Describing the association between socioeconomic inequalities and cancer survival: methodological guidelines and illustration with population-based data

Survival inequalities in head and neck cancers: A hospital-based cohort study

BACKGROUND

We investigated whether the socioeconomic status (SES) influenced survival rates in oropharynx cancers (OPC), oral cavity cancers (OCC), and larynx cancers (LC) in Brazilian patients.

METHODS

This hospital-based cohort study assessed the age-standardized 5-year relative survival (RS) using the Pohar Perme estimator.

RESULTS

Overall, we identified 37 191 cases, and 5-year RS were 24.4%, 34.1%, and 44.9% in OPC, OCC, and LC, respectively. In multiple Cox regression, the highest risk of death occurred in the most vulnerable social strata for all subsites-that is, illiterates or patients relying on publicly funded healthcare services. Disparities increased over time by 34.9% in OPC due to the rising of survival rates in the highest SES, whereas they reduced by 10.2% and 29.6% in OCC and LC.

CONCLUSIONS

The potential inequities were more significant for OPC than for OCC and LC. It is urgent to tackle social disparities to improve prognoses in highly unequal countries.

Can an Ecological Index of Deprivation Be Used at the Country Level? The Case of the French Version of the European Deprivation Index (F-EDI)

Most ecological indices of deprivation are constructed from census data at the national level, which raises questions about the relevance of their use, and their comparability across a country. We aimed to determine whether a national index can account for deprivation regardless of location characteristics. In Metropolitan France, 43,853 residential census block groups (IRIS) were divided into eight area types based on quality of life. We calculated score deprivation for each IRIS using the French version of the European Deprivation Index (F-EDI). We decomposed the score by calculating the contribution of each of its components by area type, and we assessed the impact of removing each component and recalculating the weights on the identification of deprived IRIS. The set of components most contributing to the score changed according to the area type, but the identification of deprived IRIS remained stable regardless of the component removed for recalculating the score. Not all components of the F-EDI are markers of deprivation according to location characteristics, but the multidimensional nature of the index ensures its robustness. Further research is needed to examine the limitations of using these indices depending on the purpose of the study, particularly in relation to the geographical grid used to calculate deprivation scores.

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.

Excess Mortality by Multimorbidity, Socioeconomic, and Healthcare Factors, amongst Patients Diagnosed with Diffuse Large B-Cell or Follicular Lymphoma in England

(1) Background: Socioeconomic inequalities of survival in patients with lymphoma persist, which may be explained by patients' comorbidities. We aimed to assess the association between comorbidities and the survival of patients diagnosed with diffuse large B-cell (DLBCL) or follicular lymphoma (FL) in England accounting for other socio-demographic characteristics. (2) Methods: Population-based cancer registry data were linked to Hospital Episode Statistics. We used a flexible multilevel excess hazard model to estimate excess mortality and net survival by patient's comorbidity status, adjusted for sociodemographic, economic, and healthcare factors, and accounting for the patient's area of residence. We used the latent normal joint modelling multiple imputation approach for missing data. (3) Results: Overall, 15,516 and 29,898 patients were diagnosed with FL and DLBCL in England between 2005 and 2013, respectively. Amongst DLBCL and FL patients, respectively, those in the most deprived areas showed 1.22 (95% confidence interval (CI): 1.18-1.27) and 1.45 (95% CI: 1.30-1.62) times higher excess mortality hazard compared to those in the least deprived areas, adjusted for comorbidity status, age at diagnosis, sex, ethnicity, and route to diagnosis. (4) Conclusions: Deprivation is consistently associated with poorer survival among patients diagnosed with DLBCL or FL, after adjusting for co/multimorbidities. Comorbidities and multimorbidities need to be considered when planning public health interventions targeting haematological malignancies in England.

Socioeconomic Environment and Survival in Patients with Digestive Cancers: A French Population-Based Study

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.

Dealing with missing information on covariates for excess mortality hazard regression models - Making the imputation model compatible with the substantive model

Missing data is a common issue in epidemiological databases. Among the different ways of dealing with missing data, multiple imputation has become more available in common statistical software packages. However, the incompatibility between the imputation and substantive model, which can arise when the associations between variables in the substantive model are not taken into account in the imputation models or when the substantive model is itself nonlinear, can lead to invalid inference. Aiming at analysing population-based cancer survival data, we extended the multiple imputation substantive model compatible-fully conditional specification (SMC-FCS) approach, proposed by Bartlett et al. in 2015 to accommodate excess hazard regression models. The proposed approach was compared with the standard fully conditional specification multiple imputation procedure and with the complete-case analysis using a simulation study. The SMC-FCS approach produced unbiased estimates in both scenarios tested, while the fully conditional specification produced biased estimates and poor empirical coverages probabilities. The SMC-FCS algorithm was then used for handling missing data in the evaluation of socioeconomic inequalities in survival from colorectal cancer patients diagnosed in the North Region of Portugal. The analysis using SMC-FCS showed a clearer trend in higher excess hazards for patients coming from more deprived areas. The proposed algorithm was implemented in R software and is presented as Supplementary Material.

Effects of Age and Disease Duration on Excess Mortality in Patients With Multiple Sclerosis From a French Nationwide Cohort

OBJECTIVE

To determine the effects of current age and disease duration on excess mortality in multiple sclerosis (MS), we describe the dynamics of excess death rates over these 2 time scales and study the effect of age at MS clinical onset on these dynamics, separately in each initial phenotype.

METHODS

We used data from 18 French MS expert centers participating in the Observatoire Français de la Sclérose en Plaques. Patients with MS living in metropolitan France and having a clinical onset between 1960 and 2014 were included. Vital status was updated on January 1, 2016. For each MS phenotype separately (relapsing onset [RMS] or primary progressive [PPMS]), we used an innovative statistical method to model the logarithm of excess death rates by a multidimensional penalized spline of age and disease duration.

RESULTS

Among 37,524 patients (71% women, mean age at MS onset ± SD 33.0 ± 10.6 years), 2,883 (7.7%) deaths were observed and 7.8% of patients were lost to follow-up. For patients with RMS, there was no excess mortality during the first 10 years after disease onset; afterwards, whatever the age at onset, excess death rates increased with current age. From current age 70, the excess death rate values converged and became identical whatever the age at disease onset, which means that disease duration had no more effect. Excess death rates were higher in men, with an excess hazard ratio of 1.46 (95% confidence interval 1.25-1.70). In contrast, in patients with PPMS, excess death rates rapidly increased from disease onset, and were associated with age at onset, but not with sex.

CONCLUSIONS

In RMS, current age has a stronger effect on MS mortality than disease duration, while their respective effects are not clear in PPMS.

Systematic review of the association between socioeconomic status and bladder cancer survival with hospital type, comorbidities, and treatment delay as mediators

Objectives

Materials and methods

Results

Conclusions

Are social inequalities in acute myeloid leukemia survival explained by differences in treatment utilization? Results from a French longitudinal observational study among older patients

Background

Evidences support social inequalities in cancer survival. Studies on hematological malignancies, and more specifically Acute Myeloid Leukemia (AML), are sparser. Our study assessed: 1/ the influence of patients’ socioeconomic position on survival, 2/ the role of treatment in this relationship, and 3/ the influence of patients’ socioeconomic position on treatment utilization.

Methods

This prospective multicenter study includes all patients aged 60 and older, newly diagnosed with AML, excluding promyelocytic subtypes, between 1st January 2009 to 31st December 2014 in the South-West of France. Data came from medical files. Patients’ socioeconomic position was measured by an ecological deprivation index, the European Deprivation Index. We studied first, patients’ socioeconomic position influence on overall survival (n = 592), second, on the use of intensive chemotherapy (n = 592), and third, on the use of low intensive treatment versus best supportive care among patients judged unfit for intensive chemotherapy (n = 405).

Results

We found an influence of patients’ socioeconomic position on survival (highest versus lowest position HR_Q5: 1.39 [1.05;1.87] that was downsized to become no more significant after adjustment for AML ontogeny (HR_Q5: 1.31[0.97;1.76] and cytogenetic prognosis HR_Q5: 1.30[0.97;1.75]). The treatment was strongly associated with survival. A lower proportion of intensive chemotherapy was observed among patients with lowest socioeconomic position (OR_Q5: 0.41[0.19;0.90]) which did not persist after adjustment for AML ontogeny (OR_Q5: 0.59[0.25;1.40]). No such influence of patients’ socioeconomic position was found on the treatment allocation among patients judged unfit for intensive chemotherapy.

Conclusions

Finally, these results suggest an indirect influence of patients’ socioeconomic position on survival through AML initial presentation.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-6093-3) contains supplementary material, which is available to authorized users.

Geographical Variations in Prostate Cancer Outcomes: A Systematic Review of International Evidence

Background: Previous reviews of geographical disparities in the prostate cancer continuum from diagnosis to mortality have identified a consistent pattern of poorer outcomes with increasing residential disadvantage and for rural residents. However, there are no contemporary, systematic reviews summarizing the latest available evidence. Our objective was to systematically review the published international evidence for geographical variations in prostate cancer indicators by residential rurality and disadvantage.

Methods: Systematic searches of peer-reviewed articles in English published from 1/1/1998 to 30/06/2018 using PubMed, EMBASE, CINAHL, and Informit databases. Inclusion criteria were: population was adult prostate cancer patients; outcome measure was PSA testing, prostate cancer incidence, stage at diagnosis, access to and use of services, survival, and prostate cancer mortality with quantitative results by residential rurality and/or disadvantage. Studies were critically appraised using a modified Newcastle-Ottawa Scale.

Results: Overall 169 studies met the inclusion criteria. Around 50% were assessed as high quality and 50% moderate. Men from disadvantaged areas had consistently lower prostate-specific antigen (PSA) testing and prostate cancer incidence, poorer survival, more advanced disease and a trend toward higher mortality. Although less consistent, predominant patterns by rurality were lower PSA testing, prostate cancer incidence and survival, but higher stage disease and mortality among rural men. Both geographical measures were associated with variations in access and use of prostate cancer-related services for low to high risk disease.

Conclusions: This review found substantial evidence that prostate cancer indicators varied by residential location across diverse populations and geographies. While wide variations in study design limited comparisons across studies, our review indicated that internationally, men living in disadvantaged areas, and to a lesser extent more rural areas, face a greater prostate cancer burden. This review highlights the need for a better understanding of the complex social, environmental, and behavioral reasons for these variations, recognizing that, while important, geographical access is not the only issue. Implementing research strategies to help identify these processes and to better understand the central role of disadvantage to variations in health outcome are crucial to inform the development of evidence-based targeted interventions.

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.

Summarizing and communicating on survival data according to the audience: a tutorial on different measures illustrated with population-based cancer registry data

Survival data analysis results are usually communicated through the overall survival probability. Alternative measures provide additional insights and may help in communicating the results to a wider audience. We describe these alternative measures in two data settings, the overall survival setting and the relative survival setting, the latter corresponding to the particular competing risk setting in which the cause of death is unavailable or unreliable. In the overall survival setting, we describe the overall survival probability, the conditional survival probability and the restricted mean survival time (restricted to a prespecified time window). In the relative survival setting, we describe the net survival probability, the conditional net survival probability, the restricted mean net survival time, the crude probability of death due to each cause and the number of life years lost due to each cause over a prespecified time window. These measures describe survival data either on a probability scale or on a timescale. The clinical or population health purpose of each measure is detailed, and their advantages and drawbacks are discussed. We then illustrate their use analyzing England population-based registry data of men 15-80 years old diagnosed with colon cancer in 2001-2003, aiming to describe the deprivation disparities in survival. We believe that both the provision of a detailed example of the interpretation of each measure and the software implementation will help in generalizing their use.