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  • Impact of area-level socioeconomic status and accessibility to treatment on life expectancy after a cancer diagnosis in Queensland, Australia

    Author(s)
    Kou, Kou
    Dasgupta, Paramita
    Aitken, Joanne F
    Baade, Peter D
    Griffith University Author(s)
    Dasgupta, Paramita
    Baade, Peter D.
    Year published
    2020
    Metadata
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    Abstract
    Aims: This study quantifies geographic inequities in loss of life expectancy (LOLE) by area-level socioeconomic status (SES) and accessibility to treatment. Methods: Analysis was conducted using a population-based cancer-registry cohort (n = 371,570) of Queensland (Australia) residents aged 50-89 years, diagnosed between 1997-2016. Flexible parametric survival models were used to estimate LOLE by area-level SES and accessibility for all invasive cancers and the five leading cancers. The gain in life years that could be achieved if all cancer patients experienced the same relative survival as those in the least disadvantaged-high ...
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    Aims: This study quantifies geographic inequities in loss of life expectancy (LOLE) by area-level socioeconomic status (SES) and accessibility to treatment. Methods: Analysis was conducted using a population-based cancer-registry cohort (n = 371,570) of Queensland (Australia) residents aged 50-89 years, diagnosed between 1997-2016. Flexible parametric survival models were used to estimate LOLE by area-level SES and accessibility for all invasive cancers and the five leading cancers. The gain in life years that could be achieved if all cancer patients experienced the same relative survival as those in the least disadvantaged-high accessibility category was estimated for the 2016 cohort. Results: For all invasive cancers, men living in the most disadvantaged areas lost 34 % of life expectancy due to their cancer diagnosis, while those from the least disadvantaged areas lost 25 %. The corresponding percentages for women were 33 % and 23 %. Accessibility had a lower impact on LOLE than SES, with patients from low accessibility areas losing 0-4 % more life expectancy than those from high accessibility areas. For cancer patients diagnosed in 2016 (n = 24,423), an estimated 101,387 life years will be lost. This would be reduced by 19 % if all patients experienced the same relative survival as those from the least disadvantaged-high accessibility areas. Conclusion: The impact of a cancer diagnosis on remaining life expectancy varies by geographical area. Establishing reasons why area disadvantage impacts on life expectancy is crucial to inform subsequent interventions that could increase the life expectancy of cancer patients from more disadvantaged areas.
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    Journal Title
    Cancer Epidemiology
    Volume
    69
    DOI
    https://doi.org/10.1016/j.canep.2020.101803
    Subject
    Oncology and carcinogenesis
    Health services and systems
    Public health
    Accessibility
    Australia
    Cancer
    Loss of life expectancy
    Socioeconomic
    Publication URI
    http://hdl.handle.net/10072/397651
    Collection
    • Journal articles

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