# Small-area spatio-temporal analysis of cancer risk to support effective and equitable cancer prevention

## TL;DR

This paper uses small-area mapping to show how cancer risk varies by community and socioeconomic status in Nova Scotia, aiming to guide equitable cancer prevention.

## Contribution

The study introduces a spatio-temporal Bayesian model to identify cancer risk disparities linked to socioeconomic status at the community level.

## Key findings

- Significant disparities in cancer risk were found across communities based on sex and socioeconomic status.
- Small-area mapping effectively identifies at-risk communities and highlights inequities tied to material and social capacity.
- The approach can be applied to other regions to improve cancer prevention and reduce health disparities.

## Abstract

Cancer is rapidly increasing worldwide and urgent global action towards cancer control is required. Consistent with global trends, Canada is expected to experience a near doubling in new cases and cancer deaths between 2020–2040; population growth and ageing being the primary drivers. The projected increased cancer incidence and its associated costs is expected to further exacerbate socioeconomic inequities. Focused actions to prevent cancer, to detect it earlier when more treatable, and, to lower the risk of recurrence, must be prioritized. Almost half of all cancers are preventable, caused by risk factors that are potentially avoidable and modifiable. Integrating cancer prevention with care-based models is necessary and represents the most cost-effective and sustainable approach to control cancer. To be effective, prevention efforts must consider the cancers impacting local populations and understand how community and individual factors interact within the spatial and temporal contexts in which people live. This study is part of the Nova Scotia Community Cancer Matrix project which profiles the cancers impacting communities over time; measuring associations between cancer and socioeconomic status (SES); and determining how the joint spatial distribution of cancers can be used to address inequities, identify priority populations and strengthen prevention efforts. Using Bayesian inference to model spatio-temporal variations in 58,206 cases diagnosed in 301 communities between 2001–2017, across 10 preventable cancer types, we report significant disparities in cancer risk across communities based on sex and community SES. The work highlights the utility of small-area mapping to identify at-risk communities and understand how community-SES impacts risk. It also uncovers significant inequities rooted in the differential distribution of material and social capacity, operating beyond the control of individuals. The approach is implementable to other regions to inform and strengthen prevention efforts aiming at reducing the burden of cancer or that of other diseases.