Search

Background: Cardiovascular disease is a leading cause of death in Canada, but how the major cardiovascular risk factors vary across ethnicity and immigration status has yet to be examined. Methods: Using data from the Canadian Community Health Surveys, national trends in health conditions (hypertension, diabetes, high blood cholesterol level, and obesity) and health behaviours (smoking, activity levels, and alcohol consumption) were estimated for the period 2001-2018. In this cross-sectional study, the trends were then compared across sex, age, ethnicity, and immigration status. Results: A total of 1,065,391 respondents were examined, for the period 2001-2018. During the study period, the prevalence of the following risk factors increased in Canada over time, as follows: diabetes by 54.5%; hypertension by 23.4%; and obesity by 32.3%. For health behaviours, smoking prevalence decreased overall, especially in racialized populations. Heavy drinking was most prevalent for nonracialized and non-Indigenous Canadian-born populations, and was of lowest prevalence among racialized immigrants. Physical inactivity was most prevalent for racialized immigrant populations. The prevalence of self-reported heart disease decreased by 21.0%, except for racialized established immigrants (≥ 10 years since immigration to Canada), who had a 4.2% increase. Conclusions: During this study period, decreases occurred in the prevalences of smoking and physical inactivity, along with increases in obesity, diabetes, and hypertension prevalences. By migration-group status, established immigrants in Canada had a higher prevalence of cardiovascular disease risk factors compared to that among their Canadian-born counterparts. Migration gaps should be considered in future interventions targeted at reducing these cardiovascular risk factors in Canada. Résumé: Contexte: Les maladies cardiovasculaires sont une cause majeure de décès au Canada, mais la manière dont les principaux facteurs de risque cardiovasculaire varient en fonction de l’origine ethnique et du statut d’immigration n’a encore jamais été évaluée. Méthodologie: Des tendances nationales dans certains problèmes de santé (hypertension, diabète, hypercholestérolémie et obésité) et certains comportements liés à la santé (tabagisme, niveaux d’activité et consommation d’alcool) ont été dégagées à partir des données de l’Enquête sur la santé dans les collectivités canadiennes pour la période de 2001 à 2018. Ces tendances ont ensuite été comparées en fonction du sexe, de l’âge, de l’origine ethnique et du statut d’immigration dans le cadre de la présente étude transversale. Résultats: Au total, 1 065 391 répondants ont été examinés pour la période de 2001 à 2018. Durant la période de l’étude, la prévalence des facteurs de risque suivants a graduellement augmenté au Canada : diabète, de 54,5 %; hypertension, de 23,4 %; obésité, de 32,3 %. Dans le cas des comportements liés à la santé, la prévalence du tabagisme a globalement diminué, surtout dans les populations racialisées. La consommation excessive d’alcool était plus fréquente dans les populations non racialisées et non autochtones d’origine canadienne, et moins fréquente chez les immigrants racialisés. L’inactivité physique était particulièrement répandue dans les populations immigrantes racialisées. La prévalence des maladies cardiaques auto-déclarées a diminué de 21,0 %, sauf chez les immigrants établis racialisés (≥ 10 ans depuis l’arrivée au Canada), qui a connu une hausse de 4,2 %. Conclusions: Durant la période de l’étude, la prévalence du tabagisme et de l’inactivité physique a diminué tandis que celle de l’obésité, du diabète et de l’hypertension a augmenté. D’après le statut du groupe de migration, la prévalence des facteurs de risque de maladies cardiovasculaires était plus élevée chez les immigrants établis au Canada que chez leurs homologues nés au Canada. Il convient de prendre en considération les différences liées à la migration dans les interventions futures visant à réduire ces facteurs de risque cardiovasculaire au Canada.

Objectives: This study aimed to assess the acceptability, value, and perceived barriers of using electronic risk calculators for predicting and communicating the risk of death in community-dwelling older adults. Methods: One focus group and eight interviews were conducted with 16 participants with experience caring for patients or family members at end of life. A prototype mortality risk tool was used to anchor discussions. Data were analysed using a qualitative content analysis approach. Results: Five themes emerged: acceptability, communication, barriers to use, broadening the circle of care, and tool limitations. Participants found the tool helpful for preparation, planning, and providing care, but disagreed on its community availability. Personalized risk estimates were valued for facilitating early goals of care conversations and normalizing discussions about death. However, concerns were raised about the tool's interpretation for individuals with different language, cultural, or educational backgrounds. Conclusions: While electronic risk calculators were found to be acceptable, balancing autonomy with varying preferences for receiving the information and potential need for support is crucial. Innovation: Providing patient-oriented life-expectancy estimates can enhance decisional capacity and facilitate shared decision-making between patients, their families, and healthcare professionals. Further research is needed to explore effective communication of personalized risk tools and additional benefits, harms, and barriers to implementation.

Introduction Low back pain (LBP) is commonly treated with opioid analgesics despite evidence that these medicines provide minimal or no benefit for LBP and have an established profile of harms. International guidelines discourage or urge caution with the use of opioids for back pain; however, doctors and patients lack practical strategies to help them implement the guidelines. This trial will evaluate a multifaceted intervention to support general practitioners (GPs) and their patients with LBP implement the recommendations in the latest opioid prescribing guidelines.Methods and analysis This is a cluster randomised controlled trial that will evaluate the effect of educational outreach visits to GPs promoting opioid stewardship alongside non-pharmacological interventions including heat wrap and patient education about the possible harms and benefits of opioids, on GP prescribing of opioids medicines dispensed. At least 40 general practices will be randomised in a 1:1 ratio to either the intervention or control (no outreach visits; GP provides usual care). A total of 410 patient–participants (205 in each arm) who have been prescribed an opioid for LBP will be enrolled via participating general practices. Follow-up of patient–participants will occur over a 1-year period. The primary outcome will be the cumulative dose of opioid dispensed that was prescribed by study GPs over 1 year from the enrolment visit (in morphine milligram equivalent dose). Secondary outcomes include prescription of opioid medicines, benzodiazepines, gabapentinoids, non-steroidal anti-inflammatory drugs by study GPs or any GP, health services utilisation and patient-reported outcomes such as pain, quality of life and adverse events. Analysis will be by intention to treat, with a health economics analysis also planned.Ethics and dissemination The trial received ethics approval from The University of Sydney Human Research Ethics Committee (2022/511). The results will be disseminated via publications in journals, media and conference presentations.Trial registration number ACTRN12622001505796.

Abstract Background Modern health surveillance and planning requires an understanding of how preventable risk factors impact population health, and how these effects vary between populations. In this study, we compare how smoking, alcohol consumption, diet and physical activity are associated with all-cause mortality in Canada and the United States using comparable individual-level, linked population health survey data and identical model specifications. Methods The Canadian Community Health Survey (CCHS) (2003–2007) and the United States National Health Interview Survey (NHIS) (2000, 2005) linked to individual-level mortality outcomes with follow up to December 31, 2011 were used. Consistent variable definitions were used to estimate country-specific mortality hazard ratios with sex-specific Cox proportional hazard models, including smoking, alcohol, diet and physical activity, sociodemographic indicators and proximal factors including disease history. Results A total of 296,407 respondents and 1,813,884 million person-years of follow-up from the CCHS and 58,232 respondents and 497,909 person-years from the NHIS were included. Absolute mortality risk among those with a ‘healthy profile’ was higher in the United States compared to Canada, especially among women. Adjusted mortality hazard ratios associated with health behaviours were generally of similar magnitude and direction but often stronger in Canada. Conclusion Even when methodological and population differences are minimal, the association of health behaviours and mortality can vary across populations. It is therefore important to be cautious of between-study variation when aggregating relative effect estimates from differing populations, and when using external effect estimates for population health research and policy development.

Objectives To determine the feasibility of a patient-education booklet to support patients with low back pain (LBP) to reduce paracetamol intake.Design Single group, repeated measures feasibility study.Setting Community.Participants Adults experiencing LBP of any kind and self-reporting consumption of paracetamol for LBP weekly for at least 1 month were invited to participate.Intervention Participants received a patient-education booklet 1 week after the baseline measures were collected. The intervention was designed to change beliefs, increase knowledge and self-efficacy to deprescribe paracetamol for their LBP and create discussion with a health professional through the mechanisms of motivation, capacity and opportunity.Primary outcome measures Feasibility of recruitment procedures, data collection and acceptability of the intervention.Secondary outcome measures Changes in motivation, self-efficacy, opportunity to deprescribe paracetamol for their LBP, paracetamol usage and LBP clinical outcomes at baseline, 1-week and 1-month follow-up.Results A total of 24 participants were recruited into the study within the timeframe of 3 months from study advertisement and all completed the study follow-up. There were no missing data for any outcome measure across all follow-up points, 22 (91.6%) participants were willing to participate in a future randomised control trial (RCT) and over 60% of participants responded positively to questions regarding acceptability of the patient-educational booklet. Overall, at the 1-month follow-up, approximately two thirds (15/24) of participants had an increase in motivation and self-efficacy scores and had discussed or intended to discuss their paracetamol use for LBP with a health professional.Conclusions The results of this study demonstrate that the patient-education booklet is feasible to implement, and both the intervention and study design were well-received by participants. This study supports the undertaking an RCT to assess the effects of the patient-education booklet on deprescribing paracetamol in people with LBP.

Objectives Only a few cardiovascular risk prediction models have been developed based on modifiable exposures and even fewer utilize complex dietary factors. Our objective was to develop and validate the Cardiovascular Disease Population Risk Tool (CVDPoRT)-Nutrition as a tool for estimating 5-year risk of incident cardiovascular disease (CVD) using lifestyle factors and dietary pattern scores. Approach The CVDPoRT developed and validated using the Canadian Community Health Survey (CCHS) linked with health administrative databases was modified to remove limited measures of dietary intakes (i.e., fruit and vegetable, potato, and juice intake frequency) and instead incorporate 5 different dietary pattern scores (i.e., Dietary Guidelines for Americans Adherence Index, Dietary Approaches to Stop Hypertension, Healthy Eating Index, Alternative HEI, and Mediterranean Style Dietary Pattern Score). Outcome data (i.e., CVD events and CVD-related mortality) came from linkage with the Canadian Vital Statistics – Death Database and Discharge Abstract Database. CVDPoRT-Nutrition was tested in 61 policy-relevant subgroups. Results Performance after adding in each dietary pattern score was similar to the original CVDPoRT (Brier score=2.6%, Harrell’s c-stat=0.87(0.85-0.88) for female models; Brier score=1.6%, Harrell’s c-stat=0.82(0.81-0.84) for male models). The algorithm was calibrated in 53 (female models) and 57 (male models) of 61 policy relevant subgroups. The most important predictors of CVD and CVD-related mortality were age, sex, and smoking. Conclusion Altering the dietary measures included in the CVDPoRT algorithm did not greatly improve the predictive capacity. The original CVDPoRT can continue to be used for predicting CVD and CVD-related mortality, while CVDPoRT-Nutrition may be used for predicting CVD incidence associated with poor dietary patterns.

Objectives Prognostication tools reporting personalized mortality risk and survival can improve advance care planning and discussions about end-of-life care. We developed, validated, and implemented a mortality risk algorithm for older adults with diverse care needs in long-term care (LTC) homes, called the Risk Evaluation for Support: Predictions for Elder-Life in the Community Tool for LTC (RESPECT-LTC). Approach RESPECT-LTC was developed using routinely-collected health information on residents in LTC homes in Ontario, Canada. Model development used a cohort of LTC residents aged 50 years or older with at least 1 Resident Assessment Instrument—Minimum Data Set (RAI-MDS) record between January 2010 and December 2016. The primary outcome was mortality 6 months after a RAI-MDS assessment. We used proportional hazards regression with robust standard errors to account for clustering by the individual. We validated this algorithm, temporally, in a cohort of LTC residents who were assessed between January and December 2017. We constructed 37 risk bins based on incremental increases in estimated median survival of ~3 weeks among residents at high risk of death and 3 months among residents with lower mortality risk. We implemented and are evaluating the use of RESPECT-LTC for early identification of palliative care needs in LTC homes across Ontario. Results Development and validation cohorts included 2,228,176 and 328,204 RAI-MDS assessments, respectively. Mean predicted 6-month mortality risk ranged from 1.38% (95% CI 0.63%-1.61%) in the lowest to 91.97% (95% CI 81.47%-99.9%) in the highest risk group. Estimated median survival spanned from 42 days (15 to 128 d at the 25th and 75th percentiles) in the highest risk group to over 8 years (2,066 to 3,428 d) in the lowest risk group. The algorithm had a c-statistic of 0.730 (95% CI 0.726–0.736) in our validation cohort. Conclusion RESPECT-LTC makes use of routinely-collected information to improve the identification of palliative and end-of-life care needs in LTC. Ongoing evaluation will assess its impact on referrals to palliative care, hospitalization at the end of life, and location of death.

Objectives RESPECT is a prognostic tool, developed using linked population-based data, to predict 6-month mortality in community-dwelling older adults. RESPECT is implemented and openly accessible as a web-based tool on ProjectBigLife.ca, where over 700,000 calculations have been performed to date. Our objective was to describe healthcare utilization patterns among home care (HC) users across mortality risk profiles generated from RESPECT to inform care planning for older persons who have varying mortality risks and levels of care needs as they decline. Approach We conducted a retrospective cohort study examining healthcare use among HC users in Ontario, Canada, who received at least one interRAI HC assessment between April 2018 and September 2019. Using linked health administrative data at the individual level, we examined the use of acute care (hospitalizations and emergency department (ED) visits), long-term care (LTC), and palliative home care within 6-months of each assessment and prognostication using RESPECT. Mortality risk profiles from RESPECT were created based on the median survival. Results The cohort comprised 247,377 community-dwelling older adults; 14.3% died within 6-months of an assessment. Among decedents, half (51.51%) of HC users with a predicted median survival of less than 3-months received at least one palliative care home visit; 39.17%, 34.82% and 13.84% visited the ED, were hospitalized, or were admitted to LTC, respectively. The proportion of assessments that received at least one palliative HC visit declined to 43.11% and 30.28% of assessments with a median survival between 3- and 6-months and those between 6-months and 12-months, respectively. The proportion of assessments with an acute care use increases with increasing median survival. Conclusion A considerable proportion of people at the end-of-life do not receive any palliative home care and continued to be institutionalized. This may be indication that the reduced life expectancies and palliative care needs of many older adults are not being recognized, thus demonstrating the value of prognostic models like RESPECT to inform care planning for individuals in their final years of life.

Background: Smoking, unhealthy alcohol consumption, poor diet and physical inactivity are leading risk factors for morbidity and mortality, and contribute substantially to overall healthcare costs. The availability of health surveys linked to health care provides population-based estimates of direct healthcare costs. We estimated health behaviour and socioeconomic-attribute healthcare costs, and how these have changed during a period when government policies have aimed to reduce their burden. Methods: The Ontario samples of the Canadian Community Health Surveys (conducted in 2003, 2005, and 2007-2008) were linked at the individual level to all records of health care use of publicly funded healthcare. Generalized linear models were estimated with a negative binomial distribution to ascertain the relationship of health behaviours and socioeconomic risk factors on health care costs. The multivariable cost model was applied to unlinked, Ontario CCHS samples for each year from 2004 to 2013 to examine the evolution of health behaviour and socioeconomic-attributable direct health care expenditures over a 10-year period. Results: We included 80,749 respondents, aged 25 years and older, and 312,952 person-years of follow-up. The cost model was applied to 200,324 respondents aged 25 years and older (CCHS 2004 to 2013). During the 10-year period from 2004 to 2013, smoking, unhealthy alcohol consumption, poor diet and physical inactivity attributed to 22% of Ontario’s direct health care costs. Ontarians in the most disadvantaged socioeconomic position contributed to 15% of the province’s direct health care costs. Combined, these health behaviour and socioeconomic risk factors were associated with 34% ($134 billion) of direct health care costs (2004 to 2013). Over this time period, we estimated a 1.9% reduction in health care expenditure ($5.0 billion) attributable to improvements in some health behaviours, most importantly reduced rates of smoking. Conclusions: Adverse health behaviours and socioeconomic position cause a large direct health care system cost burden.

Introduction In Ontario, only 52% of people received palliative care in their last year of life, with only 20\% of those receiving it at home, which can improve the dying experience. Existing algorithms identifying people at end-of-life can potentially improve access to palliative care but are difficult for patients to understand. Objectives and Approach To predict and communicate risk of death for community dwelling older adults using a pre-specified and published approach (Trial registration NCT02779309). All assessments from community-dwelling Ontarians (N = 488,636) who received at least one home care assessment from the residential assessment instrument – home care (RAI HC) from 2007 to 2013 (N=1,331,273) were included. The algorithm used a two-step approach by rank ordering participants into 61 groups based on six-month probability of death (from Cox-proportional hazard models) and generated Kaplan-Meier five-year survival curves for each group. Median Survival time is reported with uncertainties expressed with 25th to 75th percentiles. Results The median predicted probability of death within six-months was 0.1095 (0.1093-0.1097, 95% CI). Risk varied among the 61 groups from 0.0158 (0.0158-0.0159) to 0.9820 (0.9810-0.9830). Median observed survival time varied from 27 days (10 to 81 days, 25th and 75th percentile) in the highest risk group to 10 years (3655 days (2111 to >3655 days)) in the lowest risk group. Discrimination and calibration were satisfactory between the derivation (2007-2012 assessments) and validation (2013 assessments) cohorts, with a C statistics of 0.77 and discrimination plot intercept 0.094, slope 0.914. The Kaplan-Meier five-year survival curves for each of the 61 groups will be visually represented in six different ways displaying the risk and uncertainty, and can be altered to yield information of interest specific to each patient/caregiver. Conclusion/Implications RESPECT is adaptive and personalized, with instantaneous feedback as the user provides a response to each question. We will present RESPECT’s development and implementation processes and set up an interactive presentation of the calculator, demonstrating RESPECT’s ability to deliver patient-comprehensible end-of-life prognoses with uncertainty to patients and their caregivers.

Introduction Current approaches to the development and application of predictive studies is inefficient and difficult to reproduce. Thousands of predictive health algorithms have been developed; however, less than 2\% have been assessed outside their original setting and even fewer have been applied and evaluated in practice. Objectives and Approach Objective: To develop a standardized workflow for algorithm development, dissemination and implementation. Existing predictive analytics workflow and open standards were adapted and expanded for health research and health care settings. The approach was designed to work within multidisciplinary teams and to improve research transparency, reproducibility, quality, efficiency and application. Key components include standardized algorithm description files, documentation and code libraries. All libraries and programming packages, which were created for/with open-source software, can be used for a wide range of statistical and machine learning models. Publicly-available repositories contain the algorithms, validation data, R code and other supporting infrastructure. Results Algorithm development involves variable pre-specification and documentation of model variables, followed by creation of data preprocessing code to generate model variables from the study dataset. Preprocessing uses algorithm specification documentation and a function library, building upon and integrating with existing algorithms when possible to preventing code duplication. Models are output as a Predictive Modelling Markup Language (PMML) file, a portable industry standard for describing and scoring predictive models. A separate scoring "engine" is used to implement PMML-described algorithms in a range of settings, including algorithm validation at other research institutions. Algorithm applications currently include the Project Big Life (www.projectbiglife.ca) online calculators, population, health services and public health planning uses and an algorithm visualization tool. An API permits use of the calculator engine by other organizations. Conclusion/Implications Barriers to the implementation of predictive analytics in real-world settings—such as within electronic medical records or decision aid applications—can be mitigated with well described algorithms that are easy to replicate and implement, especially as access to big health data increases and algorithms become increasingly complex.

Introduction National health surveys, available in over 100 countries, are the most common data used for health behaviour surveillance and are increasingly being linked to individual-level health outcomes. We propose that improved health behaviour hazard estimates can be obtained from pooled international population health surveys linked to outcome data. Objectives and Approach The objective of this study was to compare smoking, alcohol, diet and physical activity all-cause mortality hazards in Canada and the United States using individual-level, linked population health survey data and common model specifications. The Canadian Community Health Survey (CCHS) (2003-2007) and the United States National Health Interview Survey (NHIS) (2000, 2005) linked to individual-level mortality outcomes with follow up to December 31, 2011 were used. Variable definitions consistent across the CCHS and NHIS were developed and used to estimate country-specific mortality hazards with sex-specific Cox proportional hazard models, including health behaviours, sociodemographic indicators and proximal factors including disease history. Results A total of 296,407 respondents and 1,813,884 million person-years of follow-up from the CCHS and 62,226 respondents and 497,909 person-years from the NHIS were included. Hazards of smoking, alcohol consumption, diet and physical activity in Canada and the United States are of similar magnitude and direction, with similar dose response relationships. The largest health behaviour mortality hazards were associated with female heavy smokers in both Canada (HR: 3.36, 95% CI: 2.86, 3.95) and the United States (Female HR: 2.63, 95% CI: 2.11, 3.27), compared to non-smokers. Conclusion/Implications Health behaviour mortality hazards are comparable in Canada and the United States, supporting the use of hazards obtained from pooled analyses for population heath. These hazards can replace those obtained from independent epidemiology studies that are often incompletely adjusted, rarely population-based and often not generalizable to the population of interest.

ABSTRACT Objectives Increasingly, national health surveys are being linked to vital statistics and health care information, providing a new and unique source of individual population health data. Given that nationally-representative health surveys are performed in over a hundred countries, these linkages create comprehensive data sets that are potentially larger than most existing cohort studies. To date, this resource has not been utilized. Approach The purpose, study base, content and methods of the Canadian Community Health Survey (CCHS) cycles 2.1 (2003-04) and 3.1 (2005-06) and the United States 2000 and 2005 National Health Interview Survey (NHIS) were examined for comparability and consistency. Smoking, alcohol, physical activity and diet questions were identified, question construct and response categorization were compared, and variable constructions possible for both national health surveys were created. All respondents 20+ years of age were identified and stratified by country and sex. Cox proportional hazard models were used to estimate 5-year hazards of mortality associated with the common smoking, alcohol, physical activity and diet variables. Results The CCHS and NHIS are highly consistent and comparable. Health behaviour questions are similar and permit the creation of smoking, alcohol, physical activity and diet variables that are comparable across surveys. A total of 284 475 survey respondents from Canada and the United States (CCHS, N= 226 731; NHIS, N= 57 744) were included. The largest mortality hazards were associated with female heavy smokers in both Canada (HR: 2.91; 95% CI: 2.52, 3.37) and the United States (Female HR: 2.96; 95% CI: 2.59, 3.38), compared to non-smokers. Moderate variation in the age adjusted all-cause mortality hazard ratios was observed; both smoking and physical activity hazard ratios were consistently higher in the United States than in Canada. Conclusion This study provides initial support for the methodological feasibility of pooling linked population health surveys however, challenges introduced by dissimilarities will require the use of innovative methodologies, and discussions regarding how to manage jurisdictional data restrictions and privacy issues are needed. Pooled population health data has the potential to improve national and international health surveillance and public health.

BackgroundBehaviours such as smoking, poor diet, physical inactivity, and unhealthy alcohol consumption are leading risk factors for death. We assessed the Canadian burden attributable to these behaviours by developing, validating, and applying a multivariable predictive model for risk of all-cause death.MethodsA predictive algorithm for 5 y risk of death-the Mortality Population Risk Tool (MPoRT)-was developed and validated using the 2001 to 2008 Canadian Community Health Surveys. There were approximately 1 million person-years of follow-up and 9,900 deaths in the development and validation datasets. After validation, MPoRT was used to predict future mortality and estimate the burden of smoking, alcohol, physical inactivity, and poor diet in the presence of sociodemographic and other risk factors using the 2010 national survey (approximately 90,000 respondents). Canadian period life tables were generated using predicted risk of death from MPoRT. The burden of behavioural risk factors attributable to life expectancy was estimated using hazard ratios from the MPoRT risk model.FindingsThe MPoRT 5 y mortality risk algorithms were discriminating (C-statistic: males 0.874 [95% CI: 0.867-0.881]; females 0.875 [0.868-0.882]) and well calibrated in all 58 predefined subgroups. Discrimination was maintained or improved in the validation cohorts. For the 2010 Canadian population, unhealthy behaviour attributable life expectancy lost was 6.0 years for both men and women (for men 95% CI: 5.8 to 6.3 for women 5.8 to 6.2). The Canadian life expectancy associated with health behaviour recommendations was 17.9 years (95% CI: 17.7 to 18.1) greater for people with the most favourable risk profile compared to those with the least favourable risk profile (88.2 years versus 70.3 years). Smoking, by itself, was associated with 32% to 39% of the difference in life expectancy across social groups (by education achieved or neighbourhood deprivation).ConclusionsMultivariable predictive algorithms such as MPoRT can be used to assess health burdens for sociodemographic groups or for small changes in population exposure to risks, thereby addressing some limitations of more commonly used measurement approaches. Unhealthy behaviours have a substantial collective burden on the life expectancy of the Canadian population.

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.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).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.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.

Diabetes has been described as one of the most important threats to the health of developed countries. Effective population strategies to prevent diabetes have not been determined but two broad strategies have been proposed: "high-risk" and "community-wide" strategies.We modelled the potential effectiveness of two strategies to prevent 10% of new cases of diabetes in Ontario, Canada over a 5-year period. The 5-year risk of developing physician-diagnosed diabetes was estimated for respondents to the Canadian Community Health Survey 2003 (CCHS 2.1, N = 26 232) using a validated and calibrated diabetes risk tool (Diabetes Population Risk Tool [DPoRT]). We estimated how many cases of diabetes could be prevented using two different strategies: a) a community-wide strategy that would uniformly reduce body mass index (BMI) in the entire population; and b) a high baseline risk strategy using either pharmacotherapy or lifestyle counselling to treat people who have an increased risk of developing diabetes.In 2003, the 5-year risk of developing diabetes was 4.7% (383 600 new diagnosed cases of diabetes in 8 189 000 Ontarians aged 20+) and risk was moderately diffused (0.5%, 3.1% and 17.9% risk in the 1(st), 5(th) (median) and 10(th) deciles of risk). A 10% reduction in new cases of diabetes would have been achieved under any of the following scenarios: if BMI was 3.5% lower in the entire population; if lifestyle counselling covered 32.2% of high-risk people (371 900 of 1 155 000 people with 5 year diabetes risk greater than 10%); or, if pharmacotherapy covered 65.2% of high-risk people.Prevention using pharmacotherapy alone requires unrealistically high coverage levels to achieve modest population reduction in new diabetes cases. On the other hand, in recent years few jurisdictions have been able to achieve a reduction in BMI at the population level, let alone a reduction of BMI of 3.5%.

Adverse events are poor patient outcomes caused by medical care. Their identification requires the peer-review of poor outcomes, which may be unreliable. Combining physician ratings might improve the accuracy of adverse event classification.To evaluate the variation in peer-reviewer ratings of adverse outcomes; determine the impact of this variation on estimates of reviewer accuracy; and determine the number of reviewers who judge an adverse event occurred that is required to ensure that the true probability of an adverse event exceeded 50%, 75% or 95%.Thirty physicians rated 319 case reports giving details of poor patient outcomes following hospital discharge. They rated whether medical management caused the outcome using a six-point ordinal scale. We conducted latent class analyses to estimate the prevalence of adverse events as well as the sensitivity and specificity of each reviewer. We used this model and Bayesian calculations to determine the probability that an adverse event truly occurred to each patient as function of their number of positive ratings.The overall median score on the 6-point ordinal scale was 3 (IQR 2,4) but the individual rater median score ranged from a minimum of 1 (in four reviewers) to a maximum median score of 5. The overall percentage of cases rated as an adverse event was 39.7% (3798/9570). The median kappa for all pair-wise combinations of the 30 reviewers was 0.26 (IQR 0.16, 0.42; Min = -0.07, Max = 0.62). Reviewer sensitivity and specificity for adverse event classification ranged from 0.06 to 0.93 and 0.50 to 0.98, respectively. The estimated prevalence of adverse events using a latent class model with a common sensitivity and specificity for all reviewers (0.64 and 0.83 respectively) was 47.6%. For patients to have a 95% chance of truly having an adverse event, at least 3 of 3 reviewers are required to deem the outcome an adverse event.Adverse event classification is unreliable. To be certain that a case truly represents an adverse event, there needs to be agreement among multiple reviewers.

Background Despite an increasing number of risk prediction models being developed within the healthcare space, few have been widely adopted and evaluated in clinical practice. RESPECT, a mortality risk communication tool powered by a prediction algorithm, has been implemented in the home care setting in Ontario, Canada, to support the identification of palliative care needs among older adults. We sought to re-estimate and validate the RESPECT algorithm in contemporary data. Methods The study and derivation cohort comprised adults living in Ontario aged 50 years and older with at least 1 interRAI Home Care (interRAI HC) record between April 1, 2018 and September 30, 2019. Algorithm validation used 500 bootstrapped samples, each containing a 5% random selection from the total cohort. The primary outcome was mortality within 6 months following an interRAI HC assessment. We used proportional hazards regression with robust standard errors to account for clustering by the individual. Kaplan–Meier survival curves were estimated to derive the observed risk of death at 6 months for assessment of calibration and median survival. Finally, 61 risk groups were constructed based on incremental increases in the observed median survival. Results The study cohort included 247,377 adults and 35,497 deaths (14.3%). The mean predicted 6-month mortality risk was 18.0% and ranged from 1.5% (95% CI 1.0%–1.542%) in the lowest to 96.0 % (95% CI 95.8%–96.2%) in the highest risk group. Estimated median survival spanned from 36 days in the highest risk group to over 3.5 years in the lowest risk group. The algorithm had a c-statistic of 0.76 (95% CI 0.75-0.77) in our validation cohort. Conclusions RESPECT demonstrates good discrimination and calibration. The algorithm, which leverages routinely-collected information, may be useful in home care settings for earlier identification of individuals who might be nearing the end of life.

Research needs to be reported transparently so readers can critically assess the strengths and weaknesses of the design, conduct, and analysis of studies. Reporting guidelines have been developed to inform reporting for a variety of study designs. The objective of this study was to identify whether there is a need to develop a reporting guideline for survey research.We conducted a three-part project: (1) a systematic review of the literature (including "Instructions to Authors" from the top five journals of 33 medical specialties and top 15 general and internal medicine journals) to identify guidance for reporting survey research; (2) a systematic review of evidence on the quality of reporting of surveys; and (3) a review of reporting of key quality criteria for survey research in 117 recently published reports of self-administered surveys. Fewer than 7% of medical journals (n = 165) provided guidance to authors on survey research despite a majority having published survey-based studies in recent years. We identified four published checklists for conducting or reporting survey research, none of which were validated. We identified eight previous reviews of survey reporting quality, which focused on issues of non-response and accessibility of questionnaires. Our own review of 117 published survey studies revealed that many items were poorly reported: few studies provided the survey or core questions (35%), reported the validity or reliability of the instrument (19%), defined the response rate (25%), discussed the representativeness of the sample (11%), or identified how missing data were handled (11%).There is limited guidance and no consensus regarding the optimal reporting of survey research. The majority of key reporting criteria are poorly reported in peer-reviewed survey research articles. Our findings highlight the need for clear and consistent reporting guidelines specific to survey research.

ObjectivesTo describe the development, validation and inter-rater reliability of an instrument to measure the quality of patient decision support technologies (decision aids).DesignScale development study, involving construct, item and scale development, validation and reliability testing.SettingThere has been increasing use of decision support technologies--adjuncts to the discussions clinicians have with patients about difficult decisions. A global interest in developing these interventions exists among both for-profit and not-for-profit organisations. It is therefore essential to have internationally accepted standards to assess the quality of their development, process, content, potential bias and method of field testing and evaluation.MethodsScale development study, involving construct, item and scale development, validation and reliability testing.ParticipantsTwenty-five researcher-members of the International Patient Decision Aid Standards Collaboration worked together to develop the instrument (IPDASi). In the fourth Stage (reliability study), eight raters assessed thirty randomly selected decision support technologies.ResultsIPDASi measures quality in 10 dimensions, using 47 items, and provides an overall quality score (scaled from 0 to 100) for each intervention. Overall IPDASi scores ranged from 33 to 82 across the decision support technologies sampled (n = 30), enabling discrimination. The inter-rater intraclass correlation for the overall quality score was 0.80. Correlations of dimension scores with the overall score were all positive (0.31 to 0.68). Cronbach's alpha values for the 8 raters ranged from 0.72 to 0.93. Cronbach's alphas based on the dimension means ranged from 0.50 to 0.81, indicating that the dimensions, although well correlated, measure different aspects of decision support technology quality. A short version (19 items) was also developed that had very similar mean scores to IPDASi and high correlation between short score and overall score 0.87 (CI 0.79 to 0.92).ConclusionsThis work demonstrates that IPDASi has the ability to assess the quality of decision support technologies. The existing IPDASi provides an assessment of the quality of a DST's components and will be used as a tool to provide formative advice to DSTs developers and summative assessments for those who want to compare their tools against an existing benchmark.

Background: Smoking, unhealthy alcohol consumption, poor diet and physical inactivity are leading risk factors for morbidity and mortality, and contribute substantially to overall healthcare costs. The availability of health surveys linked to health care provides population-based estimates of direct healthcare costs. We estimated health behaviour and socioeconomic-attribute healthcare costs, and how these have changed during a period when government policies have aimed to reduce their burden. Methods: The Ontario samples of the Canadian Community Health Surveys (conducted in 2003, 2005, and 2007-2008) were linked at the individual level to all records of health care use of publicly funded healthcare. Generalized linear models were estimated with a negative binomial distribution to ascertain the relationship of health behaviours and socioeconomic risk factors on health care costs. The multivariable cost model was then applied to unlinked, cross-sectional CCHS samples for each year from 2004 to 2013 to examine the evolution of health behaviour and socioeconomic-attributable direct health care expenditures over a 10-year period. Results: We included 80,749 respondents, aged 25 years and older, and 312,952 person-years of follow-up. The cost model was applied to 200,324 respondents aged 25 years and older (CCHS 2004 to 2013). During the 10-year period from 2004 to 2013, smoking, unhealthy alcohol consumption, poor diet and physical inactivity attributed to 22% of Ontario’s direct health care costs. Ontarians in the most disadvantaged socioeconomic position contributed to 15% of the province’s direct health care costs. Taken together, health behaviours and socioeconomic position were associated with 34% ($134 billion) of direct health care costs (2004 to 2013). Over this time period, we estimated a 1.9% reduction in health care expenditure ($5.0 billion) attributable to improvements in some health behaviours, most importantly reduced rates of smoking. Conclusions: Health behaviours and socioeconomic position cause a large direct health care system cost burden.