William M. Flanagan

Trends in risk factors for cardiovascular disease in Canada: temporal, socio-demographic and geographic factors

Background: Temporal trends in risk factors for cardiovascular disease and the impact of socio-economic status on these risk factors remain unclear. Methods: Using data from the National Population Health Survey and the Canadian Community Health Survey, we examined national trends in heart disease, hypertension, diabetes mellitus, obesity and smoking prevalence from 1994 to 2005, adjusting for age and sex. We stratified data by income adequacy category, body mass index and region of residence. Results: An estimated 1.29 million Canadians reported having heart disease in 2005, representing increases of 19% for men and 2% for women, relative to 1994. Heart disease increased significantly in the lowest income category (by 27%), in the lower middle income category (by 37%) and in the upper middle income category (by 12%); however, it increased by only 6% in the highest income group. Diabetes increased in all but the highest income group: by 56% in the lowest income group, by 93% in the lower middle income group and by 59% in the upper middle income group. Hypertension increased in all income groups: by 85% in the lowest income group, by 80% in the lower middle income group, by 91% in the upper middle income group and by 117% in the highest income group. Obesity also increased in all income groups: by 20% in the lowest income group, by 25% in the lower middle income group, by 33% in the upper middle income group and by 37% in the highest income group. In addition to socio-economic status, obesity and overweight also modified the trends in risk factors. Diabetes increased to a greater extent among obese participants (61% increase) and overweight participants (25% increase), as did hypertension, which increased by 80% among obese individuals and by 74% among overweight individuals. Trends in diabetes, hypertension and obesity were consistent for all provinces. Interpretation: During the study period, heart disease, hypertension, diabetes and obesity increased for all or most income groups in Canada. Further interventions supporting modification of lifestyle and risk factors are needed to prevent future cardiovascular disease.

Validation of population-based disease simulation models: a review of concepts and methods

BackgroundComputer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models.MethodsWe developed a comprehensive framework for validating population-based chronic disease simulation models and used this framework in a review of published model validation guidelines. Based on the review, we formulated a set of recommendations for gathering evidence of model credibility.ResultsEvidence of model credibility derives from examining: 1) the process of model development, 2) the performance of a model, and 3) the quality of decisions based on the model. Many important issues in model validation are insufficiently addressed by current guidelines. These issues include a detailed evaluation of different data sources, graphical representation of models, computer programming, model calibration, between-model comparisons, sensitivity analysis, and predictive validity. The role of external data in model validation depends on the purpose of the model (e.g., decision analysis versus prediction). More research is needed on the methods of comparing the quality of decisions based on different models.ConclusionAs the role of simulation modeling in population health is increasing and models are becoming more complex, there is a need for further improvements in model validation methodology and common standards for evaluating model credibility.

Canada's Population Health Model (POHEM): a tool for performing economic evaluations of cancer control interventions

This paper describes the Population Health Model (POHEM) developed by Statistics Canada and shows its usefulness in the evaluation of cancer control interventions and policy decision-making. Models of the costs of diagnosis and treatment of lung and breast cancer were developed and incorporated into POHEM. Then, POHEM was used to evaluate the economic impact of chemotherapy for advanced non-small cell lung cancer; reduced length of hospital stay following breast cancer surgery; and the provision of preventive tamoxifen to women at high risk of breast cancer. A lung cancer chemotherapy treatment decision framework was developed to rank order currently available chemotherapy regimens according to relative cost-effectiveness and cost-utility. Reducing post-surgical breast cancer hospitalisation with optimal home care support could produce major healthcare savings. However, the provision of preventive tamoxifen was estimated to have no population health benefit. This paper demonstrates that POHEM is an effective tool for performing economic evaluations of cancer control interventions and to inform healthcare policy decisions.

Deriving utility scores for co-morbid conditions: a test of the multiplicative model for combining individual condition scores

BackgroundThe co-morbidity of health conditions is becoming a significant health issue, particularly as populations age, and presents important methodological challenges for population health research. For example, the calculation of summary measures of population health (SMPH) can be compromised if co-morbidity is not taken into account. One popular co-morbidity adjustment used in SMPH computations relies on a straightforward multiplicative combination of the severity weights for the individual conditions involved. While the convenience and simplicity of the multiplicative model are attractive, its appropriateness has yet to be formally tested. The primary objective of the current study was therefore to examine the empirical evidence in support of this approach.MethodsThe present study drew on information on the prevalence of chronic conditions and a utility-based measure of health-related quality of life (HRQoL), namely the Health Utilities Index Mark 3 (HUI3), available from Cycle 1.1 of the Canadian Community Health Survey (CCHS; 2000–01). Average HUI3 scores were computed for both single and co-morbid conditions, and were also purified by statistically removing the loss of functional health due to health problems other than the chronic conditions reported. The co-morbidity rule was specified as a multiplicative combination of the purified average observed HUI3 utility scores for the individual conditions involved, with the addition of a synergy coefficient s for capturing any interaction between the conditions not explained by the product of their utilities. The fit of the model to the purified average observed utilities for the co-morbid conditions was optimized using ordinary least squares regression to estimate s. Replicability of the results was assessed by applying the method to triple co-morbidities from the CCHS cycle 1.1 database, as well as to double and triple co-morbidities from cycle 2.1 of the CCHS (2003–04).ResultsModel fit was optimized at s = .99 (i.e., essentially a straightforward multiplicative model). These results were closely replicated with triple co-morbidities reported on CCHS 2000–01, as well as with double and triple co-morbidities reported on CCHS 2003–04.ConclusionThe findings support the simple multiplicative model for computing utilities for co-morbid conditions from the utilities for the individual conditions involved. Future work using a wider variety of conditions and data sources could serve to further evaluate and refine the approach.

Development of a population-based microsimulation model of osteoarthritis in Canada

OBJECTIVES The purpose of the study was to develop a population-based simulation model of osteoarthritis (OA) in Canada that can be used to quantify the future health and economic burden of OA under a range of scenarios for changes in the OA risk factors and treatments. In this article we describe the overall structure of the model, sources of data, derivation of key input parameters for the epidemiological component of the model, and preliminary validation studies. DESIGN We used the Population Health Model (POHEM) platform to develop a stochastic continuous-time microsimulation model of physician-diagnosed OA. Incidence rates were calibrated to agree with administrative data for the province of British Columbia, Canada. The effect of obesity on OA incidence and the impact of OA on health-related quality of life (HRQL) were modeled using Canadian national surveys. RESULTS Incidence rates of OA in the model increase approximately linearly with age in both sexes between the ages of 50 and 80 and plateau in the very old. In those aged 50+, the rates are substantially higher in women. At baseline, the prevalence of OA is 11.5%, 13.6% in women and 9.3% in men. The OA hazard ratios for obesity are 2.0 in women and 1.7 in men. The effect of OA diagnosis on HRQL, as measured by the Health Utilities Index Mark 3 (HUI3), is to reduce it by 0.10 in women and 0.14 in men. CONCLUSIONS We describe the development of the first population-based microsimulation model of OA. Strengths of this model include the use of large population databases to derive the key parameters and the application of modern microsimulation technology. Limitations of the model reflect the limitations of administrative and survey data and gaps in the epidemiological and HRQL literature.

Cost-effectiveness of Lung Cancer Screening in Canada

IMPORTANCE The US National Lung Screening Trial supports screening for lung cancer among smokers using low-dose computed tomographic (LDCT) scans. The cost-effectiveness of screening in a publically funded health care system remains a concern. OBJECTIVE To assess the cost-effectiveness of LDCT scan screening for lung cancer within the Canadian health care system. DESIGN, SETTING, AND PARTICIPANTS The Cancer Risk Management Model (CRMM) simulated individual lives within the Canadian population from 2014 to 2034, incorporating cancer risk, disease management, outcome, and cost data. Smokers and former smokers eligible for lung cancer screening (30 pack-year smoking history, ages 55-74 years, for the reference scenario) were modeled, and performance parameters were calibrated to the National Lung Screening Trial (NLST). The reference screening scenario assumes annual scans to age 75 years, 60% participation by 10 years, 70% adherence to screening, and unchanged smoking rates. The CRMM outputs are aggregated, and costs (2008 Canadian dollars) and life-years are discounted 3% annually. MAIN OUTCOMES AND MEASURES The incremental cost-effectiveness ratio. RESULTS Compared with no screening, the reference scenario saved 51,000 quality-adjusted life-years (QALY) and had an incremental cost-effectiveness ratio of CaD

Trends in physician-diagnosed osteoarthritis incidence in an administrative database in British Columbia, Canada, 1996-1997 through 2003-2004.

52,000/QALY. If smoking history is modeled for 20 or 40 pack-years, incremental cost-effectiveness ratios of CaD

Canadian cancer risk management model: Evaluation of cancer control

62,000 and CaD

First do no harm: extending the debate on the provision of preventive tamoxifen

43,000/QALY, respectively, were generated. Changes in participation rates altered life years saved but not the incremental cost-effectiveness ratio, while the incremental cost-effectiveness ratio is sensitive to changes in adherence. An adjunct smoking cessation program improving the quit rate by 22.5% improves the incremental cost-effectiveness ratio to CaD

Projections of preventable risks for cardiovascular disease in Canada to 2021: a microsimulation modelling approach

24,000/QALY. CONCLUSIONS AND RELEVANCE Lung cancer screening with LDCT appears cost-effective in the publicly funded Canadian health care system. An adjunct smoking cessation program has the potential to improve outcomes.