Age disparities in lung cancer survival in New Zealand: the role of patient and clinical factors

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Abstract

Background Age is an important prognostic factor for lung cancer. However, no studies have investigated the age difference in lung cancer survival per se. We, therefore, described the role of patient-related and clinical factors on the age pattern in lung cancer excess mortality hazard by stage at diagnosis in New Zealand. Methods We extracted 22 487 new lung cancer cases aged 50-99 (median age = 71, 47.1% females) diagnosed between 1 January 2006 and 31 July 2017 from the New Zealand population-based cancer registry and followed up to December 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on the excess mortality hazard by stage at diagnosis, and we derived corresponding lung cancer net survival. Results The age difference in net survival was particularly marked for localised and regional lung cancers, with a sharp decline in survival from the age of 70. No identified factors influenced age disparities in patients with localised cancer. However, for other stages, females had a greater age difference in survival between middle-aged and older patients with lung cancer than males. Comorbidity and emergency presentation played a minor role. Ethnicity and deprivation did not influence age disparities in lung cancer survival. Conclusion Sex and stage at diagnosis were the most important factors of age disparities in lung cancer survival in New Zealand. Key messages What is the key question? How do patient-related and clinical factors influence age pattern in lung cancer survival? What is the bottom line? Age disparities in lung cancer survival were strongest for females and non-advanced disease. Deprivation, ethnicity, comorbidity, and emergency presentation did not influence age disparities. Why read on? Our findings reinforce the call for a better representation of older adults in clinical trials and a wider use of geriatric assessment to identify patients who will benefit treatment.

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License: CC-BY-4.0