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Background: Colorectal cancer (CRC) patients in Denmark and England have historically had worse outcomes than patients diagnosed in other high-income countries with similar healthcare coverage. This survival deficit may be partly explained by differences in stage distribution due to delays in cancer diagnosis, however, differences in stage-specific survival suggest that differences in treatment also explain the survival gap. Aim: We aim to provide a comparison of stage-specific survival of CRC patients diagnosed in Denmark, England, Norway and Sweden, and compare the probability of receiving potentially curative surgery (PCS) by patient and tumor characteristics to understand whether differences in stage and treatment help explain the international differences in survival. Methods: Population-based information on patients aged 18-99 years diagnosed with primary malignant colon or rectal cancer in Denmark, England, Norway, and Sweden during 2010-2012 was extracted from national CRC registries. We compared the prevalence of PCS by stage and age category, and estimated the probability of receiving it using multivariate logistic regression. Age-standardized net survival was estimated using a multivariable modeling approach. Results: There were 13,230 patients diagnosed with CRC in Denmark, 99,869 in England, 11,754 in Norway, and 17,584 in Sweden between 2010 and 2012. The proportion of patients with missing stage information was higher in England than in the other countries. Stage distribution was otherwise similar. Survival of patients with stage I-II tumors was generally comparable between the countries, but lower in England and - to a lesser degree - in Denmark for stage III-IV disease. Patients with unknown stage had lower survival than staged patients in all countries. We found marked disadvantages in the probability of receiving PCS in older patients with stage II-IV disease in England, and stage IV colon cancer patients in Denmark. The proportion of surgically-treated rectal cancer patients (and their survival) was comparable between Denmark, Norway and Sweden for each combination of age group and stage, except for patients diagnosed with stage I disease. The proportion of patients receiving surgery and survival from rectal cancer was generally lower in England. Conclusion: Denmark seems to be catching up with Norway and Sweden, especially in rectal cancer survival. Despite recent improvements, CRC survival in England remains lower than in Sweden and Norway. Survival generally paralleled countries' relative performance in the provision of potentially curative surgery. Differences in patient selection for surgery, especially in older patients and/or with advanced disease, may partly explain the survival deficit. Increases in the proportion treated, in combination with efforts in postoperative care and with other treatment modalities, may translate into better longer-term outcomes, especially for frail patients. [ABSTRACT FROM AUTHOR]

In this Policy Review we discuss ten key pressure points in the NHS in the delivery of cancer care services that need to be urgently addressed by a comprehensive national cancer control plan. These pressure points cover areas such as increasing workforce capacity and its productivity, delivering effective cancer survivorship services, addressing variation in quality, fixing the reimbursement system for cancer care, and balancing of the cancer research agenda. These areas have been selected based on their relative importance to ensuring sustainable cancer services, persistence as key issues in the NHS, and their impact on delivering better and more equitable and affordable patient outcomes. Many of these pressure points are not acknowledged explicitly in any current discourse. The evidence we provide points to their impact on the ability to deliver world class cancer care, but also to their amenability to affordable solutions if given the relevant prioritisation and investment. The current narrative needs to move away from a technocentric approach to improving care, to one focused on understanding the complexity of cancer services and the wider health system to drive improvements in survival, quality of life, and experience for patients., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Propensity score methods, such as inverse probability-of-treatment weighting (IPTW), have been increasingly used for covariate balancing in both observational studies and randomized trials, allowing the control of both systematic and chance imbalances. Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by applying PS weights. Thus, a variance estimator that accounts for both steps is crucial for correct inference. Using a variance estimator which ignores the first step leads to overestimated variance when the estimand is the average treatment effect (ATE), and to under or overestimated estimates when targeting the average treatment effect on the treated (ATT). In this article, we emphasize the importance of using an IPTW variance estimator that correctly considers the uncertainty in PS estimation. We present a comprehensive tutorial to obtain unbiased variance estimates, by proposing and applying a unifying formula for different types of PS weights (ATE, ATT, matching and overlap weights). This can be derived either via the linearization approach or M-estimation. Extensive R code is provided along with the corresponding large-sample theory. We perform simulation studies to illustrate the behavior of the estimators under different treatment and outcome prevalences and demonstrate appropriate behavior of the analytical variance estimator. We also use a reproducible analysis of observational lung cancer data as an illustrative example, estimating the effect of receiving a PET-CT scan on the receipt of surgery., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Cancer survival is a key component to assess the overall effectiveness of healthcare systems in their cancer management efforts. A key supporting tool for planning and decision making was introduced with the development of an index of cancer survival that summarises survival for all adults and cancer types into one single estimate, but the implementation details have not been previously described., Methods: We detail the construction of the index, including the structure, the calculation of 'sex-age-cancer' specific weights and our proposed modelling strategy to estimate net survival. We provide some practical recommendations through an illustration using a synthetic dataset ('Replica') that we generated for this purpose. An example of R code usage to estimate the index using our approach is provided., Results: The 'Replica' contains 500 000 artificial cancer records that mimic a cohort of adult cancer patients diagnosed with cancer in England between 1980 and 2004. Using this dataset, we estimated an index of cancer survival at one, five, and ten years after diagnosis for five selected periods of diagnosis, and provide an example of interpretation of these results., Discussion: We propose a flexible penalised regression modelling strategy to estimate the index's 'sex-age-cancer' specific cancer survival components that minimises the estimation challenge of these components. This tutorial will support researchers in constructing an index of cancer survival for their own setting, facilitating the enrichment of existing toolkits of cancer indicators to more effectively measure progress against cancer in their respective regions/countries., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: More deprived cancer patients are at higher risk of Emergency Presentation (EP) with most studies pointing to lower symptom awareness and increased comorbidities to explain those patterns. With the example of colon cancer, we examine patterns of hospital emergency admissions (HEAs) history in the most and least deprived patients as a potential precursor of EP., Methods: We analysed the rates of hospital admissions and their admission codes (retrieved from Hospital Episode Statistics) in the two years preceding cancer diagnosis by sex, deprivation and route to diagnosis (EP, non-EP). To select the conditions (grouped admission codes) that best predict emergency admission, we adapted the purposeful variable selection to mixed-effects logistic regression., Results: Colon cancer patients diagnosed through EP had the highest number of HEAs than all the other routes to diagnosis, especially in the last 7 months before diagnosis. Most deprived patients had an overall higher rate and higher probability of HEA but fewer conditions associated with it., Conclusions: Our findings point to higher use of emergency services for non-specific symptoms and conditions in the most deprived patients, preceding colon cancer diagnosis. Health system barriers may be a shared factor of socio-economic inequalities in EP and HEAs., (© 2024. The Author(s).)

Background: Individual and tumour factors only explain part of observed inequalities in colorectal cancer survival in England. This study aims to investigate inequalities in treatment in patients with colorectal cancer., Methods: All patients diagnosed with colorectal cancer in England between 2012 and 2016 were followed up from the date of diagnosis (state 1), to treatment (state 2), death (state 3) or censored at 1 year after the diagnosis. A multistate approach with flexible parametric model was used to investigate the effect of income deprivation on the probability of remaining alive and treated in colorectal cancer., Results: Compared to the least deprived quintile, the most deprived with stage I-IV colorectal cancer had a lower probability of being alive and treated at all the time during follow-up, and a higher probability of being untreated and of dying. The probability differences (most vs. least deprived) of being alive and treated at 6 months ranged between -2.4% (95% CI: -4.3, -1.1) and -7.4% (-9.4, -5.3) for colon; between -2.0% (-3.5, -0.4) and -6.2% (-8.9, -3.5) for rectal cancer., Conclusion: Persistent inequalities in treatment were observed in patients with colorectal cancer at every stage, due to delayed access to treatment and premature death., (© 2023. The Author(s).)

Purpose: Treatment with any adjuvant chemotherapy for stage III colon cancer has been shown to differ between population groups. Few studies, however, explore variations in the type of adjuvant chemotherapy received, none of which are from the UK. The aim of this study is to explore variation in the type of chemotherapy received by stage III colon cancer patients in England., Methods: Data from the national cancer registry was linked to the Systemic Anti-Cancer Therapy database, which provides detailed information on treatment of malignant diseases from all NHS England chemotherapy providers. Demographic and clinical characteristics were compared between those who received monotherapy (fluoropyrimidine) or combination chemotherapy (fluoropyrimidine and oxaliplatin) among stage III colon cancer patients between 2012 and 2017., Results: Of 8750 patients who received adjuvant chemotherapy, 22.3% (n = 2359) received monotherapy and 60.4% (n = 6391) received combination therapy. The odds of receiving combination therapy decreased with age. Those from the most deprived group had half the odds (OR: 0.5, CI: 0.42, 0.59, p < 0.001) of receiving combination therapy compared to the least deprived group. Women were 14% less likely to get combined therapy (OR: 0.86, CI: 0.77, 0.95, p = 0.005). Those with the largest tumour size (T4) and those with more than three lymph nodes involved (N2) had 30% (OR: 1.30; CI: 1.07, 1.59; p = 0.008) and 50% (OR: 1.50; 1.34, 1.69; p < 0.001) higher odds of receiving combination therapy compared to T1 or T2 and N1, respectively., Conclusion: There is variation in the type of chemotherapy received for stage III colon cancer patients by sociodemographic factors, despite clear clinical guidelines., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: To investigate associations between quality of life (QoL) and 1) immunotherapy and other cancer treatments received three months before QoL measurements, and 2) the comorbidities at the time of completion or in the year prior to QoL measurements, among patients with advanced cancer., Methods: A cross-sectional study is conducted on patients with advanced cancer in the Netherlands. The data come from the baseline wave of the 2017-2020 eQuiPe study. Participants were surveyed via questionnaires (including EORTC QLQ-C30). Using multivariable linear and logistic regression models, we explored statistical associations between QoL components and immunotherapy and other cancer treatments as well as pre-existing comorbidities while adjusting for age, sex, socio-economic status., Results: Of 1088 participants with median age 67 years, 51% were men. Immunotherapy was not associated with global QoL but was associated with reduced appetite loss (odds ratio (OR) = 0.6, 95%CI = [0.3,0.9]). Reduced global QoL was associated with chemotherapy (adjusted mean difference (β) = - 4.7, 95% CI [- 8.5,- 0.8]), back pain (β = - 7.4, 95% CI [- 11.0,- 3.8]), depression (β = - 13.8, 95% CI [- 21.5,- 6.2]), thyroid diseases (β = - 8.9, 95% CI [- 14.0,- 3.8]) and diabetes (β = - 4.5, 95% CI [- 8.9,- 0.5]). Chemotherapy was associated with lower physical (OR = 2.4, 95% CI [1.5,3.9]) and role (OR = 1.8, 95% CI [1.2,2.7]) functioning, and higher pain (OR = 1.9, 95% CI [1.3,2.9]) and fatigue (OR = 1.6, 95% CI [1.1,2.4])., Conclusion: Our study identified associations between specific cancer treatments, lower QoL and more symptoms. Monitoring symptoms may improve QoL of patients with advanced cancer. Producing more evidence from real life data would help physicians in better identifying patients who require additional supportive care., (© 2023. The Author(s).)

Background: The excess mortality observed in Acute Myeloblastic Leukaemia (AML) patients, partly attributed to unequal access to curative treatments, could be linked to care pathways., Methods: We included 1039 AML incident cases diagnosed between 2012-2016 from the 3 French blood cancer registries (3,625,400 inhabitants). We describe patients according to age, the medical entry unit and access to the specialised haematology unit (SHU) during follow-up. Multivariate logistic regression model was done to determine the association between covariables and access to SHU. A total of 713 patients (69%) had access to SHU during care., Results: The most common care pathway concerned referral from the general practitioner to SHU, n = 459(44%). The univariate analysis observed a downward trend for the most deprived patients. Patients who consulted in SHU were younger (66 years vs. 83, p < 0.001), and 92% had access to cytogenetic analysis (vs. 54%, p < 0.001). They also had less poor prognosis AML-subtypes (AML-MRC, t-AML/MDS and AML-NOS) (38% vs. 69%); 77% with de novo AML (vs. 67%, p < 0.003)], more favourable cytogenetic prognostic status (23% vs. 6%, p < 0.001), less comorbidities (no comorbidity = 55% vs. 34%, p < 0.001) and treatments proposed were curative 68% (vs. 5.3%, p < 0.001). Factors limiting access to SHU were age over 80 years (OR, 0.14; 95% CI, 0.04-0.38), severe comorbidities (OR, 0.39; 95% CI, 0.21-0.69), emergency unit referral (OR, 0.28; 95% CI, 0.18-0.44) and non-SHU referral (OR, 0.12; 95% CI, 0.07-0.18). Consultation in an academic hospital increased access to SHU by 8.87 times (95% CI, 5.64-14.2)., Conclusion: The high proportion of access to cytogenetic testing and curative treatment among patients admitted to SHU, and the importance of early treatment in AML underlines the importance of access to SHU for both diagnosis and treatment., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: In cancer net survival analyses, if life tables (LT) are not stratified based on socio-demographic characteristics, then the social gradient in mortality in the general population is ignored. Consequently, the social gradient estimated on cancer-related excess mortality might be inaccurate. We aimed to evaluate whether the social gradient in cancer net survival observed in France could be attributable to inaccurate LT., Methods: Deprivation-specific LT were simulated, applying the social gradient in the background mortality due to external sources to the original French LT. Cancer registries' data from a previous French study were re-analyzed using the simulated LT. Deprivation was assessed according to the European Deprivation Index (EDI). Net survival was estimated by the Pohar-Perme method and flexible excess mortality hazard models by using multidimensional penalized splines., Results: A reduction in net survival among patients living in the most-deprived areas was attenuated with simulated LT, but trends in the social gradient remained, except for prostate cancer, for which the social gradient reversed. Flexible modelling additionally showed a loss of effect of EDI upon the excess mortality hazard of esophagus, bladder and kidney cancers in men and bladder cancer in women using simulated LT., Conclusions: For most cancers the results were similar using simulated LT. However, inconsistent results, particularly for prostate cancer, highlight the need for deprivation-specific LT in order to produce accurate results.

Background: Socioeconomic inequalities in survival from non-Hodgkin lymphoma persist. Comorbidities are more prevalent amongst those in more deprived areas and are associated with diagnostic delay (emergency diagnostic route), which is also associated with poorer survival probability. We aimed to describe the effect of comorbidity on the probability of death mediated by diagnostic route (emergency vs. elective route) amongst patients with diffuse large B-cell (DLBCL) or follicular lymphoma (FL). Methods: We linked the English population-based cancer registry and hospital admission records (2005-2013) of patients aged 45-99 years. We decomposed the effect of comorbidity on survival into an indirect effect acting through diagnostic route and a direct effect not mediated by diagnostic route. Furthermore, we estimated the proportion of the comorbidity effect on survival mediated by diagnostic route. Results: For both DLBCL ( n = 27,379) and FL ( n = 14,043), those with any comorbidity, or living in more deprived areas, were more likely to experience diagnostic delay and poorer survival. The indirect effect of comorbidity on mortality through diagnostic route was highest at 12 months since diagnosis (DLBCL: Odds Ratio 1.10 [95% CI 1.07-1.13], FL: OR 1.09 [95% CI 1.04-1.14]). Within the first 12 months since diagnosis, emergency diagnostic route accounted for 24% (95% CI 17.5-29.5) and 16% (95% CI 6.0-25.6) of the comorbidity effect on mortality, for DLBCL and FL, respectively. Conclusion: Efforts to reduce diagnostic delay (emergency diagnosis) amongst patients with comorbidity would reduce inequalities in DLBCL and FL survival by 24% and 16%, respectively. Further public health programs and interventions are needed to reduce diagnostic delay amongst lymphoma patients with comorbidities.

Importance: Mental health morbidity (MHM) in patients presenting with possible cancer symptoms may be associated with prediagnostic care and time to cancer diagnosis., Objective: To compare the length of intervals to cancer diagnosis by preexisting MHM status in patients who presented with symptoms of as-yet-undiagnosed colon cancer and evaluate their risk of emergency cancer diagnosis., Design, Setting, and Participants: This cohort study was conducted using linked primary care data obtained from the population-based Clinical Practice Research Datalink, which includes primary care practices in England, linked to cancer registry and hospital data. Included participants were 3766 patients diagnosed with colon cancer between 2011 and 2015 presenting with cancer-relevant symptoms up to 24 months before their diagnosis. Data analysis was performed in January 2021 to April 2022., Exposures: Mental health conditions recorded in primary care before cancer diagnosis, including anxiety, depression, schizophrenia, bipolar disorder, alcohol addiction, anorexia, and bulimia., Main Outcomes and Measures: Fast-track (also termed 2-week wait) specialist referral for investigations, time to colonoscopy and cancer diagnosis, and risk of emergency cancer diagnosis., Results: Among 3766 patients with colon cancer (median [IQR] age, 75 [65-82] years; 1911 [50.7%] women ), 623 patients [16.5%] had preexisting MHM recorded in primary care the year before cancer diagnosis, including 562 patients (14.9%) with preexisting anxiety or depression (accounting for 90.2% of patients with preexisting MHM) and 61 patients (1.6%) with other MHM; 3143 patients (83.5%) did not have MHM. Patients with MHM had records of red-flag symptoms or signs (ie, rectal bleeding, change in bowel habit, or anemia) in the 24 months before cancer diagnosis in a smaller proportion compared with patients without MHM (308 patients [49.4%] vs 1807 patients [57.5%]; P < .001). Even when red-flag symptoms were recorded, patients with MHM had lower odds of fast-track specialist referral (adjusted odds ratio [OR] = 0.72; 95% CI, 0.55-0.94; P = .01). Among 2115 patients with red-flag symptoms or signs, 308 patients with MHM experienced a more than 2-fold longer median (IQR) time to cancer diagnosis (326 [75-552] days vs 133 [47-422] days) and higher odds of emergency diagnosis (90 patients [29.2%] vs 327 patients [18.1%]; adjusted OR = 1.63; 95% CI, 1.23-2.24; P < .001) compared with 1807 patients without MHM., Conclusions and Relevance: This study found that patients with MHM experienced large and prognostically consequential disparities in diagnostic care before a colon cancer diagnosis. These findings suggest that appropriate pathways and follow-up strategies after symptomatic presentation are needed for earlier cancer diagnoses and improved health outcomes in this large patient group.

Life tables summarise a population's mortality experience during a time period. Sex- and age-specific life tables are needed to compute various cancer survival measures. However, mortality rates vary according to socioeconomic status. We present sex- and age-specific life tables based on socioeconomic status at the census tract level in Spain during 2011-2013 that will allow estimating cancer relative survival estimates and life expectancy measures by socioeconomic status. Population and mortality data were obtained from the Spanish Statistical Office. Socioeconomic level was measured using the Spanish Deprivation Index by census tract. We produced sex- and age-specific life expectancies at birth by quintiles of deprivation, and life tables by census tract and province. Life expectancy at birth was higher among women than among men. Women and men in the most deprived census tracts in Spain lived 3.2 and 3.8 years less than their counterparts in the least deprived areas. A higher life expectancy in the northern regions of Spain was discovered. Life expectancy was higher in provincial capitals than in rural areas. We found a significant life expectancy gap and geographical variation by sex and socioeconomic status in Spain. The gap was more pronounced among men than among women. Understanding the association between life expectancy and socioeconomic status could help in developing appropriate public health programs. Furthermore, the life tables we produced are needed to estimate cancer specific survival measures by socioeconomic status. Therefore, they are important for cancer control in Spain., (© 2022. The Author(s).)

Introduction: The burden of stomach cancer remains high, particularly among Asian countries. Although Japan is known to achieve high survival from stomach cancer, little is known regarding the survival trends for recent years and survival by subsite and stage. We report age-standardised 1-, 3-, 5- and 10-year net survival for patients diagnosed with stomach cancer in Osaka, Japan., Methods: We analysed patients diagnosed with primary stomach cancer and registered in the population-based cancer registry in Osaka Prefecture between 2001 and 2014. We used the non-parametric Pohar Perme method to derive net survival for each year. Both cohort and period approaches were used. Age was standardised using weights of the external population of the International Cancer Survival Standard. Multiple imputation was applied to handle missing information on subsite and stage before estimating age-standardised net survival by subsite (cardia and non-cardia) and stage (localised, regional and distant metastasis). We then examined general trends in the cohort-based survival estimates, as well as by subsite and stage, using linear regression., Results: A total of 97,276 patients were included in the analysis. Age-standardised net survival improved steadily (mean annual absolute change ≥1.2%). Net survival for both subsites improved, but cardia cancer showed 7-23% lower survival than non-cardia cancer throughout the study period. Five-year net survival remained high (≥80%) in the localised stage from the beginning of this study. Net survival increased steeply (≥1.4% per year) in the regional stage. Although 1-year net survival increased by 14% in the distant stage, 5-year and 10-year net survival remained below 10%., Conclusion: Age-standardised net survival for stomach cancer in Japan improved during the study period owing to an increase in the number of patients with localised stage at diagnosis and improved treatment. Monitoring both short- and long-term survival should be continued as management of stomach cancer progresses., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Purpose: System science offers a unique set of tools, including causal loop diagrams (CLDs), for stakeholders to better grasp the complexity of factors surrounding quality of life. Because the health-related quality of life (HRQoL) of cancer immunotherapy patients exists within an intricate system affected by and affecting many factors across multiple dimensions, the development of a systems-level model can provide a powerful framework to aid the understanding of this complexity. We developed a CLD for HRQoL of cancer immunotherapy patients., Methods: We first applied a literature-based approach to construct a CLD for patients following immunotherapy. We then iteratively reviewed and enhanced the CLD through interviews with subject matter experts., Results: Based on the reviewed literature and subject matter expert input, we produced a CLD representing the system surrounding cancer immunotherapy patients' HRQoL. Several feedback loops are identified that span clinical experiences, oncology teams' perceptions about immunotherapy, social support structures, and further research and development in cancer immunotherapy, in addition to other components. The CLD enables visualization of thought experiments regarding how a change anywhere in the system can ultimately worsen or improve patients' HRQoL., Conclusion: The CLD illustrates the valuable contribution of a systems perspective to quality-of-life research. This systems-based qualitative representation gives insight on strategies to inhibit harmful effects, enhance beneficial effects, and inherent tradeoffs within the system. The CLD identifies gaps in the literature and offers a communication tool for diverse stakeholders. Our research method provides an example for studying the complexities of quality of life in other health domains., (© 2022. The Author(s).)

Background: We aimed to investigate the impact of socio-economic inequalities in cancer survival in England on the Number of Life-Years Lost (NLYL) due to cancer., Methods: We analysed 1.2 million patients diagnosed with one of the 23 most common cancers (92.3% of all incident cancers in England) between 2010 and 2014. Socio-economic deprivation of patients was based on the income domain of the English Index of Deprivation. We estimated the NLYL due to cancer within 3 years since diagnosis for each cancer and stratified by sex, age and deprivation, using a non-parametric approach. The relative survival framework enables us to disentangle death from cancer and death from other causes without the information on the cause of death., Results: The largest socio-economic inequalities were seen mostly in adults <45 years with poor-prognosis cancers. In this age group, the most deprived patients with lung, pancreatic and oesophageal cancer lost up to 6 additional months within 3 years since diagnosis than the least deprived. For most moderate/good prognosis cancers, the socio-economic inequalities widened with age., Conclusions: More deprived patients and particularly the young with more lethal cancers, lose systematically more life-years than the less deprived. To reduce these inequalities, cancer policies should systematically encompass the inequities component., (© 2022. The Author(s).)

Background: Associations between socioeconomic status (SES) and breast cancer survival are most pronounced in young patients. We further investigated the relation between SES, subsequent recurrent events and mortality in breast cancer patients < 40 years. Using detailed data on all recurrences that occur between date of diagnosis of the primary tumor and last observation, we provide a unique insight in the prognosis of young breast cancer patients according to SES., Methods: All women < 40 years diagnosed with primary operated stage I-III breast cancer in 2005 were selected from the nationwide population-based Netherlands Cancer Registry. Data on all recurrences within 10 years from primary tumor diagnosis were collected directly from patient files. Recurrence patterns and absolute risks of recurrence, contralateral breast cancer (CBC) and mortality - accounting for competing risks - were analysed according to SES. Relationships between SES, recurrence patterns and excess mortality were estimated using a multivariable joint model, wherein the association between recurrent events and excess mortality (expected mortality derived from the general population) was included., Results: We included 525 patients. The 10-year recurrence risk was lowest in high SES (18.1%), highest in low SES (29.8%). Death and CBC as first events were rare. In high, medium and low SES 13.2%, 15.3% and 19.1% died following a recurrence. Low SES patients had shorter median time intervals between diagnosis, first recurrence and 10-year mortality (2.6 and 2.7 years, respectively) compared to high SES (3.5 and 3.3 years, respectively). In multivariable joint modeling, high SES was significantly related to lower recurrence rates over 10-year follow-up, compared to low SES. A strong association between the recurrent event process and excess mortality was found., Conclusions: High SES is associated with lower recurrence risks, less subsequent events and better prognosis after recurrence over 10 years than low SES. Breast cancer risk factors, adjuvant treatment adherence and treatment of recurrence may possibly play a role in this association., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Cancer patients often have pre-existing comorbidities, which can influence timeliness of cancer diagnosis. We examined symptoms, investigations and emergency presentation (EP) risk among colorectal cancer (CRC) patients by comorbidity status., Methods: Using linked cancer registration, primary care and hospital records of 4836 CRC patients (2011-2015), and multivariate quantile and logistic regression, we examined variations in specialist investigations, diagnostic intervals and EP risk., Results: Among colon cancer patients, 46% had at least one pre-existing hospital-recorded comorbidity, most frequently cardiovascular disease (CVD, 18%). Comorbid versus non-comorbid cancer patients more frequently had records of anaemia (43% vs 38%), less frequently rectal bleeding/change in bowel habit (20% vs 27%), and longer intervals from symptom-to-first relevant test (median 136 vs 74 days). Comorbid patients were less likely investigated with colonoscopy/sigmoidoscopy, independently of symptoms (adjusted OR = 0.7[0.6, 0.9] for Charlson comorbidity score 1-2 and OR = 0.5 [0.4-0.7] for score 3+ versus 0. EP risk increased with comorbidity score 0, 1, 2, 3+: 23%, 35%, 33%, 47%; adjusted OR = 1.8 [1.4, 2.2]; 1.7 [1.3, 2.3]; 3.0 [2.3, 4.0]) and for patients with CVD (adjusted OR = 2.0 [1.5, 2.5])., Conclusions: Comorbid individuals with as-yet-undiagnosed CRC often present with general rather than localising symptoms and are less likely promptly investigated with colonoscopy/sigmoidoscopy. Comorbidity is a risk factor for diagnostic delay and has potential, additionally to symptoms, as risk-stratifier for prioritising patients needing prompt assessment to reduce EP., (© 2021. The Author(s).)

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., Competing Interests: Disclosure The authors declare no competing financial interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Marked geographical disparities in survival from colon cancer have been consistently described in England. Similar patterns have been observed within London, almost mimicking a microcosm of the country's survival patterns. This evidence has suggested that the area of residence plays an important role in the survival from cancer., Methods: We analysed the survival from colon cancer of patients diagnosed in 2006-2013, in a pre-pandemic period, living in London at their diagnosis and received care in a London hospital. We examined the patterns of patient pathways between the area of residence and the hospital of care using flow maps, and we investigated whether geographical variations in survival from colon cancer are associated with the hospital of care. To estimate survival, we applied a Bayesian excess hazard model which accounts for the hierarchical structure of the data., Results: Geographical disparities in colon cancer survival disappeared once controlled for hospitals, and the disparities seemed to be augmented between hospitals. However, close examination of patient pathways revealed that the poorer survival observed in some hospitals was mostly associated with higher proportions of emergency diagnosis, while their performance was generally as expected for patients diagnosed through non-emergency routes., Discussion: This study highlights the need to better coordinate primary and secondary care sectors in some areas of London to improve timely access to specialised clinicians and diagnostic tests. This challenge remains crucially relevant after the recent successive regroupings of Clinical Commissioning Groups (which grouped struggling areas together) and the observed exacerbation of disparities during the COVID-19 pandemic., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial&#95;Computational&#95;Causal&#95;Inference&#95;Estimators., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

In population-based cancer studies, net survival is a crucial measure for population comparison purposes. However, alternative measures, namely the crude probability of death (CPr) and the number of life years lost (LYL) due to death according to different causes, are useful as complementary measures for reflecting different dimensions in terms of prognosis, treatment choice, or development of a control strategy. When the cause of death (COD) information is available, both measures can be estimated in competing risks setting using either cause-specific or subdistribution hazard regression models or with the pseudo-observation approach through direct modeling. We extended the pseudo-observation approach in order to model the CPr and the LYL due to different causes when information on COD is unavailable or unreliable (i.e., in relative survival setting). In a simulation study, we assessed the performance of the proposed approach in estimating regression parameters and examined models with different link functions that can provide an easier interpretation of the parameters. We showed that the pseudo-observation approach performs well for both measures and we illustrated their use on cervical cancer data from the England population-based cancer registry. A tutorial showing how to implement the method in R software is also provided., (© The Author 2020. Published by Oxford University Press.)

Background: Despite persistent reports of socioeconomic inequalities in colorectal cancer survival in England, the magnitude of survival differences has not been fully evaluated., Methods: Patients diagnosed with colon cancer (n=68 169) and rectal cancer (n=38 267) in England (diagnosed between January 2010 and March 2013) were analysed as a retrospective cohort study using the National Cancer Registry data linked with other population-based healthcare records. The flexible parametric model incorporating time-varying covariates was used to assess the difference in excess hazard of death and in net survival between the most affluent and the most deprived groups over time., Results: Survival analyses showed a clear pattern by deprivation. Hazard ratio of death was consistently higher in the most deprived group than the least deprived for both colon and rectal cancer, ranging from 1.08 to 1.17 depending on the model. On the net survival scale, the socioeconomic gap between the most and the least deprived groups reached approximately -4% at the maximum (-3.7%, 95% CI -1.6 to -5.7% in men, -3.6%, 95% CI -1.6 to -5.7% in women) in stages III for colon and approximately -2% (-2.3%, 95% CI -0.2 to -4.5% in men, -2.3%, 95% CI -0.2 to -4.3% in women) in stage II for rectal cancer at 3 years from diagnosis, after controlling for age, emergency presentation, receipt of resection and comorbidities. The gap was smaller in other stages and sites. For both cancers, patients with emergency presentation persistently had a higher excess hazard of death than those without emergency presentation., Conclusion: Survival disparities were profound particularly among patients in the stages, which benefit from appropriate and timely treatment. For the patients with emergency presentation, excess hazard of death remained high throughout three years from the diagnosis. Public health measures should be taken to reduce access inequalities to improve survival disparities., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objectives: We aimed to assess the association between multimorbidity and deprivation on short-term mortality among patients with diffuse large B-cell (DLBCL) and follicular lymphoma (FL) in England., Setting: The association of multimorbidity and socioeconomic deprivation on survival among patients diagnosed with DLBCL and FL in England between 2005 and 2013. We linked the English population-based cancer registry with electronic health records databases and estimated adjusted mortality rate ratios by multimorbidity and deprivation status. Using flexible hazard-based regression models, we computed DLBCL and FL standardised mortality risk by deprivation and multimorbidity at 1 year., Results: Overall, 41 422 patients aged 45-99 years were diagnosed with DLBCL or FL in England during 2005-2015. Most deprived patients with FL with multimorbidities had three times higher hazard of 1-year mortality (HR: 3.3, CI 2.48 to 4.28, p<0.001) than least deprived patients without comorbidity; among DLBCL, there was approximately twice the hazard (HR: 1.9, CI 1.70 to 2.07, p<0.001)., Conclusions: Multimorbidity, deprivation and their combination are strong and independent predictors of an increased short-term mortality risk among patients with DLBCL and FL in England. Public health measures targeting the reduction of multimorbidity among most deprived patients with DLBCL and FL are needed to reduce the short-term mortality gap., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

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

Introduction: Diagnostic delay is associated with lower chances of cancer survival. Underlying comorbidities are known to affect the timely diagnosis of cancer. Diffuse large B-cell (DLBCL) and follicular lymphomas (FL) are primarily diagnosed amongst older patients, who are more likely to have comorbidities. Characteristics of clinical commissioning groups (CCG) are also known to impact diagnostic delay. We assess the association between comorbidities and diagnostic delay amongst patients with DLBCL or FL in England during 2005-2013., Methods: Multivariable generalised linear mixed-effect models were used to assess the main association. Empirical Bayes estimates of the random effects were used to explore between-cluster variation. The latent normal joint modelling multiple imputation approach was used to account for partially observed variables., Results: We included 30,078 and 15,551 patients diagnosed with DLBCL or FL, respectively. Amongst patients from the same CCG, having multimorbidity was strongly associated with the emergency route to diagnosis (DLBCL: odds ratio 1.56, CI 1.40-1.73; FL: odds ratio 1.80, CI 1.45-2.23). Amongst DLBCL patients, the diagnostic delay was possibly correlated with CCGs that had higher population densities., Conclusions: Underlying comorbidity is associated with diagnostic delay amongst patients with DLBCL or FL. Results suggest a possible correlation between CCGs with higher population densities and diagnostic delay of aggressive lymphomas., (© 2021. The Author(s).)

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

This study aimed to evaluate the association between thyroid dysfunction and breast cancer risk. We included 239,436 females of the UK Biobank cohort. Information on thyroid dysfunction, personal and family medical history, medications, reproductive factors, lifestyle, and socioeconomic characteristics was retrieved from baseline self-reported data and hospital inpatient databases. Breast cancer diagnoses were identified through population-based registries. We computed Cox models to estimate hazard ratios (HRs) of breast cancer incidence for thyroid dysfunction diagnosis and treatments, and examined potential confounding and effect modification by comorbidities and breast cancer risk factors. In our study, 3,227 (1.3%) and 20,762 (8.7%) women had hyper- and hypothyroidism prior to the baseline. During a median follow-up of 7.1 years, 5,326 (2.2%) women developed breast cancer. Compared to no thyroid dysfunction, there was no association between hypothyroidism and breast cancer risk overall (HR = 0.93, 95% confidence interval (CI): 0.84-1.02, 442 cases), but we found a decreased risk more than 10 years after hypothyroidism diagnosis (HR=0.85, 95%CI 0.74-0.97, 226 cases). There was no association with hyperthyroidism overall (HR=1.08, 95%CI 0.86-1.35, 79 cases) but breast cancer risk was elevated among women with treated hyperthyroidism (HR=1.38, 95%CI: 1.03-1.86, 44 cases) or aged 60 years or more at hyperthyroidism diagnosis (HR=1.74, 95%CI: 1.01-3.00, 113 cases), and 5-10 years after hyperthyroidism diagnosis (HR=1.58, 95%CI: 1.06-2.33, 25 cases). In conclusion, breast cancer risk was reduced long after hypothyroidism diagnosis, but increased among women with treated hyperthyroidism. Future studies are needed to determine whether the higher breast cancer risk observed among treated hyperthyroidism could be explained by hyperthyroidism severity, type of treatment or aetiology., (© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Introduction: Delays in cancer diagnosis arose from the commencement of non-pharmaceutical interventions (NPI) introduced in the UK in March 2020 in response to the COVID-19 pandemic. Our earlier work predicted this will lead to approximately 3620 avoidable deaths for four major tumour types (breast, bowel, lung, and oesophageal cancer) in the next 5 years. Here, using national population-based modelling, we estimate the health and economic losses resulting from these avoidable cancer deaths. We also compare these with the impact of an equivalent number of COVID-19 deaths to understand the welfare consequences of the different health conditions., Methods: We estimate health losses using quality-adjusted life years (QALYs) and lost economic productivity using the human capital (HC) approach. The analysis uses linked English National Health Service (NHS) cancer registration and hospital administrative datasets for patients aged 15-84 years, diagnosed with breast, colorectal, and oesophageal cancer between 1st Jan to 31st Dec 2010, with follow-up data until 31st Dec 2014, and diagnosed with lung cancer between 1st Jan to 31st Dec 31 2012, with follow-up data until 31st Dec 2015. Productivity losses are based on the estimation of excess additional deaths due to cancer at 1, 3 and 5 years for the four cancer types, which were derived from a previous analysis using this dataset. A total of 500 random samples drawn from the total number of COVID-19 deaths reported by the Office for National Statistics, stratified by gender, were used to estimate productivity losses for an equivalent number of deaths (n = 3620) due to SARS-CoV-2 infection., Results: We collected data for 32,583 patients with breast cancer, 24,975 with colorectal cancer, 6744 with oesophageal cancer, and 29,305 with lung cancer. We estimate that across the four site-specific cancers combined in England alone, additional excess cancer deaths would amount to a loss of 32,700 QALYs (95% CI 31,300-34,100) and productivity losses of £103.8million GBP (73.2-132.2) in the next five years. For breast cancer, we estimate a loss of 4100 QALYS (3900-4400) and productivity losses of £23.2 m (18.2-28.6); for colorectal cancer, 15,000 QALYS (14,100-16,000) lost and productivity losses of £35.7 m (22.4-48.7); for lung cancer 10,900 QALYS (9,900-11,700) lost and productivity losses of £38.3 m (14.0-59.9) for lung cancer; and for oesophageal cancer, 2700 QALYS (2300-3,100) lost and productivity losses of £6.6 m (-6 to -17.6). In comparison, the equivalent number of COVID-19 deaths caused approximately 21,450 QALYs lost, as well as productivity losses amounting to £76.4 m (73.5-79.2)., Conclusion: Premature cancer deaths resulting from diagnostic delays during the first wave of the COVID-19 pandemic in the UK will result in significant economic losses. On a per-capita basis, this impact is, in fact, greater than that of deaths directly attributable to COVID-19. These results emphasise the importance of robust evaluation of the trade-offs of the wider health, welfare and economic effects of NPI to support both resource allocation and the prioritisation of time-critical health services directly impacted in a pandemic, such as cancer care., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful., Methods: We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%., Results: We were able to identify 41.3% of patients < = 60 years at 'high risk' of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours., Conclusion: After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease., Competing Interests: The authors declare no competing interests.

Background: Patients living in more deprived localities have lower cancer survival in England, but the role of individual health status at diagnosis and the utilisation of primary health care in explaining these differentials has not been widely considered. We set out to evaluate whether pre-existing individual health status at diagnosis and primary care consultation history (peri-diagnostic factors) could explain socio-economic differentials in survival amongst women diagnosed with breast cancer., Methods: We conducted a retrospective cohort study of women aged 15-99 years diagnosed in England using linked routine data. Ecologically-derived measures of income deprivation were combined with individually-linked data from the English National Cancer Registry, Clinical Practice Research Datalink (CPRD) and Hospital Episodes Statistics (HES) databases. Smoking status, alcohol consumption, BMI, comorbidity, and consultation histories were derived for all patients. Time to breast surgery was derived for women diagnosed after 2005. We estimated net survival and modelled the excess hazard ratio of breast cancer death using flexible parametric models. We accounted for missing data using multiple imputation., Results: Net survival was lower amongst more deprived women, with a single unit increase in deprivation quintile inferring a 4.4% (95% CI 1.4-8.8) increase in excess mortality. Peri-diagnostic co-variables varied by deprivation but did not explain the differentials in multivariable analyses., Conclusions: These data show that socio-economic inequalities in survival cannot be explained by consultation history or by pre-existing individual health status, as measured in primary care. Differentials in the effectiveness of treatment, beyond those measuring the inclusion of breast surgery and the timing of surgery, should be considered as part of the wider effort to reduce inequalities in premature mortality.

Background: Subsequent epidemic waves have already emerged in many countries and in the absence of highly effective preventive and curative options, the role of patient characteristics on the development of outcomes needs to be thoroughly examined, especially in middle-east countries where such epidemiological studies are lacking. There is a huge pressure on the hospital services and in particular, on the Intensive Care Units (ICU). Describing the need for critical care as well as the chance of being discharged from hospital according to patient characteristics, is essential for a more efficient hospital management. The objective of this study is to describe the probabilities of admission to the ICU and the probabilities of hospital discharge among positive COVID-19 patients according to demographics and comorbidities recorded at hospital admission., Methods: A prospective cohort study of all patients with COVID-19 found in the Electronic Medical Records of Jaber Al-Ahmad Al-Sabah Hospital in Kuwait was conducted. The study included 3995 individuals (symptomatic and asymptomatic) of all ages who tested positive from February 24th to May 27th, 2020, out of which 315 were treated in the ICU and 3619 were discharged including those who were transferred to a different healthcare unit without having previously entered the ICU. A competing risk analysis considering two events, namely, ICU admission and hospital discharge using flexible hazard models was performed to describe the association between event-specific probabilities and patient characteristics., Results: Results showed that being male, increasing age and comorbidities such as chronic kidney disease (CKD), asthma or chronic obstructive pulmonary disease and weakened immune system increased the risk of ICU admission within 10 days of entering the hospital. CKD and weakened immune system decreased the probabilities of discharge in both females and males however, the age-related pattern differed by gender. Diabetes, which was the most prevalent comorbid condition, had only a moderate impact on both probabilities (18% overall) in contrast to CKD which had the largest effect, but presented only in 7% of those admitted to ICU and in 1% of those who got discharged. For instance, within 5 days a 50-year-old male had 19% (95% C.I.: [15,23]) probability of entering the ICU if he had none of these comorbidities, yet this risk jumped to 31% (95% C.I.: [20,46]) if he had also CKD, and to 27% in the presence of asthma/COPD (95% C.I.: [19,36]) or of weakened immune system (95% C.I.: [16,42])., Conclusions: This study provides useful insight in describing the probabilities of ICU admission and hospital discharge according to age, gender, and comorbidities among confirmed COVID-19 cases in Kuwait. A web-tool is also provided to allow the user to estimate these probabilities for any combination of these covariates. These probabilities enable deeper understanding of the hospital demand according to patient characteristics which is essential to hospital management and useful for developing a vaccination strategy.

Background: A persistent socioeconomic gap in colon cancer survival is observed in England. Provision of cancer care may also vary by socioeconomic status (SES). We investigated population-based data to explore differential surgical care by SES., Methods: We analysed a retrospective cohort of patients diagnosed with colon cancer in England (2010-2013). We examined patterns of presentation and surgery by SES, and whether socioeconomic differences exist in the length of time from diagnosis to elective major resection using linear regression., Results: Among a total of 68 169 patients with colon cancer, 21.0 % (3138/14 917) in the most affluent group had emergency presentation (EP) whereas 27.9 % (2901/10 386) in the most deprived. Among 45 332 (66.5 %) patients who underwent resection, the proportion of patients receiving urgent surgery (surgery before or ≤ 7 days of diagnosis) was higher in the most deprived group (39.9 %, 2685/6733) than the most affluent (35.4 %, 3595/10 146). Days from diagnosis to elective surgery (surgery > 7 days after diagnosis) ranged from 33.9 (95 % CI 33.1-34.8) in stage II to 38.2 (95 % CI 36.8-39.7) in stage I, but no socioeconomic differences in time were seen in all stages., Conclusions: Time to elective surgery for colon cancer did not differ by SES, whereas a higher proportion among deprived patients tended to be diagnosed through EP and to receive urgent surgery. These results suggest that the waiting time target may not be an appropriate measure to assess access to cancer care. Reducing both EP and urgent surgery should be a key policy target., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

We explored the role of socioeconomic inequalities in COVID-19 incidence among cancer patients during the first wave of the pandemic. We conducted a case-control study within the UK Biobank cohort linked to the COVID-19 tests results available from 16 March 2020 until 23 August 2020. The main exposure variable was socioeconomic status, assessed using the Townsend Deprivation Index. Among 18,917 participants with an incident malignancy in the UK Biobank cohort, 89 tested positive for COVID-19. The overall COVID-19 incidence was 4.7 cases per 1000 incident cancer patients (95%CI 3.8-5.8). Compared with the least deprived cancer patients, those living in the most deprived areas had an almost three times higher risk of testing positive (RR 2.6, 95%CI 1.1-5.8). Other independent risk factors were ethnic minority background, obesity, unemployment, smoking, and being diagnosed with a haematological cancer for less than five years. A consistent pattern of socioeconomic inequalities in COVID-19 among incident cancer patients in the UK highlights the need to prioritise the cancer patients living in the most deprived areas in vaccination planning. This socio-demographic profiling of vulnerable cancer patients at increased risk of infection can inform prevention strategies and policy improvements for the coming pandemic waves.

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., (© The Authors 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Myeloma is primarily a disease of the elderly, but older patients experience poorer outcomes. Effective treatments may not be offered to older patients over fears about toxicity, particularly for those with comorbidities. We aimed to characterise the trend in survival disparity between older and younger patients and assess to what extent comorbidity might explain these disparities., Methods: We examined records for 56 010 patients diagnosed with myeloma in England between 1998-2014. The Hospital Episode Statistics database provided information on comorbidity. Net survival was estimated for each diagnosis period. Adjusted excess hazard ratios for the effect of age were estimated using flexible parametric models., Results: Net survival increased for all age groups over the study period. However, older patients experienced a higher risk of death from their disease consistent between 1998 and 2014. Adjusting for comorbidity made little difference to the estimates., Conclusion: Factors other than comorbidity must explain the poorer survival experience of elderly patients. Treatment data were not examined and should be employed by future population studies. Inconsistent treatment of elderly patients with myeloma may be prevented by further use of pragmatic clinical trials which are inclusive of older adults, and also wider utilisation of frailty scores., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Despite a large choice of models, functional forms and types of effects, the selection of excess hazard models for prediction of population cancer survival is not widespread in the literature. We propose multi-model inference based on excess hazard model(s) selected using Akaike information criteria or Bayesian information criteria for prediction and projection of cancer survival. We evaluate the properties of this approach using empirical data of patients diagnosed with breast, colon or lung cancer in 1990-2011. We artificially censor the data on 31 December 2010 and predict five-year survival for the 2010 and 2011 cohorts. We compare these predictions to the observed five-year cohort estimates of cancer survival and contrast them to predictions from an a priori selected simple model, and from the period approach. We illustrate the approach by replicating it for cohorts of patients for which stage at diagnosis and other important prognosis factors are available. We find that model-averaged predictions and projections of survival have close to minimal differences with the Pohar-Perme estimation of survival in many instances, particularly in subgroups of the population. Advantages of information-criterion based model selection include (i) transparent model-building strategy, (ii) accounting for model selection uncertainty, (iii) no a priori assumption for effects, and (iv) projections for patients outside of the sample.

Acquiring real-world evidence is crucial to support health policy, but observational studies are prone to serious biases. An approach was recently proposed to overcome confounding and immortal-time biases within the emulated trial framework. This tutorial provides a step-by-step description of the design and analysis of emulated trials, as well as R and Stata code, to facilitate its use in practice. The steps consist in: (i) specifying the target trial and inclusion criteria; (ii) cloning patients; (iii) defining censoring and survival times; (iv) estimating the weights to account for informative censoring introduced by design; and (v) analysing these data. These steps are illustrated with observational data to assess the benefit of surgery among 70-89-year-old patients diagnosed with early-stage lung cancer. Because of the severe unbalance of the patient characteristics between treatment arms (surgery yes/no), a naïve Kaplan-Meier survival analysis of the initial cohort severely overestimated the benefit of surgery on 1-year survival (22% difference), as did a survival analysis of the cloned dataset when informative censoring was ignored (17% difference). By contrast, the estimated weights adequately removed the covariate imbalance. The weighted analysis still showed evidence of a benefit, though smaller (11% difference), of surgery among older lung cancer patients on 1-year survival. Complementing the CERBOT tool, this tutorial explains how to proceed to conduct emulated trials using observational data in the presence of immortal-time bias. The strength of this approach is its transparency and its principles that are easily understandable by non-specialists., (© The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association.)