Estimating the impact of a cancer diagnosis on life expectancy by socio-economic group for a range of cancer types in England

Abstract

Background:Differences in cancer survival exist across socio-economic groups for many cancer types. Standard metrics fail to show the overall impact for patients and the population.Methods:The available data consist of a population of ∼2.5 million patients and include all patients recorded as being diagnosed with melanoma, prostate, bladder, breast, colon, rectum, lung, ovarian and stomach cancers in England between 1998 and 2013. We estimated the average loss in expectation of life per patient in years and the proportion of life lost for a range of cancer types, separately by deprivation group. In addition, estimates for the total number of years lost due to each cancer were also obtained.Results:Lung and stomach cancers result in the highest overall loss for males and females in all deprivation groups in terms of both absolute life years lost and loss as a proportion of expected life remaining. Female lung cancer patients in the least- and most-deprived group lose 14.4 and 13.8 years on average, respectively, that is translated as 86.1% and 87.3% of their average expected life years remaining. Melanoma, prostate and breast cancers have the lowest overall loss. On the basis of the number of patients diagnosed in 2013, lung cancer results in the most life years lost in total followed by breast cancer. Melanoma and bladder cancer account for the lowest total life years lost.Conclusions:There are wide differences in the impact of cancer on life expectancy across deprivation groups, and for most cancers the most affluent lose less years.

Emerging cancer incidence, mortality, hospitalisation and associated burden among Australian cancer patients, 1982 – 2014: an incidence-based approach in terms of trends, determinants and inequality

“…Similarly, patients in the least advantaged group experienced a significantly higher burden of cancer in terms of YLL (6.50% to 7.57%) compared with the richest (1.11%) from 2011 to 2015. Previous studies have also reported similar inequalities in YLL,75 76 whereas, a high proportion of patients in the most-deprived groups experienced very high years loss of life. Even though survival rates after cancer diagnosis have improved in recent years,7 disparities in cancer outcomes between the least-deprived and the most-deprived groups continue to persist.…”

“…Similarly, the burden of cancer was high in New South Wales, Victoria and Queensland; however, the magnitude of fatal burden was unchanged during 2011 to 2015. Some previous studies have shown consistent findings, which have confirmed that the proportion of life lost for patients in geographical disadvantaged or low-resource settings had a higher cancer burden than their more advantaged counterparts 75 76. Socioeconomic inequalities in terms of poorer survival for geographically isolated patients was observed in cancer types in Australia including breast and colorectal cancer 86.…”

Loss in life expectancy and gain in life years as measures of cancer impact

“…Andersson et al [7] have developed a model-based approach that involves extrapolating the excess and expected mortality curves in order to estimate the longterm all-cause survival. This approach has been shown to perform well empirically using historical data and has been applied in a number of settings [7,12,13,14,17]. Further steps can be taken to ensure the estimates are up-to-date by undertaking a period analysis approach [18,19].…”

“…We can calculate proportional, rather than the absolute loss in life expectancy, in order to report a metric that is more comparable across age, and is also a useful further metric for reporting [6,14]. Figure 4 shows the average proportion of life years lost for female cancer patients across the four cancer sites; the average number of years of life lost as a proportion of the average life years without cancer.…”

“…The dataset contains information on tumour site, age, time since diagnosis, as well as a categorical variable with a crude measure of deprivation split into five categories based on postcode areas. The data are the same as those used by Syriopoulou et al [14], but we restrict to a subset of cancer sites to exemplify the approach. We also use a population lifetable specific to England whereby information on all-cause mortality is available stratified by age, sex, calendar year and deprivation group [15].…”