than those developed with traditional statistical approaches. Objectives and ApproachThe objective of this work is to explore the use of machine learning methods for the development, validation and implementation of predictive risk algorithms designed specifically for population health planning purposes. Algorithms to predict risk of dementia and avoidable hospitalizations are in development using the Canadian Community Health Survey, geographic sociodemographic information, administrative health care utilization data and vital statistics. Methods being explored include naïve Bayes, gradient boosting, support vector machines and neural networks. ResultsRisk algorithms for population health should generally prioritize calibration over discrimination due to implications for resource allocation decisions. Approaches to minimize the risk of overfitting should be used and reweighting of unbalanced data avoided as it distorts the population-level nature of the data. It is important to be aware of propagating underlying bias in the data or exacerbating existing health inequities, which can be evaluated in part through assessment of calibration across relevant population subgroups. Approaches that consider multi-level data structures are needed to appropriately incorporate neighbourhood-level measures with individual-level information. To maximize population health impact and acceptability, model transparency and interpretability should be prioritized. ConclusionThere is tremendous potential for machine learning approaches to leverage large volumes of linked population data to produce predictive risk algorithms that will inform population health decision-making. Future work will explore use of complex environmental remote sensing and built environment data.