Personalizing Breast Cancer Screening Based on Polygenic Risk and Family History.

BACKGROUND: We assessed the clinical utility of a first-degree breast cancer family history and polygenic risk score (PRS) to inform screening decisions among women aged 30-50 years. METHODS: Two established breast cancer models evaluated digital mammography screening strategies in the 1985 US birth cohort by risk groups defined by family history and PRS based on 313 single nucleotide polymorphisms. Strategies varied in initiation age (30, 35, 40, 45, and 50 years) and interval (annual, hybrid, biennial, triennial). The benefits (breast cancer deaths averted, life-years gained) and harms (false-positive mammograms, overdiagnoses) were compared with those seen with 3 established screening guidelines. RESULTS: Women with a breast cancer family history who initiated biennial screening at age 40 years (vs 50 years) had a 36% (model range = 29%-40%) increase in life-years gained and 20% (model range = 16%-24%) more breast cancer deaths averted, but 21% (model range = 17%-23%) more overdiagnoses and 63% (model range = 62%-64%) more false positives. Screening tailored to PRS vs biennial screening from 50 to 74 years had smaller positive effects on life-years gained (20%) and breast cancer deaths averted (11%) but also smaller increases in overdiagnoses (10%) and false positives (26%). Combined use of family history and PRS vs biennial screening from 50 to 74 years had the greatest increase in life-years gained (29%) and breast cancer deaths averted (18%). CONCLUSIONS: Our results suggest that breast cancer family history and PRS could guide screening decisions before age 50 years among women at increased risk for breast cancer but expected increases in overdiagnoses and false positives should be expected.

Biennial lung cancer screening in Canada with smoking cessation-outcomes and cost-effectiveness.

BACKGROUND: Guidelines recommend low-dose CT (LDCT) screening to detect lung cancer among eligible at-risk individuals. We used the OncoSim model (formerly Cancer Risk Management Model) to compare outcomes and costs between annual and biennial LDCT screening. METHODS: OncoSim incorporates vital statistics, cancer registry data, health survey and utility data, cost, and other data, and simulates individual lives, aggregating outcomes over millions of individuals. Using OncoSim and National Lung Screening Trial eligibility criteria (age 55-74, minimum 30 pack-year smoking history, smoking cessation less than 15 years from time of first screen) and data, we have modeled screening parameters, cancer stage distribution, and mortality shifts for screen diagnosed cancer. Costs (in 2008 Canadian dollars) and quality of life years gained are discounted at 3% annually. RESULTS: Compared with annual LDCT screening, biennial screening used fewer resources, gained fewer life-years (61,000 vs. 77,000), but resulted in very similar quality-adjusted life-years (QALYs) (24,000 vs. 23,000) over 20 years. The incremental cost-effectiveness ratio (ICER) of annual compared with biennial screening was $54,000-$4.8 million/QALY gained. Average incremental CT scan use in biennial screening was 52% of that in annual screening. A smoking cessation intervention decreased the average cost-effectiveness ratio in most scenarios by half. CONCLUSIONS: Over 20 years, biennial LDCT screening for lung cancer appears to provide similar benefit in terms of QALYs gained to annual screening and is more cost-effective. Further study of biennial screening should be undertaken in population screening programs. A smoking cessation program should be integrated into either screening strategy.

Effects of Reductions in Body Mass Index on the Future Osteoarthritis Burden in Canada: A Population-Based Microsimulation Study.

OBJECTIVE: Osteoarthritis (OA) is the most common joint disease and a major cause of disability. Incidence and prevalence of OA are expected to increase due to population aging and increased levels of obesity. The purpose of this study was to project the effect of hypothetical interventions that change the distribution of body mass index (BMI) on OA burden in Canada. METHODS: We used a microsimulation computer model of OA based on the Population Health Model platform. The model used demographic predictions for Canada and population data from an administrative database in British Columbia and national Canadian surveys. RESULTS: Under the base-case scenario, between 2010 and 2030, OA prevalence is expected to increase from 11.5% to 15.6% in men and 16.3% to 21.1% in women. In scenarios assuming, on average, a 0.3-, 0.5-, or 1-unit drop in BMI per year, OA prevalence in 2030 would reach 14.9%, 14.6%, and 14.2% in men and 20.3%, 19.7%, and 18.5%, in women, respectively. Under these scenarios, the proportion of new cases prevented would be 9.5%, 13.2%, and 16.7%, respectively, in men, and 9.1%, 15.2%, and 25.0% in women. Targeting only those people ages ≥50 years for weight reduction would achieve approximately 70% of the impact of a full population strategy. Targeting only the obese (BMI ≥30) would likely result in a larger benefit for men than women. CONCLUSION: Due to the aging of the population, OA will remain a major and growing health issue in Canada over the next 2 decades, regardless of the course of the obesity epidemic.

The Population Health Model (POHEM): an overview of rationale, methods and applications.

The POpulation HEalth Model (POHEM) is a health microsimulation model that was developed at Statistics Canada in the early 1990s. POHEM draws together rich multivariate data from a wide range of sources to simulate the lifecycle of the Canadian population, specifically focusing on aspects of health. The model dynamically simulates individuals' disease states, risk factors, and health determinants, in order to describe and project health outcomes, including disease incidence, prevalence, life expectancy, health-adjusted life expectancy, quality of life, and healthcare costs. Additionally, POHEM was conceptualized and built with the ability to assess the impact of policy and program interventions, not limited to those taking place in the healthcare system, on the health status of Canadians. Internationally, POHEM and other microsimulation models have been used to inform clinical guidelines and health policies in relation to complex health and health system problems. This paper provides a high-level overview of the rationale, methodology, and applications of POHEM. Applications of POHEM to cardiovascular disease, physical activity, cancer, osteoarthritis, and neurological diseases are highlighted.

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 $52,000/QALY. If smoking history is modeled for 20 or 40 pack-years, incremental cost-effectiveness ratios of CaD $62,000 and CaD $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 $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.

Performance of the cancer risk management model lung cancer screening module.

BACKGROUND: The National Lung Screening Trial (NLST) demonstrated that low-dose computed tomography (LDCT) screening reduces lung cancer mortality in a high-risk U.S. population. A microsimulation model of LDCT screening was developed to estimate the impact of introducing population-based screening in Canada. DATA AND METHODS: LDCT screening was simulated using the lung cancer module of the Cancer Risk Management Model (CRMM-LC), which generates large, representative samples of the Canadian population from which a cohort with characteristics similar to NLST participants was selected. Screening parameters were estimated for stage shift, LDCT sensitivity and specificity, lead time, and survival to fit to NLST incidence and mortality results. The estimation process was a step-wise directed search. RESULTS: Simulated mortality reduction from LDCT screening was 23% in the CRMM-LC, compared with 20% in the NLST. The difference in the number of lung cancer cases over six years varied by, at most, 2.3% in the screen arm. The difference in cumulative incidence at six years was less than 2% in both screen and control arms. The estimated percentage over-diagnosed was 24.8%, which was 6% higher than NLST results. INTERPRETATION: Simulated screening reproduces NLST results. The CRMM-LC can evaluate a variety of population-based screening strategies. Sensitivity analyses are recommended to provide a range of projections to reflect model uncertainty.

Canadian Cancer Risk Management Model: evaluation of cancer control.

OBJECTIVES: The aim of this study was to develop a decision support tool to assess the potential benefits and costs of new healthcare interventions. METHODS: The Canadian Partnership Against Cancer (CPAC) commissioned the development of a Cancer Risk Management Model (CRMM)--a computer microsimulation model that simulates individual lives one at a time, from birth to death, taking account of Canadian demographic and labor force characteristics, risk factor exposures, and health histories. Information from all the simulated lives is combined to produce aggregate measures of health outcomes for the population or for particular subpopulations. RESULTS: The CRMM can project the population health and economic impacts of cancer control programs in Canada and the impacts of major risk factors, cancer prevention, and screening programs and new cancer treatments on population health and costs to the healthcare system. It estimates both the direct costs of medical care, as well as lost earnings and impacts on tax revenues. The lung and colorectal modules are available through the CPAC Web site (www.cancerview.ca/cancerrriskmanagement) to registered users where structured scenarios can be explored for their projected impacts. Advanced users will be able to specify new scenarios or change existing modules by varying input parameters or by accessing open source code. Model development is now being extended to cervical and breast cancers.

Making sense of health rankings.

In an era of increasingly complex medical care and escalating costs, healthcare decision-makers often rely on a broad range of indicators to gauge the health of a population, the quality of hospital care and the performance of healthcare systems. Reports that rank the health of Canadians and Canada's healthcare systems according to these indicators are widely cited in the media. These reports attempt to condense a complicated array of statistics into a relatively simple number, a rank that is used to make international and provincial comparisons. These reports have often been inconsistent. Unlike a familiar economic indicator - the gross domestic product (GDP), which represents a complex entity with a single number calculated according to an internationally agreed-upon methodology - rankings of health and healthcare are not yet standardized or well understood. This article aims to improve readers' understanding of ranking reports. It outlines the components and processes that underlie health rankings and explores why such rankings can be difficult to interpret.

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

BACKGROUND: Computer 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. METHODS: We 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. RESULTS: Evidence 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. CONCLUSION: As 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.

Income disparities in health-adjusted life expectancy for Canadian adults, 1991 to 2001.

BACKGROUND: Health-adjusted life expectancy is a summary measure of population health that combines mortality and morbidity data into a single index. This article profiles differences in health-adjusted life expectancy across income categories for a representative sample of the Canadian population. DATA AND METHODS: Mortality data were obtained from the 1991-2001 Canadian census mortality follow-up study, which linked a 15% sample of the 1991 adult non-institutional population with 11 years of death records from the Canadian Mortality Data Base. Information on morbidity was obtained from the Health Utilities Index Mark 3 instrument on the 2000/2001 Canadian Community Health Survey. The Sullivan method was used to compute health-adjusted life expectancy for national deciles of population ranked by income. MAIN RESULTS: For both sexes, and with few exceptions, a nearly linear gradient across income deciles emerged for health-adjusted life expectancy at age 25. Compared with people in higher-income deciles, those in lower-income deciles had fewer years of health-adjusted life expectancy. These disparities were substantially larger than those revealed by life expectancy alone. INTERPRETATION: These findings highlight the generally worse health-related quality of life of lower-income groups. The results demonstrate that assessments of socio-economic disparities in health should include the effects of both mortality and morbidity.

Income inequality and mortality: a multilevel prospective study of 521 248 individuals in 50 US states.

BACKGROUND: Some of the most consistent evidence in favour of an association between income inequality and health has been among US states. However, in multilevel studies of mortality, only two out of five studies have reported a positive relationship with income inequality after adjustment for the compositional characteristics of the state's inhabitants. In this study, we attempt to clarify these mixed results by analysing the relationship within age-sex groups and by applying a previously unused analytical method to a database that contains more deaths than any multilevel study to date. METHODS: The US National Longitudinal Mortality Study (NLMS) was used to model the relationship between income inequality in US states and mortality using both a novel and previously used methodologies that fall into the general framework of multilevel regression. We adjust age-sex specific models for nine socioeconomic and demographic variables at the individual level and percentage black and region at the state level. RESULTS: The preponderance of evidence from this study suggests that 1990 state-level income inequality is associated with a 40% differential in state level mortality rates (95% CI = 26-56%) for men 25-64 years and a 14% (95% CI = 3-27%) differential for women 25-64 years after adjustment for compositional factors. No such relationship was found for men or women over 65. CONCLUSIONS: The relationship between income inequality and mortality is only robust to adjustment for compositional factors in men and women under 65. This explains why income inequality is not a major driver of mortality trends in the United States because most deaths occur at ages 65 and over. This analysis does suggest, however, the certain causes of death that occur primarily in the population under 65 may be associated with income inequality. Comparison of analytical techniques also suggests coefficients for income inequality in previous multilevel mortality studies may be biased, but further research is needed to provide a definitive answer.

Relation between income inequality and mortality in Canada and in the United States: cross sectional assessment using census data and vital statistics

Relation between income inequality and mortality in Canada and in the United States: cross sectional assessment using census data and vital statistics. To compare the relation between Mortality and income inequality in Canada with out in the United States.