Understanding disparities in cancer prognosis: An extension of mediation analysis to the relative survival framework

The Impact of Age in the Treatment of Non-comorbid Patients with Rectal Cancer: Survival Outcomes from the National Cancer Database

BACKGROUND

Multimodal therapy has improved survival outcomes for rectal cancer (RC) significantly with an exemption for older patients. We sought to assess whether older non-comorbid patients receive substandard oncological treatment for localized RC referring to the National Comprehensive Cancer Network (NCCN) guidelines and whether it affects survival outcomes.

METHODS

This is a retrospective study using patient data from the National Cancer Data Base (NCDB) for histologically confirmed RC from 2002 to 2014. Non-comorbid patients between ≥50 and ≤85 years and defined treatment for localized RC were included and assigned to a younger (<75 years) and an older group (≥75 years). Treatment approaches and their impact on relative survival (RS) were analyzed using loess regression models and compared between both groups. Furthermore, mediation analysis was performed to measure the independent relative effect on age and other variables on RS. Data were assessed using the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) checklist.

RESULTS

Of 59,769 included patients, 48,389 (81.0%) were assigned to the younger group (<75 years). Oncologic resection was performed in 79.6% of the younger patients compared to 67.2% of the older patients (p < 0.001). Chemotherapy (74.3% vs. 56.1%) and radiotherapy (72.0% vs. 58.1%) were provided less often in older patients, respectively (p < 0.001). Increasing age was associated with enhanced 30- and 90-day mortality with 0.6% and 1.1% in the younger and 2.0% and 4.1% in the elderly group (p < 0.001) and worse RS rates [multivariable adjusted HR: 1.93 (95% CI 1.87-2.00), p < 0.001]. Adherence to standard oncological therapy resulted in a significant increase in 5-year RS (multivariable adjusted HR: 0.80 (95% CI 0.74-0.86), p < 0.001). Mediation analysis revealed that RS was mainly affected by age itself (84%) rather than the choice of therapy.

CONCLUSIONS

The likelihood to receive substandard oncological therapy increases in the older population and negatively affects RS. Since age itself has a major impact on RS, better patient selection should be performed to identify those that are potentially eligible for standard oncological care regardless of their age.

Impact of a Prior Cancer Diagnosis on Quality of Care and Survival Following Acute Myocardial Infarction: Retrospective Population-Based Cohort Study in England

BACKGROUND

An increasing proportion of patients with cancer experience acute myocardial infarction (AMI). We investigated differences in quality of AMI care and survival between patients with and without previous cancer diagnoses.

METHODS

A retrospective cohort study using Virtual Cardio-Oncology Research Initiative data. Patients aged 40+ years hospitalized in England with AMI between January 2010 and March 2018 were assessed, ascertaining previous cancers diagnosed within 15 years. Multivariable regression was used to assess effects of cancer diagnosis, time, stage, and site on international quality indicators and mortality.

RESULTS

Of 512 388 patients with AMI (mean age, 69.3 years; 33.5% women), 42 187 (8.2%) had previous cancers. Patients with cancer had significantly lower use of ACE (angiotensin-converting enzyme) inhibitors/angiotensin receptor blockers (mean percentage point decrease [mppd], 2.6% [95% CI, 1.8-3.4]) and lower overall composite care (mppd, 1.2% [95% CI, 0.9-1.6]). Poorer quality indicator attainment was observed in patients with cancer diagnosed in the last year (mppd, 1.4% [95% CI, 1.8-1.0]), with later stage disease (mppd, 2.5% [95% CI, 3.3-1.4]), and with lung cancer (mppd, 2.2% [95% CI, 3.0-1.3]). Twelve-month all-cause survival was 90.5% in noncancer controls and 86.3% in adjusted counterfactual controls. Differences in post-AMI survival were driven by cancer-related deaths. Modeling improving quality indicator attainment to noncancer patient levels showed modest 12-month survival benefits (lung cancer, 0.6%; other cancers, 0.3%).

CONCLUSIONS

Measures of quality of AMI care are poorer in patients with cancer, with lower use of secondary prevention medications. Findings are primarily driven by differences in age and comorbidities between cancer and noncancer populations and attenuated after adjustment. The largest impact was observed in recent cancer diagnoses (<1 year) and lung cancer. Further investigation will determine whether differences reflect appropriate management according to cancer prognosis or whether opportunities to improve AMI outcomes in patients with cancer exist.

How recreational farm operators use dynamic capabilities to respond to COVID-19 pandemic

The outbreak of the COVID-19 Pandemic has devastated economic activities around the world. The tourism industry is facing severe challenges, such as reduced tourist flow and the lack of tourist consumption at destinations. Recreational farms are one of the business types of agricultural tourism in Taiwan and have the characteristics of small and medium-sized agriculture businesses. The operator is facing an uncertain environment in the epidemic market, and the operator's dynamic capability is considered to be effective in coping with the current environment. In this study, 20 selected recreational farms were interviewed to explore how operators use dynamic capabilities to make responses. The results show that recreational farms have the three elements of dynamic capabilities: sense, seize, and transform, to change farm marketing channels and develop new products or services to respond to the new market. Recreational farm operators adopted resource optimization, computerization, and cost control strategies to respond to the market.

Management implications

A successful implementation of recreational farm offers requires-

the reallocation of resources,

-

the planning of new products and services, and - the improvement of service processes to create new business and to address additional target groups.

The COVID-19 pandemic forces a systematic positioning or repositioning of the business.

Socioeconomic inequalities in breast cancer survival in Reunion Island: The contribution of stage at diagnosis as a mediator

INTRODUCTION

Although breast cancer survival has improved in France, it appears that women living in deprived areas are more likely to die from breast cancer. However, no study has yet examined socioeconomic inequalities in breast cancer survival in La Réunion. Our objective was to examine whether socioeconomic inequalities in breast cancer survival exist in Reunion Island and whether stage at diagnosis could partly explain these differences.

METHODS

A population-based cohort study of all women on Reunion Island with primary breast cancer diagnosed between 2008 and 2016 was conducted. Each woman was assigned a deprivation index based on her area of residence at diagnosis. Net survival by deprivation group and stage at diagnosis was estimated by the non parametric Pohar Perme method. The role of stage (indirect effect) was assessed using a mediation analysis extended to the relative survival framework.

RESULTS

At five years, net survival was significantly lower in women living in the most deprived areas than in women living in the least deprived areas (81 % (95 % CI 77-86) and 91 % (95 % CI 89-94), respectively, p < 0.0001), and mediation analysis showed that the contribution of stage at diagnosis to these survival differences was 43 %.

DISCUSSION

Our result shows that although measures to promote earlier diagnosis are important, they would only reduce socioeconomic inequalities in breast cancer survival by 43 %. To further investigate these inequalities, future research should explore the role of unmeasured mediators, such as comorbidities and treatment received, as well as the impact of specific interventions that might address the differences in mediator distribution.

Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv

BACKGROUND

When interested in a time-to-event outcome, competing events that prevent the occurrence of the event of interest may be present. In the presence of competing events, various estimands have been suggested for defining the causal effect of treatment on the event of interest. Depending on the estimand, the competing events are either accommodated or eliminated, resulting in causal effects with different interpretations. The former approach captures the total effect of treatment on the event of interest while the latter approach captures the direct effect of treatment on the event of interest that is not mediated by the competing event. Separable effects have also been defined for settings where the treatment can be partitioned into two components that affect the event of interest and the competing event through different causal pathways.

METHODS

We outline various causal effects that may be of interest in the presence of competing events, including total, direct and separable effects, and describe how to obtain estimates using regression standardisation with the Stata command standsurv. Regression standardisation is applied by obtaining the average of individual estimates across all individuals in a study population after fitting a survival model.

RESULTS

With standsurv several contrasts of interest can be calculated including differences, ratios and other user-defined functions. Confidence intervals can also be obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer to allow the reader to replicate the analysis and further explore the different effects of interest.

CONCLUSIONS

Several causal effects can be defined in the presence of competing events and, under assumptions, estimates of those can be obtained using regression standardisation with the Stata command standsurv. The choice of which causal effect to define should be given careful consideration based on the research question and the audience to which the findings will be communicated.

Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility

BACKGROUND

A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on.

METHODS

We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to generate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented.

RESULTS

We successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research.

CONCLUSIONS

We evaluate the effectiveness of the methods for constructing synthetic data, as well as providing evidence that there is minimal risk that a given patient from the original data could be identified from their individual unique patient information. Synthetic datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research.

Understanding disparities in cancer prognosis: An extension of mediation analysis to the relative survival framework

Mediation analysis can be applied to investigate the effect of a third variable on the pathway between an exposure and the outcome. Such applications include investigating the determinants that drive differences in cancer survival across subgroups. However, cancer disparities may be the result of complex mechanisms that involve both cancer-related and other-cause mortality differences making it difficult to identify the causing factors. Relative survival, a commonly used measure in cancer epidemiology, can be used to focus on cancer-related differences. We extended mediation analysis to the relative survival framework for exploring cancer inequalities. The marginal effects were obtained using regression standardization, after fitting a relative survival model. Contrasts of interests included both marginal relative survival and marginal all-cause survival differences between exposure groups. Such contrasts include the indirect effect due to a mediator that is identifiable under certain assumptions. A separate model was fitted for the mediator and uncertainty was estimated using parametric bootstrapping. The avoidable deaths under interventions can also be estimated to quantify the impact of eliminating differences. The methods are illustrated using data for individuals diagnosed with colon cancer. Mediation analysis within relative survival allows focus on factors that account for cancer-related differences instead of all-cause differences and helps improve our understanding on cancer inequalities.