Geographical Variation in Underlying Social Deprivation, Cardiovascular and Other Comorbidities in Patients with Potentially Curable Cancers in England: Results from a National Registry Dataset Analysis.

AIMS: To describe the prevalence of cardiovascular disease (CVD), multiple comorbidities and social deprivation in patients with a potentially curable cancer in 20 English Cancer Alliances. MATERIALS AND METHODS: This National Registry Dataset Analysis used national cancer registry data and CVD databases to describe rates of CVD, comorbidities and social deprivation in patients diagnosed with a potentially curable malignancy (stage I-III breast cancer, stage I-III colon cancer, stage I-III rectal cancer, stage I-III prostate cancer, stage I-IIIA non-small cell lung cancer, stage I-IV diffuse large B-cell lymphoma, stage I-IV Hodgkin lymphoma) between 2013 and 2018. Outcome measures included observation of CVD prevalence, other comorbidities (evaluated by the Charlson Comorbidity Index) and deprivation (using the Index of Multiple Deprivation) according to tumour site and allocation to Cancer Alliance. Patients were allocated to CVD prevalence tertiles (minimum: <33.3rd percentile; middle: 33.3rd to 66.6th percentile; maximum: >66.6th percentile). RESULTS: In total, 634 240 patients with a potentially curable malignancy were eligible. The total CVD prevalence for all cancer sites varied between 13.4% (CVD n = 2058; 95% confidence interval 12.8, 13.9) and 19.6% (CVD n = 7818; 95% confidence interval 19.2, 20.0) between Cancer Alliances. CVD prevalence showed regional variation both for male (16-26%) and female patients (8-16%) towards higher CVD prevalence in northern Cancer Alliances. Similar variation was observed for social deprivation, with the proportion of cancer patients being identified as most deprived varying between 3.3% and 32.2%, depending on Cancer Alliance. The variation between Cancer Alliance for total comorbidities was much smaller. CONCLUSION: Social deprivation, CVD and other comorbidities in patients with a potentially curable malignancy in England show significant regional variations, which may partly contribute to differences observed in treatments and outcomes.

Cardiovascular diseases among diffuse large B-cell lymphoma long-term survivors in Asia: a multistate model study.

BACKGROUND: We modeled the clinical course of a cohort of diffuse large B-cell lymphoma (DLBCL) patients with no prior cardiovascular diseases (CVDs) using a multistate modeling framework. PATIENTS AND METHODS: Data on 2600 patients with DLBCL diagnosed between 2000 and 2018 and had received chemotherapy with or without radiotherapy were obtained from a population-wide electronic health database of Hong Kong. We used the Markov illness-death model to quantify the impact of doxorubicin and various risk factors (therapeutic exposure, demographic, comorbidities, cardiovascular risk factors, and lifestyle factors which included smoking) on the clinical course of DLBCL (transitions into incident CVD, lymphoma death, and other causes of death). RESULTS: A total of 613 (23.6%) and 230 (8.8%) of 2600 subjects died of lymphoma and developed incident CVD, respectively. Median follow-up was 7.0 years (interquartile range 3.8-10.8 years). Older ages [hazard ratio (HR) for >75 versus ≤60 years 1.88; 95% confidence interval (CI) 1.25-2.82 and HR for 61-75 versus ≤60 years 1.60; 95% CI 1.12-2.30], hypertension (HR 4.92; 95% CI 2.61-9.26), diabetes (HR 1.43; 95% CI 1.09-1.87), and baseline use of aspirin (HR 5.30; 95% CI 3.93-7.16) were associated with an increased risk of incident CVD. In a subgroup of anticipated higher-risk patients (aged 61-75 years, smoked, had diabetes, and received doxorubicin), we found that they remained on average 7.9 (95% CI 7.2-8.8) years in the DLBCL state and 0.1 (95% CI 0.0-0.4) years in the CVD state, if they could be followed up for 10 years. The brief time in the CVD state is consistent with the high chance of death in patients who developed CVD. Other causes of death have overtaken DLBCL-related death after about 5 years. CONCLUSIONS: In this Asian population-based cohort, we found that incident CVDs can occur soon after DLBCL treatment and continued to occur throughout survivorship. Clinicians are advised to balance the risks and benefits of treatment choices to minimize the risk of CVD.

Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method.

Estimates of the overall survival benefit of new cancer treatments are often confounded by treatment switching in randomised controlled trials (RCTs) - whereby patients randomised to the control group are permitted to switch onto the experimental treatment upon disease progression. In health technology assessment, estimates of the unconfounded overall survival benefit associated with the new treatment are needed. Several switching adjustment methods have been advocated in the literature, some of which have been used in health technology assessment. However, it is unclear which methods are likely to produce least bias in realistic RCT-based scenarios. We simulated RCTs in which switching, associated with patient prognosis, was permitted. Treatment effect size and time dependency, switching proportions and disease severity were varied across scenarios. We assessed the performance of alternative adjustment methods based upon bias, coverage and mean squared error, related to the estimation of true restricted mean survival in the absence of switching in the control group. We found that when the treatment effect was not time-dependent, rank preserving structural failure time models (RPSFTM) and iterative parameter estimation methods produced low levels of bias. However, in the presence of a time-dependent treatment effect, these methods produced higher levels of bias, similar to those produced by an inverse probability of censoring weights method. The inverse probability of censoring weights and structural nested models produced high levels of bias when switching proportions exceeded 85%. A simplified two-stage Weibull method produced low bias across all scenarios and provided the treatment switching mechanism is suitable, represents an appropriate adjustment method.

Continued improvement in survival of acute myeloid leukemia patients: an application of the loss in expectation of life.

We evaluated temporal trends in survival of Swedish acute myeloid leukemia (AML) patients diagnosed between 1973 and 2011 using relative survival ratios (RSRs) and a measure called the loss in expectation of life (LEL). RSRs increased most for patients <60 years at diagnosis during the first calendar periods, but between 1997-2005 and 2006-2011 the most pronounced increase was for those aged 61-70 years at diagnosis; RSR changed from 0.16 (95% confidence interval (CI): 0.13-0.19) to 0.28 (95% CI: 0.23-0.33), respectively. The LEL for males aged 35 years at diagnosis was 41.0 (95% CI: 40.1-41.8) years in 1975 and 19.5 (95% CI: 16.4-22.5) years in 2011. For males aged 65 years, the corresponding figures were 13.8 (95% CI: 13.7-14.0) and 12.0 (95% CI: 11.3-12.8). Conditional LEL estimates suggested that patients who survive 5 years postdiagnosis have shorter remaining lifespan than the general population. The proportion of expected life lost (PELL) suggested that male 65-year-old patients lost 75% of their life expectancy in 2005 and 66% if they were diagnosed in 2011. Survival continued to increase to 2011, with larger improvements in those aged 61-70 years at diagnosis. The LEL and PELL are intuitive measures that may be useful in communicating survival statistics to patients, clinicians and health-care providers.

Estimating the potential survival gains by eliminating socioeconomic and sex inequalities in stage at diagnosis of melanoma.

BACKGROUND: Although inequalities in cancer survival are thought to reflect inequalities in stage at diagnosis, little evidence exists about the size of potential survival gains from eliminating inequalities in stage at diagnosis. METHODS: We used data on patients diagnosed with malignant melanoma in the East of England (2006-2010) to estimate the number of deaths that could be postponed by completely eliminating socioeconomic and sex differences in stage at diagnosis after fitting a flexible parametric excess mortality model. RESULTS: Stage was a strong predictor of survival. There were pronounced socioeconomic and sex inequalities in the proportion of patients diagnosed at stages III-IV (12 and 8% for least deprived men and women and 25 and 18% for most deprived men and women, respectively). For an annual cohort of 1025 incident cases in the East of England, eliminating sex and deprivation differences in stage at diagnosis would postpone approximately 24 deaths to beyond 5 years from diagnosis. Using appropriate weighting, the equivalent estimate for England would be around 215 deaths, representing 11% of all deaths observed within 5 years from diagnosis in this population. CONCLUSIONS: Reducing socioeconomic and sex inequalities in stage at diagnosis would result in substantial reductions in deaths within 5 years of a melanoma diagnosis.

The impact of eliminating age inequalities in stage at diagnosis on breast cancer survival for older women.

BACKGROUND: Older women with breast cancer have poorer relative survival outcomes, but whether achieving earlier stage at diagnosis would translate to substantial reductions in mortality is uncertain. METHODS: We analysed data on East of England women with breast cancer (2006-2010) aged 70+ years. We estimated survival for different stage-deprivation-age group strata using both the observed and a hypothetical stage distribution (assuming that all women aged 75+ years acquired the stage distribution of those aged 70-74 years). We subsequently estimated deaths that could be postponed beyond 5 years from diagnosis if women aged 75+ years had the hypothetical stage distribution. We projected findings to the English population using appropriate age and socioeconomic group weights. RESULTS: For a typically sized annual cohort in the East of England, 27 deaths in women with breast cancer aged 75+ years can be postponed within 5 years from diagnosis if their stage distribution matched that of the women aged 70-74 years (4.8% of all 566 deaths within 5 years post diagnosis in this population). Under assumptions, we estimate that the respective number for England would be 280 deaths (5.0% of all deaths within 5 years post diagnosis in this population). CONCLUSIONS: The findings support ongoing development of targeted campaigns aimed at encouraging prompt presentation in older women.

Understanding the impact of socioeconomic differences in breast cancer survival in England and Wales: avoidable deaths and potential gain in expectation of life.

BACKGROUND: Socioeconomic differences in cancer patient survival are known to exist for women diagnosed with breast cancer. Standard metrics tend not to place great emphasis on evaluating the actual impact of these differences. METHODS: We used two alternative, but related, methods of reporting the impact of socioeconomic differences for breast cancer patients in England and Wales. We calculated the average gain in life years for each patient should socioeconomic differences in relative survival be removed and show how this is related to the number of all-cause deaths that could be postponed by removing socioeconomic differences in cancer patient survival. RESULTS: Our results indicate that deprivation differences for women with breast cancer exist and result in women from more deprived areas losing a larger proportion of their life due to a diagnosis of cancer. We also estimate that on average 1.1 years could be gained for a 60 year old breast cancer patient in the most deprived group by improving their relative survival to match the least deprived group. However, our results also show that deprivation differences in general survival have a large impact on life expectancy; showing that over two-thirds of the gap in differential life expectancy is explained by differences in other-cause survival. CONCLUSION: Socioeconomic differences in relative survival have an impact on life expectancy for patients and result in higher early mortality for more deprived patients. However, differences in general survival across socioeconomic groups explain a larger proportion of the deprivation gap in life expectancy for breast cancer patients.

Serum selenium in relation to measures of glucose metabolism and incidence of Type 2 diabetes in an older Swedish population.

AIMS: The relation between selenium status and risk of Type 2 diabetes is controversial. We aimed to evaluate associations of serum selenium, a marker of dietary selenium, with measures of glucose metabolism and risk of diabetes. METHODS: We used data from a population-based, longitudinal cohort of 1925 Swedish men who were 50 years old and did not have diabetes at baseline in the 1970s. At baseline, an intravenous glucose tolerance test was performed and, at a follow-up examination after 20 years, an oral glucose tolerance test and a hyperinsulinaemic euglycaemic clamp for the assessment of insulin sensitivity were conducted. RESULTS: At baseline, the mean (standard deviation) selenium concentration was 75.6 (14.3) µg/l. During 20 years of follow-up, 88 incident cases of diabetes occurred in 1024 participants with follow-up data. Baseline serum selenium levels were not associated with risk of diabetes (odds ratio 1.06; 95% CI 0.83-1.38). Higher selenium levels were associated with lower early insulin response (standardized ß -0.08; 95% CI -0.14 to -0.03) at baseline after adjusting for potential confounders, but not with any other measures of ß-cell function or insulin sensitivity at baseline or follow-up. The association with early insulin response was non-significant after taking multiple testing into account. CONCLUSIONS: Our results do not support a role of dietary selenium in the development of disturbances in glucose metabolism or diabetes in older individuals.

Estimating the proportion cured of cancer: some practical advice for users.

BACKGROUND: Cure models can provide improved possibilities for inference if used appropriately, but there is potential for misleading results if care is not taken. In this study, we compared five commonly used approaches for modelling cure in a relative survival framework and provide some practical advice on the use of these approaches. PATIENTS AND METHODS: Data for colon, female breast, and ovarian cancers were used to illustrate these approaches. The proportion cured was estimated for each of these three cancers within each of three age groups. We then graphically assessed the assumption of cure and the model fit, by comparing the predicted relative survival from the cure models to empirical life table estimates. RESULTS: Where both cure and distributional assumptions are appropriate (e.g., for colon or ovarian cancer patients aged <75 years), all five approaches led to similar estimates of the proportion cured. The estimates varied slightly when cure was a reasonable assumption but the distributional assumption was not (e.g., for colon cancer patients ≥75 years). Greater variability in the estimates was observed when the cure assumption was not supported by the data (breast cancer). CONCLUSIONS: If the data suggest cure is not a reasonable assumption then we advise against fitting cure models. In the scenarios where cure was reasonable, we found that flexible parametric cure models performed at least as well, or better, than the other modelling approaches. We recommend that, regardless of the model used, the underlying assumptions for cure and model fit should always be graphically assessed.

How much of the deprivation gap in cancer survival can be explained by variation in stage at diagnosis: an example from breast cancer in the East of England.

Socioeconomic differences in cancer patient survival exist in many countries and across cancer sites. In our article, we estimated the number of deaths in women with breast cancer that could be avoided within 5 years from diagnosis if it were possible to eliminate socioeconomic differences in stage at diagnosis. We analysed data on East of England women with breast cancer (2006-2010). We estimated survival for different stage-age-deprivation strata using both the observed and a hypothetical stage distribution (assuming all women acquired the stage distribution of the most affluent women). Data were analysed on 20,738 women with complete stage information (92%). Affluent women were less likely to be diagnosed in advanced stage. Relative survival decreased with increasing level of deprivation. Eliminating differences in stage at diagnosis could be expected to nearly eliminate differences in relative survival for women in deprivation groups 3 and 4, but would only approximately halve the difference in relative survival for women in the most deprived group (5). This means, for a typical cohort of women diagnosed in a calendar year with breast cancer, eliminating deprivation differences in stage at diagnosis would prevent ∼40 deaths in the East of England from occurring within 5 years from diagnosis. Using appropriate weighting we estimated the respective number of avoidable deaths for the whole of England to be ∼450. The findings suggest that policies aimed at reducing inequalities in stage at diagnosis between women with breast cancer are important to reduce inequalities in breast cancer survival.

A comprehensive assessment of the impact of errors in the cancer registration process on 1- and 5-year relative survival estimates.

BACKGROUND: When making international comparisons of cancer survival, it is essential reported differences are real effects and not an artefact of potential errors in cancer registration. METHODS: We use simulation methods to assess the impact of various cancer registration errors on commonly reported outcomes of cancer survival (1-, and 5-year relative survival estimates). We draw two samples of patients diagnosed with cancer from one population and introduce potential registration errors in one of the sample populations under various assumptions. We investigate the effect of errors individually as well as the composite effect when combined with other registration errors. RESULTS: The results indicate that high levels of cancer registration errors are necessary to make a noticeable effect on commonly reported metrics of cancer survival. Differences of up to 3 percentage units in the 5-year relative survival proportion are seen under plausible scenarios. CONCLUSION: This study is a comprehensive assessment of cancer registration errors and the consequent impact on commonly reported survival statistics. We show that under plausible scenarios, it is very unlikely that these biases are large enough to explain the variation in international comparisons of cancer survival. Registration errors will also impact on other metrics reported from registry data, such as incidence.

Cure by age and stage at diagnosis for colorectal cancer patients in North West England, 1997-2004: a population-based study.

BACKGROUND: Stage and age at diagnosis are important prognostic factors for patients with colorectal cancer. However, the proportion cured by stage and age is unknown in England. MATERIALS AND METHODS: This population-based study includes 29,563 adult patients who were diagnosed and registered with colorectal cancer during 1997-2004 and followed till 2007 in North West England. Multiple imputation was used to provide more reliable estimates of stage at diagnosis, when these data were missing. Cure mixture models were used to estimate the proportion 'cured' and the median survival of the uncured by age and stage. RESULTS: For both colon and rectal cancer the proportion of patients cured and median survival time of the uncured decreased with advancing stage and increasing age. Patients aged under 65 years had the highest proportion cured and longest median survival of the uncured. CONCLUSION: Cure of colorectal cancer patients is dependent on stage and age at diagnosis with younger patients or those with less advanced disease having a better prognosis. Further efforts are required, in order to reduce the proportion of patients presenting with stage III and IV disease and ultimately increase the chance of cure.

Should relative survival be used with lung cancer data?

BACKGROUND: Under certain assumptions, relative survival is a measure of net survival based on estimating the excess mortality in a study population when compared with the general population. Background mortality estimates are usually taken from national life tables that are broken down by age, sex and calendar year. A fundamental assumption of relative survival methods is that if a patient did not have the disease of interest then their probability of survival would be comparable to that of the general population. It is argued, as most lung cancer patients are smokers and therefore carry a higher risk of smoking-related mortalities, that they are not comparable to a population where the majority are likely to be non-smokers. METHODS: We use data from the Finnish Cancer Registry to assess the impact that the non-comparability assumption has on the estimates of relative survival through the use of a sensitivity analysis. RESULTS: Under realistic estimates of increased all-cause mortality for smokers compared with non-smokers, the bias in the estimates of relative survival caused by the non-comparability assumption is negligible. CONCLUSION: Although the assumption of comparability underlying the relative survival method may not be reasonable, it does not have a concerning impact on the estimates of relative survival, as most lung cancer patients die within the first 2 years following diagnosis. This should serve to reassure critics of the use of relative survival when applied to lung cancer data.

A comparison of prostate cancer survival in England, Norway and Sweden: a population-based study.

PURPOSE: The objective of the study was to compare patterns of survival 2001-2004 in prostate cancer patients from England, Norway and Sweden in relation to age and period of follow-up. SUBJECTS AND METHODS: Excess mortality in men with prostate cancer was estimated using nation-wide cancer register data using a period approach for relative survival. 179,112 men in England, 23,192 in Norway and 59,697 in Sweden were included. RESULTS: In all age groups, England had the lowest survival, particularly so among men aged 80+. Overall age-standardised five-year survival was 76.4%, 80.3% and 83.0% for England, Norway and Sweden, respectively. The majority of the excess deaths in England were confined to the first year of follow-up. CONCLUSION: The results indicate that a small but important group of older patients present at a late stage and succumb early to their cancers, possibly in combination with severe comorbidity, and this situation is more common in England than in Norway or Sweden.

Does socioeconomic status influence the prospect of cure from colon cancer--a population-based study in Sweden 1965-2000.

AIM OF STUDY: Differences in the survival of colon cancer patients by socioeconomic status have been demonstrated in several populations, but the underlying reasons for the differences are not well understood. By simultaneously estimating the proportion of patients cured from colon cancer and the survival times of the 'uncured' we hope to increase understanding of how socioeconomic status affects survival following a diagnosis of colon cancer. METHODS: We conducted a population-based cohort study of 58,873 patients diagnosed with colon cancer in Sweden 1965-2000. Socioeconomic status was classified based on occupation. We fitted mixture cure models and Poisson regression models adjusted for age, sex and calendar period. RESULTS: We observed higher excess mortality, lower proportion cured and shorter survival times among the uncured in patients from lower socioeconomic groups compared to the highest socioeconomic group. There was no evidence that the gap between the socioeconomic groups reduced over time. Farmers had the lowest odds of cure (odds ratio (OR) 0.85, 95% confidence interval (CI) 0.75-0.95) compared to higher non-manual workers followed by self-employed (0.91, 0.81-1.03), manual workers (0.93, 0.85-1.03) and lower non-manual workers (0.98, 0.89-1.08). CONCLUSION: Patients from lower socioeconomic groups in Sweden experience worse survival following a diagnosis of colon cancer. Differences exist in both the cure proportion and the survival time of the uncured, suggesting that socioeconomic differences cannot be attributed solely to lead time bias.Although this study has furthered our understanding of socioeconomic differences in survival, more detailed studies are required in order to identify, and subsequently remove, the underlying reasons for the differences.

Estimating the crude probability of death due to cancer and other causes using relative survival models.

Relative survival is used extensively in population-based cancer studies to measure patient survival correcting for causes of death not related to the disease of interest. An advantage of relative survival is that it provides a measure of mortality associated with a particular disease, without the need for information on cause of death. Relative survival provides a measure of net mortality, i.e. the probability of death due to cancer in the absence of other causes. This is a useful measure, but it is also of interest to measure crude mortality, i.e. the probability of death due to cancer in the presence of other causes. A previous approach to estimate the crude probability of death in population-based cancer studies used life table methods, but we show how the estimates can be obtained after fitting a relative survival model. We adopt flexible parametric models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects. We illustrate the approach using an example of men diagnosed with prostate cancer in England and Wales showing the differences in net and crude survival for different ages.

'Cure' from breast cancer among two populations of women followed for 23 years after diagnosis.

BACKGROUND: Although survival from breast cancer has greatly improved over the past three decades, there is little consensus as to whether a population of women diagnosed with breast cancer can ever be considered 'cured' of the disease. PATIENTS AND METHODS: We examined population 'cure' among women aged 15-99 years diagnosed with breast cancer from 1980 to 1995 in the West Midlands (England) and New South Wales (Australia). We calculated interval-specific excess mortality rates and fitted a number of statistical models to evaluate 'cure'. RESULTS: There was little evidence that these women could ever be considered cured of the disease because excess mortality due to breast cancer was evident among young and middle-aged women up to 23 years after their diagnosis. Older women diagnosed in New South Wales displayed some evidence of 'cure'. However, this was estimated to occur only after the women's 75th birthday. CONCLUSIONS: There is no strong evidence of the existence of a 'cured' subpopulation among young or middle-aged women diagnosed with breast cancer in either West Midlands or New South Wales during the period 1980-1995. Additional follow-up data would permit 'cure' to be assessed for women diagnosed more recently than 1995.

An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes.

Often multiple outcomes are of interest in each study identified by a systematic review, and in this situation a separate univariate meta-analysis is usually applied to synthesize the evidence for each outcome independently; an alternative approach is a single multivariate meta-analysis model that utilizes any correlation between outcomes and obtains all the pooled estimates jointly. Surprisingly, multivariate meta-analysis is rarely considered in practice, so in this paper we illustrate the benefits and limitations of the approach to provide helpful insight for practitioners. We compare a bivariate random-effects meta-analysis (BRMA) to two independent univariate random-effects meta-analyses (URMA), and show how and why a BRMA is able to 'borrow strength' across outcomes. Then, on application to two examples in healthcare, we show: (i) given complete data for both outcomes in each study, BRMA is likely to produce individual pooled estimates with very similar standard errors to those from URMA; (ii) given some studies where one of the outcomes is missing at random, the 'borrowing of strength' is likely to allow BRMA to produce individual pooled estimates with noticeably smaller standard errors than those from URMA; (iii) for either complete data or missing data, BRMA will produce a more appropriate standard error of the pooled difference between outcomes as it incorporates their correlation, which is not possible using URMA; and (iv) despite its advantages, BRMA may often not be possible due to the difficulty in obtaining the within-study correlations required to fit the model. Bivariate meta-regression and further research priorities are also discussed.

Recalibration methods to enhance information on prevalence rates from large mental health surveys.

Comparisons between self-report and clinical psychiatric measures have revealed considerable disagreement. It is unsafe to consider these measures as directly equivalent, so it would be valuable to have a reliable recalibration of one measure in terms of the other. We evaluated multiple imputation incorporating a Bayesian approach, and a fully Bayesian method, to recalibrate diagnoses from a self-report survey interview in terms of those from a clinical interview with data from a two-phase national household survey for a practical application, and artificial data for simulation studies. The most important factors in obtaining a precise and accurate 'clinical' prevalence estimate from self-report data were (a) good agreement between the two diagnostic measures and (b) a sufficiently large set of calibration data with diagnoses based on both kinds of interview from the same group of subjects. From the case study, calibration data on 612 subjects were sufficient to yield estimates of the total prevalence of anxiety, depression or neurosis with a precision in the region of +/-2%. The limitations of the calibration method demonstrate the need to increase agreement between survey and reference measures by improving lay interviews and their diagnostic algorithms.