[Prognosis of colorectal cancer and socio-economic inequalities].

It is well established that socio-economic status is a major prognostic factor for many cancers, including colorectal cancer. The aims of this review are (i) to report epidemiological data showing how socio-economic status influences colorectal cancer survival, (ii) to attempt to describe the mechanisms underlying these survival inequalities, and (iii) to assess their impact on survival of colorectal cancer

Trends and inequalities in laryngeal cancer survival in men and women: England and Wales 1991-2006.

Laryngeal cancer in men is a relatively common malignancy, with a marked socioeconomic gradient in survival between affluent and deprived patients. Cancer of the larynx in women is rare. Survival tends to lower than for men, and little is known about the association between deprivation and survival in women with laryngeal cancer. This paper explores the trends and socio-economic inequalities in laryngeal cancer survival in women, with comparison to men. We examined relative survival among men and women diagnosed with laryngeal cancer in England and Wales during 1991-2006, followed up to 31 December 2007. We estimated the difference in survival between the most deprived and most affluent groups (the 'deprivation gap') at one and five years after diagnosis, for each sex, anatomical subsite and calendar period. Five year survival for all laryngeal cancers combined was up to 8% lower in women than in men. This difference is only partially explained by the differential distribution of anatomical subsites in men and women. Disparities in survival between men and women were also present within specific subsites. In contrast to men, there was little evidence of a consistent deprivation gap in survival for women at any of the anatomical subsites. The stark socioeconomic inequalities in laryngeal cancer survival in men do not appear to be replicated in women. The origins of the socio-economic inequalities in survival among men, and the disparities in survival between men and women at specific tumour subsites remains unclear

Estimating excess hazard ratios and net survival when covariate data are missing: strategies for multiple imputation.

BACKGROUND: Net survival is the survival probability we would observe if the disease under study were the only cause of death. When estimated from routinely collected population-based cancer registry data, this indicator is a key metric for cancer control. Unfortunately, such data typically contain a non-negligible proportion of missing values on important prognostic factors (eg, tumor stage). METHODS: We carried out an empirical study to compare the performance of complete records analysis and several multiple imputation strategies when net survival is estimated via a flexible parametric proportional hazards model that includes stage, a partially observed categorical covariate. Starting from fully observed cancer registry data, we induced missingness on stage under three scenarios. For each of these scenarios, we simulated 100 incomplete datasets and evaluated the performance of the different strategies. RESULTS: Ordinal logistic models are not suitable for the imputation of tumor stage. Complete records analysis may lead to grossly misleading estimates of net survival, even when the missing data mechanism is conditionally independent of survival time given the covariates and the bias on the excess hazard ratios estimates is negligible. CONCLUSIONS: As key covariates are unlikely missing completely at random, studies estimating net survival should not use complete records. When the missingness can be inferred from available data, appropriate multiple imputation should be performed. In the context of flexible parametric proportional hazards models with a partially observed stage covariate, a multinomial logistic imputation model for stage should be used and should include the Nelson-Aalen cumulative hazard estimate and the event indicator

Prediction of cancer survival for cohorts of patients most recently diagnosed using multi-model inference.

Despite a large choice of models, functional forms and types of effects, the selection of excess hazard models for prediction of population cancer survival is not widespread in the literature. We propose multi-model inference based on excess hazard model(s) selected using Akaike information criteria or Bayesian information criteria for prediction and projection of cancer survival. We evaluate the properties of this approach using empirical data of patients diagnosed with breast, colon or lung cancer in 1990-2011. We artificially censor the data on 31 December 2010 and predict five-year survival for the 2010 and 2011 cohorts. We compare these predictions to the observed five-year cohort estimates of cancer survival and contrast them to predictions from an a priori selected simple model, and from the period approach. We illustrate the approach by replicating it for cohorts of patients for which stage at diagnosis and other important prognosis factors are available. We find that model-averaged predictions and projections of survival have close to minimal differences with the Pohar-Perme estimation of survival in many instances, particularly in subgroups of the population. Advantages of information-criterion based model selection include (i) transparent model-building strategy, (ii) accounting for model selection uncertainty, (iii) no a priori assumption for effects, and (iv) projections for patients outside of the sample

How much do tumor stage and treatment explain socioeconomic inequalities in breast cancer survival? Applying causal mediation analysis to population-based data

Substantial socioeconomic inequalities in breast cancer survival persist in England, possibly due to more advanced cancer at diagnosis and differential access to treatment. We aim to disentangle the contributions of differential stage at diagnosis and differential treatment to the socioeconomic inequalities in cancer survival. Information on 36,793 women diagnosed with breast cancer during 2000–2007 was routinely collected by an English population-based cancer registry. Deprivation was determined for each patient according to her area of residence at the time of diagnosis. A parametric implementation of the mediation formula using Monte Carlo simulation was used to estimate the proportion of the effect of deprivation on survival mediated by stage and by treatment. One-third (35 % [23–48 %]) of the higher mortality experienced by most deprived patients at 6 months after diagnosis, and one tenth (14 % [−3 to 31 %]) at 5 years, was mediated by adverse stage distribution. We initially found no evidence of mediation via differential surgical treatment. However, sensitivity analyses testing some of our study limitations showed in particular that up to thirty per cent of the higher mortality in most deprived patients could be mediated by differential surgical treatment. This study illustrates the importance of using causal inference methods with routine medical data and the need for testing key assumptions through sensitivity analyses. Our results suggest that, although effort for earlier diagnosis is important, this would reduce the cancer survival inequalities only by a third. Because of data limitations, role of differential surgical treatment may have been under-estimated

A novel ecological methodology for constructing ethnic-majority life tables in the absence of individual ethnicity information.

BACKGROUND: Deprivation-specific life tables have been in use for some time, but health outcomes are also known to vary by ethnicity over and above deprivation. The mortality experiences of ethnic groups are little studied in the UK, however, because ethnicity is not captured on death certificates. METHODS: Population data for all Output Areas (OAs) in England and Wales were stratified by age-group, sex and ethnic proportion, and matched to the deaths counts in that OA from 2000 to 2002. We modelled the relationship between mortality, age, deprivation and ethnic proportion. We predicted mortality rates for an area that contained the maximum proportion of each ethnic group reported in any area in England and Wales, using a generalised linear model with a Poisson distribution adjusted for deprivation. RESULTS: After adjustment, Asian and White life expectancies between 1 and 80 years were very similar. Black men and women had lower life expectancies: men by 4 years and women by around 1.5 years. The Asian population had the lowest mortality of all groups over age 45 in women and over 50 in men, whereas the Black population had the highest rates throughout, except in girls under 15. CONCLUSIONS: We adopted a novel ecological method of constructing ethnic-majority life tables, adjusted for deprivation. There is still diversity within these three broad ethnic groups, but our data show important residual differences in mortality for Black men and women. These ethnic life tables can be used to inform public health planning and correctly account for background mortality in ethnic subgroups of the population

Analysing population-based cancer survival - settling the controversies.

BACKGROUND: The relative survival field has seen a lot of development in the last decade, resulting in many different and even opposing suggestions on how to approach the analysis. METHODS: We carefully define and explain the differences between the various measures of survival (overall survival, crude mortality, net survival and relative survival ratio) and study their differences using colon and prostate cancer data extracted from the national population-based cancer registry of Slovenia as well as simulated data. RESULTS: The colon and prostate cancer data demonstrate clearly that when analysing population-based data, it is useful to split the overall mortality in crude probabilities of dying from cancer and from other causes. Complemented by net survival, it provides a complete picture of cancer survival in a given population. But when comparisons of different populations as defined for example by place or time are of interest, our simulated data demonstrate that net survival is the only measure to be used. CONCLUSIONS: The choice of the method should be done in two steps: first, one should determine the measure of interest and second, one should choose among the methods that estimate that measure consistently

What might explain deprivation-specific differences in the excess hazard of breast cancer death amongst screen-detected women? Analysis of patients diagnosed in the West Midlands region of England from 1989 to 2011.

BACKGROUND: Breast cancer survival is higher in less deprived women, even amongst women whose tumor was screen-detected, but reasons behind this have not been comprehensively investigated. METHODS: The excess hazard of breast cancer death in 20,265 women diagnosed with breast cancer, followed up to 2012, was estimated for screen-detected and non-screen-detected women, comparing more deprived to less deprived women using flexible parametric models. Models were adjusted for individual and tumor factors, treatment received and comorbidity. For screen-detected women, estimates were also corrected for lead-time and overdiagnosis. RESULTS: The excess hazard ratio (EHR) of breast cancer death in the most deprived group, adjusted only for age and year of diagnosis, was twice that of the least deprived among screen-detected women (EHR=2.12, 95%CI 1.48-2.76) and 64% higher among non-screen-detected women (EHR=1.64, 95%CI 1.41-1.87). Adjustment for stage at diagnosis lowered these estimates by 25%. Further adjustment had little extra impact. In the final models, the excess hazard for the most deprived women was 54% higher (EHR=1.54, 95%CI 1.10-1.98) among screen-detected women and 39% higher (EHR=1.39, 95%CI 1.20-1.59) among non-screen-detected women. CONCLUSION: A persistent socio-economic gradient in breast cancer-related death exists in this cohort, even for screen-detected women. The impact of differential lifestyles, management and treatment warrant further investigation

No inequalities in survival from colorectal cancer by education and socioeconomic deprivation - a population-based study in the North Region of Portugal, 2000-2002.

BACKGROUND: Association between cancer survival and socioeconomic status has been reported in various countries but it has never been studied in Portugal. We aimed here to study the role of education and socioeconomic deprivation level on survival from colorectal cancer in the North Region of Portugal using a population-based cancer registry dataset. METHODS: We analysed a cohort of patients aged 15-84 years, diagnosed with a colorectal cancer in the North Region of Portugal between 2000 and 2002. Education and socioeconomic deprivation level was assigned to each patient based on their area of residence. We measured socioeconomic deprivation using the recently developed European Deprivation Index. Net survival was estimated using Pohar-Perme estimator and age-adjusted excess hazard ratios were estimated using parametric flexible models. Since no deprivation-specific life tables were available, we performed a sensitivity analysis to test the robustness of the results to life tables adjusted for education and socioeconomic deprivation level. RESULTS: A total of 4,105 cases were included in the analysis. In male patients (56.3 %), a pattern of worse 5- and 10-year net survival in the less educated (survival gap between extreme education groups: -7 % and -10 % at 5 and 10 years, respectively) and more deprived groups (survival gap between extreme EDI groups: -5 % both at 5 and 10 years) was observed when using general life tables. No such clear pattern was found among female patients. In both sexes, when likely differences in background mortality by education or deprivation were accounted for in the sensitivity analysis, any differences in net survival between education or deprivation groups vanished. CONCLUSIONS: Our study shows that observed differences in survival by education and EDI level are most likely attributable to inequalities in background survival. Also, it confirms the importance of using the relevant life tables and of performing sensitivity analysis when evaluating socioeconomic inequalities in cancer survival. Comparison studies of different healthcare systems organization should be performed to better understand its influence on cancer survival inequalities