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. 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. 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. PRS information would be the most important advance in enabling effective risk stratification for population-wide breast cancer screening.

Background: Low-dose computed tomography (LDCT) screening has been shown to reduce mortality from lung cancer; however, the optimal screening duration and “at risk” population are not known. Methods: The Cancer Risk Management Model developed by Statistics Canada for the Canadian Partnership Against Cancer includes a lung screening module based on data from the U.S. National Lung Screening Trial (NLST). The base-case scenario reproduces NLST outcomes with high fidelity. The impact in Canada of annual screening on the number of incident cases and life-years gained, with a wider range of age and smoking history eligibility criteria and varied participation rates, was modelled to show the magnitude of clinical benefit nationally and by province. Life-years gained, costs (discounted and undiscounted), and resource requirements were also estimated. Results: In 2014, 1.4 million Canadians were eligible for screening according to NLST criteria. Over 10 years, screening would detect 12,500 more lung cancers than the expected 268,300 and would gain 9200 life-years. The computed tomography imaging requirement of 24,000–30,000 at program initiation would rise to between 87,000 and 113,000 by the 5th year of an annual NLST-like screening program. Costs would increase from approximately $75 million to $128 million at 10 years, and the cumulative cost nationally over 10 years would approach $1 billion, partially offset by a reduction in the costs of managing advanced lung cancer. Conclusions: Modelling various ways in which LDCT might be implemented provides decision-makers with estimates of the effect on clinical benefit and on resource needs that clinical trial results are unable to provide.

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, p0.1) 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.

Describing recent pandemic experiences modeling the disease. Throughout the pandemic, health officials and government leaders showed graphs of projected epidemic curves, and alternative curves depending on whether a particular intervention like physical distancing or school closures was implemented. Underlying these projections are computer simulation models. Xia et al. (2024) surveyed and characterized the variety of these models for six provinces where information was available. There was some informal information sharing among these modelers and regular crosscountry virtual conversations convened by the Public Health Agency of Canada (PHAC). Still, important lessons merit being more widely shared, for which this CJPH article provides an important starting point. As in all models, the veracity of the projections depends critically on the quality of the data on which they are based, and the methodologies applied. Canada is well endowed with infectious disease modeling expertise, primarily based in universities. However, the data needed to support these modeling efforts were too often limited. In many cases, the data did not exist; for example, at the start of the pandemic, accurate counts of new infections, hospitalizations, and deaths due to COVID, as well as the availability of ventilators and personal protective equipment (PPE) were unavailable.