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Simple Summary: Studies investigating the social gradient in digestive cancer survival are scarce, and the statistical methods used do not always consider important assumptions in survival analysis for adequate assessment. Using an ecological index (European Deprivation Index), we found a negative impact of social environment in digestive cancers net survival (especially for esophagus, stomach, bile ducts among females; colon and rectum for both sexes) and provided insight into how this social gradient in cancer survival builds up, and at what time of follow-up it appears. These results can guide clinical practice/public health actions to address social inequalities in survival by targeting digestive cancers with the greatest impact and identifying key follow-up periods to implement actions. Social inequalities are an important prognostic factor in cancer survival, but little is known regarding digestive cancers specifically. We aimed to provide in-depth analysis of the contextual social disparities in net survival of patients with digestive cancer in France, using population-based data and relevant modeling. Digestive cancers (n = 54,507) diagnosed between 2006–2009, collected through the French network of cancer registries, were included (end of follow-up 30 June 2013). Social environment was assessed by the European Deprivation Index. Multidimensional penalized splines were used to model excess mortality hazard. We found that net survival was significantly worse for individuals living in a more deprived environment as compared to those living in a less deprived one for esophageal, liver, pancreatic, colon and rectal cancers, and for stomach and bile duct cancers among females. Excess mortality hazard was up to 57% higher among females living in the most deprived areas (vs. least deprived) at 1 year of follow-up for bile duct cancer, and up to 21% higher among males living in the most deprived areas (vs. least deprived) regarding colon cancer. To conclude, we provide a better understanding of how the (contextual) social gradient in survival is constructed, offering new perspectives for tackling social inequalities in digestive cancer survival. [ABSTRACT FROM AUTHOR]

Background: Many international ecological studies that examine the link between social environment and cancer incidence use a deprivation index based on the subjects' address at the time of diagnosis to evaluate socioeconomic status. Thus, social past details are ignored, which leads to misclassification bias in the estimations. The objectives of this study were to include the latency delay in such estimations and to observe the effects. Methods: We adapted a previous methodology to correct estimates of the influence of socioeconomic environment on cancer incidence considering the latency delay in measuring socioeconomic status. We implemented this method using French data. We evaluated the misclassification due to social mobility with census data and corrected the relative risks. Results: Inclusion of misclassification affected the values of relative risks, and the corrected values showed a greater departure from the value 1 than the uncorrected ones. For cancer of lung, colonrectum, lips-mouth-pharynx, kidney and esophagus in men, the over incidence in the deprived categories was augmented by the correction. Conclusions: By not taking into account the latency period in measuring socioeconomic status, the burden of cancer associated with social inequality may be underestimated. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the relationship between socioeconomic deprivation and postoperative outcomes in patients who underwent colonic resection for sigmoid diverticulitis (SD)., Background: The potential impact of socioeconomic inequalities on the management of SD has been scarcely studied in the literature. Considering other gastrointestinal pathologies for which lesser access to optimal treatment and poorer survival have been shown, we hypothesize that deprivation could be associated with outcomes for SD., Methods: This multicenter retrospective study was conducted at 41 French hospitals between January 1, 2010, and August 31, 2021. The main outcome was the occurrence of severe postoperative complications on postoperative day 90, according to the Clavien-Dindo scale (≥3). The European Deprivation Index was used to approximate deprivation for each patient. Multiple imputations by a chained equation were performed to consider the influence of missing data on the results., Results: Twenty percent of the 6415 patients operated on had severe postoperative complications at 90 days. In the multivariate regression analysis, increasing age, male sex, American Society of Anesthesiologists score ≥3, conversion to laparotomy or upfront open approach, surgical procedures, and perioperative transfusion were independent risk factors for severe postoperative complications. After adjusting for age, sex, body mass index, American Society of Anesthesiologists score, emergent setting, blood transfusion, indications for surgery, surgical approach, and procedures, the probability of severe postoperative complications increased with socioeconomic deprivation (P=0.026) by day 90., Conclusions: This study highlights the potential influence of socioeconomic deprivation on the surgical outcomes of SD. Socioeconomic deprivation should be considered as a risk factor for severe postoperative complications during the preoperative assessment of the patient's medical conditions., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The impact of geographical accessibility on cancer survival has been investigated in few studies, with most research focusing on access to reference care centers, using overall mortality and limited to specific cancer sites. This study aims to examine the association of access to primary care with mortality in excess of patients with the 10 most frequent cancers in France, while controlling for socioeconomic deprivation., Methods: This study included a total of 151,984 cases diagnosed with the 10 most common cancer sites in 21 French cancer registries between 2013 and 2015. Access to primary care was estimated using two indexes: the Accessibilité Potentielle Localisée index (access to general practitioners) and the Scale index (access to a range of primary care clinicians). Mortality in excess was modelized using an additive framework based on expected mortality based on lifetables and observed mortality., Findings: Patients living in areas with less access to primary care had a greater mortality in excess for some very common cancer sites like breast (women), lung (men), liver (men and women), and colorectal cancer (men), representing 46% of patients diagnosed in our sample. The maximum effect was found for breast cancer; the excess hazard ratio was estimated to be 1.69 (95% CI, 1.20-2.38) 1 year after diagnosis and 2.26 (95% CI, 1.07-4.80) 5 years after diagnosis., Interpretation: This study revealed that this differential access to primary care was associated with mortality in excess for patients with cancer and should become a priority for health policymakers to reduce these inequalities in health care accessibility., (© 2024 American Cancer Society.)

Background: The observed increase in the incidence of complicated diverticulitis may lead to the performance of more emergency surgeries. This study aimed to assess the rate and risk factors of emergency surgery for sigmoid diverticulitis., Method: The primary outcomes were the rate of emergency surgery for sigmoid diverticulitis and its associated risk factors. The urgent or elective nature of the surgical intervention was provided by the surgeon and in accordance with the indication for surgical treatment. A mixed logistic regression with a random intercept after multiple imputations by the chained equation was performed to consider the influence of missing data on the results., Results: Between 2010 and 2021, 6,867 patients underwent surgery for sigmoid diverticulitis in the participating centers, of which one-third (n = 2317) were emergency cases. In multivariate regression analysis with multiple imputation by chained equation, increasing age, body mass index <18.5 kg/m 2 , neurologic and pulmonary comorbidities, use of anticoagulant drugs, immunocompromised status, and first attack of sigmoid diverticulitis were independent risk factors for emergency surgery. The likelihood of emergency surgery was significantly more frequent after national guidelines, which were implemented in 2017, only in patients with a history of sigmoid diverticulitis attacks., Conclusion: The present study highlights a high rate (33%) of emergency surgery for sigmoid diverticulitis in France, which was significantly associated with patient features and the first attack of diverticulitis., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The presence of missing data, a constant problem in medical research, has several consequences: systematic loss of power, associated or not with a reduction in the representativeness of the sample analyzed. There are three types of missing data: 1) missing completely at random (MCAR); 2) missing at random (MAR); 3) missing not at random (MNAR). Multiple imputation by chained equations allows for the correct handling of missing data under the MCAR and MAR assumptions. It allows to simulate for each missing data j, a number m of simulated values which seem plausible with regard to the other variables. A random effect is included in this simulation to express the uncertainty. Several data sets are thus created and analyzed individually, in an identical way. Then the estimators of each data set are combined to obtain a global estimator. Multiple imputation increases power, corrects for some biases and has the advantage of being applicable to many types of variables. Complete case analysis should no longer be the norm. The objective of this guide is to help the reader in conducting an analysis with multiple imputed data. We cover the following points: the different types of missing data, the different historical approaches to handling them, and then we detail the multiple imputation method using chained equations. We provide a code example for the mice package of R®.

Background: The relationship between socioeconomic status (SES) and mortality among persons with multiple sclerosis (PwMS) is poorly understood., Objective: To investigate the association between SES and mortality risk in PwMS., Methods: From health-administrative data, we identified 12,126 incident MS cases with a first demyelinating event (MS 'onset') occurring between 1994 and 2017. Cox proportional hazard model assessed the association between socioeconomic status quintiles (SES-Qs) at MS onset and all-cause mortality., Results: Lower SES-Qs were associated with higher mortality risk; adjusted hazard ratios: SES-Q1 (most deprived) =1.61 (95% confidence interval (CI) = 1.36-1.91); SES-Q2 = 1.26 (95% CI = 1.05-1.50); SES-Q3 = 1.22 (95% CI = 1.02-1.46); SES-Q4 = 1.13 (95% CI = 0.94-1.35) versus SES-Q5 (least deprived)., Conclusion: A lower SES was associated with higher mortality risk in PwMS.

Background: The effects of socio-economic status on mortality in patients with multiple sclerosis is not well known. The objective was to examine mortality due to multiple sclerosis according to socio-economic status., Methods: A retrospective observational cohort design was used with recruitment from 18 French multiple sclerosis expert centers participating in the Observatoire Français de la Sclérose en Plaques. All patients lived in metropolitan France and had a definite or probable diagnosis of multiple sclerosis according to either Poser or McDonald criteria with an onset of disease between 1960 and 2015. Initial phenotype was either relapsing-onset or primary progressive onset. Vital status was updated on January 1st 2016. Socio-economic status was measured by an ecological index, the European Deprivation Index and was attributed to each patient according to their home address. Excess death rates were studied according to socio-economic status using additive excess hazard models with multidimensional penalised splines. The initial hypothesis was a potential socio-economic gradient in excess mortality., Findings: A total of 34,169 multiple sclerosis patients were included (88% relapsing onset (n = 30,083), 12% progressive onset (n = 4086)), female/male sex ratio 2.7 for relapsing-onset and 1.3 for progressive-onset). Mean age at disease onset was 31.6 (SD = 9.8) for relapsing-onset and 42.7 (SD = 10.8) for progressive-onset. At the end of follow-up, 1849 patients had died (4.4% for relapsing-onset (n = 1311) and 13.2% for progressive-onset (n = 538)). A socio-economic gradient was found for relapsing-onset patients; more deprived patients had a greater excess death rate. At thirty years of disease duration and a year of onset of symptoms of 1980, survival probability difference (or deprivation gap) between less deprived relapsing-onset patients (EDI = -6) and more deprived relapsing-onset patients (EDI = 12) was 16.6% (95% confidence interval (CI) [10.3%-22.9%]) for men and 12.3% (95%CI [7.6%-17.0%]) for women. No clear socio-economic mortality gradient was found in progressive-onset patients., Interpretation: Socio-economic status was associated with mortality due to multiple sclerosis in relapsing-onset patients. Improvements in overall care of more socio-economically deprived patients with multiple sclerosis could help reduce these socio-economic inequalities in multiple sclerosis-related mortality., Funding: This study was funded by the ARSEP foundation "Fondation pour l'aide à la recherche sur la Sclérose en Plaques" (Grant Reference Number 1122). Data collection has been supported by a grant provided by the French State and handled by the "Agence Nationale de la Recherche," within the framework of the "Investments for the Future" programme, under the reference ANR-10-COHO-002, Observatoire Français de la Sclérose en Plaques (OFSEP)., Competing Interests: Sarah Wilson, Fabien Rollot, Mathieu Fauvernier, Laurent Remontet, Laure Tron, Marc Debouverie, Jérôme de Sèze, Thibault Moreau, Christine Lebrun Frenay, Pierre Labauge, Jean Pelletier and Olivier Dejardin report no disclosures. Floriane Calocer: received funding for the present research from the ARSEP foundation for a Postdoctoral Fellowship (payment to the institution), from the “Réseau Bas-Normand pour la SEP” for a Postdoctoral Fellowship (payment to the institution), from the Regional Council of Normandy (payment to the institution), from the Ecole Doctorale of Caen University for a training in LSHTD to conduct this research (payment to the institution). She received support for attending meetings and/or travel from the ARSEP Foundation (paid directly to herself, unrelated to this work). Sandra Vukusic: received grants or contracts (paid to her university hospital) from Biogen, BMS-Celgene, Janssen, Merck, Novartis, Roche, Sanofi-Genzyme and Teva; received consulting fees from Biogen, BMS-Celgene, Janssen, Merck, Novartis, Roche, Sanofi-Genzyme and Teva (paid to her university hospital); received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Biogen, Merck, Novartis, Roche, Sanofi-Genzyme and Teva (paid to her university hospital); received support for attending meetings and/or travel from Biogen, Merck, Novartis, Roche, Sanofi-Genzyme and Teva, participated on a Data Safety Monitoring Board or Advisory Board for Biogen (contracts with her university hospital), all of the above unrelated to this work. Emmanuelle Le Page: received payment or honoraria for consulting or lectures from Biogen, Merck, Teva, Sanofi-Genzyme, Novartis Alexion; received research support from Teva and Biogen, and received academic research grants from PHRC and LFSEP, and a travel grant from the ARSEP Foundation; received payment for consulting from Biogen, Merck, Sanofi-Genzyme, and Novartis; received invitations for national and international congresses from Biogen, Merck, Sanofi-Genzyme, Novartis Alexion, all of the above unrelated to this work. Jonathan Ciron: participated on a Data Safety Monitory Board of Advisory Board with Biogen, Novartis, Merck, Sanofi, Roche, Alexion and BMS-Celgene (all unrelated to this work). Aurélie Ruet: Consultancy fees from Roche and Biogen, payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Merck, Roche, Biogen, research grants (paid to the institution) from Roche, Biogen and Sanofi-Genzyme, and support for attending meetings and/or travel from Biogen, Novartis and Alexion, all of the above unrelated to this work. Hélène Zephir: received research support for one PhD student from Roche, and research support for one MD student from FHU Imminent, consulting fees from Biogen IDEC (Symposium Biogen Idec in ISNI congress); received payment or honoraria for lectures from Merck, received payment or honoraria for lectures and boards from Novartis, all of the above unrelated to this work. David-Axel Laplaud: received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Biogen, Merck, Alexion, BMS, Roche and Novartis, all of the above unrelated to this work. Pierre Clavelou: received consulting fees from Biogen, Janssen, Medday, Merck, Novartis, Roche, Sanofi-Genzyme and Teva Pharma; and support for attending meetings and/or travel from Sanofi-Genzyme, and participated on a Data Safety Monitoring Board or Advisory Board for Medday, Merck and Novartis. All of the above unrelated to this work. Eric Berger: received consulting fees from Novartis, Sanofi Aventis, Biogen, Genzyme, Roche and Teva Pharma; received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novartis, Sanofi Aventis, Biogen, Genyme, Roche and Teva Pharma (all of the above unrelated to this work). Olivier Heinzlef: consulting fees from Bayer Schering, Merck, Teva, Genzyme, Novartis, Almirall and BiogenIdec, support for attending meetings and/or travel grants from Novartis, Teva, Genzyme, Merck Serono and Biogen Idec and other financial or non-financial interests from Novartis, Teva, Genzyme, Merck Serono and BiogenIdec (all of the above unrelated to this work). Eric Thouvenot: received grants or contracts from Novartis and Biogen (paid to the institution), consulting fees from Merck, Novartis, Biogen and Celgene (paid directly to himself); received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Merck, Novartis, Biogen, Celgene (paid directly to himself). All of the above unrelated to this work. Jean Philippe Camdessanché: received grants or contracts from CSL-Behring, Grifols, Laboratoire Français des Biotechnologies, consulting fees from Akcea, Alexion, Alnylam, Argenx, Bristol Myers Squibb, Laboratoire Français des Biotechnologies, Pfizer, UCB Pharma, SNF-Floeger, received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Akcea, Alexion, Alnylam, Argenx, Biogen, CSL-Behring, Genzyme, Grifols, Laboratoire Français des Biotechnologies, Merck-Serono, Natus, Novartis, Pfizer, UCB Pharma and Teva. Received support for attending meetings and/or travel from Akcea, Alexion, Alnylam, Argenx, Biogen, CSL-Behring, Genzyme, Grifols, Laboratoire Français des Biotechnologies, Merck-Serono, Natus, Novartis, Pfizer, Teva, SNF-Floeger, all of the above unrelated to this work. Emmanuelle Leray: received consulting fees from Alexion, Merck, Novartis, Roche and Biogen, received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Sanofi Genzyme, and received support for attending meetings and/or travel from Sanofi Genzyme, all of the above unrelated to this work. Gilles Defer Received research grants (paid to institution) from Biogen, Merck Serono, Novartis, Sanofi Genzyme; payment for speaker honoraria from Biogen, Merck Serono, Novartis, Sanofi Genzyme, Teva Pharmaceuticals, BMS; funding for travel from Biogen, Merck Serono, Novartis, Sanofi Genzyme, Teva Pharmaceuticals; and personal compensation for scientific advisory boards from Biogen, Merck Serono, Novartis, Sanofi Genzyme, Teva Pharmaceuticals, and BMS. All of the above unrelated to this work., (© 2022 The Author(s).)

Background: To investigate the value of European deprivation index (EDI) and hepatocellular carcinoma (HCC) characteristics and their relationships with outcome after liver transplantation (LT)., Methods: Patients undergoing LT for HCC were included from a national database (from "Agence de la Biomédecine" between 2006 and 2016. Characteristics of the patients were blindly extracted from the database. Thus, EDI was calculated in 5 quintiles and prognosis factors of survival were determined according to a Cox model., Results: Among the 3865 included patients, 33.9% were in the fifth quintile (quintile 1, N = 562 [14.5%]; quintile 2, N = 647 [16.7%]; quintile 3, N = 654 [16.9%]; quintile 4, N = 688 [17.8%]). Patients in each quintile were comparable regarding HCC history, especially median size of HCC, number of nodules of HCC and alpha-fetoprotein score. In the univariate analysis of the crude survival, having >2 nodules of HCC before LT and time on waiting list were associated with a higher risk of death (P < 0.0001 and P = 0.03, respectively). EDI, size of HCC, model for end-stage liver disease score, Child-Pugh score were not statistically significant in the crude and net survival. In both survival, time on waiting list and number of HCC ≥2 were independent factor of mortality after LT for HCC (P = 0.009 and 0.001, respectively, and P = 0.03 and 0.02, respectively)., Conclusions: EDI does not impact overall survival after LT for HCC. Number of HCC and time on waiting list are independent prognostic factors of survival after LT for HCC., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

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., Competing Interests: Disclosure The authors report no conflicts of interest in this work.

The survival of patients with diffuse large B-cell lymphoma has increased during the last decade as a result of addition of anti-CD20 to anthracycline-based chemotherapy. Although the trend is encouraging, there are persistent differences in survival within and between the USA and European countries suggesting that non-biological factors play a role. Our aim was to investigate the influence of such factors on relative survival of patients with diffuse large B-cell lymphoma. We conducted a retrospective, multicenter, registry-based study in France on 1165 incident cases of diffuse large B-cell lymphoma between 2002 and 2008. Relative survival analyses were performed and missing data were controlled with the multiple imputation method. In a multivariate analysis, adjusted for age, sex and International Prognostic Index, we confirmed that time period was associated with a better 5-year relative survival. The registry area, the medical specialty of the care department (onco-hematology versus other), the time to travel to the nearest teaching hospital, the place of treatment (teaching versus not-teaching hospital -borderline significance), a comorbidity burden and marital status were independently associated with the 5-year relative survival. Adjusted for first-course treatment, inclusion in a clinical trial and treatment discussion in a multidisciplinary meeting were strongly associated with a better survival outcome. In contrast, socio-economic status (determined using the European Deprivation Index) was not associated with outcome. Despite therapeutic advances, various non-biological factors affected the relative survival of patients with diffuse large B-cell lymphoma. The notion of lymphoma-specific expertise seems to be essential to achieve optimal care management and reopens the debate regarding centralization of these patients' care in hematology/oncology departments., (Copyright© Ferrata Storti Foundation.)

Pancreatic survival is one of the worst in oncology. To what extent wait times affect outcomes in unknown No population-based study has previously explored patient and treatment delays among individuals with pancreatic cancer. The aim of this study was to estimate patient and treatment delays in patients with pancreatic cancer and to measure their association with survival in a nonselected population. All patients diagnosed with pancreatic cancer for the first time between 2009 and 2011 and registered in two French digestive cancer registries were included. Patient delay (time from onset of symptoms until the first consultation categorized into <1 or ≥1 month), and treatment delay (time between the first consultation and treatment categorized into less or more than 29 days, the median time) were collected. Overall delay was used to test associations between survival and the timeliness of care by combining patient delay and treatment delay. Patient delay was longer than 1 month in 46% of patients. A patient delay longer than one month was associated with the absence of jaundice (p < 0.001) and the presence of metastasis (p = 0.003). After adjusting for other covariates, such as symptoms and treatment, the presence of metastasis was negatively associated with treatment delay longer than 29 days (p = 0.025). After adjustment for other covariates, especially metastatic dissemination and the result of the resection, overall delay was not significantly associated with prognosis. We found little evidence to suggest that timely care was associated with the survival of patients., (© 2016 UICC.)

The excess hazard regression model is an approach developed for the analysis of cancer registry data to estimate net survival, that is, the survival of cancer patients that would be observed if cancer was the only cause of death. Cancer registry data typically possess a hierarchical structure: individuals from the same geographical unit share common characteristics such as proximity to a large hospital that may influence access to and quality of health care, so that their survival times might be correlated. As a consequence, correct statistical inference regarding the estimation of net survival and the effect of covariates should take this hierarchical structure into account. It becomes particularly important as many studies in cancer epidemiology aim at studying the effect on the excess mortality hazard of variables, such as deprivation indexes, often available only at the ecological level rather than at the individual level. We developed here an approach to fit a flexible excess hazard model including a random effect to describe the unobserved heterogeneity existing between different clusters of individuals, and with the possibility to estimate non-linear and time-dependent effects of covariates. We demonstrated the overall good performance of the proposed approach in a simulation study that assessed the impact on parameter estimates of the number of clusters, their size and their level of unbalance. We then used this multilevel model to describe the effect of a deprivation index defined at the geographical level on the excess mortality hazard of patients diagnosed with cancer of the oral cavity. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)