Ethnic disparities in lung cancer incidence and differences in diagnostic characteristics: a population-based cohort study in England

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Background: Lung cancer is a leading cause of mortality, yet disparities in lung cancer across different sociodemographic groups in the UK remain unclear. This study investigates ethnicity and sociodemographic disparities and differences in lung cancer in a nationally representative English cohort, aiming to highlight inequalities and promote equitable access to diagnostic advancements. Methods: We conducted a population-based cohort study using health care records from QResearch, a large primary care database in England. The study included adults aged 25 and over, spanning the period of 2005-2019. Lung cancer incidence rates were calculated using age-standardized methods. Multinomial logistic regression was applied to assess associations between ethnicity/sociodemographic factors and diagnostic characteristics (histological type, stage, and cancer grade), adjusting for confounders. Findings: From a cohort of over 17.5 million people, we identified disparities in incidence rates across ethnic groups from 2005 to 2019. Analysis of 84,253 lung cancer cases revealed that younger woman and Individuals of Indian, other Asian, Black African, Caribbean and Chinese backgrounds had a significantly higher risks of adenocarcinoma compared with squamous cell carcinoma than their White counterparts (relative risk ratios [RRR] spanning from 1.52 (95% CI 1.18-1.94) to 2.69 (95% CI 1.43-5.05). Men and current smokers were more likely to be diagnosed at an advanced stage than women and never smokers (RRR: 1.72 [95% CI 1.56-1.90]-2.45 [95% CI 2.16-2.78]). Socioeconomic deprivation was associated with higher risks of moderate or poorly differentiated adenocarcinoma compared with well differentiated (RRRs between 1.35 [CI: 1.02-1.79] and 1.37 [1.05-1.80]). Interpretation: Our study highlights significant differences in lung cancer incidence and in lung cancer diagnostic characteristics related to ethnicity, deprivation and other demographic factors. These findings have important implications for the provision of equitable screening and prevention programmes to mitigate health inequalities. Funding: DART (The Integration and Analysis of Data using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases) project, Innovate UK (UK Research and Innovation), QResearch® and grants from the NIHR Biomedical Research Centre (Oxford), John Fell Oxford University Press Research Fund, Cancer Research UK, and the Oxford Wellcome Institutional Strategic Support Fund.

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