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. 2025 Jul 23;20(7):e0328778.
doi: 10.1371/journal.pone.0328778. eCollection 2025.

Spatial insights into colorectal cancer risk factors and priority areas for screening in the United Kingdom based on data from the UK Biobank

Mei Yang  1   2 , Vagheesh M Narasimhan  3   4 , Franklin Benjamin Zhan  2   5
Affiliations

Spatial insights into colorectal cancer risk factors and priority areas for screening in the United Kingdom based on data from the UK Biobank

Mei Yang et al. PLoS One. .

Abstract

Geography and geospatial data science hold the potential to make unique contributions to the reduction of the burden of cancer on society. Here we use colorectal cancer (CRC) as an example to show how spatial insights into CRC risk factors and priority areas for screening may be obtained to achieve geographically targeted screening. We obtained data from the UK Biobank and divided the participants into the older (50<=age < 70) and young (age < 50) adult groups. The data consists of 2,080 CRC cases and 8,062 controls. We used a case-control study and geographically weighted logistic regression (GWLR) to explore spatial variations in risk levels of significant factors at a fine geographic resolution. Analysis results reveal that, among all significant risk factors, polygenic risk score (PRS) is the most important risk factor for both age groups. Findings suggest that the top priority screening areas for older adults, using PRS as the sole risk factor, are between Sheffield, Birmingham, Cardiff, Bristol, and west of Greater London. For young adults, the top priority areas are between the south of Glasgow and Edinburgh and northwest of Greater London. Furthermore, the approach used in this study holds promise for developing more effective targeted cancer screening.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Geographic distribution of CRC cases and controls.
(A) the older group (50<=age < 70). (B) the younger group (age < 50). Shown on the maps are the densities of CRC cases and controls at the local authority level. (Note: Country and local authority boundary data were obtained from UK Census data, available through the UK Data Service: https://ukdataservice.ac.uk/help/data-types/census-data/.).
Fig 2
Fig 2. Population density in the UK.
(Note: UK boundary data were obtained from UK Census data, available through the UK Data Service: https://ukdataservice.ac.uk/help/data-types/census-data/.).
Fig 3
Fig 3. Spatial variations in CRC risk factors among older participants (50<=age < 70).
(A) PRS. (B) employment status. (C) sex. (D) alcohol intake frequency. (E) age. (F) BMI. Shown on the maps are the median odds ratios in local authority’s areas. (Note: Local authority boundary data were obtained from UK Census data, available through the UK Data Service: https://ukdataservice.ac.uk/help/data-types/census-data/).
Fig 4
Fig 4. Spatial variations in CRC risk factors among younger participants (age < 50).
(A) PRS. (B) smoking. Shown on the maps are the median odds ratios in local authority’s areas. (Note: Local authority boundary data were obtained from UK Census data, available through the UK Data Service: https://ukdataservice.ac.uk/help/data-types/census-data/).
See this image and copyright information in PMC

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