How much of the deprivation gap in cancer survival can be explained by variation in stage at diagnosis: An example from breast cancer in the East of England

Abstract

Socioeconomic differences in cancer patient survival exist in many countries and across cancer sites. In our article, we estimated the number of deaths in women with breast cancer that could be avoided within 5 years from diagnosis if it were possible to eliminate socioeconomic differences in stage at diagnosis. We analysed data on East of England women with breast cancer (2006)(2007)(2008)(2009)(2010). We estimated survival for different stage-age-deprivation strata using both the observed and a hypothetical stage distribution (assuming all women acquired the stage distribution of the most affluent women). Data were analysed on 20,738 women with complete stage information (92%). Affluent women were less likely to be diagnosed in advanced stage. Relative survival decreased with increasing level of deprivation. Eliminating differences in stage at diagnosis could be expected to nearly eliminate differences in relative survival for women in deprivation groups 3 and 4, but would only approximately halve the difference in relative survival for women in the most deprived group (5). This means, for a typical cohort of women diagnosed in a calendar year with breast cancer, eliminating deprivation differences in stage at diagnosis would prevent~40 deaths in the East of England from occurring within 5 years from diagnosis. Using appropriate weighting we estimated the respective number of avoidable deaths for the whole of England to be~450. The findings suggest that policies aimed at reducing inequalities in stage at diagnosis between women with breast cancer are important to reduce inequalities in breast cancer survival.

Socioeconomic inequalities in prostate cancer survival: A review of the evidence and explanatory factors

“…Finally, later stage at diagnosis can partly explain socioeconomic differences in survival but due to statistical artefacts conclusions must be drawn carefully. Results of studies analysing the impact of stage among other cancer sites differ in their results (Booth et al, 2010;Byers et al, 2008;Frederiksen et al, 2009;Jeffreys et al, 2009;Rutherford et al, 2013;Schrijvers et al, 1995). The two further explanatory factors effecting reductions of survival inequalities are treatment and, to a lesser extent, comorbidity.…”

Impact of socioeconomic status on disease phenotype, genomic landscape and outcomes in myelodysplastic syndromes

“…Based on the established SES-dependent variation in clinicopathological characteristics and outcomes in many diseases (Barker, 1981;Gomez et al, 2015;Stringhini et al, 2013) including cancer (DeSantis et al, 2011;Kolahdooz et al, 2014;Lyratzopoulos et al, 2012;Roberts et al, 2015;Rutherford et al, 2013), we hypothesised a role for SES in influencing the biological and clinical heterogeneity in patients with MDS. In previous studies, Wang et al (2009) investigated the prognostic role of neighbourhood SES in MDS using data from the Surveillance, Epidemiology and End Results (SEER) cancer registries linked to the national insurance programme Medicare in the USA and identified poorer survival in patients belonging to lower SES groups.…”

“…In patients with solid organ and other haematological neoplasia differences in time to diagnosis, access to clinical care or trials and comorbidity between patients that are SES-dependent are known to affect survival (Kolahdooz et al, 2014;Munro, 2005;Roberts et al, 2015;Rutherford et al, 2013). For example, the lower incidence of childhood acute lymphoblastic leukaemia across levels of increasing relative deprivation could reflect under-diagnosis in lower socioeconomic communities (Kroll et al, 2012;Lightfoot et al, 2012), and the mortality in AML and myeloma appears to be reduced in higher SES groups in some studies (Kristinsson et al, 2009).…”

Carbohydrate derivative-functionalized biosensing toward highly sensitive electrochemical detection of cell surface glycan expression as cancer biomarker

“…Taking into account of the critical requirements for cancer detection with high sensitivity in early diagnosis (Rutherford et al, 2013), we applied the thiomannosyl-functionalized biosensor to detect A549, QGY-7703 and LNCaP cells on the basis of the specific recognition between Con A and cell surface mannose. Under optimal conditions, the DPV peak current for the quantification of QGY-7703 cells is depicted in Fig.…”