The impact of life tables adjusted for smoking on the socio-economic difference in net survival for laryngeal and lung cancer

The impact of life tables on age standardized net survival of real-life example databases

BACKGROUND

Population-based, age-standardized net survival estimates provide valuable insights for comparing the effectiveness of cancer treatment and the prospects of cure in an international context. Although numerous studies have previously assessed survival, the choice of life tables may crucially impact the feasibility of such analyses. Therefore, based on available studies, our aim was to understand the critical influence of life tables on net survival estimates.

METHODS

Record-level data of approximately 50,000 breast, cervical, and ovarian cancer patients were extracted from the Hungarian National Cancer Registry. These patients were diagnosed between 2010 and 2014 and were followed up until December 31, 2019. Life tables for the Hungarian female population were taken from the Human Mortality Database, the Human Life-Table Database and were compiled according to the EUROCARE, CONCORD both multivariable flexible and Ewbank methodology. Regarding the last due to the lack of specific parameters, simulations were performed to assess the missing values​. The calculation of 5-year age-standardized net survival using different life tables revealed limitations in the methodology, highlighting the impact of life table selection on survival estimates.

FINDINGS

Minor biases were observed in age-standardized net survival when using life tables from different international databases. However, the net survival of breast cancer, which had the most favorable prognosis of the studied malignancies, showed significant discrepancies. Moreover, this research highlights the extreme sensitivity of the applied κ parameter in the CONCORD Ewbank method, underscoring the need for careful consideration when applying this approach.

INTERPRETATION

Present study shed light on how the choice of life tables can lead to differences in survival estimates for the same cancer population. It also emphasizes the importance of open methodological discussions to improve validity and accuracy of international comparability.

The long-lasting impacts of the COVID-19 pandemic on population-based cancer survival: what are the implications for data analysis?

Monitoring trends of cancer incidence, mortality and survival is vital for the planning and delivery of health services, and the evaluation of diagnostics and treatment at the population level. Furthermore, comparisons are often made between population subgroups to explore inequalities in outcomes. During the COVID-19 pandemic routine delivery of health services were severely disrupted. Resources were redeployed to COVID-19 services and patient risk of COVID-19 infection required serious consideration. Cancer screening services were paused, the availability of healthcare providers was reduced and, in some cases, patients faced difficulty in accessing optimal treatment in a timely manner. Given these major disruptions, much care should be taken when interpreting changes in cancer survival estimates during this period. The impact on cancer incidence and mortality statistics that have already been reported in some jurisdictions should drive further thought on the corresponding impact on cancer survival, and whether any differences observed are real, artificial or a combination of the two. We discuss the likely impact on key cancer metrics, the likely implications for the analysis of cancer registration data impacted by the pandemic and the implications for comparative analyses between population groups and other risk factor groups when using data spanning the pandemic period.

Estimating cure and risk of death from other causes of cancer patients: EUROCARE-6 data on head & neck, colorectal, and breast cancers

BACKGROUND

To estimate net survival and cancer cure fraction (CF), i.e. the proportion of patients no longer at risk of dying from cancer progression/relapse, a clear distinction needs to be made between mortality from cancer and from other causes. Conventionally, CF is estimated assuming no excess mortality compared to the general population.

METHODS

A new modelling approach, that corrects for patients' extra risk of dying (RR) from causes other than the diagnosed cancer, was considered to estimate both indicators. We analysed EUROCARE-6 data on head and neck (H&N), colorectal, and breast cancer patients aged 40-79, diagnosed from 1998 to 2002 and followed-up to 31/12/2014, provided by 65 European cancer registries.

FINDINGS

Young male H&N cancer patients have 4 times the risk of dying from other causes than their peers, this risk decreases with age to 1.6. Similar results were observed for female. We observed an absolute increase in CF of 30 % using the new model instead of the conventional one. For colorectal cancer, CF with the new model increased by a maximum of 3 % for older patients and the RR ranged from 1 to 1.2 for both sexes. CF of female breast cancer ranged from 73 % to 79 % using the new cure model, with RR between 1.2 and 1.4.

INTERPRETATION

Not considering a RR> 1 leads to underestimate the proportion of patients not bound to die of their diagnosed cancer. Estimates of cancer mortality risk have an important impact on patients' quality of life.

Including uncertainty of the expected mortality rates in the prediction of loss in life expectancy

PURPOSE

This study introduces a novel method for estimating the variance of life expectancy since diagnosis (LEC) and loss in life expectancy (LLE) for cancer patients within a relative survival framework in situations where life tables based on the entire general population are not accessible. LEC and LLE are useful summary measures of survival in population-based cancer studies, but require information on the mortality in the general population. Our method addresses the challenge of incorporating the uncertainty of expected mortality rates when using a sample from the general population.

METHODS

To illustrate the approach, we estimated LEC and LLE for patients diagnosed with colon and breast cancer in Sweden. General population mortality rates were based on a random sample drawn from comparators of a matched cohort. Flexible parametric survival models were used to model the mortality among cancer patients and the mortality in the random sample from the general population. Based on the models, LEC and LLE together with their variances were estimated. The results were compared with those obtained using fixed expected mortality rates.

RESULTS

By accounting for the uncertainty of expected mortality rates, the proposed method ensures more accurate estimates of variances and, therefore, confidence intervals of LEC and LLE for cancer patients. This is particularly valuable for older patients and some cancer types, where underestimation of the variance can be substantial when the entire general population data are not accessible.

CONCLUSION

The method can be implemented using existing software, making it accessible for use in various cancer studies. The provided example of Stata code further facilitates its adoption.

Persistent High Long-term Excess Mortality After Elective AAA Repair Especially in Women: A Large Population-based Study

OBJECTIVE

The aim of this time-trend analysis is to estimate long-term excess mortality and associated cardiovascular risk for abdominal aortic aneurysm (AAA) patients after elective repair while addressing the changes in AAA management and patient selection over time.

BACKGROUND

Despite the intensification of endovascular aneurysm repair and cardiovascular risk management, Swedish population data suggest that AAA patients retain a persistently high long-term mortality after elective repair. The question is whether this reflects suboptimal treatment, a changing patient population over time, or a national phenomenon.

METHODS

Nationwide time-trend analysis including 40,730 patients (87% men) following elective AAA repair between 1995 and 2017. Three timeframes were compared, each reflecting changes in the use of endovascular aneurysm repair and intensification of cardiovascular risk management. Relative survival analyses were used to estimate disease-specific excess mortality. Competing risk of death analysis evaluated the risk of cardiovascular versus noncardiovascular death. Sensitivity analysis evaluated the impact of changes in patient selection over time.

RESULTS

Short-term excess mortality significantly improved over time. Long-term excess mortality remained high with a doubled mortality risk for women (relative excess risk=1.87, 95% CI: 1.73-2.02). Excess mortality did not differ between age categories. The risk of cardiovascular versus noncardiovascular death remained similar over time, with a higher risk of cardiovascular death for women. Changes in patient population (ie, older and more comorbid patients in the latter period) marginally impacted excess mortality (2%).

CONCLUSIONS

Despite changes in AAA care, patients retain a high long-term excess mortality after elective repair with a persistent high cardiovascular mortality risk. In this, a clear sex - but no age - disparity stands out.

A new cure model that corrects for increased risk of non-cancer death: analysis of reliability and robustness, and application to real-life data

BACKGROUND

Non-cancer mortality in cancer patients may be higher than overall mortality in the general population due to a combination of factors, such as long-term adverse effects of treatments, and genetic, environmental or lifestyle-related factors. If so, conventional indicators may underestimate net survival and cure fraction. Our aim was to propose and evaluate a mixture cure survival model that takes into account the increased risk of non-cancer death for cancer patients.

METHODS

We assessed the performance of a corrected mixture cure survival model derived from a conventional mixture cure model to estimate the cure fraction, the survival of uncured patients, and the increased risk of non-cancer death in two settings of net survival estimation, grouped life-table data and individual patients' data. We measured the model's performance in terms of bias, standard deviation of the estimates and coverage rate, using an extensive simulation study. This study included reliability assessments through violation of some of the model's assumptions. We also applied the models to colon cancer data from the FRANCIM network.

RESULTS

When the assumptions were satisfied, the corrected cure model provided unbiased estimates of parameters expressing the increased risk of non-cancer death, the cure fraction, and net survival in uncured patients. No major difference was found when the model was applied to individual or grouped data. The absolute bias was < 1% for all parameters, while coverage ranged from 89 to 97%. When some of the assumptions were violated, parameter estimates appeared more robust when obtained from grouped than from individual data. As expected, the uncorrected cure model performed poorly and underestimated net survival and cure fractions in the simulation study. When applied to colon cancer real-life data, cure fractions estimated using the proposed model were higher than those in the conventional model, e.g. 5% higher in males at age 60 (57% vs. 52%).

CONCLUSIONS

The present analysis supports the use of the corrected mixture cure model, with the inclusion of increased risk of non-cancer death for cancer patients to provide better estimates of indicators based on cancer survival. These are important to public health decision-making; they improve patients' awareness and facilitate their return to normal life.

Inverse probability weighting and doubly robust standardization in the relative survival framework

A commonly reported measure when interested in the survival of cancer patients is relative survival. Relative survival circumvents issues with inaccurate cause of death information by incorporating the expected mortality rates of cancer individuals from population lifetables of the general population. A summary of the cancer population prognosis can be obtained using the marginal relative survival. To explore differences between exposure groups, such as socioeconomic groups, the difference in marginal relative survival between exposed and unexposed can be obtained and under assumptions is interpreted as the average causal effect of exposure to survival. In a modeling context, this is usually estimated by applying regression standardization as the average of the individual-specific estimates after fitting a relative survival model. Regression standardization yields an estimator that consistently estimates the causal effect under standard causal inference assumptions and if the relative survival model is correctly specified. We extend inverse probability weighting (IPW) and doubly robust standardization methods in the relative survival framework as additional valuable tools for obtaining average causal effects when correct model specification might not hold for the relative survival model. IPW yields an unbiased estimate of the average causal effect if a correctly specified model has been fitted for the exposure (propensity score) whereas doubly robust standardization requires that at least one of the propensity score model or the relative survival model is correctly specified. An example using data on melanoma is provided and a simulation study is conducted to investigate how sensitive are the methods to model misspecification, including different ways for obtaining standard errors.

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.

How do age and social environment affect the dynamics of death hazard and survival in patients with breast or gynecological cancer in France?

Several studies have investigated the association between net survival (NS) and social inequalities in people with cancer, highlighting a varying influence of deprivation depending on the type of cancer studied. However, few of these studies have accounted for the effect of social inequalities over the follow-up period, and/or according to the age of the patients. Thus, using recent and more relevant statistical models, we investigated the effect of social environment on NS in women with breast or gynecological cancer in France. The data were derived from population-based cancer registries, and women diagnosed with breast or gynecological cancer between 2006 and 2009 were included. We used the European deprivation index (EDI), an aggregated index, to define the social environment of the women included. Multidimensional penalized splines were used to model excess mortality hazard. We observed a significant effect of the EDI on NS in women with breast cancer throughout the follow-up period, and especially at 1.5 years of follow-up in women with cervical cancer. Regarding corpus uteri and ovarian cancer patients, the effect of deprivation on NS was less pronounced. These results highlight the impact of social environment on NS in women with breast or gynecological cancer in France thanks to a relevant statistical approach, and identify the follow-up periods during which the social environment may have a particular influence. These findings could help investigate targeted actions for each cancer type, particularly in the most deprived areas, at the time of diagnosis and during follow-up.

Age disparities in lung cancer survival in New Zealand: The role of patient and clinical factors

OBJECTIVE

Age is an important prognostic factor for lung cancer. However, no studies have investigated the age difference in lung cancer survival per se. We, therefore, described the role of patient-related and clinical factors on the age pattern in lung cancer excess mortality hazard by stage at diagnosis in New Zealand.

MATERIALS AND METHODS

We extracted 22 487 new lung cancer cases aged 50-99 (median age = 71, 47.1 % females) diagnosed between 1 January 2006 and 31 July 2017 from the New Zealand population-based cancer registry and followed up to December 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on the excess mortality hazard by stage at diagnosis, and we derived corresponding lung cancer net survival.

RESULTS

The age difference in net survival was particularly marked for localised and regional lung cancers, with a sharp decline in survival from the age of 70. No identified factors influenced age disparities in patients with localised cancer. However, for other stages, females had a greater difference in survival between middle-age and older-age than males. Comorbidity and emergency presentation played a minor role. Ethnicity and deprivation did not influence age disparities in lung cancer survival.

CONCLUSION

Sex and stage at diagnosis were the most important factors of age disparities in lung cancer survival in New Zealand.

Marginal measures and causal effects using the relative survival framework

BACKGROUND

In population-based cancer survival studies, the event of interest is usually death due to cancer. However, other competing events may be present. Relative survival is a commonly used measure in cancer studies that circumvents problems caused by the inaccuracy of the cause of death information. A summary of the prognosis of the cancer population and potential differences between subgroups can be obtained using marginal estimates of relative survival.

METHODS

We utilize regression standardization to obtain marginal estimates of interest in a relative survival framework. Such measures include the standardized relative survival, standardized all-cause survival and standardized crude probabilities of death. Contrasts of these can be formed to explore differences between exposure groups and under certain assumptions are interpreted as causal effects. The difference in standardized all-cause survival can also provide an estimate for the impact of eliminating cancer-related differences between exposure groups. The potential avoidable deaths after such hypothetical scenarios can also be estimated. To illustrate the methods we use the example of survival differences across socio-economic groups for colon cancer.

RESULTS

Using relative survival, a range of marginal measures and contrasts were estimated. For these measures we either focused on cancer-related differences only or chose to incorporate both cancer and other cause differences. The impact of eliminating differences between groups was also estimated. Another useful way for quantifying that impact is the avoidable deaths under hypothetical scenarios.

CONCLUSIONS

Marginal estimates within the relative survival framework provide useful summary measures and can be applied to better understand differences across exposure groups.

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.

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.

Correcting inaccurate background mortality in excess hazard models through breakpoints

BACKGROUND

Methods for estimating relative survival are widely used in population-based cancer survival studies. These methods are based on splitting the observed (the overall) mortality into excess mortality (due to cancer) and background mortality (due to other causes, as expected in the general population). The latter is derived from life tables usually stratified by age, sex, and calendar year but not by other covariates (such as the deprivation level or the socioeconomic status) which may lack though they would influence background mortality. The absence of these covariates leads to inaccurate background mortality, thus to biases in estimating the excess mortality. These biases may be avoided by adjusting the background mortality for these covariates whenever available.

METHODS

In this work, we propose a regression model of excess mortality that corrects for potentially inaccurate background mortality by introducing age-dependent multiplicative parameters through breakpoints, which gives some flexibility. The performance of this model was first assessed with a single and two breakpoints in an intensive simulation study, then the method was applied to French population-based data on colorectal cancer.

RESULTS

The proposed model proved to be interesting in the simulations and the applications to real data; it limited the bias in parameter estimates of the excess mortality in several scenarios and improved the results and the generalizability of Touraine's proportional hazards model.

CONCLUSION

Finally, the proposed model is a good approach to correct reliably inaccurate background mortality by introducing multiplicative parameters that depend on age and on an additional variable through breakpoints.

Volatile organic compound breath testing detects in-situ squamous cell carcinoma of bronchial and laryngeal regions and shows distinct profiles of each tumour

Volatile organic compound (VOC) breath testing of lung and head and neck squamous cell carcinoma (SCC) has been widely studied, however little is known regarding VOC profiles of in-situ SCC. A prospective study of VOC in patients with histologically proven SCC, either in-situ or advanced, and controls. Breath samples were analysed using the E-nose Cyranose ^®320 and by gas chromatography/mass spectroscopy. Predictive models were developed using bootstrap forest using all 32 sensors. Data from 55 participants was analysed: 42 SCC cases comprising 20 bronchial (10 in-situ, 10 advanced) and 22 laryngeal (12 in-situ, 10 advanced), and 13 controls. There were 32 (76%) male SCC cases with mean age 63.6 (SD = 9.5) compared with 11 (85%) male controls with mean age 61.9 (SD = 10.1). Predictive models for in situ cases had good sensitivity and specificity compared to controls (overall, 95% and 69%; laryngeal, 100% and 85%; bronchial, 77% and 80%). When distinguishing in-situ and advanced tumours, sensitivity and specificity 82% and 75% respectively. For different tumour types (bronchial versus advanced laryngeal) sensitivity and specificity were 100% and 80% respectively. VOCs isolated from in-situ cancers included some previously demonstrated in advanced cancers and some novel VOCs. In-situ bronchial and laryngeal cancer can be detected by VOC analysis. Distinction from normal controls and between the two tumour types could allow screening in high risk groups for these curable lesions.

Errors in determination of net survival: cause-specific and relative survival settings

BACKGROUND

Cause-specific and relative survival estimates differ. We aimed to examine these differences in common cancers where by possible identifying the most plausible sources of error in each estimate.

METHODS

Ten-year cause-specific and relative survival were estimated for lung, breast, prostate, ovary, oesophagus and colorectal cancers. The cause-specific survival was corrected for misclassification of cause of death. The Pohar-Perme relative survival estimator was modified by (1) correcting for differences in deaths from ischaemic heart disease (IHD) between cancers and general population; or (2) correcting the population hazard for smoking (lung cancer only).

RESULTS

For all cancers except breast and prostate, relative survival was lower than cause-specific. Correction for published error rates in cause of death gave implausible results. Correction for rates of IHD death gave slightly different relative survival estimates for lung, oesophagus and colorectal cancers. For lung cancer, when the population hazard was inflated for smoking, survival estimates were increased.

CONCLUSION

Results agreed with the consensus that relative survival is usually preferable. However, for some cancers, relative survival might be inaccurate (e.g. lung and prostate). Likely solutions include enhancing life tables to include other demographic variables than age and sex, and to stratify relative survival calculation by cause of death.

Reporting net survival in populations: a sensitivity analysis in lung cancer demonstrates the differential implications of reporting relative survival and cause-specific survival

Background

Net survival is commonly quantified as relative survival (observed survival among lung cancer patients versus expected survival among the general population) and cause-specific survival (lung cancer–specific survival among lung cancer patients). These approaches have drastically different assumptions; hence, failure to distinguish between them results in significant implications for study findings. We quantified the differences between relative and cause-specific survival when reporting net survival of patients with non-small cell lung cancer (NSCLC).

Methods

Cases of NSCLC diagnosed between 2004 and 2014 were extracted from the Surveillance, Epidemiology, and End Results database. The net survival of each stage-by-age stratum was expressed as cause-specific survival (Kaplan-Meier approach) and relative survival (Ederer II approach); percentage-point (pp) differences between the survival estimates were quantified up to 10 years postdiagnosis.

Results

Analyses included 263,894 cases. Cause-specific survival estimates were higher than relative survival estimates across all strata. Although the differences were negligible at 1 year postdiagnosis, they increased with increasing years of follow-up, up to 9.3 pp at 10 years (eg, aged 60–74 with stage I disease: 53.0% vs 43.7%). Differences in survival estimates between the methods also increased by increasing age groups (eg, at 10 years postdiagnosis: 5.1 pp for ages 18–44, 8.8 pp for ages 45–59, and 9.3 pp for ages 60–74) but decreased drastically for those aged ≥75 (3.1 pp).

Conclusion

Relative survival and cause-specific survival are not interchangeable. The type of survival estimate used in cancer studies should be specified, particularly for long-term survival.

Differences in Cancer Survival with Relative versus Cause-Specific Approaches: An Update Using More Accurate Life Tables

BACKGROUND

We investigated differences in net cancer survival (survival observed if the only possible cause of death was the cancer under study) estimated using new approaches for relative survival (RS) and cause-specific survival (CSS).

METHODS

We used SEER data for patients diagnosed in 2000 to 2013, followed-up through December 31, 2014. For RS, we used new life tables accounting for geography and socio-economic status. For CSS, we used the SEER cause of death algorithm for attributing cancer-specific death. Estimates were compared by site, age, stage, race, and time since diagnosis.

RESULTS

Differences between 5-year RS and CSS were generally small. RS was always higher in screen-detectable cancers, for example, female breast (89.2% vs. 87.8%) and prostate (98.5% vs. 93.7%) cancers; differences increased with age or time since diagnosis. CSS was usually higher in the remaining cancer sites, particularly those related to specific risk factors, for example, cervix (70.9% vs. 68.3%) and liver (20.7% vs. 17.1%) cancers. For most cancer sites, the gap between estimates was smaller with more advanced stage.Conclusion: RS is the preferred approach to report cancer survival from registry data because cause of death may be inaccurate, particularly for older patients and long-term survivors as comorbidities increase challenges in determining cause of death. However, CSS proved to be more reliable in patients diagnosed with localized disease or cancers related to specific risk factors as general population life tables may not capture other causes of mortality.

IMPACT

Different approaches for net survival estimation should be considered depending on cancer under study.

Meta-analysis of long-term survival after elective endovascular or open repair of abdominal aortic aneurysm

BACKGROUND

Endovascular aneurysm repair (EVAR) has become the preferred strategy for elective repair of abdominal aortic aneurysm (AAA) for many patients. However, the superiority of the endovascular procedure has recently been challenged by reports of impaired long-term survival in patients who underwent EVAR. A systematic review of long-term survival following AAA repair was therefore undertaken.

METHODS

A systematic review was performed according to PRISMA guidelines. Articles reporting short- and/or long-term mortality of EVAR and open surgical repair (OSR) of AAA were identified. Pooled overall survival estimates (hazard ratios (HRs) with corresponding 95 per cent c.i. for EVAR versus OSR) were calculated using a random-effects model. Possible confounding owing to age differences between patients receiving EVAR or OSR was addressed by estimating relative survival.

RESULTS

Some 53 studies were identified. The 30-day mortality rate was lower for EVAR compared with OSR: 1·16 (95 per cent c.i. 0·92 to 1·39) versus 3·27 (2·71 to 3·83) per cent. Long-term survival rates were similar for EVAR versus OSR (HRs 1·01, 1·00 and 0·98 for 3, 5 and 10 years respectively; P = 0·721, P = 0·912 and P = 0·777). Correction of age inequality by means of relative survival analysis showed equal long-term survival: 0·94, 0·91 and 0·76 at 3, 5 and 10 years for EVAR, and 0·96, 0·91 and 0·76 respectively for OSR.

CONCLUSION

Long-term overall survival rates were similar for EVAR and OSR. Available data do not allow extension beyond the 10-year survival window or analysis of specific subgroups.

More accurate cancer-related excess mortality through correcting background mortality for extra variables

Relative survival methods used to estimate the excess mortality of cancer patients rely on the background (or expected) mortality derived from general population life tables. These methods are based on splitting the observed mortality into the excess mortality and the background mortality. By assuming a regression model for the excess mortality, usually a Cox-type model, one may investigate the effects of certain covariates on the excess mortality. Some covariates are cancer-specific whereas others are variables that may influence the background mortality as well. The latter should be taken into account in the background mortality to avoid biases in estimating their effects on the excess mortality. Unfortunately, the available life table might not include such variables and, consequently, might provide inaccurate values of the background mortality. We propose a model that uses multiplicative parameters to correct potentially inaccurate background mortality. The model can be seen as an extension of the frequently used Estève model because we assume a Cox-type model for the excess mortality with a piecewise constant baseline function and introduce additional parameters that multiply the background mortality. The original and the extended model are compared, first in a simulation study, then in an application to colon cancer registry data.

Socioeconomic Differences and Lung Cancer Survival-Systematic Review and Meta-Analysis

Background: The impact of socioeconomic differences on cancer survival has been investigated for several cancer types showing lower cancer survival in patients from lower socioeconomic groups. However, little is known about the relation between the strength of association and the level of adjustment and level of aggregation of the socioeconomic status measure. Here, we conduct the first systematic review and meta-analysis on the association of individual and area-based measures of socioeconomic status with lung cancer survival.

Methods: In accordance with PRISMA guidelines, we searched for studies on socioeconomic differences in lung cancer survival in four electronic databases. A study was included if it reported a measure of survival in relation to education, income, occupation, or composite measures (indices). If possible, meta-analyses were conducted for studies reporting on individual and area-based socioeconomic measures.

Results: We included 94 studies in the review, of which 23 measured socioeconomic status on an individual level and 71 on an area-based level. Seventeen studies were eligible to be included in the meta-analyses. The meta-analyses revealed a poorer prognosis for patients with low individual income (pooled hazard ratio: 1.13, 95 % confidence interval: 1.08–1.19, reference: high income), but not for individual education. Group comparisons for hazard ratios of area-based studies indicated a poorer prognosis for lower socioeconomic groups, irrespective of the socioeconomic measure. In most studies, reported 1-, 3-, and 5-year survival rates across socioeconomic status groups showed decreasing rates with decreasing socioeconomic status for both individual and area-based measures. We cannot confirm a consistent relationship between level of aggregation and effect size, however, comparability across studies was hampered by heterogeneous reporting of socioeconomic status and survival measures. Only eight studies considered smoking status in the analysis.

Conclusions: Our findings suggest a weak positive association between individual income and lung cancer survival. Studies reporting on socioeconomic differences in lung cancer survival should consider including smoking status of the patients in their analysis and to stratify by relevant prognostic factors to further explore the reasons for socioeconomic differences. A common definition for socioeconomic status measures is desirable to further enhance comparisons between nations and across different levels of aggregation.

Effect of DACH1 on proliferation and invasion of laryngeal squamous cell carcinoma

BACKGROUND

To investigate the effect of DACH1 over-expression on proliferation and invasion of laryngeal squamous cell carcinoma (LSCC).

METHODS

The 120 cases of LSCC tumors and 114 adjacent non-neoplastic tissues were collected to detect the expression of DACH1 by immunohistochemistry. The changes of DACH1 expression from each group were assessed and correlated to the clinical parameters of the patients. Plasmid-DACH1 was transfected into Hep-2 cells to up-regulate the expression of DACH1C. Real-time PCR, Western blot, CCK8 and transwell assay were used to verify the cell proliferation and invasion after plasmid-DACH1 transfection.

RESULTS

The results indicated that DACH1 was downregulated in LSCC tissues as compared to corresponding adjacent non-neoplastic tissues. Decreased expression of DACH1 was found in the tumors upraglottic tumor, lymph node metastases, T3-4 stage and advanced clinical stage. In Hep-2 cells, transfection with plasmid-DACH1 could suppress cell proliferation, invasion and induce G1 phase extension in cell cycle.

CONCLUSIONS

DACH1 may act as a tumor suppressor gene and could be a potential target for therapeutic intervention of LSCC.

Geographical, racial and socio-economic variation in life expectancy in the US and their impact on cancer relative survival

PURPOSE

Despite gains in life expectancy between 1992 to 2012, large disparities in life expectancy continue to exist in the United States between subgroups of the population. This study aimed to develop detailed life tables (LT), accounting for mortality differences by race, geography, and socio-economic status (SES), to more accurately measure relative cancer survival and life expectancy patterns in the United States.

METHODS

We estimated an extensive set of County SES-LT by fitting Poisson regression models to deaths and population counts for U.S. counties by age, year, gender, race, ethnicity and county-level SES index. We reported life expectancy patterns and evaluated the impact of the County SES-LT on relative survival using data from the Surveillance Epidemiology and End Results (SEER) Program cancer registries.

RESULTS

Between 1992 and 2012, the largest increase in life expectancy was among black men (6.8 years), however there were still large geographical differences. Life expectancy was highest for Asian or Pacific Islanders (API), and lowest for American Indians and Alaskan Natives (AIAN). In 2010, life expectancies by state ranged from 73 to 82 years for white males, 78 to 86 years for white females, 66 to 75 for black males, and 75 to 81 for black females. Comparisons of relative survival using National LT and the new County SES-LT showed that relative survival using County SES-LT improved relative survival estimates for some demographic groups, particularly in low and high SES areas, among Hispanics and AIAN, and among older male cancer patients. Relative survival using County SES-LT was 7.3% and 6.7% survival points closer to cause-specific survival compared to the National LT relative survival for AIAN and Hispanic cancer patients diagnosed between ages 75 and 84 years, respectively. Importantly, the County SES-LT relative survival estimates were higher in lower SES areas and lower in higher SES areas, reducing differences in relative survival comparisons.

CONCLUSION

The use of these new socio-economic life tables (County SES-LT) can provide more accurate estimates of relative survival, improve comparisons of relative survival among registries, better illustrate disparities and cancer control efforts, and should be used as default for cancer relative survival using U.S. data.

Adjusting Expected Mortality Rates Using Information From a Control Population: An Example Using Socioeconomic Status

Expected or reference mortality rates are commonly used in the calculation of measures such as relative survival in population-based cancer survival studies and standardized mortality ratios. These expected rates are usually presented according to age, sex, and calendar year. In certain situations, stratification of expected rates by other factors is required to avoid potential bias if interest lies in quantifying measures according to such factors as, for example, socioeconomic status. If data are not available on a population level, information from a control population could be used to adjust expected rates. We have presented two approaches for adjusting expected mortality rates using information from a control population: a Poisson generalized linear model and a flexible parametric survival model. We used a control group from BCBaSe-a register-based, matched breast cancer cohort in Sweden with diagnoses between 1992 and 2012-to illustrate the two methods using socioeconomic status as a risk factor of interest. Results showed that Poisson and flexible parametric survival approaches estimate similar adjusted mortality rates according to socioeconomic status. Additional uncertainty involved in the methods to estimate stratified, expected mortality rates described in this study can be accounted for using a parametric bootstrap, but this might make little difference if using a large control population.

Critical Points for Interpreting Patients' Survival Rate Using Cancer Registries: A Literature Review

Background

Survival rate is used to develop cancer control plans. However, there are limitations and biases when interpreting patient survival rate data. This study aimed to identify and account for potential biases and/or limitations on estimating survival rate to enable more effective control of cancer.

Methods

The authors searched PubMed from December 2010 to December 2015 for articles that investigated or described biases in estimating patient survival using cancer registries. Articles that only described the tendency of survival rate and investigated relationships between patient characteristics, treatment, and survival rate were excluded.

Results

In total, 50 articles met the inclusion criteria. The identified potential biases were categorized into three areas, as follows: 1) the quality of registry data (eg, the completeness of cancer patients, accuracy of data, and follow-up rates); 2) limitations related to estimated methods of survival rates (eg, misclassification of cause of death for cause-specific survival rate or a lack of comparability of background mortality for relative survival rate); and 3) the comparability of survival rates among different groups (eg, age-adjustment or patients with multiple cancers).

Conclusion

We concluded that survival rate can be suitable for answering questions related to health policy and research. Several factors should be considered when interpreting survival rates estimated using cancer registries.

Survival from cancer in the north region of Portugal: results from the first decade of the millennium

The aim of this study was to evaluate net survival from cancer diagnosed during the period 2001-2010 in the north region of Portugal to identify the tumours that need actions to improve the outcomes. Data were retrieved from the North Region Cancer Registry of Portugal database. The top 20 cancer sites in adults were considered: oesophagus, stomach, colon, rectum, pancreas, liver, larynx, lung, skin melanoma, breast, cervix, corpus uteri, ovary, prostate, kidney, bladder, brain and central nervous system, thyroid, non-Hodgkin lymphoma and multiple myeloma. Net survival was estimated using the Pohar-Perme estimator. The effect of diagnosis period was evaluated using flexible parametric models adjusted for age and sex where appropriate. Thyroid and prostate cancers presented the best 5-year survival (>90%), whereas oesophagus, pancreas, liver and lung cancers the worst 5-year survival (<20%). The largest increase in survival was observed for the larynx. A significant decrease in age-adjusted and sex-adjusted excess mortality was observed for stomach, colon, pancreas, larynx, melanoma, breast, brain and central nervous system, thyroid, non-Hodgkin lymphoma and multiple myeloma. For the other cancer sites, no significant trends were observed. For some of these sites, the downward trend in excess mortality was only observed in the short term. An important picture of population-based cancer survival outcomes for the first decade of the millennium in the north region of Portugal was presented in this study. It has been shown that improvements in survival were not universal for all cancer sites. These results should be used to highlight tumours where intervention is needed the most.

Different survival analysis methods for measuring long-term outcomes of Indigenous and non-Indigenous Australian cancer patients in the presence and absence of competing risks

BACKGROUND

Net survival is the most common measure of cancer prognosis and has been used to study differentials in cancer survival between ethnic or racial population subgroups. However, net survival ignores competing risks of deaths and so provides incomplete prognostic information for cancer patients, and when comparing survival between populations with different all-cause mortality. Another prognosis measure, "crude probability of death", which takes competing risk of death into account, overcomes this limitation. Similar to net survival, it can be calculated using either life tables (using Cronin-Feuer method) or cause of death data (using Fine-Gray method). The aim of this study is two-fold: (1) to compare the multivariable results produced by different survival analysis methods; and (2) to compare the Cronin-Feuer with the Fine-Gray methods, in estimating the cancer and non-cancer death probability of both Indigenous and non-Indigenous cancer patients and the Indigenous cancer disparities.

METHODS

Cancer survival was investigated for 9,595 people (18.5% Indigenous) diagnosed with cancer in the Northern Territory of Australia between 1991 and 2009. The Cox proportional hazard model along with Poisson and Fine-Gray regression were used in the multivariable analysis. The crude probabilities of cancer and non-cancer methods were estimated in two ways: first, using cause of death data with the Fine-Gray method, and second, using life tables with the Cronin-Feuer method.

RESULTS

Multivariable regression using the relative survival, cause-specific survival, and competing risk analysis produced similar results. In the presence of competing risks, the Cronin-Feuer method produced similar results to Fine-Gray in the estimation of cancer death probability (higher Indigenous cancer death probabilities for all cancers) and non-cancer death probabilities (higher Indigenous non-cancer death probabilities for all cancers except lung cancer and head and neck cancers). Cronin-Feuer estimated much lower non-cancer death probabilities than Fine-Gray for non-Indigenous patients with head and neck cancers and lung cancers (both smoking-related cancers).

CONCLUSION

Despite the limitations of the Cronin-Feuer method, it is a reasonable alternative to the Fine-Gray method for assessing the Indigenous survival differential in the presence of competing risks when valid and reliable subgroup-specific life tables are available and cause of death data are unavailable or unreliable.

Incidence and Survival of urothelial carcinoma of the urinary bladder in Norway 1981-2014

Background

Urothelial carcinoma of the urinary bladder (UCB) is the 4^th most common cancer type in men in developed countries, and tumor recurrence or progression occurs in more than half of the patients. Previous studies report contradictory trends in incidence and survival over the past decades. This article describes the trends of UCB incidence and survival from 1981 to 2014, including both invasive and non-invasive UCB using data from the Cancer Registry of Norway.

Methods

In Norway, 33,761 patients were diagnosed with UCB between 1981 and 2014. Incidence and 5-year relative survival were calculated, stratified by sex, morphology, stage, age and diagnostic period. Age-period-cohort models were used to distinguish period- and cohort effects. Temporal trends were summarized by calculating the average absolute annual change in incidence and relative survival allowing for breaks in this trend by incorporating a joinpoint analysis. Excess mortality rate ratios (EMRR) quantify the relative risks by using a proportional excess hazard model.

Results

The incidence of UCB in men increased from 18.5 (1981-85) to 21.1 (1991-95) per 100 000 person-years and was rather stable thereafter (1996–2014). The incidence rates of UCB were lower in women increasing linearly from 4.7 to 6.2 over the past 34 years (p = 5.9 · 10^-7). These trends could be explained by an increase of the incidence rates of non-invasive tumors. Furthermore, the observed pattern seemed to represent a birth cohort effect. Five-year relative survival increased annually with 0.004 in men (p = 1.3 · 10^-6) and 0.003 in women (p = 4.5 · 10^-6). There is a significant increase over the past 34 years in survival of UCB in both genders for local tumors but not for advanced stages.

Conclusions

Increasing and stable incidence trends mirror little improvement in primary and secondary prevention of UCB for more than three decades. Survival proportions increased only marginally. Thus, any changes in treatment and follow-up care did not lead to notable improvement with respect to survival of the patients. High estimates of preventable cases together with large recurrence rates of this particular cancer type, demand more research on prevention guidelines, diagnostic tools and treatment for UCB.

Cause-specific or relative survival setting to estimate population-based net survival from cancer? An empirical evaluation using women diagnosed with breast cancer in Geneva between 1981 and 1991 and followed for 20 years after diagnosis

BACKGROUND

Both cause-specific and relative survival settings can be used to estimate net survival, the survival that would be observed if the only possible underlying cause of death was the disease under study. Both resulting net survival estimators are biased by informative censoring and prone to biases related to the data settings within which each is derived. We took into account informative censoring to derive theoretically unbiased estimators and examine which of the two data settings was the most robust against incorrect assumptions in the data.

PATIENTS AND METHODS

We identified 2489 women in the Geneva Cancer Registry, diagnosed with breast cancer between 1981 and 1991, and estimated net survival up to 20-years using both cause-specific and relative survival settings, by tackling the informative censoring with weights. To understand the possible origins of differences between the survival estimates, we performed sensitivity analyses within each setting. We evaluated the impact of misclassification of cause of death and of using inappropriate life tables on survival estimates.

RESULTS

Net survival was highest using the cause-specific setting, by 1% at one year and by up to around 11% twenty years after diagnosis. Differences between both sets of net survival estimates were eliminated after recoding between 15% and 20% of the non-specific deaths as breast cancer deaths. By contrast, a dramatic increase in the general population mortality rates was needed to see the survival estimates based on relative survival setting become closer to those derived from cause-specific setting.

CONCLUSION

Net survival estimates derived using the cause-specific setting are very sensitive to misclassification of cause of death. Net survival estimates derived using the relative-survival setting were robust to large changes in expected mortality. The relative survival setting is recommended for estimation of long-term net survival among patients with breast cancer.