Inequalities in treatment among patients with colon and rectal cancer: a multistate survival model using data from England national cancer registry 2012-2016.

BACKGROUND: Individual and tumour factors only explain part of observed inequalities in colorectal cancer survival in England. This study aims to investigate inequalities in treatment in patients with colorectal cancer. METHODS: All patients diagnosed with colorectal cancer in England between 2012 and 2016 were followed up from the date of diagnosis (state 1), to treatment (state 2), death (state 3) or censored at 1 year after the diagnosis. A multistate approach with flexible parametric model was used to investigate the effect of income deprivation on the probability of remaining alive and treated in colorectal cancer. RESULTS: Compared to the least deprived quintile, the most deprived with stage I-IV colorectal cancer had a lower probability of being alive and treated at all the time during follow-up, and a higher probability of being untreated and of dying. The probability differences (most vs. least deprived) of being alive and treated at 6 months ranged between -2.4% (95% CI: -4.3, -1.1) and -7.4% (-9.4, -5.3) for colon; between -2.0% (-3.5, -0.4) and -6.2% (-8.9, -3.5) for rectal cancer. CONCLUSION: Persistent inequalities in treatment were observed in patients with colorectal cancer at every stage, due to delayed access to treatment and premature death.

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights.

Propensity score methods, such as inverse probability-of-treatment weighting (IPTW), have been increasingly used for covariate balancing in both observational studies and randomized trials, allowing the control of both systematic and chance imbalances. Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by applying PS weights. Thus, a variance estimator that accounts for both steps is crucial for correct inference. Using a variance estimator which ignores the first step leads to overestimated variance when the estimand is the average treatment effect (ATE), and to under or overestimated estimates when targeting the average treatment effect on the treated (ATT). In this article, we emphasize the importance of using an IPTW variance estimator that correctly considers the uncertainty in PS estimation. We present a comprehensive tutorial to obtain unbiased variance estimates, by proposing and applying a unifying formula for different types of PS weights (ATE, ATT, matching and overlap weights). This can be derived either via the linearization approach or M-estimation. Extensive R code is provided along with the corresponding large-sample theory. We perform simulation studies to illustrate the behavior of the estimators under different treatment and outcome prevalences and demonstrate appropriate behavior of the analytical variance estimator. We also use a reproducible analysis of observational lung cancer data as an illustrative example, estimating the effect of receiving a PET-CT scan on the receipt of surgery.

Long-term excess mortality and net survival among elderly diffuse large B-cell lymphoma patients after front-line R-CHOP treatment.

In the era of immunochemotherapy, data on the long-term prognosis of elderly patients diagnosed with a diffuse large B-cell lymphoma (DLBCL) are scarce. In this population and on the longer term, other-cause mortality is an important competing risk that needs to be accounted for. Using clinical trial data and relative survival approaches, we estimated the 10-year net survival (NS) and we described the excess mortality hazard (EMH) due (directly or indirectly) to the DLBCL, over time and according to main prognosis factors using flexible regression modelling. The 10-year NS was 65% [59; 71]. Using the flexible modelling, we showed that the EMH decreases steeply after diagnosis. The variables 'performance status', 'number of extra-nodal sites' and the serum 'lactate dehydrogenase' were strongly associated with the EMH, even after adjustment on other important variables. EMH is very close to zero at 10 years for the whole population, so DLBCL patients do not experience an increased mortality compared to the general population in the long term. The number of extra-nodal sites was an important prognostic factor shortly after diagnosis, suggesting that it is correlated with an important but unmeasured prognostic factor that would lead to this selection effect over time.

Direct modeling of the crude probability of cancer death and the number of life years lost due to cancer without the need of cause of death: a pseudo-observation approach in the relative survival setting.

In population-based cancer studies, net survival is a crucial measure for population comparison purposes. However, alternative measures, namely the crude probability of death (CPr) and the number of life years lost (LYL) due to death according to different causes, are useful as complementary measures for reflecting different dimensions in terms of prognosis, treatment choice, or development of a control strategy. When the cause of death (COD) information is available, both measures can be estimated in competing risks setting using either cause-specific or subdistribution hazard regression models or with the pseudo-observation approach through direct modeling. We extended the pseudo-observation approach in order to model the CPr and the LYL due to different causes when information on COD is unavailable or unreliable (i.e., in relative survival setting). In a simulation study, we assessed the performance of the proposed approach in estimating regression parameters and examined models with different link functions that can provide an easier interpretation of the parameters. We showed that the pseudo-observation approach performs well for both measures and we illustrated their use on cervical cancer data from the England population-based cancer registry. A tutorial showing how to implement the method in R software is also provided.

Individual frailty excess hazard models in cancer epidemiology.

Unobserved individual heterogeneity is a common challenge in population cancer survival studies. This heterogeneity is usually associated with the combination of model misspecification and the failure to record truly relevant variables. We investigate the effects of unobserved individual heterogeneity in the context of excess hazard models, one of the main tools in cancer epidemiology. We propose an individual excess hazard frailty model to account for individual heterogeneity. This represents an extension of frailty modeling to the relative survival framework. In order to facilitate the inference on the parameters of the proposed model, we select frailty distributions which produce closed-form expressions of the marginal hazard and survival functions. The resulting model allows for an intuitive interpretation, in which the frailties induce a selection of the healthier individuals among survivors. We model the excess hazard using a flexible parametric model with a general hazard structure which facilitates the inclusion of time-dependent effects. We illustrate the performance of the proposed methodology through a simulation study. We present a real-data example using data from lung cancer patients diagnosed in England, and discuss the impact of not accounting for unobserved heterogeneity on the estimation of net survival. The methodology is implemented in the R package IFNS.

Functional forms of socio-territorial inequities in breast cancer screening - A French cross-sectional study using hierarchical generalised additive models.

To reduce the breast cancer burden, the French National Organised Breast Cancer Screening Programme (FNOBCSP) was implemented in 2004. The recommended participation rate has never been achieved and socio-territorial inequities in participation have been reported on several occasions. We investigated the functional forms and consistency of the relationships between neighbourhood deprivation, travel time to the nearest accredited radiology centre and screening uptake. We used two-level hierarchical generalised additive models in 8 types of territories classified by socio-demographic and economic factors. The first level was 368,201 women aged 50-72 invited to the 2013-2014 screening campaign in metropolitan France. They were nested in 41 départements, the level of organisation of the FNOBCSP. The effect of travel time showed two main patterns: it was either linear (with participation decreasing as travel time increased) or participation first increased with increasing travel time to a peak around 5-15 min and decreased afterward. In nearly all types and départements, the probability of participation decreased linearly with increasing deprivation. Territorial inequities in participation were more context-dependent and complex than social inequities. Inequities in participation represent a loss of opportunity for individuals who already have the worst cancer outcomes. Evidence-based public health policies are needed to increase the effectiveness and equity of breast cancer screening.

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome Associated with COVID-19: An Emulated Target Trial Analysis.

Rationale: Whether patients with coronavirus disease (COVID-19) may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. Objectives: To estimate the effect of ECMO on 90-day mortality versus IMV only. Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO versus no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 < 80 or PaCO2 ⩾ 60 mm Hg). We controlled for confounding using a multivariable Cox model on the basis of predefined variables. Measurements and Main Results: A total of 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability on Day 7 from the onset of eligibility criteria (87% vs. 83%; risk difference, 4%; 95% confidence interval, 0-9%), which decreased during follow-up (survival on Day 90: 63% vs. 65%; risk difference, -2%; 95% confidence interval, -10 to 5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand and when initiated within the first 4 days of IMV and in patients who are profoundly hypoxemic. Conclusions: In an emulated trial on the basis of a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and regions with ECMO capacities specifically organized to handle high demand.

Socio-economic inequalities in cancer survival: how do they translate into Number of Life-Years Lost?

BACKGROUND: We aimed to investigate the impact of socio-economic inequalities in cancer survival in England on the Number of Life-Years Lost (NLYL) due to cancer. METHODS: We analysed 1.2 million patients diagnosed with one of the 23 most common cancers (92.3% of all incident cancers in England) between 2010 and 2014. Socio-economic deprivation of patients was based on the income domain of the English Index of Deprivation. We estimated the NLYL due to cancer within 3 years since diagnosis for each cancer and stratified by sex, age and deprivation, using a non-parametric approach. The relative survival framework enables us to disentangle death from cancer and death from other causes without the information on the cause of death. RESULTS: The largest socio-economic inequalities were seen mostly in adults <45 years with poor-prognosis cancers. In this age group, the most deprived patients with lung, pancreatic and oesophageal cancer lost up to 6 additional months within 3 years since diagnosis than the least deprived. For most moderate/good prognosis cancers, the socio-economic inequalities widened with age. CONCLUSIONS: More deprived patients and particularly the young with more lethal cancers, lose systematically more life-years than the less deprived. To reduce these inequalities, cancer policies should systematically encompass the inequities component.

On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables.

In cancer epidemiology using population-based data, regression models for the excess mortality hazard is a useful method to estimate cancer survival and to describe the association between prognosis factors and excess mortality. This method requires expected mortality rates from general population life tables: each cancer patient is assigned an expected (background) mortality rate obtained from the life tables, typically at least according to their age and sex, from the population they belong to. However, those life tables may be insufficiently stratified, as some characteristics such as deprivation, ethnicity, and comorbidities, are not available in the life tables for a number of countries. This may affect the background mortality rate allocated to each patient, and it has been shown that not including relevant information for assigning an expected mortality rate to each patient induces a bias in the estimation of the regression parameters of the excess hazard model. We propose two parametric corrections in excess hazard regression models, including a single-parameter or a random effect (frailty), to account for possible mismatches in the life table and thus misspecification of the background mortality rate. In an extensive simulation study, the good statistical performance of the proposed approach is demonstrated, and we illustrate their use on real population-based data of lung cancer patients. We present conditions and limitations of these methods and provide some recommendations for their use in practice.

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial.

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators.

An investigation of cancer survival inequalities associated with individual-level socio-economic status, area-level deprivation, and contextual effects, in a cancer patient cohort in England and Wales.

BACKGROUND: People living in more deprived areas of high-income countries have lower cancer survival than those in less deprived areas. However, associations between individual-level socio-economic circumstances and cancer survival are relatively poorly understood. Moreover, few studies have addressed contextual effects, where associations between individual-level socio-economic status and cancer survival vary depending on area-based deprivation. METHODS: Using 9276 individual-level observations from a longitudinal study in England and Wales, we examined the association with cancer survival of area-level deprivation and individual-level occupation, education, and income, for colorectal, prostate and breast cancer patients aged 20-99 at diagnosis. With flexible parametric excess hazard models, we estimated excess mortality across individual-level and area-level socio-economic variables and investigated contextual effects. RESULTS: For colorectal cancers, we found evidence of an association between education and cancer survival in men with Excess Hazard Ratio (EHR) = 0.80, 95% Confidence Interval (CI) = 0.60;1.08 comparing "degree-level qualification and higher" to "no qualification" and EHR = 0.74 [0.56;0.97] comparing "apprenticeships and vocational qualification" to "no qualification", adjusted on occupation and income; and between occupation and cancer survival for women with EHR = 0.77 [0.54;1.10] comparing "managerial/professional occupations" to "manual/technical," and EHR = 0.81 [0.63;1.06] comparing "intermediate" to "manual/technical", adjusted on education and income. For breast cancer in women, we found evidence of an association with income (EHR = 0.52 [0.29;0.95] for the highest income quintile compared to the lowest, adjusted on education and occupation), while for prostate cancer, all three individual-level socio-economic variables were associated to some extent with cancer survival. We found contextual effects of area-level deprivation on survival inequalities between occupation types for breast and prostate cancers, suggesting wider individual-level inequalities in more deprived areas compared to least deprived areas. Individual-level income inequalities for breast cancer were more evident than an area-level differential, suggesting that area-level deprivation might not be the most effective measure of inequality for this cancer. For colorectal cancer in both sexes, we found evidence suggesting area- and individual-level inequalities, but no evidence of contextual effects. CONCLUSIONS: Findings highlight that both individual and contextual effects contribute to inequalities in cancer outcomes. These insights provide potential avenues for more effective policy and practice.

Investigating the inequalities in route to diagnosis amongst patients with diffuse large B-cell or follicular lymphoma in England.

INTRODUCTION: Diagnostic delay is associated with lower chances of cancer survival. Underlying comorbidities are known to affect the timely diagnosis of cancer. Diffuse large B-cell (DLBCL) and follicular lymphomas (FL) are primarily diagnosed amongst older patients, who are more likely to have comorbidities. Characteristics of clinical commissioning groups (CCG) are also known to impact diagnostic delay. We assess the association between comorbidities and diagnostic delay amongst patients with DLBCL or FL in England during 2005-2013. METHODS: Multivariable generalised linear mixed-effect models were used to assess the main association. Empirical Bayes estimates of the random effects were used to explore between-cluster variation. The latent normal joint modelling multiple imputation approach was used to account for partially observed variables. RESULTS: We included 30,078 and 15,551 patients diagnosed with DLBCL or FL, respectively. Amongst patients from the same CCG, having multimorbidity was strongly associated with the emergency route to diagnosis (DLBCL: odds ratio 1.56, CI 1.40-1.73; FL: odds ratio 1.80, CI 1.45-2.23). Amongst DLBCL patients, the diagnostic delay was possibly correlated with CCGs that had higher population densities. CONCLUSIONS: Underlying comorbidity is associated with diagnostic delay amongst patients with DLBCL or FL. Results suggest a possible correlation between CCGs with higher population densities and diagnostic delay of aggressive lymphomas.

Major Depression and Survival in People With Cancer.

OBJECTIVE: The question of whether depression is associated with worse survival in people with cancer remains unanswered because of methodological criticism of the published research on the topic. We aimed to study the association in a large methodologically robust study. METHODS: We analyzed data on 20,582 patients with breast, colorectal, gynecological, lung, and prostate cancers who had attended cancer outpatient clinics in Scotland, United Kingdom. Patients had completed two-stage screening for major depression as part of their cancer care. These data on depression status were linked to demographic, cancer, and subsequent mortality data from national databases. We estimated the association of major depression with survival for each cancer using Cox regression. We adjusted for potential confounders and interactions between potentially time-varying confounders and the interval between cancer diagnosis and depression screening, and used multiple imputation for missing depression and confounder data. We pooled the cancer-specific results using fixed-effects meta-analysis. RESULTS: Major depression was associated with worse survival for all cancers, with similar adjusted hazard ratios (HRs): breast cancer (HR = 1.42, 95% confidence interval [CI] = 1.15-1.75), colorectal cancer (HR = 1.47, 95% CI = 1.11-1.94), gynecological cancer (HR = 1.36, 95% CI = 1.08-1.71), lung cancer (HR = 1.39, 95% CI = 1.24-1.56), and prostate cancer (HR = 1.76, 95% CI = 1.08-2.85). The pooled HR was 1.41 (95% CI = 1.29-1.54, p < .001, I2 = 0%). These findings were not materially different when we only considered the deaths (90%) that were attributed to cancer. CONCLUSIONS: Major depression is associated with worse survival in patients with common cancers. The mechanisms of this association and the clinical implications require further study.

Probabilities of ICU admission and hospital discharge according to patient characteristics in the designated COVID-19 hospital of Kuwait.

BACKGROUND: Subsequent epidemic waves have already emerged in many countries and in the absence of highly effective preventive and curative options, the role of patient characteristics on the development of outcomes needs to be thoroughly examined, especially in middle-east countries where such epidemiological studies are lacking. There is a huge pressure on the hospital services and in particular, on the Intensive Care Units (ICU). Describing the need for critical care as well as the chance of being discharged from hospital according to patient characteristics, is essential for a more efficient hospital management. The objective of this study is to describe the probabilities of admission to the ICU and the probabilities of hospital discharge among positive COVID-19 patients according to demographics and comorbidities recorded at hospital admission. METHODS: A prospective cohort study of all patients with COVID-19 found in the Electronic Medical Records of Jaber Al-Ahmad Al-Sabah Hospital in Kuwait was conducted. The study included 3995 individuals (symptomatic and asymptomatic) of all ages who tested positive from February 24th to May 27th, 2020, out of which 315 were treated in the ICU and 3619 were discharged including those who were transferred to a different healthcare unit without having previously entered the ICU. A competing risk analysis considering two events, namely, ICU admission and hospital discharge using flexible hazard models was performed to describe the association between event-specific probabilities and patient characteristics. RESULTS: Results showed that being male, increasing age and comorbidities such as chronic kidney disease (CKD), asthma or chronic obstructive pulmonary disease and weakened immune system increased the risk of ICU admission within 10 days of entering the hospital. CKD and weakened immune system decreased the probabilities of discharge in both females and males however, the age-related pattern differed by gender. Diabetes, which was the most prevalent comorbid condition, had only a moderate impact on both probabilities (18% overall) in contrast to CKD which had the largest effect, but presented only in 7% of those admitted to ICU and in 1% of those who got discharged. For instance, within 5 days a 50-year-old male had 19% (95% C.I.: [15,23]) probability of entering the ICU if he had none of these comorbidities, yet this risk jumped to 31% (95% C.I.: [20,46]) if he had also CKD, and to 27% in the presence of asthma/COPD (95% C.I.: [19,36]) or of weakened immune system (95% C.I.: [16,42]). CONCLUSIONS: This study provides useful insight in describing the probabilities of ICU admission and hospital discharge according to age, gender, and comorbidities among confirmed COVID-19 cases in Kuwait. A web-tool is also provided to allow the user to estimate these probabilities for any combination of these covariates. These probabilities enable deeper understanding of the hospital demand according to patient characteristics which is essential to hospital management and useful for developing a vaccination strategy.

A French multicentric prospective prognostic cohort with epidemiological, clinical, biological and treatment information to improve knowledge on lymphoma patients: study protocol of the "REal world dAta in LYmphoma and survival in adults" (REALYSA) cohort.

BACKGROUND: Age-adjusted lymphoma incidence rates continue to rise in France since the early 80's, although rates have slowed since 2010 and vary across subtypes. Recent improvements in patient survival in major lymphoma subtypes at population level raise new questions about patient outcomes (i.e. quality of life, long-term sequelae). Epidemiological studies have investigated factors related to lymphoma risk, but few have addressed the extent to which socioeconomic status, social institutional context (i.e. healthcare system), social relationships, environmental context (exposures), individual behaviours (lifestyle) or genetic determinants influence lymphoma outcomes, especially in the general population. Moreover, the knowledge of the disease behaviour mainly obtained from clinical trials data is partly biased because of patient selection. METHODS: The REALYSA ("REal world dAta in LYmphoma and Survival in Adults") study is a real-life multicentric cohort set up in French areas covered by population-based cancer registries to study the prognostic value of epidemiological, clinical and biological factors with a prospective 9-year follow-up. We aim to include 6000 patients over 4 to 5 years. Adult patients without lymphoma history and newly diagnosed with one of the following 7 lymphoma subtypes (diffuse large B-cell, follicular, marginal zone, mantle cell, Burkitt, Hodgkin, mature T-cell) are invited to participate during a medical consultation with their hematologist. Exclusion criteria are: having already received anti-lymphoma treatment (except pre-phase) and having a documented HIV infection. Patients are treated according to the standard practice in their center. Clinical data, including treatment received, are extracted from patients' medical records. Patients' risk factors exposures and other epidemiological data are obtained at baseline by filling out a questionnaire during an interview led by a clinical research assistant. Biological samples are collected at baseline and during treatment. A virtual tumor biobank is constituted for baseline tumor samples. Follow-up data, both clinical and epidemiological, are collected every 6 months in the first 3 years and every year thereafter. DISCUSSION: This cohort constitutes an innovative platform for clinical, biological, epidemiological and socio-economic research projects and provides an opportunity to improve knowledge on factors associated to outcome of lymphoma patients in real life. TRIAL REGISTRATION: 2018-A01332-53, ClinicalTrials.gov identifier: NCT03869619 .

Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers.

BACKGROUND: The presence of comorbidity affects the care of cancer patients, many of whom are living with multiple comorbidities. The prevalence of cancer comorbidity, beyond summary metrics, is not well known. This study aims to estimate the prevalence of comorbid conditions among cancer patients in England, and describe the association between cancer comorbidity and socio-economic position, using population-based electronic health records. METHODS: We linked England cancer registry records of patients diagnosed with cancer of the colon, rectum, lung or Hodgkin lymphoma between 2009 and 2013, with hospital admissions records. A comorbidity was any one of fourteen specific conditions, diagnosed during hospital admission up to 6 years prior to cancer diagnosis. We calculated the crude and age-sex adjusted prevalence of each condition, the frequency of multiple comorbidity combinations, and used logistic regression and multinomial logistic regression to estimate the adjusted odds of having each condition and the probability of having each condition as a single or one of multiple comorbidities, respectively, by cancer type. RESULTS: Comorbidity was most prevalent in patients with lung cancer and least prevalent in Hodgkin lymphoma patients. Up to two-thirds of patients within each of the four cancer patient cohorts we studied had at least one comorbidity, and around half of the comorbid patients had multiple comorbidities. Our study highlighted common comorbid conditions among the cancer patient cohorts. In all four cohorts, the odds of having a comorbidity and the probability of multiple comorbidity were consistently highest in the most deprived cancer patients. CONCLUSIONS: Cancer healthcare guidelines may need to consider prominent comorbid conditions, particularly to benefit the prognosis of the most deprived patients who carry the greater burden of comorbidity. Insight into patterns of cancer comorbidity may inform further research into the influence of specific comorbidities on socio-economic inequalities in receipt of cancer treatment and in short-term mortality.

Socioeconomic environment and disparities in cancer survival for 19 solid tumor sites: An analysis of the French Network of Cancer Registries (FRANCIM) data.

Social inequalities are concerning along the cancer continuum. In France, social gradient in health is particularly marked but little is known about social gradient in cancer survival. We aimed to investigate the influence of socioeconomic environment on cancer survival, for all cancers reported in the French Network of Cancer Registries. We analyzed 189,657 solid tumors diagnosed between 2006 and 2009, recorded in 18 registries. The European Deprivation Index (EDI), an ecological index measuring relative poverty in small geographic areas, assessed social environment. The EDI was categorized into quintiles of the national distribution. One- and five-year age-standardized net survival (ASNS) were estimated for each solid tumor site and deprivation quintile, among men and among women. We found that 5-year ASNS was lower among patients living in the most deprived areas compared to those living in the least deprived ones for 14/16 cancers among men and 16/18 cancers among women. The extent of cancer survival disparities according to deprivation varied substantially across the cancer sites. The reduction in ASNS between the least and the most deprived quintile reached 34% for liver cancer among men and 59% for bile duct cancer among women. For pancreas, stomach and esophagus cancer (among men), and ovary and stomach cancer (among women), deprivation gaps were larger at 1-year than 5-year survival. In conclusion, survival was worse in the most deprived areas for almost all cancers. Our results from population-based cancer registries data highlight the need for implementing actions to reduce social inequalities in cancer survival in France.

Association between age, deprivation and specific comorbid conditions and the receipt of major surgery in patients with non-small cell lung cancer in England: A population-based study.

INTRODUCTION: We investigated socioeconomic disparities and the role of the main prognostic factors in receiving major surgical treatment in patients with lung cancer in England. METHODS: Our study comprised 31 351 patients diagnosed with non-small cell lung cancer in England in 2012. Data from the national population-based cancer registry were linked to Hospital Episode Statistics and National Lung Cancer Audit data to obtain information on stage, performance status and comorbidities, and to identify patients receiving major surgical treatment. To describe the association between prognostic factors and surgery, we performed two different analyses: one using multivariable logistic regression and one estimating cause-specific hazards for death and surgery. In both analyses, we used multiple imputation to deal with missing data. RESULTS: We showed strong evidence that the comorbidities 'congestive heart failure', 'cerebrovascular disease' and 'chronic obstructive pulmonary disease' reduced the receipt of surgery in early stage patients. We also observed gender differences and substantial age differences in the receipt of surgery. Despite accounting for sex, age at diagnosis, comorbidities, stage at diagnosis, performance status and indication of having had a PET-CT scan, the socioeconomic differences persisted in both analyses: more deprived people had lower odds and lower rates of receiving surgery in early stage lung cancer. DISCUSSION: Comorbidities play an important role in whether patients undergo surgery, but do not completely explain the socioeconomic difference observed in early stage patients. Future work investigating access to and distance from specialist hospitals, as well as patient perceptions and patient choice in receiving surgery, could help disentangle these persistent socioeconomic inequalities.

Estimation of the adjusted cause-specific cumulative probability using flexible regression models for the cause-specific hazards.

In competing risks setting, we account for death according to a specific cause and the quantities of interest are usually the cause-specific hazards (CSHs) and the cause-specific cumulative probabilities. A cause-specific cumulative probability can be obtained with a combination of the CSHs or via the subdistribution hazard. Here, we modeled the CSH with flexible hazard-based regression models using B-splines for the baseline hazard and time-dependent (TD) effects. We derived the variance of the cause-specific cumulative probabilities at the population level using the multivariate delta method and showed how we could easily quantify the impact of a covariate on the cumulative probability scale using covariate-adjusted cause-specific cumulative probabilities and their difference. We conducted a simulation study to evaluate the performance of this approach in its ability to estimate the cumulative probabilities using different functions for the cause-specific log baseline hazard and with or without a TD effect. In the scenario with TD effect, we tested both well-specified and misspecified models. We showed that the flexible regression models perform nearly as well as the nonparametric method, if we allow enough flexibility for the baseline hazards. Moreover, neglecting the TD effect hardly affects the cumulative probabilities estimates of the whole population but impacts them in the various subgroups. We illustrated our approach using data from people diagnosed with monoclonal gammopathy of undetermined significance and provided the R-code to derive those quantities, as an extension of the R-package mexhaz.