Syndemic Geographic Patterns of Cancer Types in a Health Deprived Area of England: a new Paradigm for Public Health Cancer Interventions?
Cancer poses a significant public health challenge, and accurate tools are crucial for effective intervention, especially in high-risk areas. The North West of England, historically identified as a region with high cancer incidence, has become a focus for public health initiatives. This study aims to analyse cancer risk factors, demographic trends and spatial patterns in this region by employing a novel spatial joint modelling framework designed to account for large frequencies of left-censored data. Cancer diagnoses were collected at the postcode sector level. The dataset was left-censored due to confidentiality issues, and categorised as interval censored. Demographic and behavioural factors, alongside socio-economic variables, both at individual and geographic unit levels, were obtained from the linkage of primary and secondary health data and various open source datasets. An ecological investigation was conducted using joint spatial modelling on nine cancer types (breast, colorectal, gynaecology, haematology, head and neck, lung, skin, upper GI, urology), for which 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 patterns of different cancer types. The study included 15,506 cancer diagnoses from 2017 to 2022, with the highest incidence in skin, breast and urology cancers. Preliminary censoring adjustments reduced censored records from 86% to 60%. Factors such as age, ethnicity, frailty and comorbidities were associated with cancer risk. The analysis identified 22 relevant variables, with comorbidities and ethnicity being prominent. The spatial distribution of the risk and cumulative risk of the cancer types revealed regional variations, with five clusters identified. Rural areas were the least affected by cancer and Barrow-in-Furness was the area with the highest cancer risk. This study emphasizes the need for targeted interventions addressing health inequalities in different geographical regions. 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..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 27. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jones, Catherine [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2024.02.24.24303312 |
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| PPN (Katalog-ID): |
XBI042647614 |
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| 245 | 1 | 0 | |a Syndemic Geographic Patterns of Cancer Types in a Health Deprived Area of England: a new Paradigm for Public Health Cancer Interventions? |
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| 520 | |a Cancer poses a significant public health challenge, and accurate tools are crucial for effective intervention, especially in high-risk areas. The North West of England, historically identified as a region with high cancer incidence, has become a focus for public health initiatives. This study aims to analyse cancer risk factors, demographic trends and spatial patterns in this region by employing a novel spatial joint modelling framework designed to account for large frequencies of left-censored data. Cancer diagnoses were collected at the postcode sector level. The dataset was left-censored due to confidentiality issues, and categorised as interval censored. Demographic and behavioural factors, alongside socio-economic variables, both at individual and geographic unit levels, were obtained from the linkage of primary and secondary health data and various open source datasets. An ecological investigation was conducted using joint spatial modelling on nine cancer types (breast, colorectal, gynaecology, haematology, head and neck, lung, skin, upper GI, urology), for which 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 patterns of different cancer types. The study included 15,506 cancer diagnoses from 2017 to 2022, with the highest incidence in skin, breast and urology cancers. Preliminary censoring adjustments reduced censored records from 86% to 60%. Factors such as age, ethnicity, frailty and comorbidities were associated with cancer risk. The analysis identified 22 relevant variables, with comorbidities and ethnicity being prominent. The spatial distribution of the risk and cumulative risk of the cancer types revealed regional variations, with five clusters identified. Rural areas were the least affected by cancer and Barrow-in-Furness was the area with the highest cancer risk. This study emphasizes the need for targeted interventions addressing health inequalities in different geographical regions. 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. | ||
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