Small-area spatio-temporal analyses of bladder and kidney cancer risk in Nova Scotia, Canada

The Nova Scotia Community Cancer Matrix: A geospatial tool to support cancer prevention

Globally, cancer is a leading cause of death and morbidity and its burden is increasing worldwide. It is established that medical approaches alone will not solve this cancer crisis. Moreover, while cancer treatment can be effective, it is costly and access to treatment and health care is vastly inequitable. However, almost 50% of cancers are caused by potentially avoidable risk factors and are thus preventable. Cancer prevention represents the most cost-effective, feasible and sustainable pathway towards global cancer control. While much is known about cancer risk factors, prevention programs often lack consideration of how place impacts cancer risk over time. Maximizing cancer prevention investment requires an understanding of the geographic context for why some people develop cancer while others do not. Data on how community and individual level risk factors interact is therefore required. The Nova Scotia Community Cancer Matrix (NS-Matrix) study was established in Nova Scotia (NS), a small province in Eastern Canada with a population of 1 million. The study integrates small-area profiles of cancer incidence with cancer risk factors and socioeconomic conditions, to inform locally relevant and equitable cancer prevention strategies. The NS-Matrix Study includes over 99,000 incident cancers diagnosed in NS between 2001 and 2017, georeferenced to small-area communities. In this analysis we used Bayesian inference to identify communities with high and low risk for lung and bladder cancer: two highly preventable cancers with rates in NS exceeding the Canadian average, and for which key risk factors are high. We report significant spatial heterogeneity in lung and bladder cancer risk. The identification of spatial disparities relating to a community's socioeconomic profile and other spatially varying factors, such as environmental exposures, can inform prevention. Adopting Bayesian spatial analysis methods and utilizing high quality cancer registry data provides a model to support geographically-focused cancer prevention efforts, tailored to local community needs.

Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package

INTRODUCTION

There is an increasing interest in small area analyses in cancer surveillance; however, technical capacity is limited and accessible analytical approaches remain to be determined. This study demonstrates an accessible approach for small area cancer risk estimation using Bayesian hierarchical models and data visualization through the smallareamapp R package.

MATERIALS AND METHODS

Incident lung (N = 26,448), female breast (N = 28,466), cervical (N = 1,478), and colorectal (N = 25,457) cancers diagnosed among British Columbia (BC) residents between 2011 and 2018 were obtained from the BC Cancer Registry. Indirect age-standardization was used to derive age-adjusted expected counts and standardized incidence ratios (SIRs) relative to provincial rates. Moran's I was used to assess the strength and direction of spatial autocorrelation. A modified Besag, York and Mollie model (BYM2) was used for model incidence counts to calculate posterior median relative risks (RR) by Community Health Service Areas (CHSA; N = 218), adjusting for spatial dependencies. Integrated Nested Laplace Approximation (INLA) was used for Bayesian model implementation. Areas with exceedance probabilities (above a threshold RR = 1.1) greater or equal to 80% were considered to have an elevated risk. The posterior median and 95% credible intervals (CrI) for the spatially structured effect were reported. Predictive posterior checks were conducted through predictive integral transformation values and observed versus fitted values.

RESULTS

The proportion of variance in the RR explained by a spatial effect ranged from 4.4% (male colorectal) to 19.2% (female breast). Lung cancer showed the greatest number of CHSAs with elevated risk (Nwomen = 50/218, Nmen = 44/218), representing 2357 total excess cases. The largest lung cancer RRs were 1.67 (95% CrI = 1.06-2.50; exceedance probability = 96%; cases = 13) among women and 2.49 (95% CrI = 2.14-2.88; exceedance probability = 100%; cases = 174) among men. Areas with small population sizes and extreme SIRs were generally smoothed towards the null (RR = 1.0).

DISCUSSION

We present a ready-to-use approach for small area cancer risk estimation and disease mapping using BYM2 and exceedance probabilities. We developed the smallareamapp R package, which provides a user-friendly interface through an R-Shiny application, for epidemiologists and surveillance experts to examine geographic variation in risk. These methods and tools can be used to estimate risk, generate hypotheses, and examine ecologic associations while adjusting for spatial dependency.

Associations between Neighborhood Walkability, Physical Activity, and Chronic Disease in Nova Scotian Adults: An Atlantic PATH Cohort Study

Background: While neighborhood walkability has been shown to positively influence health behaviors, less is known about its impact on chronic disease. Our aim was to examine the association between walkability and self-reported physical activity in relation to chronic health conditions in an Atlantic Canadian population. Methods: Using data from the Atlantic Partnership for Tomorrow's Health, a prospective cohort study, we employed both a cross-sectional and a prospective analytical approach to investigate associations of walkability and physical activity with five prevalent chronic diseases and multimorbidity. Results: The cross-sectional data show that participants with the lowest neighborhood walkability were more likely to have reported a pre-existing history of cancer and depression and least likely to report chronic respiratory conditions. Participants with low physical activity were more likely to have a pre-existing history of diabetes, chronic respiratory disease, and multimorbidity. Follow-up analyses showed no significant associations between walkability and chronic disease incidence. Low levels of physical activity were significantly associated with diabetes, cancer and multimorbidity. Conclusions: Our data provides evidence for the health protective benefits of higher levels of physical activity, and a reduction in prevalence of some chronic diseases in more walkable communities.

Current State of Geospatial Methodologic Approaches in Canadian Population Oncology Research

Geospatial analyses are increasingly used in population oncology. We provide a first review of geospatial analysis in Canadian population oncology research, compare to international peers, and identify future directions. Geospatial-focused peer-reviewed publications from 1992-2020 were compiled using PubMed, MEDLINE, Web of Science, and Google Scholar. Abstracts were screened for data derived from a Canadian cancer registry and use of geographic information systems. Studies were classified by geospatial methodology, geospatial unit, location, cancer site, and study year. Common limitations were documented from article discussion sections. Our search identified 71 publications using data from all provincial and national cancer registries. Thirty-nine percent (N = 28) were published in the most recent 5-year period (2016-2020). Geospatial methodologies included exposure assessment (32.4%), identifying spatial associations (21.1%), proximity analysis (16.9%), cluster detection (15.5%), and descriptive mapping (14.1%). Common limitations included confounding, ecologic fallacy, not accounting for residential mobility, and small case/population sizes. Geospatial analyses are increasingly used in Canadian population oncology; however, efforts are concentrated among a few provinces and common cancer sites, and data are over a decade old. Limitations were similar to those documented internationally, and more work is needed to address them. Organized efforts are needed to identify common challenges, develop leading practices, and identify shared priorities.

Estimating the risk of bladder and kidney cancer from exposure to low-levels of arsenic in drinking water, Nova Scotia, Canada

Arsenic in drinking water impacts health. Highest levels of arsenic have been historically observed in Taiwan and Bangladesh but the contaminant has been affecting the health of people globally. Strong associations have been confirmed between exposure to high-levels of arsenic in drinking water and a wide range of diseases, including cancer. However, at lower levels of exposure, especially near the current World Health Organization regulatory limit (10μg/L), this association is inconsistent as the effects are mostly extrapolated from high exposure studies. This ecological study used Bayesian inference to model the relative risk of bladder and kidney cancer at these lower concentrations-0-2μg/L; 2-5μg/L and; ≥5μg/L of arsenic-in 864 bladder and 525 kidney cancers diagnosed in the study area, Nova Scotia, Canada between 1998 and 2010. The model included proxy measures of lifestyle (e.g. smoking) and accounted for spatial dependencies. Overall, bladder cancer risk was 16% (2-5μg/L) and 18% (≥5μg/L) greater than that of the referent group (<2μg/L), with posterior probabilities of 88% and 93% for these risks being above 1. Effect sizes for kidney cancer were 5% (2-5μg/L) and 14% (≥5μg/L) above that of the referent group (<2μg/L), with probabilities of 61% and 84%. High-risk areas were common in southwestern areas, where higher arsenic-levels are associated with the local geology. The study suggests an increased bladder cancer, and potentially kidney cancer, risk from exposure to drinking water arsenic-levels within the current the World Health Organization maximum acceptable concentration.