Elisavet Syriopoulou

Aim: Whether socioeconomic factors are associated with colorectal cancer (CRC) risk remains unknown, with inconsistent results from different countries. The hypothesis was that both socioeconomic factors and the presence of psychiatric comorbidities are risk factors for CRC. Method: CRCBaSe, a registry linkage project, was used for a nested case‒control study with 78,043 CRC patients and 431,105 controls matched by age, sex, and county of residence. Socioeconomic factors (income, education, civil status, and birth country) and history of mental illness were used to obtain incidence rate ratios (hazard ratios). Results: Patients in the middle-income quartile had an increased rate of CRC compared with those in the lowest-income quartile (HR 1.05, 95%CI 1.03–1.08 for Q2 vs. Q1, and HR 1.04 95%CI 1.021.07 for Q3 vs. Q1), whereas there was no association with high income or educational level. Compared with those born in Sweden, those born in other Nordic countries had an increased rate of CRC (HR 1.06 95%CI 1.02–1.10), and those born outside of the EU had a decreased rate (HR 0.80 95%CI 0.77–0.83). A history of mental illness was associated with the rate of CRC even after adjusting for socioeconomic factors: mild mental illness, HR 0.81 (95%CI 0.78–0.83); and severe mental illness, HR 1.85 (95%CI 1.74–1.96). Conclusion: These findings confirm that the effect of socioeconomic position on CRC risk is heterogeneous and small in Sweden and that severe mental illness is a separate risk factor. Further investigations into the mechanisms that drive the increased risk of CRC in patients with mental illness are warranted.

Objective: Unlike survival measures, life expectancy readily illustrates the burden of cancer on society and the average impact on individuals diagnosed with cancer. Cancer stage at diagnosis is a key prognostic factor and hence it is important to obtain stage-specific life expectancy estimates. However, completeness of recording for cancer stage at diagnosis is often historically poor in cancer registries. Therefore, it can be challenging to obtain the long-term stage-specific survival estimates required for estimating stage-specific life expectancy. We provide the first stage-specific life expectancy estimates using whole population data in England. Methods and analysis: Multiple imputation was used to impute values of cancer stage at diagnosis for patients with missing stage at diagnosis information. The simultaneous application of period analysis to obtain up-to-date estimates restricts the contribution of patients with historical diagnoses and hence improves the overall completeness of stage at diagnosis information. For each of the 10 cancer sites in this study, we fit a flexible parametric excess hazard model for each cancer stage on the cumulative excess hazard scale and estimated stage-specific life expectancy using the relative survival framework. Results: The differences in stage-specific and sex-specific life expectancy were evaluated from 40 to 90 years of age. Colorectal cancer, prostate cancer and bladder cancer yield much lower estimates of life expectancy for patients diagnosed with stage IV cancer compared with stages I–III. For example, female patients diagnosed with stage IV colorectal cancer at 70 years of age have a life expectancy of 72.3 years, while those with stages I–III can expect to live beyond 82.0 years. The remaining cancer sites yield approximately equal reductions in life expectancy with each increase in stage at diagnosis from I to IV. Conclusion: We offer the first stage-specific life expectancy estimates for a range of cancer sites in England using data from the National Cancer Registration and Analysis Service, with follow-up until February 2020. Estimates of stage-specific life expectancy provide a real-world, intuitive metric to evaluate the impact of cancer stage at diagnosis on prognosis up to a lifetime horizon. Stage-specific life expectancy estimates also provide key information regarding the potential benefits of early diagnosis initiatives in terms of gains in life years.

Background: Loss in life expectancy (LLE) by socioeconomic position (SEP) is obtained to explore cancer disparities. Often education or income are used to determine SEP. However, the low income of some older individuals or females within households with greater overall resources may not accurately reflect their SEP, potentially leading to biased estimates. Our study investigates how various conceptualizations of SEP influence the extent of these disparities. Methods: Data included all colon cancer diagnoses in Sweden between 2008 and 2016. We estimated LLE using different SEP indicators: education, individual income, part of household income. Household income accounts for individuals with low individual income but within high-income households. We allocated patients to income groups creating quartiles, overall or separately by sex, age-groups, or both, to account for SEP misclassification. We analysed data using flexible parametric survival models. Results: Standardised LLE varied from 5.05 to 5.62 years for the lowest to the highest education, resulting in a 0.57-year difference. When income was used to determine SEP, LLE was similar with disparities ranging from 0.29 to 0.77 across definitions. Age and sex specific LLE disparities varied more across definitions. Disparities between 60-year-old females in the highest-lowest income groups were 0.94 when using household income and − 0.17 when using individual income. Conclusions: When studying LLE by SEP, thoughtful consideration must be given to selecting the most suitable indicator for conceptualizing SEP. The choice should align with the specific SEP pathway of interest and address potential misclassification concerns relevant to the study population.

Background: Survival differences between socioeconomic groups in colorectal cancer have been studied for patients diagnosed in the 90s and 00s, but research on recent patients using individual measures of socioeconomic position is limited. Methods: CRCBaSe, a database of linked national registry data, was used to analyse stage I–III colorectal cancer patients diagnosed in Sweden between 2008 and 2021. The exposures of interest were income and education. Flexible parametric survival models were fitted and standardised survival probabilities and hazard ratios (HR) were calculated for cancer-specific survival, recurrence, and overall survival. Results: Analysis of 59,995 patients showed better 5-year standardised cancer-specific survival in the least deprived income group, 77.8% (95%CI 76.9–78.6) vs. 73.2% (95%CI 72.6–73.9) in the most deprived income group, HR 0.93 (95%CI 0.87–0.99). Time to recurrence was not statistically different between socioeconomic groups. Overall survival was better in the least deprived income group, with a 5-year standardised overall survival of 70.0% (95%CI 69.1–70.8) vs. 63.5% (95%CI 62.9–64.1) in the most deprived income group, HR 0.82 (95%CI 0.79–0.86). Conclusion: We found large disparities in cancer-specific and overall survival between the highest and most deprived income and education groups, despite improvements in care and the introduction of guidelines.

Background and purpose: The impact of mental illness on treatment and outcomes for patients with colorectal cancer (CRC) has not been investigated with potential confounders and mediators accounted for. Patients and methods: Colorectal Cancer Database (CRCBaSe), a linked national registry database, was used to analyse stage I–III CRC patients diagnosed in Sweden between 2008 and 2021. The exposure of interest was a history of mental illness. Treatment outcomes were analysed with logistic regressions. Flexible parametric models were fitted for survival analysis. Analyses were adjusted for pre-specified confounders. Results: Patients with a history of severe mental illness presented with more advanced tumours and comorbidities. They were more likely to undergo emergency surgery (OR 1.56, 95% CI 1.32–1.84) and less likely to receive adjuvant treatment (OR 0.65, 95% CI 0.53–0.80) than patients with no history of mental illness. Five-year standardised overall survival (OS) was worse for those with a history of mild and severe mental illness, 64.6% (95%CI 63.9–65.3) and 61.8% (95%CI 59.7–63.8) compared to those without 69.3% (95%CI 68.9–69.7). Although time to recurrence was not significantly impacted, standardised survival after recurrence was worse for patients with a history of severe mental illness, with a 3-year survival after recurrence of 24% compared to 30% in those without a history of mental illness. Interpretation: Although the differences were smaller compared to previous studies, patients with a history of mental illnesses still do worse. The management of CRC patients with psychiatric comorbidities presents complex challenges necessitating personalised solutions.

Background Differences in the prognosis after colorectal cancer (CRC) by socioeconomic position (SEP) have been reported previously; however, most studies focused on survival differences at a particular time since diagnosis. We quantified the lifetime impact of CRC and its variation by SEP, using individualised income to conceptualise SEP. Methods Data included all adults with a first-time diagnosis of colon or rectal cancers in Sweden between 2008 and 2021. The analysis was done separately for colon and rectal cancers using flexible parametric models. For each cancer and income group, we estimated the life expectancy in the absence of cancer, the life expectancy in the presence of cancer and the loss in life expectancy (LLE). Results We found large income disparities in life expectancy after a cancer diagnosis, with larger differences among the youngest patients. Higher income resulted in more years lost following a cancer diagnosis. For example, 40-year-old females with colon cancer lost 17.64 years if in the highest-income group and 13.68 years if in the lowest-income group. Rectal cancer resulted in higher LLE compared with colon cancer. Males lost a larger proportion of their lives. All patients, including the oldest, lost more than 30% of their remaining life expectancy. Based on the number of colon and rectal cancer diagnoses in 2021, colon cancer results in almost double the number of years lost compared with rectal cancer (24 669 and 12 105 years, respectively). Conclusion While our results should be interpreted in line with what individualised income represents, they highlight the need to address inequalities.

Background: When interested in studying the effect of a treatment (or other exposure) on a time-to-event outcome, the most popular approach is to estimate survival probabilities using the Kaplan–Meier estimator. In the presence of confounding, regression models are fitted, and results are often summarised as hazard ratios. However, despite their broad use, hazard ratios are frequently misinterpreted as relative risks instead of relative rates.Methods: We discuss measures for summarising the analysis from a regression model that overcome some of the limitations associated with hazard ratios. Such measures are the standardised survival probabilities for treated and untreated: survival probabilities if everyone in the population received treatment and if everyone did not. The difference between treatment arms can be calculated to provide a measure for the treatment effect.Results: Using publicly available data on breast cancer, we demonstrated the usefulness of standardised survival probabilities for comparing the experience between treated and untreated after adjusting for confounding. We also showed that additional important research questions can be addressed by standardising among subgroups of the total population.Discussion: Standardised survival probabilities are a useful way to report the treatment effect while adjusting for confounding and have an informative interpretation in terms of risk.

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 . 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 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 . 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.

Background: An increasingly popular measure for summarising cancer prognosis is the loss in life expectancy (LLE), i.e. the reduction in life expectancy following a cancer diagnosis. The proportion of life lost (PLL) can also be derived, improving comparability across age groups as LLE is highly age-dependent. LLE and PLL are often used to assess the impact of cancer over the remaining lifespan and across groups (e.g. socioeconomic groups). However, in the presence of screening, it is unclear whether part of the differences across population groups could be attributed to lead time bias. Lead time is the extra time added due to early diagnosis, that is, the time from tumour detection through screening to the time that cancer would have been diagnosed symptomatically. It leads to artificially inflated survival estimates even when there are no real survival improvements. Methods: In this paper, we used a simulation-based approach to assess the impact of lead time due to mammography screening on the estimation of LLE and PLL in breast cancer patients. A natural history model developed in a Swedish setting was used to simulate the growth of breast cancer tumours and age at symptomatic detection. Then, a screening programme similar to current guidelines in Sweden was imposed, with individuals aged 40–74 invited to participate every second year; different scenarios were considered for screening sensitivity and attendance. To isolate the lead time bias of screening, we assumed that screening does not affect the actual time of death. Finally, estimates of LLE and PLL were obtained in the absence and presence of screening, and their difference was used to derive the lead time bias. Results: The largest absolute bias for LLE was 0.61 years for a high screening sensitivity scenario and assuming perfect screening attendance. The absolute bias was reduced to 0.46 years when the perfect attendance assumption was relaxed to allow for imperfect attendance across screening visits. Bias was also present for the PLL estimates. Conclusions: The results of the analysis suggested that lead time bias influences LLE and PLL metrics, thus requiring special consideration when interpreting comparisons across calendar time or population groups.

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.