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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Are socio-economic inequalities in breast cancer survival explained by peri-diagnostic factors?
Laura M Woods1, Bernard Rachet2, Melanie Morris3
1Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel St, London, WC1E 7HT, UK. laura.woods@lshtm.ac.uk.
Background:
Patients living in more deprived localities have lower cancer survival in England, but the role of individual health status at diagnosis and the utilisation of primary health care in explaining these differentials has not been widely considered. We set out to evaluate whether pre-existing individual health status at diagnosis and primary care consultation history (peri-diagnostic factors) could explain socio-economic differentials in survival amongst women diagnosed with breast cancer.
Methods:
We conducted a retrospective cohort study of women aged 15-99 years diagnosed in England using linked routine data. Ecologically-derived measures of income deprivation were combined with individually-linked data from the English National Cancer Registry, Clinical Practice Research Datalink (CPRD) and Hospital Episodes Statistics (HES) databases. Smoking status, alcohol consumption, BMI, comorbidity, and consultation histories were derived for all patients. Time to breast surgery was derived for women diagnosed after 2005. We estimated net survival and modelled the excess hazard ratio of breast cancer death using flexible parametric models. We accounted for missing data using multiple imputation.
Results:
Net survival was lower amongst more deprived women, with a single unit increase in deprivation quintile inferring a 4.4% (95% CI 1.4-8.8) increase in excess mortality. Peri-diagnostic co-variables varied by deprivation but did not explain the differentials in multivariable analyses.
Conclusions:
These data show that socio-economic inequalities in survival cannot be explained by consultation history or by pre-existing individual health status, as measured in primary care. Differentials in the effectiveness of treatment, beyond those measuring the inclusion of breast surgery and the timing of surgery, should be considered as part of the wider effort to reduce inequalities in premature mortality.
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