Clinical Epidemiology (May 2018)
Describing the association between socioeconomic inequalities and cancer survival: methodological guidelines and illustration with population-based data
Abstract
Aurélien Belot,1-3 Laurent Remontet,3,4 Bernard Rachet,1 Olivier Dejardin,5,6 Hadrien Charvat,7 Simona Bara,8 Anne-Valérie Guizard,5,9 Laurent Roche,3,4 Guy Launoy,5,6 Nadine Bossard3,4 1Cancer Survival Group, Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom; 2Non-Communicable Diseases and Trauma Direction, The French Public Health Agency, Saint-Maurice, France; 3Department of Biostatistics and Bioinformatics, Hospices Civils de Lyon, Lyon, France; 4UMR 5558, Biometry and Evolutionary Biology Laboratory, Biostatistics Health Group, CNRS, University Lyon 1, Lyon, France; 5National Institute of Health and Medical Research U1086 ANTICIPE, Caen, France; 6Calvados Digestive Cancer Registry, Centre Hospitalier Universitaire, Caen, France; 7Prevention Division, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan; 8Manche General Cancer Registry, Centre Hospitalier Public du Cotentin, Cherbourg-en-Cotentin, France; 9Calvados General Cancer Registry, Centre François Baclesse, Caen, France Background: Describing the relationship between socioeconomic inequalities and cancer survival is important but methodologically challenging. We propose guidelines for addressing these challenges and illustrate their implementation on French population-based data. Methods: We analyzed 17 cancers. Socioeconomic deprivation was measured by an ecological measure, the European Deprivation Index (EDI). The Excess Mortality Hazard (EMH), ie, the mortality hazard among cancer patients after accounting for other causes of death, was modeled using a flexible parametric model, allowing for nonlinear and/or time-dependent association between the EDI and the EMH. The model included a cluster-specific random effect to deal with the hierarchical structure of the data. Results: We reported the conventional age-standardized net survival (ASNS) and described the changes of the EMH over the time since diagnosis at different levels of deprivation. We illustrated nonlinear and/or time-dependent associations between the EDI and the EMH by plotting the excess hazard ratio according to EDI values at different times after diagnosis. The median excess hazard ratio quantified the general contextual effect. Lip–oral cavity–pharynx cancer in men showed the widest deprivation gap, with 5-year ASNS at 41% and 29% for deprivation quintiles 1 and 5, respectively, and we found a nonlinear association between the EDI and the EMH. The EDI accounted for a substantial part of the general contextual effect on the EMH. The association between the EDI and the EMH was time dependent in stomach and pancreas cancers in men and in cervix cancer. Conclusion: The methodological guidelines proved efficient in describing the way socioeconomic inequalities influence cancer survival. Their use would allow comparisons between different health care systems. Keywords: cancer net survival, socioeconomic inequalities, European Deprivation Index, excess mortality hazard, flexible parametric model
