Extent and socioeconomic correlates of small area variations in life expectancy in Canada and the United States.

Background: An extensive literature documents substantial variations in life expectancy (LE) between countries and at various levels of subnational geography. These variations in LE are significantly correlated with socioeconomic covariates, though no analyses have been produced at the finest feasible census tract (CT) level of geographic disaggregation in Canada or designed to compare Canada with the United States. Data and methods: Abridged life tables for each CT where robust estimates were feasible were estimated comparably with U.S. data. Cross-tabulations and graphical visualizations are used to explore patterns of LE across Canada, for Canada's 15 largest cities, and for the 6 largest U.S. cities. Results: LE varies by as much as two decades across CTs in both countries' largest cities. There are notable differences in the strength of associations with socioeconomic status (SES) factors across Canada's largest cities, though these associations with income-poverty rates are noticeably weaker for Canada's largest cities than for the United States' largest cities. Interpretation: Small area geographic variations in LE signal major health inequalities. The association of CT-level LE with SES factors supports and extends similar findings across many studies. The variability in these associations within Canada and compared with those in the United States reinforces the importance for population health of better understanding differences in social structures and public policies not only at the national and provincial or state levels, but also within municipalities to better inform interventions to ameliorate health inequalities.

Regulating cancer risk prediction: legal considerations and stakeholder perspectives on the Canadian context.

Risk prediction models hold great promise to reduce the impact of cancer in society through advanced warning of risk and improved preventative modalities. These models are evolving and becoming more complex, increasingly integrating genetic screening data and polygenic risk scores as well as calculating risk for multiple types of a disease. However, unclear regulatory compliance requirements applicable to these models raise significant legal uncertainty and new questions about the regulation of medical devices. This paper aims to address these novel regulatory questions by presenting an initial assessment of the legal status likely applicable to risk prediction models in Canada, using the CanRisk tool for breast and ovarian cancer as an exemplar. Legal analysis is supplemented with qualitative perspectives from expert stakeholders regarding the accessibility and compliance challenges of the Canadian regulatory framework. While the paper focuses on the Canadian context, it also refers to European and U.S. regulations in this domain to contrast them. Legal analysis and stakeholder perspectives highlight the need to clarify and update the Canadian regulatory framework for Software as a Medical Device as it applies to risk prediction models. Findings demonstrate how normative guidance perceived as convoluted, contradictory or overly burdensome can discourage innovation, compliance, and ultimately, implementation. This contribution aims to initiate discussion about a more optimal legal framework for risk prediction models as they continue to evolve and are increasingly integrated into landscape for public health.

Potential of polygenic risk scores for improving population estimates of women's breast cancer genetic risks.

PURPOSE: Breast cancer risk has conventionally been assessed using family history (FH) and rare high/moderate penetrance pathogenic variants (PVs), notably in BRCA1/2, and more recently PALB2, CHEK2, and ATM. In addition to these PVs, it is now possible to use increasingly predictive polygenic risk scores (PRS) as well. The comparative population-level predictive capability of these three different indicators of genetic risk for risk stratification is, however, unknown. METHODS: The Canadian heritable breast cancer risk distribution was estimated using a novel genetic mixing model (GMM). A realistically representative sample of women was synthesized based on empirically observed demographic patterns for appropriately correlated family history, inheritance of rare PVs, PRS, and residual risk from an unknown polygenotype. Risk assessment was simulated using the BOADICEA risk algorithm for 10-year absolute breast cancer incidence, and compared to heritable risks as if the overall polygene, including its measured PRS component, and PV risks were fully known. RESULTS: Generally, the PRS was most predictive for identifying women at high risk, while family history was the weakest. Only the PRS identified any women at low risk of breast cancer. CONCLUSION: PRS information would be the most important advance in enabling effective risk stratification for population-wide breast cancer screening.

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.

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.

Publicly financed healthcare and income inequality in Canada.

Income inequality is currently the focus of considerable public and policy attention. Public services such as healthcare and education play a role in reducing income inequality in the population. This study looks at how healthcare affects the distribution of income across five income groups. Specifically, it estimates the tax contributions and the value of benefits received from physician services, drugs and hospital services over a person's lifetime. We found that benefits received from publicly funded healthcare in Canada reduce the income gap between the highest- and lowest-income groups by 16%. This analysis provides a starting point for future research to explore the distributional effects of different options for financing healthcare.

State-level association between income inequality and mortality in the USA, 1989-2019: ecological study.

BACKGROUND: Prior studies have shown a positive relationship between income inequality and population-level mortality. This study investigates whether the relationship between US state-level income inequality and all-cause mortality persisted from 1989 to 2019 and whether changes in income inequality were correlated with changes in mortality rates. METHODS: We perform repeated cross-sectional regressions of mortality on state-level inequality measures (Gini coefficients) at 10-year intervals. We also estimate the correlation between within-state changes in income inequality and changes in mortality rates using two time-series models, one with state- and year-fixed effects and one with a lagged dependent variable. Our primary regressions control for median income and are weighted by population. MAIN OUTCOME MEASURES: The two primary outcomes are male and female age-adjusted mortality rates for the working-age (25-64) population in each state. The secondary outcome is all-age mortality. RESULTS: There is a strong positive correlation between Gini and mortality in 1989. A 0.01 increase in Gini is associated with more deaths: 9.6/100 000 (95% CI 5.7, 13.5, p<0.01) for working-age females and 29.1 (21.2, 36.9, p<0.01) for working-age males. This correlation disappears or reverses by 2019 when a 0.01 increase in Gini is associated with fewer deaths: -6.7 (-12.2, -1.2, p<0.05) for working-age females and -6.2 (-15.5, 3.1, p>0.1) for working-age males. The correlation between the change in Gini and change in mortality is also negative for all outcomes using either time-series method. These results are generally robust for a range of income inequality measures. CONCLUSION: The absence or reversal of correlation after 1989 and the presence of an inverse correlation between change in inequality and change in all-cause mortality represents a significant reversal from the findings of a number of other studies. It also raises questions about the conditions under which income inequality may be an important policy target for improving population health.

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.

Income inequality and population health: a political-economic research agenda.

There is more than 30 years of research on relationships between income inequality and population health. In this article, we propose a research agenda with five recommendations for future research to refine existing knowledge and examine new questions. First, we recommend that future research prioritise analyses with broader time horizons, exploring multiple temporal aspects of the relationship. Second, we recommend expanding research on the effect of public expenditures on the inequality-health relationship. Third, we introduce a new area of inquiry focused on interactions between social mobility, income inequality and population health. Fourth, we argue the need to examine new perspectives on 21st century capitalism, specifically the population health impacts of inequality in income from capital (especially housing), in contrast to inequality in income from labour. Finally, we propose that this research broaden beyond all-cause mortality, to cause-specific mortality, avoidable mortality and subcategories thereof. We believe that such a research agenda is important for policy to respond to the changes following the COVID-19 pandemic.

Using Real-World Data to Determine Health System Costs of Ontario Women Screened for Breast Cancer.

Our study was to determine breast cancer screening costs in Ontario, Canada for screenings conducted through a formal (Ontario Breast Screening Program, OBSP) and informal (non-OBSP) screening program using administrative databases. Included women were 49-74 years of age when receiving screening mammograms between 1 January 2013 to 31 December 2019. Each woman was followed for a screening episode with screening and diagnostic components, and costs were calculated as an average cost per woman per month in 2021 Canadian dollars. The final cohort of 1,546,386 women screened had a mean age of 59.4 ± 7.1 years and ~87% were screened via OBSP. The average total cost per woman per month was $136 ± $103, $134 ± $103 and $155 ± $104 for the entire, OBSP and non-OBSP cohorts, respectively. This was further disaggregated into the average total screening cost per month, which was $103 ± $8, $100 ± $4 and $117 ± $9 per woman, and the average total diagnostic cost per woman per month at $219 ± $166, $228 ± $165 and $178 ± $159. for the entire, OBSP and non-OBSP cohorts, respectively. These results indicate similar screening costs across the different cohorts, but higher diagnostic costs for the OBSP cohort.

Should Age-Dependent Absolute Risk Thresholds Be Used for Risk Stratification in Risk-Stratified Breast Cancer Screening?

In risk-stratified cancer screening, multiple risk factors are incorporated into the risk assessment. An individual's estimated absolute cancer risk is linked to risk categories with tailored screening recommendations for each risk category. Absolute risk, expressed as either remaining lifetime risk or shorter-term (five- or ten-year) risk, is estimated from the age at assessment. These risk estimates vary by age; however, some clinical guidelines (e.g., enhanced breast cancer surveillance guidelines) and ongoing personalised breast screening trials, stratify women based on absolute risk thresholds that do not vary by age. We examine an alternative approach in which the risk thresholds used for risk stratification vary by age and consider the implications of using age-independent risk thresholds on risk stratification. We demonstrate that using an age-independent remaining lifetime risk threshold approach could identify high-risk younger women but would miss high-risk older women, whereas an age-independent 5-year or 10-year absolute risk threshold could miss high-risk younger women and classify lower-risk older women as high risk. With risk misclassification, women with an equivalent risk level would be offered a different screening plan. To mitigate these problems, age-dependent absolute risk thresholds should be used to inform risk stratification.

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.

Risk-Stratified Approach to Breast Cancer Screening in Canada: Women's Knowledge of the Legislative Context and Concerns about Discrimination from Genetic and Other Predictive Health Data.

The success of risk-stratified approaches in improving population-based breast cancer screening programs depends in no small part on women's buy-in. Fear of genetic discrimination (GD) could be a potential barrier to genetic testing uptake as part of risk assessment. Thus, the objective of this study was twofold. First, to evaluate Canadian women's knowledge of the legislative context governing GD. Second, to assess their concerns about the possible use of breast cancer risk levels by insurance companies or employers. We use a cross-sectional survey of 4293 (age: 30-69) women, conducted in four Canadian provinces (Alberta, British Colombia, Ontario and Québec). Canadian women's knowledge of the regulatory framework for GD is relatively limited, with some gaps and misconceptions noted. About a third (34.7%) of the participants had a lot of concerns about the use of their health information by employers or insurers; another third had some concerns (31.9%), while 20% had no concerns. There is a need to further educate and inform the Canadian public about GD and the legal protections that exist to prevent it. Enhanced knowledge could facilitate the implementation and uptake of risk prediction informed by genetic factors, such as the risk-stratified approach to breast cancer screening that includes risk levels.

Women's Views on Multifactorial Breast Cancer Risk Assessment and Risk-Stratified Screening: A Population-Based Survey from Four Provinces in Canada.

Risk-stratified screening for breast cancer (BC) is increasingly considered as a promising approach. However, its implementation is challenging and needs to be acceptable to women. We examined Canadian women's attitudes towards, comfort level about, and willingness to take part in BC risk-stratified screening. We conducted an online survey in women aged 30 to 69 years in four Canadian provinces. In total, 4293 women completed the questionnaire (response rate of 63%). The majority of women (63.5% to 72.8%) expressed favorable attitudes towards BC risk-stratified screening. Most women reported that they would be comfortable providing personal and genetic information for BC risk assessment (61.5% to 67.4%) and showed a willingness to have their BC risk assessed if offered (74.8%). Most women (85.9%) would also accept an increase in screening frequency if they were at higher risk, but fewer (49.3%) would accept a reduction in screening frequency if they were at lower risk. There were few differences by province; however, outcomes varied by age, education level, marital status, income, perceived risk, history of BC, prior mammography, and history of genetic test for BC (all p ≤ 0.01). Risk-based BC screening using multifactorial risk assessment appears to be acceptable to most women. This suggests that the implementation of this approach is likely to be well-supported by Canadian women.

Personalized Risk Assessment for Prevention and Early Detection of Breast Cancer: Integration and Implementation (PERSPECTIVE I&I).

Early detection of breast cancer through screening reduces breast cancer mortality. The benefits of screening must also be considered within the context of potential harms (e.g., false positives, overdiagnosis). Furthermore, while breast cancer risk is highly variable within the population, most screening programs use age to determine eligibility. A risk-based approach is expected to improve the benefit-harm ratio of breast cancer screening programs. The PERSPECTIVE I&I (Personalized Risk Assessment for Prevention and Early Detection of Breast Cancer: Integration and Implementation) project seeks to improve personalized risk assessment to allow for a cost-effective, population-based approach to risk-based screening and determine best practices for implementation in Canada. This commentary describes the four inter-related activities that comprise the PERSPECTIVE I&I project. 1: Identification and validation of novel moderate to high-risk susceptibility genes. 2: Improvement, validation, and adaptation of a risk prediction web-tool for the Canadian context. 3: Development and piloting of a socio-ethical framework to support implementation of risk-based breast cancer screening. 4: Economic analysis to optimize the implementation of risk-based screening. Risk-based screening and prevention is expected to benefit all women, empowering them to work with their healthcare provider to make informed decisions about screening and prevention.

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.