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Purpose: The objective of this study was to explore Canadian rural‐urban differences in the use of primary and secondary preventive diagnostic services for acute myocardial infarction (AMI)—a condition that is amenable to primary health care prevention efforts. Methods: We examined primary and secondary preventive care services utilized 2 years prior to a patient's first AMI in a cohort of 30,491 patients in Ontario, Canada, from 2010 to 2012. Using logistic regression, rural‐urban differences in lipid testing, glucose testing, stress testing, electrocardiograms, and echocardiograms in middle‐age and senior patients were examined. Findings: The odds of rural patients receiving care for primary preventive screening were more than 50% lower than the odds for urban patients, with rural seniors being the most affected. Lipid testing: middle‐age OR 0.519 (95% CI, 0.469‐0.574), senior OR 0.422 (0.386‐0.460); glucose testing: middle‐age OR 0.471 (0.426‐0.521), senior OR 0.359 (0.328‐0.394). The odds of rural patients receiving secondary preventive diagnostic care were also lower than the odds for urban patients, but differences between the age groups were not as apparent. Stress testing: middle‐age OR 0.745 (95%CI, 0.642‐0.866), senior OR 0.726 (0.643‐0.820); electrocardiogram: middle‐age OR 0.815 (0.737‐0.901), senior OR 0.724 (0.659‐0.795); echocardiogram: middle‐age OR 0.755 (0.655‐0.869), senior OR 0.746 (0.681‐0.818). Conclusions: Study results support ongoing concerns related to health care for rural Canadians by demonstrating underutilization of AMI preventive diagnostic care among rural patients. Rural seniors are most at risk. These results have implications for rural health care as well as seniors’ health policy in Canada. [ABSTRACT FROM AUTHOR]

Objectives: Previous patient-level acute myocardial infarction (AMI) research has found higher hospital spending to be associated with improved survival; however, survivor-treatment selection bias traditionally has been overlooked. The purpose of this study was to examine the AMI cost-outcome relationship, taking into account this form of bias.Data Sources: Hospital Discharge Abstract data tracked costs for AMI hospitalizations. Ontario Vital Statistics data tracked patient mortality.Study Design: A standard Cox survival model was compared to an extended Cox model using hospital costs as a time-varying covariate to examine the impact of cost on 1-year survival in a cohort of 30,939 first-time AMI patients in Ontario, Canada, from 2007 to 2010.Principal Findings: Higher patient-level AMI spending decreased the hazard of dying (Standard Model: log-cost hazard ratio: 0.513, 95 percent CI: 0.479-0.549; Extended Model: log-cost hazard ratio: 0.700, 95 percent CI: 0.645-0.758); however, the protective effect was overestimated by 62 percent when survivor-treatment bias was overlooked. In the extended model, a 10 percent increase in spending was associated with a 3.6 percent decrease in hazard of death.Conclusion: The findings of this study suggest that if survivor-treatment bias is overlooked, future research may materially overstate the protective effect of patient-level spending on outcomes. [ABSTRACT FROM AUTHOR]

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., (© 2024 The Authors. Journal of the American Geriatrics Society published by Wiley Periodicals LLC on behalf of The American Geriatrics Society.)

Background: Routinely collected data from large population health surveys linked to chronic disease outcomes create an opportunity to develop more complex risk-prediction algorithms. We developed a predictive algorithm to estimate 5-year risk of incident cardiovascular disease in the community setting., Methods: We derived the Cardiovascular Disease Population Risk Tool (CVDPoRT) using prospectively collected data from Ontario respondents of the Canadian Community Health Surveys, representing 98% of the Ontario population (survey years 2001 to 2007; follow-up from 2001 to 2012) linked to hospital admission and vital statistics databases. Predictors included body mass index, hypertension, diabetes, and multiple behavioural, demographic and general health risk factors. The primary outcome was the first major cardiovascular event resulting in hospital admission or death. Death from a noncardiovascular cause was considered a competing risk., Results: We included 104 219 respondents aged 20 to 105 years. There were 3709 cardiovascular events and 818 478 person-years follow-up in the combined derivation and validation cohorts (5-year cumulative incidence function, men: 0.026, 95% confidence interval [CI] 0.025-0.028; women: 0.018, 95% 0.017-0.019). The final CVDPoRT algorithm contained 12 variables, was discriminating (men: C statistic 0.82, 95% CI 0.81-0.83; women: 0.86, 95% CI 0.85-0.87) and was well-calibrated in the overall population (5-year observed cumulative incidence function v. predicted risk, men: 0.28%; women: 0.38%) and in nearly all predefined policy-relevant subgroups (206 of 208 groups)., Interpretation: The CVDPoRT algorithm can accurately discriminate cardiovascular disease risk for a wide range of health profiles without the aid of clinical measures. Such algorithms hold potential to support precision medicine for individual or population uses. Study registration: ClinicalTrials.gov, no. NCT02267447., Competing Interests: Competing interests: None declared., (© 2018 Joule Inc. or its licensors.)

Background: Health behaviours, important factors in cardiovascular disease, are increasingly a focus of prevention. We appraised whether stroke risk can be accurately assessed using self-reported information focused on health behaviours., Methods: Behavioural, sociodemographic and other risk factors were assessed in a population-based survey of 82,259 Ontarians who were followed for a median of 8.6 years (688,000 person-years follow-up) starting in 2001. Predictive algorithms for 5-year incident stroke resulting in hospitalization were created and then validated in a similar 2007 survey of 28,605 respondents (median 4.2 years follow-up)., Results: We observed 3236 incident stroke events (1551 resulting in hospitalization; 1685 in the community setting without hospital admission). The final algorithms were discriminating (C-stat: 0.85, men; 0.87, women) and well-calibrated (in 65 of 67 subgroups for men; 61 of 65 for women). An index was developed to summarize cumulative relative risk of incident stroke from health behaviours and stress. For men, each point on the index corresponded to a 12% relative risk increase (180% risk difference, lowest (0) to highest (9) scores). For women, each point corresponded to a 14% relative risk increase (340% difference, lowest (0) to highest (11) scores). Algorithms for secondary stroke outcomes (stroke resulting in death; classified as ischemic; excluding transient ischemic attack; and in the community setting) had similar health behaviour risk hazards., Conclusion: Incident stroke can be accurately predicted using self-reported information focused on health behaviours. Risk assessment can be performed with population health surveys to support population health planning or outside of clinical settings to support patient-focused prevention.

Introduction: Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting., Methods and Analysis: The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers., Ethics and Dissemination: This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible electronically for population and individual uses., Trial Registration Number: ClinicalTrials.gov NCT02267447., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objective: To assess the influence of neighbourhood immigrant concentration on cardiovascular-disease-related hospitalizations in Canada (CVDH), while adjusting for individual-level immigrant status and socio-economic indicators at individual and neighbourhood levels., Methods: Data were from the 2006 Canadian Census linked to the hospital Discharge Abstract Data (DAD) for the province of Ontario. Adults (n=1,459,950) aged ≥18 years at baseline and grouped by place of birth (Canada, China, South Asia, Europe, and other) were followed between Census Day May 16, 2006 and March 31, 2008. Information on CVDH was obtained from the DAD, while that on immigration and socio-economic indicators was obtained from the Census. The analysis used multilevel logistic regression., Results: Unadjusted results showed that CVDH was significantly lower among people living in neighbourhoods with medium and high immigrant concentration. Neighbourhood immigrant concentration tended to have no independent effect on CVDH after adjustment for individual-level immigrant status. Immigrants were less likely to experience CVDH irrespective of their country of birth. However, cross-level interaction showed that neighbourhood immigrant concentration provided additional protection to individual-level immigrant status against CVDH for most female immigrant groups, but only for South Asian males., Conclusion: This study resulted from the first-ever linkage of census data to hospitalization data in Canada. It is also the first Canadian study to report on neighbourhood variation and the effect of immigrant concentration on CVDH. The study shows that understanding immigrant health requires both individual and neighbourhood approaches, and a consideration of country of origin.