Assessing Breast Cancer Screening and Outcomes Among First Nations Women in Alberta.

BACKGROUND: Breast cancer (BC) incidence rates for First Nations (FN) women in Canada have been steadily increasing and are often diagnosed at a later stage. Despite efforts to expand the reach of BC screening programs for FN populations in Alberta (AB), gaps in screening and outcomes exist. METHODS: Existing population-based administrative databases including the AB BC Screening Program, the AB Cancer Registry, and an AB-specific FN registry data were linked to evaluate BC screening participation, detection, and timeliness of outcomes in this retrospective study. Tests of proportions and trends compared the findings between FN and non-FN women, aged 50-74 years, beginning in 2008. Incorporation of FN principles of ownership, control, access, and possession (OCAP®) managed respectful sharing and utilization of FN data and findings. RESULTS: The average age-standardized participation (2013-8) and retention rates (2015-6) for FN women compared to non-FN women in AB were 23.8% (P < .0001) and 10.3% (P = .059) lower per year, respectively. FN women were diagnosed with an invasive cancer more often in Stage II (P-value = .02). Following 90% completion of diagnostic assessments, it took 2-4 weeks longer for FN women to receive their first diagnosis as well as definitive diagnoses than non-FN women. CONCLUSION: Collectively, these findings suggest that access to and provision of screening services for FN women may not be equitable and may contribute to higher BC incidence and mortality rates. Collaborations between FN groups and screening programs are needed to eliminate these inequities to prevent more cancers in FN women.

Individual-level models of disease transmission incorporating piecewise spatial risk functions.

Modelling epidemics is crucial for understanding the emergence, transmission, impact and control of diseases. Spatial individual-level models (ILMs) that account for population heterogeneity are a useful tool, accounting for factors such as location, vaccination status and genetic information. Parametric forms for spatial risk functions, or kernels, are often used, but rely on strong assumptions about underlying transmission mechanisms. Here, we propose a class of non-parametric spatial disease transmission model, fitted within a Bayesian Markov chain Monte Carlo (MCMC) framework, allowing for more flexible assumptions when estimating the effect on spatial distance and infection risk. We focus upon two specific forms of non-parametric spatial infection kernel: piecewise constant and piecewise linear. Although these are relatively simple forms, we find them to produce results in line with, or superior to, parametric spatial ILMs. The performance of these models is examined using simulated data, including under circumstances of model misspecification, and then applied to data from the UK 2001 foot-and-mouth disease.