An index of cancer survival to measure progress in cancer control: A tutorial.

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.

What can hospital emergency admissions prior to cancer diagnosis tell us about socio-economic inequalities in cancer diagnosis? Evidence from population-based data in England.

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.

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights.

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.

Inequalities in treatment among patients with colon and rectal cancer: a multistate survival model using data from England national cancer registry 2012-2016.

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.

Associations between treatments, comorbidities and multidimensional aspects of quality of life among patients with advanced cancer in the Netherlands-a 2017-2020 multicentre cross-sectional study.

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 (ß) = - 4.7, 95% CI [- 8.5,- 0.8]), back pain (ß = - 7.4, 95% CI [- 11.0,- 3.8]), depression (ß = - 13.8, 95% CI [- 21.5,- 6.2]), thyroid diseases (ß = - 8.9, 95% CI [- 14.0,- 3.8]) and diabetes (ß = - 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.

Is the Social Gradient in Net Survival Observed in France the Result of Inequalities in Cancer-Specific Mortality or Inequalities in General Mortality?

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.

Factors influencing access to specialised haematology units during acute myeloblastic leukaemia patient care: A population-based study in France.

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.

Variations in the Type of Adjuvant Chemotherapy Among Stage III Colon Cancer Patients in England.

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.

Mental Health Morbidities and Time to Cancer Diagnosis Among Adults With Colon Cancer in England.

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.

Mediating Effects of Diagnostic Route on the Comorbidity Gap in Survival of Patients with Diffuse Large B-Cell or Follicular Lymphoma in England.

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.

Association of socioeconomic deprivation with life expectancy and all-cause mortality in Spain, 2011-2013.

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.

Trends in age-standardised net survival of stomach cancer by subsite and stage: A population-based study in Osaka, Japan, 2001-2014.

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.

Health-related quality of life in cancer immunotherapy: a systematic perspective, using causal loop diagrams.

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.

Socioeconomic status and its relation with breast cancer recurrence and survival in young women in the Netherlands.

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.

Socio-economic inequalities in cancer survival: how do they translate into Number of Life-Years Lost?

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.

Direct modeling of the crude probability of cancer death and the number of life years lost due to cancer without the need of cause of death: a pseudo-observation approach in the relative survival setting.

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.

Variation in colon cancer survival for patients living and receiving care in London, 2006-2013: does where you live matter?

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.

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial.

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_Computational_Causal_Inference_Estimators.

Do presenting symptoms, use of pre-diagnostic endoscopy and risk of emergency cancer diagnosis vary by comorbidity burden and type in patients with colorectal cancer?

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.