Syndemic geographic patterns of cancer risk in a health-deprived area of England.

Objectives: This study aims to analyse the geographical co-occurrence of cancers and their individual and shared risk factors in a highly deprived area of the North West of England to aid the identification of potential interventions. Study design: An ecological study design was employed and applied at postcode sector level in the Morecambe Bay region. Methods: A novel spatial joint modelling framework designed to account for large frequencies of left-censored cancer data was employed. Nine cancer types (breast, colorectal, gynaecology, haematology, head and neck, lung, skin, upper gastrointestinal, urology) alongside demographic, behavioural factors and socio-economic variables were included in the model. Explanatory factors were selected by employing an accelerated failure model with lognormal distribution. Post-processing included principal components analysis and hierarchical clustering to delineate geographic areas with similar spatial risk patterns of different cancer types. Results: 15,506 cancers were diagnosed from 2017 to 2022, with the highest incidence in skin, breast and urology cancers. Factors such as age, ethnicity, frailty and comorbidities were associated with cancer risk for most of the cancer types. A positive geographical association was found mostly between the colorectal, haematology, upper GI, urology and head and neck cancer types. That is, these cancers had their largest risk in the same areas, similarly to their lowest risk values. The spatial distribution of the risk and cumulative risk of the cancer types revealed regional variations, with five clusters identified based on cancer type risk, demographic and socio-economic characteristics. Rural areas were the least affected by cancer and the urban area of Barrow-in-Furness was the area with the highest cancer risk, three times greater than the risk in the surrounding rural areas. Conclusions: This study emphasizes the utility of joint disease mapping by geographically identifying common or shared factors that, if targeted, could lead to reduced risk of multiple cancers simultaneously. The findings suggest the need for tailored public health interventions, considering specific risk factors and socio-economic disparities. Policymakers can utilize the spatial patterns identified to allocate resources effectively and implement targeted cancer prevention programmes.