Effectiveness of post-COVID-19 primary care attendance in improving survival in very old patients with multimorbidity: a territory-wide target trial emulation.

OBJECTIVES: Older individuals with multimorbidity are at an elevated risk of infection and complications from COVID-19. Effectiveness of post-COVID-19 interventions or care models in reducing subsequent adverse outcomes in these individuals have rarely been examined. This study aims to examine the effectiveness of attending general outpatient within 30 days after discharge from COVID-19 on 1-year survival among older adults aged 85 years or above with multimorbidity. DESIGN: Retrospective cohort study emulating a randomised target trial using electronic health records. SETTING: We used data from the Hospital Authority and the Department of Health in Hong Kong, which provided comprehensive electronic health records, COVID-19 confirmed case data, population-based vaccination records and other individual characteristics for the study. PARTICIPANTS: Adults aged 85 years or above with multimorbidity who were discharged after hospitalisation for COVID-19 between January 2020 and August 2022. INTERVENTIONS: Attending a general outpatient within 30 days of last COVID-19 discharge defined the exposure, compared to no outpatient visit. MAIN OUTCOME MEASURES: Primary outcome was all-cause mortality within one year. Secondary outcomes included mortality from respiratory, cardiovascular and cancer causes. RESULTS: A total of 6183 eligible COVID-19 survivors were included in the analysis. The all-cause mortality rate following COVID-19 hospitalisation was lower in the general outpatient visit group (17.1 deaths per 100 person-year) compared with non-visit group (42.8 deaths per 100 person-year). After adjustment, primary care consultations within 30 days after discharge were associated with a significantly greater 1-year survival (difference in 1-year survival: 11.2%, 95% CI 8.1% to 14.4%). We also observed significantly better survival from respiratory diseases in the general outpatient visit group (difference in 1-year survival: 6.3%, 95% CI 3.5% to 8.9%). In a sensitivity analysis for different grace period lengths, we found that the earlier participants had a general outpatient visit after COVID-19 discharge, the better the survival. CONCLUSIONS: Timely primary care consultations after COVID-19 hospitalisation may improve survival following COVID-19 hospitalisation among older adults aged 85 or above with multimorbidity. Expanding primary care services and implementing follow-up mechanisms are crucial to support this vulnerable population's recovery and well-being.

Effectiveness of post-COVID-19 primary care attendance in improving survival in very old patients with multimorbidity: a territory-wide target trial emulation.

BACKGROUND: Older adults with multimorbidity are at high risk of mortality following COVID-19 hospitalisation. However, the potential benefit of timely primary care follow-up on severe outcomes post-COVID-19 has not been well established. AIM: To examine the effectiveness of attending general outpatient within 30 days after discharge from COVID-19 on 1-year survival among older adults aged ≥85 years, with multimorbidity. METHOD: We emulated a target trial using a comprehensive public healthcare database in Hong Kong. The cloning-censoring-weighting technique was used to minimise immortal time bias and confounding bias by adjusting for demographics, hospitalisation duration and ICU admission, baseline chronic conditions, and medication history. The outcome included all-cause and cause-specific mortality. RESULTS: Of 6183 eligible COVID-19 survivors, the all-cause mortality rate following COVID-19 hospitalisation was lower in general out-patient clinics (GOPC) group compared to non-GOPC group (17.1 versus 42.8 deaths per 100 person-year). After adjustment, primary care consultations within 30 days after discharge were associated with a significantly greater 1-year survival (difference in 1-year survival: 11.2%, 95% CI = 8.1% to 14.4%). We also observed better survival from respiratory diseases in the GOPC group. In a sensitivity analysis for different grace period lengths, we found that the earlier participants had a GOPC visit after COVID-19 discharge, the better the survival. CONCLUSION: Timely primary care consultations after discharge may improve survival following COVID-19 hospitalisation among older adults aged ≥85 years, with multimorbidity. Expanding primary care services and implementing follow-up mechanisms are crucial to support this vulnerable population's recovery and well-being.

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.

Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data.

BACKGROUND: Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks. METHODS: For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented. RESULTS: Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results. CONCLUSIONS: Extending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.

Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review.

PURPOSE: The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments. METHODS: We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results. RESULTS: Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE. CONCLUSIONS: There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.

Immortal-time bias in older vs younger age groups: a simulation study with application to a population-based cohort of patients with colon cancer.

BACKGROUND: In observational studies, the risk of immortal-time bias (ITB) increases with the likelihood of early death, itself increasing with age. We investigated how age impacts the magnitude of ITB when estimating the effect of surgery on 1-year overall survival (OS) in patients with Stage IV colon cancer aged 50-74 and 75-84 in England. METHODS: Using simulations, we compared estimates from a time-fixed exposure model to three statistical methods addressing ITB: time-varying exposure, delayed entry and landmark methods. We then estimated the effect of surgery on OS using a population-based cohort of patients from the CORECT-R resource and conducted the analysis using the emulated target trial framework. RESULTS: In simulations, the magnitude of ITB was larger among older patients when their probability of early death increased or treatment was delayed. The bias was corrected using the methods addressing ITB. When applied to CORECT-R data, these methods yielded a smaller effect of surgery than the time-fixed exposure approach but effects were similar in both age groups. CONCLUSION: ITB must be addressed in all longitudinal studies, particularly, when investigating the effect of exposure on an outcome in different groups of people (e.g., age groups) with different distributions of exposure and outcomes.

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome Associated with COVID-19: An Emulated Target Trial Analysis.

Rationale: Whether patients with coronavirus disease (COVID-19) may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. Objectives: To estimate the effect of ECMO on 90-day mortality versus IMV only. Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO versus no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 < 80 or PaCO2 ⩾ 60 mm Hg). We controlled for confounding using a multivariable Cox model on the basis of predefined variables. Measurements and Main Results: A total of 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability on Day 7 from the onset of eligibility criteria (87% vs. 83%; risk difference, 4%; 95% confidence interval, 0-9%), which decreased during follow-up (survival on Day 90: 63% vs. 65%; risk difference, -2%; 95% confidence interval, -10 to 5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand and when initiated within the first 4 days of IMV and in patients who are profoundly hypoxemic. Conclusions: In an emulated trial on the basis of a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and regions with ECMO capacities specifically organized to handle high demand.

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.

Thyroid dysfunction and breast cancer risk among women in the UK Biobank cohort.

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.

Economic impact of avoidable cancer deaths caused by diagnostic delay during the COVID-19 pandemic: A national population-based modelling study in England, UK.

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.

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

OBJECTIVE: Age is an important prognostic factor for lung cancer. However, no studies have investigated the age difference in lung cancer survival per se. We, therefore, described the role of patient-related and clinical factors on the age pattern in lung cancer excess mortality hazard by stage at diagnosis in New Zealand. MATERIALS AND METHODS: We extracted 22 487 new lung cancer cases aged 50-99 (median age = 71, 47.1 % females) diagnosed between 1 January 2006 and 31 July 2017 from the New Zealand population-based cancer registry and followed up to December 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on the excess mortality hazard by stage at diagnosis, and we derived corresponding lung cancer net survival. RESULTS: The age difference in net survival was particularly marked for localised and regional lung cancers, with a sharp decline in survival from the age of 70. No identified factors influenced age disparities in patients with localised cancer. However, for other stages, females had a greater difference in survival between middle-age and older-age than males. Comorbidity and emergency presentation played a minor role. Ethnicity and deprivation did not influence age disparities in lung cancer survival. CONCLUSION: Sex and stage at diagnosis were the most important factors of age disparities in lung cancer survival in New Zealand.

The impact of timely cancer diagnosis on age disparities in colon cancer survival.

OBJECTIVE: We described the role of patient-related and clinical factors on age disparities in colon cancer survival among patients aged 50-99 using New Zealand population-based cancer registry data linked to hospitalisation data. METHOD: We included 21,270 new colon cancer cases diagnosed between 1 January 2006 and 31 July 2017, followed up to end 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on colon cancer survival by stage at diagnosis using flexible excess hazard regression models. RESULTS: The excess mortality in older patients was minimal for localised cancers, maximal during the first six months for regional cancers, the first eighteen months for distant cancers, and over the three years for missing stages. The age pattern of the excess mortality hazard varied according to sex for distant cancers, emergency presentation for regional and distant cancers, and comorbidity for cancer with missing stages. Ethnicity and deprivation did not influence age disparities in colon cancer survival. CONCLUSION: Factors reflecting timeliness of cancer diagnosis most affected age-related disparities in colon cancer survival, probably by impacting treatment strategy. Because of the high risk of poor outcomes related to treatment in older patients, efforts made to improve earlier diagnosis in older patients are likely to help reduce age disparities in colon cancer survival in New Zealand.

On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables.

In cancer epidemiology using population-based data, regression models for the excess mortality hazard is a useful method to estimate cancer survival and to describe the association between prognosis factors and excess mortality. This method requires expected mortality rates from general population life tables: each cancer patient is assigned an expected (background) mortality rate obtained from the life tables, typically at least according to their age and sex, from the population they belong to. However, those life tables may be insufficiently stratified, as some characteristics such as deprivation, ethnicity, and comorbidities, are not available in the life tables for a number of countries. This may affect the background mortality rate allocated to each patient, and it has been shown that not including relevant information for assigning an expected mortality rate to each patient induces a bias in the estimation of the regression parameters of the excess hazard model. We propose two parametric corrections in excess hazard regression models, including a single-parameter or a random effect (frailty), to account for possible mismatches in the life table and thus misspecification of the background mortality rate. In an extensive simulation study, the good statistical performance of the proposed approach is demonstrated, and we illustrate their use on real population-based data of lung cancer patients. We present conditions and limitations of these methods and provide some recommendations for their use in practice.

Prediction of cancer survival for cohorts of patients most recently diagnosed using multi-model inference.

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.

The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study.

BACKGROUND: Since a national lockdown was introduced across the UK in March, 2020, in response to the COVID-19 pandemic, cancer screening has been suspended, routine diagnostic work deferred, and only urgent symptomatic cases prioritised for diagnostic intervention. In this study, we estimated the impact of delays in diagnosis on cancer survival outcomes in four major tumour types. METHODS: In this national population-based modelling study, we used 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 Jan 1, 2010, and Dec 31, 2010, with follow-up data until Dec 31, 2014, and diagnosed with lung cancer between Jan 1, 2012, and Dec 31, 2012, with follow-up data until Dec 31, 2015. We use a routes-to-diagnosis framework to estimate the impact of diagnostic delays over a 12-month period from the commencement of physical distancing measures, on March 16, 2020, up to 1, 3, and 5 years after diagnosis. To model the subsequent impact of diagnostic delays on survival, we reallocated patients who were on screening and routine referral pathways to urgent and emergency pathways that are associated with more advanced stage of disease at diagnosis. We considered three reallocation scenarios representing the best to worst case scenarios and reflect actual changes in the diagnostic pathway being seen in the NHS, as of March 16, 2020, and estimated the impact on net survival at 1, 3, and 5 years after diagnosis to calculate the additional deaths that can be attributed to cancer, and the total years of life lost (YLLs) compared with pre-pandemic data. FINDINGS: 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. Across the three different scenarios, compared with pre-pandemic figures, we estimate a 7·9-9·6% increase in the number of deaths due to breast cancer up to year 5 after diagnosis, corresponding to between 281 (95% CI 266-295) and 344 (329-358) additional deaths. For colorectal cancer, we estimate 1445 (1392-1591) to 1563 (1534-1592) additional deaths, a 15·3-16·6% increase; for lung cancer, 1235 (1220-1254) to 1372 (1343-1401) additional deaths, a 4·8-5·3% increase; and for oesophageal cancer, 330 (324-335) to 342 (336-348) additional deaths, 5·8-6·0% increase up to 5 years after diagnosis. For these four tumour types, these data correspond with 3291-3621 additional deaths across the scenarios within 5 years. The total additional YLLs across these cancers is estimated to be 59 204-63 229 years. INTERPRETATION: Substantial increases in the number of avoidable cancer deaths in England are to be expected as a result of diagnostic delays due to the COVID-19 pandemic in the UK. Urgent policy interventions are necessary, particularly the need to manage the backlog within routine diagnostic services to mitigate the expected impact of the COVID-19 pandemic on patients with cancer. FUNDING: UK Research and Innovation Economic and Social Research Council.

Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data.

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.

Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers.

BACKGROUND: The presence of comorbidity affects the care of cancer patients, many of whom are living with multiple comorbidities. The prevalence of cancer comorbidity, beyond summary metrics, is not well known. This study aims to estimate the prevalence of comorbid conditions among cancer patients in England, and describe the association between cancer comorbidity and socio-economic position, using population-based electronic health records. METHODS: We linked England cancer registry records of patients diagnosed with cancer of the colon, rectum, lung or Hodgkin lymphoma between 2009 and 2013, with hospital admissions records. A comorbidity was any one of fourteen specific conditions, diagnosed during hospital admission up to 6 years prior to cancer diagnosis. We calculated the crude and age-sex adjusted prevalence of each condition, the frequency of multiple comorbidity combinations, and used logistic regression and multinomial logistic regression to estimate the adjusted odds of having each condition and the probability of having each condition as a single or one of multiple comorbidities, respectively, by cancer type. RESULTS: Comorbidity was most prevalent in patients with lung cancer and least prevalent in Hodgkin lymphoma patients. Up to two-thirds of patients within each of the four cancer patient cohorts we studied had at least one comorbidity, and around half of the comorbid patients had multiple comorbidities. Our study highlighted common comorbid conditions among the cancer patient cohorts. In all four cohorts, the odds of having a comorbidity and the probability of multiple comorbidity were consistently highest in the most deprived cancer patients. CONCLUSIONS: Cancer healthcare guidelines may need to consider prominent comorbid conditions, particularly to benefit the prognosis of the most deprived patients who carry the greater burden of comorbidity. Insight into patterns of cancer comorbidity may inform further research into the influence of specific comorbidities on socio-economic inequalities in receipt of cancer treatment and in short-term mortality.

Comparison of model-building strategies for excess hazard regression models in the context of cancer epidemiology.

BACKGROUND: Large and complex population-based cancer data are becoming broadly available, thanks to purposeful linkage between cancer registry data and health electronic records. Aiming at understanding the explanatory power of factors on cancer survival, the modelling and selection of variables need to be understood and exploited properly for improving model-based estimates of cancer survival. METHOD: We assess the performances of well-known model selection strategies developed by Royston and Sauerbrei and Wynant and Abrahamowicz that we adapt to the relative survival data setting and to test for interaction terms. RESULTS: We apply these to all male patients diagnosed with lung cancer in England in 2012 (N = 15,688), and followed-up until 31/12/2015. We model the effects of age at diagnosis, tumour stage, deprivation, comorbidity and emergency presentation, as well as interactions between age and all of the above. Given the size of the dataset, all model selection strategies favoured virtually the same model, except for a non-linear effect of age at diagnosis selected by the backward-based selection strategies (versus a linear effect selected otherwise). CONCLUSION: The results from extensive simulations evaluating varying model complexity and sample sizes provide guidelines on a model selection strategy in the context of excess hazard modelling.

Contrasting effects of comorbidities on emergency colon cancer diagnosis: a longitudinal data-linkage study in England.

BACKGROUND: One in three colon cancers are diagnosed as an emergency, which is associated with worse cancer outcomes. Chronic conditions (comorbidities) affect large proportions of adults and they might influence the risk of emergency presentations (EP). METHODS: We aimed to evaluate the effect of specific pre-existing comorbidities on the risk of colon cancer being diagnosed following an EP rather than through non-emergency routes. The cohort study included 5745 colon cancer patients diagnosed in England 2005-2010, with individually-linked cancer registry, primary and secondary care data. In addition to multivariable analyses we also used potential-outcomes methods. RESULTS: Colon cancer patients with comorbidities consulted their GP more frequently with cancer symptoms during the pre-diagnostic year, compared with non-comorbid cancer patients. EP occurred more frequently in patients with 'serious' or complex comorbidities (diabetes, cardiac and respiratory diseases) diagnosed/treated in hospital during the years pre-cancer diagnosis (43% EP in comorbid versus 27% in non-comorbid individuals; multivariable analysis Odds Ratio (OR), controlling for socio-demographic factors and symptoms: men OR = 2.40; 95% CI 2.0-2.9 and women OR = 1.98; 95% CI 1.6-2.4. Among women younger than 60, gynaecological (OR = 3.41; 95% CI 1.2-9.9) or recent onset gastro-intestinal conditions (OR = 2.84; 95% CI 1.1-7.7) increased the risk of EP. In contrast, primary care visits for hypertension monitoring decreased EPs for both genders. CONCLUSIONS: Patients with comorbidities have a greater risk of being diagnosed with cancer as an emergency, although they consult more frequently with cancer symptoms during the year pre-cancer diagnosis. This suggests that comorbidities may interfere with diagnostic reasoning or investigations due to 'competing demands' or because they provide 'alternative explanations'. In contrast, the management of chronic risk factors such as hypertension may offer opportunities for earlier diagnosis. Interventions are needed to support the diagnostic process in comorbid patients. Appropriate guidelines and diagnostic services to support the evaluation of new or changing symptoms in comorbid patients may be useful.