Publications (25)119.84 Total impact
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Article: Pandemic H1N1 in Canada and the use of evidence in developing public health policies - A policy analysis.
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ABSTRACT: When responding to a novel infectious disease outbreak, policies are set under time constraints and uncertainty which can limit the ability to control the outbreak and result in unintended consequences including lack of public confidence. The H1N1 pandemic highlighted challenges in public health decision-making during a public health emergency. Understanding this process to identify barriers and modifiable influences is important to improve the response to future emergencies. The purpose of this study is to examine the H1N1 pandemic decision-making process in Canada with an emphasis on the use of evidence for public health decisions. Using semi-structured key informant interviews conducted after the pandemic (July-November 2010) and a document analysis, we examined four highly debated pandemic policies: use of adjuvanted vaccine by pregnant women, vaccine priority groups and sequencing, school closures and personal protective equipment. Data were analysed for thematic content guided by Lomas' policy decision-making framework as well as indicative coding using iterative methods. We interviewed 40 public health officials and scientific advisors across Canada and reviewed 76 pandemic policy documents. Our analysis revealed that pandemic pre-planning resulted in strong beliefs, which defined the decision-making process. Existing ideological perspectives of evidence strongly influenced how information was used such that the same evidentiary sources were interpreted differently according to the ideological perspective. Participants recognized that current models for public health decision-making failed to make explicit the roles of scientific evidence in relation to contextual factors. Conflict avoidance theory explained policy decisions that went against the prevailing evidence. Clarification of roles and responsibilities within the public health system would reduce duplication and maintain credibility. A more transparent and iterative approach to incorporating evidence into public health decision-making that reflects the realities of the external pressures present during a public health emergency is needed.Social Science [?] Medicine 04/2013; 83:1-9. · 2.70 Impact Factor -
Article: Effectiveness of community-wide and individual high-risk strategies to prevent diabetes: a modelling study.
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ABSTRACT: 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%.PLoS ONE 01/2013; 8(1):e52963. · 4.09 Impact Factor -
Article: The influence of measurement error on calibration, discrimination, and overall estimation of a risk prediction model.
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ABSTRACT: BACKGROUND: Self-reported height and weight are commonly collected at the population level; however, they can be subject to measurement error. The impact of this error on predicted risk, discrimination, and calibration of a model that uses body mass index (BMI) to predict risk of diabetes incidence is not known. The objective of this study is to use simulation to quantify and describe the effect of random and systematic error in self-reported height and weight on the performance of a model for predicting diabetes. METHODS: Two general categories of error were examined: random (nondirectional) error and systematic (directional) error on an algorithm relating BMI in kg/m2 to probability of developing diabetes. The cohort used to develop the risk algorithm was derived from 23,403 Ontario residents that responded to the 1996/1997 National Population Health Survey linked to a population-based diabetes registry. The data and algorithm were then simulated to allow for estimation of the impact of these errors on predicted risk using the Hosmer-Lemeshow goodness-of-fit chi2 and C-statistic. Simulations were done 500 times with sample sizes of 9,177 for males and 10,618 for females. RESULTS: Simulation data successfully reproduced discrimination and calibration generated from population data. Increasing levels of random error in height and weight reduced the calibration and discrimination of the model. Random error biased the predicted risk upwards whereas systematic error biased predicted risk in the direction of the bias and reduced calibration; however, it did not affect discrimination. CONCLUSION: This study demonstrates that random and systematic errors in self-reported health data have the potential to influence the performance of risk algorithms. Further research that quantifies the amount and direction of error can improve model performance by allowing for adjustments in exposure measurements.Population Health Metrics 11/2012; 10(1):20. · 2.11 Impact Factor -
Article: The use of syndromic surveillance for decision-making during the H1N1 pandemic: A qualitative study.
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ABSTRACT: BACKGROUND: Although an increasing number of studies are documenting uses of syndromic surveillance by front line public health, few detail the value added from linking syndromic data to public health decision-making. This study seeks to understand how syndromic data informed specific public health actions during the 2009 H1N1 pandemic. METHODS: Semi-structured telephone interviews were conducted with participants from Ontario's public health departments, the provincial ministry of health and federal public health agency to gather information about syndromic surveillance systems used and the role of syndromic data in informing specific public health actions taken during the pandemic. Responses were compared with how the same decisions were made by non-syndromic surveillance users. RESULTS: Findings from 56 interviews (82% response) show that syndromic data was most used for monitoring virus activity, measuring impact on the health care system and informing the opening of influenza assessment centres in several jurisdictions, and supporting communications and messaging, rather than its intended purpose of early outbreak detection. Syndromic data had limited impact on decisions that involved the operation of immunization clinics, school closures, sending information letters home with school children or providing recommendations to health care providers. Both syndromic surveillance users and non-users reported that guidance from the provincial ministry of health, communications with stakeholders and vaccine availability were driving factors in these public health decisions. CONCLUSIONS: Syndromic surveillance had limited use in decision-making during the 2009 H1N1 pandemic in Ontario. This study provides insights into the reasons why this occurred. Despite this, syndromic data were valued for providing situational awareness and confidence to support public communications and recommendations. Developing an understanding of how syndromic data is utilized during public health events provides valuable evidence to support future investments in public health surveillance.BMC Public Health 10/2012; 12(1):929. · 2.00 Impact Factor -
Article: Predictive risk algorithms in a population setting: an overview.
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ABSTRACT: The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health. To describe the role of predictive risk algorithms in a population setting. First, predictive risk algorithms and how clinicians use them are described. Second, the population uses of risk algorithms are described, highlighting the strengths of risk algorithms for health planning. Lastly, the way in which predictive risk algorithms are developed is discussed briefly and a guide for algorithm assessment in population health presented. For the past 20 years, absolute and baseline risk has been a cornerstone of population health planning. The most accurate and discriminating method to generate such estimates is the use of multivariable risk algorithms. Routinely collected data can be used to develop algorithms with characteristics that are well suited to health planning and such data are increasingly available. The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health.Journal of epidemiology and community health 08/2012; 66(10):859-65. · 3.04 Impact Factor -
Article: The potential effect of temporary immunity as a result of bias associated with healthy users and social determinants on observations of influenza vaccine effectiveness; could unmeasured confounding explain observed links between seasonal influenza vaccine and pandemic H1N1 infection?
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ABSTRACT: BACKGROUND: Five observational studies from Canada found an association between seasonal influenza vaccine receipt and increased risk of pandemic influenza H1N1 2009 infection. This association remains unexplained. Although uncontrolled confounding has been suggested as a possible explanation, the nature of such confounding has not been identified. Observational studies of influenza vaccination can be affected by confounding due to healthy users and the influence of social determinants on health. The purpose of this study was to investigate the influence that these two potential confounders may have in combination with temporary immunity, using stratified tables. The hypothesis is that respiratory virus infections may activate a temporary immunity that provides short-term non-specific protection against influenza and that the relationship with being a healthy user or having a social determinant may result in confounding. METHODS: We simulated the effect of confounding on vaccine effectiveness assuming that this could result from both social determinants and healthy user effects as they both influence the risk of seasonal influenza and non-influenza respiratory virus infections as well as the likelihood of being vaccinated. We then examined what impact this may have had on measurement of seasonal influenza vaccine effectiveness against pandemic influenza. RESULTS: In this simulation, failure to adjust for healthy users and social determinants would result in an erroneously increased risk of pandemic influenza infection associated with seasonal influenza vaccination. The effect sizes were not however large. CONCLUSIONS: We found that unmeasured healthy user effects and social determinants could result in an apparent association between seasonal influenza vaccine and pandemic influenza infection by virtue of being related to temporary immunity. Adjustment for social determinants of health and the healthy user effects are required in order to improve the quality of observational studies of influenza vaccine effectiveness.BMC Public Health 06/2012; 12(1):458. · 2.00 Impact Factor -
Article: The social determinants of health and pandemic H1N1 2009 influenza severity.
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ABSTRACT: We explored the effects of social determinants of health on pandemic H1N1 2009 influenza severity and the role of clinical risk factors in mediating such associations. We used multivariate logistic regression with generalized estimating equations to examine the associations between individual- and ecological-level social determinants of health and hospitalization for pandemic H1N1 2009 illness in a case-control study in Ontario, Canada. During the first pandemic phase (April 23-July 20, 2009), hospitalization was associated with having a high school education or less and living in a neighborhood with high material or total deprivation. We also observed the association with education in the second phase (August 1-November 6, 2009). Clinical risk factors for severe pandemic H1N1 2009 illness mediated approximately 39% of the observed association. The main clinical risk factors for severe pandemic H1N1 2009 illness explain only a portion of the associations observed between social determinants of health and hospitalization, suggesting that the means by which the social determinants of health affect pandemic H1N1 2009 outcomes extend beyond clinically recognized risk factors.American Journal of Public Health 06/2012; 102(8):e51-8. · 3.93 Impact Factor -
Article: The role of ethnicity in predicting diabetes risk at the population level.
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ABSTRACT: Background. The current form of the Diabetes Population Risk Tool (DPoRT) includes a non-specific category of ethnicity in concordance with publicly data available. Given the importance of ethnicity in influencing diabetes risk and its significance in a multi-ethnic population, it is prudent to determine its influence on a population-based risk prediction tool. Objective. To apply and compare the DPoRT with a modified version that includes detailed ethnic information in Canada's largest and most ethnically diverse province. Methods. Two additional diabetes prediction models were created: a model that contained predictors specific to the following ethnic groups - White, Black, Asian, south Asian, and First Nation; and a reference model which did not include a term for ethnicity. In addition to discrimination and calibration, 10-year diabetes incidence was compared. The algorithms were developed in Ontario using the 1996-1997 National Population Health Survey (N=19,861) and validated in the 2000/2001 Canadian Community Health Survey (N=26,465). Results. All non-white ethnicities were associated with higher risk for developing diabetes with south Asians having the highest risk. Discrimination was similar (0.75-0.77) and sufficient calibration was maintained for all models except the detailed ethnicity models for males. DPoRT produced the lowest overall ratio between observed and predicted diabetes risk. DPoRT identified more high risk cases than the other algorithms in males, whereas in females both DPoRT and the full ethnicity model identified more high risk cases. Overall DPoRT and full ethnicity algorithms were very similar in terms of predictive accuracy and population risk. Conclusion. Although from the individual risk perspective, incorporating information on ethnicity is important, when predicting new cases of diabetes at the population level and accounting for other risk factors, detailed ethnic information did not improve the discrimination and accuracy of the model or identify significantly more diabetes cases in the population.Ethnicity and Health 01/2012; 17(4):419-37. · 1.64 Impact Factor -
Article: Perceived usefulness of syndromic surveillance in Ontario during the H1N1 pandemic.
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ABSTRACT: Despite the growing popularity of syndromic surveillance, little is known about if or how these systems are accepted, utilized and valued by end users. This study seeks to describe the use of syndromic surveillance systems in Ontario and users' perceptions of the value of these systems within the context of other surveillance systems. Ontario's 36 public health units, the provincial ministry of health and federal public health agency completed a web survey to identify traditional and syndromic surveillance systems used routinely and during the pandemic and to describe system attributes and utility in monitoring pandemic activity and informing decision-making. Syndromic surveillance systems are used by 20/38 (53%) organizations. For routine surveillance, laboratory, integrated Public Health Information System and school absenteeism data are the most frequently used sources. Laboratory data received the highest ratings for reliability, timeliness and accuracy ('very acceptable' by 92, 51 and 89%). Hospital/clinic screening data were rated as the most reliable and timely syndromic data source (50 and 43%) and ED visit data the most accurate (48%). During the pandemic, laboratory data were considered the most useful for monitoring the epidemiology and informing decision-making while ED screening and visit data were considered the most useful syndromic sources. End user perceptions are valuable for identifying opportunities for improvement and guiding further investments in public health surveillance.Journal of Public Health 12/2011; 34(2):195-202. · 2.06 Impact Factor -
Article: Assessing the impact of confounding (measured and unmeasured) in a case-control study to examine the increased risk of pandemic A/H1N1 associated with receipt of the 2008-9 seasonal influenza vaccine.
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ABSTRACT: This study examines the role of measured and unmeasured confounding in the relationship between the 2008-9 seasonal influenza vaccine and pandemic H1N1 (pH1N1) influenza virus. Data were taken from a test-negative case-control study of 462 lab confirmed pandemic A/H1N1 (pH1N1) cases and 484 test-negative controls. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were derived using multivariate logistic regression. The analysis was repeated using propensity matching. A sensitivity analysis was conducted to quantify the impact of a hypothetical unmeasured confounder. Cases were more likely to have received the seasonal influenza vaccine after adjusting for multiple confounders using multivariate regression (OR 1.82, 95% CI: 1.25-2.65), using propensity matching (OR 1.86, 95% CI: 1.19-2.92) and in subsequent sensitivity analyses. An unmeasured confounder would need a prevalence of 20%, an odds ratio with the vaccine and pH1N1 of ≥3.5 and ≥3.0 (respectively) to result in a non-significant association. Using a prevalence of 40% the respective associations were 3.0 and 2.5. A significant positive association between the seasonal influenza vaccine and lab confirmed pH1N1 was observed after considering multiple confounders and using different methods for confounder adjustment. This was not likely explained by an unmeasured confounder given the prevalence and strength of association needed to result in a non-significant association.Vaccine 11/2011; 29(49):9194-200. · 3.77 Impact Factor -
Article: Self-reported pH1N1 influenza vaccination coverage for Ontario.
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ABSTRACT: BACKGROUND: In the fall of 2009, Canada undertook a mass vaccination campaign against pH1N1. This report provides an overview of self-reported pH1N1 vaccination coverage of the Ontario population, building on an existing random digit-dialling telephone survey, in which 9,010 Ontario adults participated. Based on the results, 34.5% of Ontario residents were vaccinated: 33.3% of adults aged 18 or older and 38.6% of children and adolescents younger than age 18. Respondents reporting high-risk chronic conditions were significantly more likely to report being vaccinated than were people who did not report such conditions. Determining vaccination uptake for the Ontario population is important in the evaluation of the province's pH1N1 prevention program.Health reports / Statistics Canada, Canadian Centre for Health Information = Rapports sur la santé / Statistique Canada, Centre canadien d'information sur la santé 09/2011; 22(3):29-33. · 3.26 Impact Factor -
Article: Obesity and respiratory hospitalizations during influenza seasons in Ontario, Canada: a cohort study.
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ABSTRACT: Previous studies suggest that obesity may be a risk factor for complications from pandemic influenza A(H1N1) infection. We aimed to examine the association between obesity and respiratory hospitalizations during seasonal influenza epidemics and to determine the extent of this association among individuals without established risk factors for serious complications due to influenza infection. We conducted a cohort study over 12 influenza seasons (1996-1997 through 2007-2008) of 82545 respondents to population health surveys in Ontario, Canada. We included individuals aged 18-64 years who had responded to a survey within 5 years prior to the start of an influenza season. We used logistic regression to examine the association between self-reported body mass index (BMI) and hospitalization for selected respiratory diseases (pneumonia and influenza, acute respiratory diseases, and chronic lung diseases), both in the entire cohort and stratified by chronic condition status. Obese class I (BMI, 30-34.9) (odds ratio [OR], 1.45 [95% confidence interval {CI}, 1.03-2.05]) and obese class II or III (BMI, ≥35) individuals (OR, 2.12 [95% CI, 1.45-3.10]) were more likely than normal weight individuals to have a respiratory hospitalization during influenza seasons. Among obese class II or III individuals, the association was present both for those without previously identified risk factors (OR, 5.10 [95% CI, 2.53-10.24]) and for those with 1 risk factor (OR, 2.11 [95% CI, 1.10-4.06]). Severely obese individuals with and without chronic conditions are at increased risk for respiratory hospitalizations during influenza seasons. They should be considered a priority group for preventive influenza measures, such as vaccination and treatment with antiviral medications.Clinical Infectious Diseases 09/2011; 53(5):413-21. · 9.15 Impact Factor -
Article: A population-based risk algorithm for the development of diabetes: development and validation of the Diabetes Population Risk Tool (DPoRT).
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ABSTRACT: National estimates of the upcoming diabetes epidemic are needed to understand the distribution of diabetes risk in the population and to inform health policy. To create and validate a population-based risk prediction tool for incident diabetes using commonly collected national survey data. With the use of a cohort design that links baseline risk factors to a validated population-based diabetes registry, a model (Diabetes Population Risk Tool (DPoRT)) was developed to predict 9-year risk for diabetes. The probability of developing diabetes was modelled using sex-specific Weibull survival functions for people > 20 years of age without diabetes (N=19,861). The model was validated in two external cohorts in Ontario (N=26,465) and Manitoba (N=9899). Predictive accuracy and model performance were assessed by comparing observed diabetes rates with predicted estimates. Discrimination and calibration were measured using a C statistic and Hosmer-Lemeshow χ² statistic (χ²(H-L)). Predictive factors included were body mass index, age, ethnicity, hypertension, immigrant status, smoking, education status and heart disease. DPoRT showed good discrimination (C=0.77-0.80) and calibration (χ²(H-L) < 20) in both external validation cohorts. This algorithm can be used to estimate diabetes incidence and quantify the effect of interventions using routinely collected survey data.Journal of epidemiology and community health 07/2011; 65(7):613-20. · 3.04 Impact Factor -
Article: Assessing secondary attack rates among household contacts at the beginning of the influenza A (H1N1) pandemic in Ontario, Canada, April-June 2009: a prospective, observational study.
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ABSTRACT: Understanding transmission dynamics of the pandemic influenza A (H1N1) virus in various exposure settings and determining whether transmissibility differed from seasonal influenza viruses was a priority for decision making on mitigation strategies at the beginning of the pandemic. The objective of this study was to estimate household secondary attack rates for pandemic influenza in a susceptible population where control measures had yet to be implemented. All Ontario local health units were invited to participate; seven health units volunteered. For all laboratory-confirmed cases reported between April 24 and June 18, 2009, participating health units performed contact tracing to detect secondary cases among household contacts. In total, 87 cases and 266 household contacts were included in this study. Secondary cases were defined as any household member with new onset of acute respiratory illness (fever or two or more respiratory symptoms) or influenza-like illness (fever plus one additional respiratory symptom). Attack rates were estimated using both case definitions. Secondary attack rates were estimated at 10.3% (95% CI 6.8-14.7) for secondary cases with influenza-like illness and 20.2% (95% CI 15.4-25.6) for secondary cases with acute respiratory illness. For both case definitions, attack rates were significantly higher in children under 16 years than adults (25.4% and 42.4% compared to 7.6% and 17.2%). The median time between symptom onset in the primary case and the secondary case was estimated at 3.0 days. Secondary attack rates for pandemic influenza A (H1N1) were comparable to seasonal influenza estimates suggesting similarities in transmission. High secondary attack rates in children provide additional support for increased susceptibility to infection.BMC Public Health 01/2011; 11:234. · 2.00 Impact Factor -
Article: Influenza vaccination and all-cause mortality in community-dwelling elderly in Ontario, Canada, a cohort study.
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ABSTRACT: The objective of this study was to evaluate the effectiveness of influenza vaccines in reducing all-cause mortality among community-dwelling elderly. We included 25,922 Ontario residents over age 65 who responded to population health surveys. After full adjustment, influenza vaccination was associated with a statistically significant reduction in all-cause mortality during influenza seasons (hazard ratio (HR)=0.61; 95% CI 0.47-0.79). Contrary to expectations, statistically significant associations between influenza vaccination and mortality were also observed during periods preceding (HR=0.55; 95% CI 0.40-0.75) and following (HR=0.74; 95% CI 0.59-0.94) influenza seasons, indicating the presence of residual confounding. Adjustment for functional status indicators, excluding individuals with high one-year predicted mortality at baseline, and moving the start date of follow-up failed to eliminate the refractory confounding. Since observational studies are prone to bias, future efforts to estimate vaccine effectiveness in the elderly should strive to minimize bias through improved data quality, novel data sources, and/or new analytical techniques.Vaccine 10/2010; 29(2):240-6. · 3.77 Impact Factor -
Article: Commentary: assessing population (baseline) risk is a cornerstone of population health planning--looking forward to address new challenges.
International Journal of Epidemiology 04/2010; 39(2):380-2. · 6.41 Impact Factor -
Article: School-based influenza vaccine delivery, vaccination rates, and healthcare use in the context of a universal influenza immunization program: an ecological study.
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ABSTRACT: Influenza vaccines are universally funded in Ontario, Canada. Some public health units (PHUs) vaccinate children in schools. We examined the impact of school-based delivery on vaccination rates and healthcare use of the entire population over seven influenza seasons (2000-2007) using population-based survey and health administrative data. School-based vaccination was associated with higher vaccination rates in school-age children only. Doctors' office visits were lower for PHUs with school-based vaccination for children aged 12-19 but not for other age groups. Emergency department use and hospitalizations were similar between the two groups. In the context of universal influenza vaccination, school-based delivery is associated with higher vaccination rates and modest reductions in healthcare use in school-age children.Vaccine 03/2010; 28(15):2722-9. · 3.77 Impact Factor -
Article: Seasonal vaccine effectiveness against pandemic A/H1N1.
The Lancet 03/2010; 375(9717):801-2; author reply 802-3. · 38.28 Impact Factor -
Article: Importance of accurately identifying disease in studies using electronic health records.
BMJ (Clinical research ed.). 01/2010; 341:c4226. -
Article: The effectiveness and efficiency of diabetes screening in Ontario, Canada: a population-based cohort study.
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ABSTRACT: Little is known about the efficiency and effectiveness of the current level of diabetes screening activity in Ontario where there is universal access to health services. Our study aims were to: (i) determine how often Ontarians are screened for diabetes; (ii) estimate screening efficiency based on the number needed to screen (NNS) to diagnosis one diabetes case; (iii) examine the population effectiveness of screening as estimated by the number of undiagnosed diabetes cases. Ontario respondents of the Canadian Community Health Survey who agreed to have their responses linked to health care data (n = 37,400) provided the cohort. The five-year probabilities of glucose testing and diabetes diagnoses were estimated using a Cox Proportional Hazards Model. We defined NNS as the ratio of diabetes tests to number of diabetes diagnoses over the study period. We estimated the number of undiagnosed diabetes by dividing the number not tested at the end of study period by the NNS. 80% of women and 66% of men had a blood glucose test within 5 years. The efficiency of screening was estimated by a NNS of 14 among men and 22 among women. 127,100 cases of undiagnosed diabetes were estimated, representing 1.4% of the Ontario adult population. Increasing age, hypertension, immigrant and non-white ethnicity, and number of general practitioner visits were associated with an increased likelihood of having a glucose test (LR chi2 p < 0.001). Low income men were less likely to be tested. Diabetes screening was high in this population-based cohort of Ontarians. Screening efficiency varied considerably in the population. Undiagnosed diabetes continues to be prevalent and remains concentrated in the highest risk groups for diabetes, especially among men.BMC Public Health 01/2010; 10:506. · 2.00 Impact Factor
Top co-authors
Top Journals
Institutions
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2013
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University of Toronto
Toronto, Ontario, Canada
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2011–2012
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Public Health Ontario
Toronto, Ontario, Canada -
Institute for Clinical Evaluative Sciences
Toronto, Ontario, Canada
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2010
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Ottawa Hospital Research Institute
Ottawa, Ontario, Canada -
BC Centre for Disease Control
Vancouver, British Columbia, Canada
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