Loss in life expectancy and gain in life years as measures of cancer impact. (June 2019)

• Record Type: Journal Article
• Authors: Rutherford, Mark J.; Andersson, T M-L; Björkholm, Magnus; Lambert, Paul C.
• Is Part Of: Cancer epidemiology. Volume 60(2019:Jun.)
• Page Start: 168
• Page End: 173
• Publication Date: 2019-06
• Subjects: Life expectancy -- Inequalities -- Cancer survival -- Population-based studies -- Statistical methods; Cancer -- Epidemiology -- Periodicals; Cancer -- Prevention -- Periodicals; Cancer -- Diagnosis -- Periodicals; Carcinogenesis -- Periodicals; 616.994005
• DOI: 10.1016/j.canep.2019.04.005
• Languages: English
• ISSNs: 1877-7821

Abstract

Highlights: A combination of survival metrics can improve comprehensibility and give a fuller understanding of the impact of cancer. Loss in life expectancy offers a summary measure that distils information of the lifetime impact of cancer on survival. Conditional and proportional measures of life expectancy can offer further insight into the impact of cancer. Life expectancy measures are easy to calculate measures that can supplement reporting of cancer survival metrics. Abstract: There are a broad range of survival-based metrics that are available to report from cancer survival studies, with varying advantages and disadvantages. A combination of metrics should be considered to improve comprehensibility and give a fuller understanding of the impact of cancer. In this article, we discuss the utility of loss in life expectancy and gain in life years as measures of cancer impact, and to quantify differences across population groups. These measures are simple to interpret, have a real-world meaning, and evaluate impact over a life-time horizon. We illustrate the use of the loss in life expectancy measures through a range of examples using data on women diagnosed with cancer in England. We use four different examples across a number of tumour types to illustrate different uses of the metrics, and highlight how they can be interpreted and used in practice in population-based oncology studies. Extensions of the measures conditional on survival to specific times after diagnosis can be used to give updated prognosis for cancer patients. Furthermore, we show how the measures can be used to understand the impact of population differences seen across patient groups. We believe that these under-used, and relatively easy to calculate, measures of overall impact can supplement reporting of cancer survival metrics and improve the comprehensibility compared to the metrics typically reported.