Disparities in care and outcomes for primary liver cancer in England during 2008–2018: a cohort study of 8.52 million primary care population using the QResearch database

Coupland, Carol; Campbell, Cori; Barnes, Eleanor; Culver, Emma L.; Fischer, Román; Hippisley‐Cox, Julia; Innes, Hamish; Irving, William L.; Jepsen, Peter; Kelly, Catherine; Klenerman, Paul; Liao, Weiqi; Mann, Derek A.; Marshall, Aileen; Matthews, Philippa C.; Pavlides, Michael; Peters, Rory; Pickles, Elisabeth; Robineau, James; Schuster-Böckler, Benjamin; Song, Chunxiao; Tomlinson, Jeremy; Welberry, Christopher

doi:10.1016/j.eclinm.2023.101969

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…Deprivation was found to be not significant, in contrast with the large amount of literature linking deprivation or socio-economic status with cancer diagnosis and survival rates (Liao et al, 2023, Bithell et al, 2013, Chambers et al, 2020, Shack et al, 2008, Edwards et al, 2006, Rogers et al, 2019, Rafiq et al, 2019, Phillips et al, 2019, Taib et al, 2018).…”

Section: Discussioncontrasting

confidence: 80%

“…Spatial heterogeneity has been observed in other studies where similar risk factors were considered. For example, age, socioeconomic deprivation, ethnicity, and geographical region were all significantly associated with an incident diagnosis of liver cancer (from primary care data) at the population level (Liao et al, 2023, Burton et al, 2022); for certain leukaemias and lymphomas in the North West (McNally et al, 2003); head and neck cancer in UK (Taib et al, 2018); and for joint modelling of lung, bowel and melanoma cancer (Jahan et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Most of the factors associated to cancer risk by type were grouped by ethnicity and comorbidities, which may help health commissioners and policymakers to consider health equality in different geographical regions and reduce health inequities in ethnic minority groups (Liao et al, 2023), but also to potentially include comorbidities in screening programmes (Vrinzen et al, 2023). This complies with the goals set by the NHS Long Term Plan 7 which aims by 2028 to reach a target of 55,000 people each year that will survive for five years or more following their cancer diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…These variables, obtained from the best fitting model where all the cancer types are modelled simultaneously, can be grouped into three domains: demographic (age, frailty, ethnicity and comorbidities), behavioural (smoking status) and socio-economic (employment). Despite the absence of sex, all these domains are commonly associated with incident diagnosis of cancer (Liao, Coupland et al 2023). Six out of 22 selected variables are area-based variables (or ecological variables) while the rest ( 16) are defined at the individual level (variables associated to cancer patients instead of the area where they live).…”

Section: Variable Selectionmentioning

confidence: 99%

“…In fact, it had the highest oral cancer incidence rates in the period 1990-1999 (for both males and females driven by the over 45 years old) (Conway et al, 2006); the highest uveal melanoma admission rates between 1999-2010 (Keenan et al, 2012) with Lancashire also experiencing the highest cutaneous melanoma; the second highest glioblastoma (a fast growing type of brain tumour) incidence and the lowest survival in 2007-2011 (Brodbelt et al, 2015); higher incidence (2008)(2009)(2010) and mortality (2009)(2010)(2011) rates, compared to England, of head and neck cancer in the Merseyside and Cheshire region (Taib et al, 2018); and finally the lowest incidence of basal cell carcinoma (the most common skin cancer) between 2004 and 2010 (Musah et al, 2013). Compared with London, the North West of England had a higher incidence rate for liver cancer subtypes hepatocellular carcinoma and intrahepatic cholangiocarcinoma between 2008 and 2018 (Liao et al, 2023); while compared to the whole UK, the North of England had the highest rates of lung cancer in non-smokers for men and women during the period 1998 to 2007 (Rait and Horsfall, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Syndemic Geographic Patterns of Cancer Types in a Health Deprived Area of England: a new Paradigm for Public Health Cancer Interventions?

Jones,

Keegan,

Knox

et al. 2024

Preprint

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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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