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

Author

Smith, Matthew J., Phillips, Rachael V., Luque-Fernandez, Miguel Angel, and Maringe, Camille

Subjects
Statistics - Applications, Statistics - Methodology, Statistics - Machine Learning
Abstract

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. We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarised the epidemiological discipline, geographical location, expertise 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. Of the 81 publications included, 25% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By the first half of 2022, 70% of the publications originated from outside the United States and explored up to 7 different epidemiological disciplines in 2021-22. Double-robustness, bias reduction and model misspecification were the main motivations that drew researchers towards 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. 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., Comment: 42 pages, 2 figures, 2 tables

Published
2023

4. The Delta-Method and Influence Function in Medical Statistics: a Reproducible Tutorial

Author

Zepeda-Tello, Rodrigo, Schomaker, Michael, Maringe, Camille, Smith, Matthew J., Belot, Aurelien, Rachet, Bernard, Schnitzer, Mireille E., and Luque-Fernandez, Miguel Angel

Subjects
Statistics - Methodology
Abstract

Approximate statistical inference via determination of the asymptotic distribution of a statistic is routinely used for inference in applied medical statistics (e.g. to estimate the standard error of the marginal or conditional risk ratio). One method for variance estimation is the classical Delta-method but there is a knowledge gap as this method is not routinely included in training for applied medical statistics and its uses are not widely understood. Given that a smooth function of an asymptotically normal estimator is also asymptotically normally distributed, the Delta-method allows approximating the large-sample variance of a function of an estimator with known large-sample properties. In a more general setting, it is a technique for approximating the variance of a functional (i.e., an estimand) that takes a function as an input and applies another function to it (e.g. the expectation function). Specifically, we may approximate the variance of the function using the functional Delta-method based on the influence function (IF). The IF explores how a functional $\phi(\theta)$ changes in response to small perturbations in the sample distribution of the estimator and allows computing the empirical standard error of the distribution of the functional. The ongoing development of new methods and techniques may pose a challenge for applied statisticians who are interested in mastering the application of these methods. In this tutorial, we review the use of the classical and functional Delta-method and their links to the IF from a practical perspective. We illustrate the methods using a cancer epidemiology example and we provide reproducible and commented code in R and Python using symbolic programming. The code can be accessed at https://github.com/migariane/DeltaMethodInfluenceFunction

Published
2022

5. Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code

Author

Smith, Matthew J., Maringe, Camille, Rachet, Bernard, Mansournia, Mohammad A., Zivich, Paul N., Cole, Stephen R., and Luque-Fernandez, Miguel Angel

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

The purpose of many health studies is to estimate the effect of an exposure on an outcome. It is not always ethical to assign an exposure to individuals in randomised controlled trials, instead observational data and appropriate study design must be used. There are major challenges with observational studies, one of which is confounding that can lead to biased estimates of the causal effects. Controlling for confounding is commonly performed by simple adjustment for measured confounders; although, often this is not enough. Recent advances in the field of causal inference have dealt with confounding by building on classical standardisation 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 different estimators were developed to overcome the limitations of the previous one. Furthermore, we also briefly introduce the potential outcomes framework, illustrate the use of different methods using an illustration from the health care setting, and most importantly, we provide reproducible and commented code in Stata, R and Python for researchers to apply in their own observational study. The code can be accessed at https://github.com/migariane/TutorialCausalInferenceEstimators

Published
2020

6. Educational Note: Paradoxical Collider Effect in the Analysis of Non-Communicable Disease Epidemiological Data: a reproducible illustration and web application

Author

Luque-Fernandez, Miguel Angel, Schomaker, Michael, Redondo-Sanchez, Daniel, Perez, Maria Jose Sanchez, Vaidya, Anand, and Schnitzer, Mireille E.

Subjects
Statistics - Methodology
Abstract

Classical epidemiology has focused on the control of confounding but it is only recently that epidemiologists have started to focus on the bias produced by colliders. A collider for a certain pair of variables (e.g., an outcome Y and an exposure A) is a third variable (C) that is caused by both. In a directed acyclic graph (DAG), a collider is the variable in the middle of an inverted fork (i.e., the variable C in A -> C <- Y). Controlling for, or conditioning an analysis on a collider (i.e., through stratification or regression) can introduce a spurious association between its causes. This potentially explains many paradoxical findings in the medical literature, where established risk factors for a particular outcome appear protective. We use an example from non-communicable disease epidemiology to contextualize and explain the effect of conditioning on a collider. We generate a dataset with 1,000 observations and run Monte-Carlo simulations to estimate the effect of 24-hour dietary sodium intake on systolic blood pressure, controlling for age, which acts as a confounder, and 24-hour urinary protein excretion, which acts as a collider. We illustrate how adding a collider to a regression model introduces bias. Thus, to prevent paradoxical associations, epidemiologists estimating causal effects should be wary of conditioning on colliders. We provide R-code in easy-to-read boxes throughout the manuscript and a GitHub repository (https://github.com/migariane/ColliderApp) for the reader to reproduce our example. We also provide an educational web application allowing real-time interaction to visualize the paradoxical effect of conditioning on a collider http://watzilei.com/shiny/collider/.

Published
2018
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7. Using Longitudinal Targeted Maximum Likelihood Estimation in Complex Settings with Dynamic Interventions

Author

Schomaker, Michael, Luque-Fernandez, Miguel Angel, Leroy, Valeriane, and Davies, Mary-Ann

Subjects
Statistics - Methodology
Abstract

Longitudinal targeted maximum likelihood estimation (LTMLE) has very rarely been used to estimate dynamic treatment effects in the context of time-dependent confounding affected by prior treatment when faced with long follow-up times, multiple time-varying confounders, and complex associational relationships simultaneously. Reasons for this include the potential computational burden, technical challenges, restricted modeling options for long follow-up times, and limited practical guidance in the literature. However, LTMLE has desirable asymptotic properties, i.e. it is doubly robust, and can yield valid inference when used in conjunction with machine learning. We use a topical and sophisticated question from HIV treatment research to show that LTMLE can be used successfully in complex realistic settings and compare results to competing estimators. Our example illustrates the following practical challenges common to many epidemiological studies 1) long follow-up time (30 months), 2) gradually declining sample size 3) limited support for some intervention rules of interest 4) a high-dimensional set of potential adjustment variables, increasing both the need and the challenge of integrating appropriate machine learning methods 5) consideration of collider bias. Our analyses, as well as simulations, shed new light on the application of LTMLE in complex and realistic settings: we show that (i) LTMLE can yield stable and good estimates, even when confronted with small samples and limited modeling options; (ii) machine learning utilized with a small set of simple learners (if more complex ones can't be fitted) can outperform a single, complex model, which is tailored to incorporate prior clinical knowledge; (iii) performance can vary considerably depending on interventions and their support in the data, and therefore critical quality checks should accompany every LTMLE analysis.

Published
2018
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10. The Spectrum of Subclinical Primary Aldosteronism and Incident Hypertension: A Cohort Study.

Author

Brown, Jenifer M, Robinson-Cohen, Cassianne, Luque-Fernandez, Miguel Angel, Allison, Matthew A, Baudrand, Rene, Ix, Joachim H, Kestenbaum, Bryan, de Boer, Ian H, and Vaidya, Anand

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Cardiovascular, Genetics, Clinical Research, Hypertension, Aetiology, 2.1 Biological and endogenous factors, Aged, Aged, 80 and over, Aldosterone, Cross-Sectional Studies, Female, Humans, Hyperaldosteronism, Incidence, Longitudinal Studies, Male, Middle Aged, Potassium, Receptors, Mineralocorticoid, Renin, Risk Factors, Public Health and Health Services
Abstract

BackgroundPrimary aldosteronism is recognized as a severe form of renin-independent aldosteronism that results in excessive mineralocorticoid receptor (MR) activation.ObjectiveTo investigate whether a spectrum of subclinical renin-independent aldosteronism that increases risk for hypertension exists among normotensive persons.DesignCohort study.SettingNational community-based study.Participants850 untreated normotensive participants in MESA (Multi-Ethnic Study of Atherosclerosis) with measurements of serum aldosterone and plasma renin activity (PRA).MeasurementsLongitudinal analyses investigated whether aldosterone concentrations, in the context of physiologic PRA phenotypes (suppressed, ≤0.50 µg/L per hour; indeterminate, 0.51 to 0.99 µg/L per hour; unsuppressed, ≥1.0 µg/L per hour), were associated with incident hypertension (defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or initiation of antihypertensive medications). Cross-sectional analyses investigated associations between aldosterone and MR activity, assessed via serum potassium and urinary fractional excretion of potassium.ResultsA suppressed renin phenotype was associated with a higher rate of incident hypertension than other PRA phenotypes (incidence rates per 1000 person-years of follow-up: suppressed renin phenotype, 85.4 events [95% CI, 73.4 to 99.3 events]; indeterminate renin phenotype, 53.3 events [CI, 42.8 to 66.4 events]; unsuppressed renin phenotype, 54.5 events [CI, 41.8 to 71.0 events]). With renin suppression, higher aldosterone concentrations were independently associated with an increased risk for incident hypertension, whereas no association between aldosterone and hypertension was seen when renin was not suppressed. Higher aldosterone concentrations were associated with lower serum potassium and higher urinary excretion of potassium, but only when renin was suppressed.LimitationSodium and potassium were measured several years before renin and aldosterone.ConclusionSuppression of renin and higher aldosterone concentrations in the context of this renin suppression are associated with an increased risk for hypertension and possibly also with increased MR activity. These findings suggest a clinically relevant spectrum of subclinical primary aldosteronism (renin-independent aldosteronism) in normotension.Primary funding sourceNational Institutes of Health.

Published
2017

11. Protocolo del Estudio Poblacional Multinivel de las Desigualdades Socioeconómicas en la Distribución Geográfica de la Incidencia, la Mortalidad y la Supervivencia Neta del Cáncer en España: Estudio DESOCANES

Author

Luque-Fernández, Miguel Ángel, Redondo-Sánchez, Daniel, Fernández, Pablo, Salamanca-Fernández, Elena, Marcos-Gragera, Rafael, Guevara, Marcela, Carulla Aresté, Marià, Jiménez, Rosario, Núñez, Olivier, Sabater, Consol, López de Munain Marqués, Arantxa, Chirlaque, María Dolores, Mateos, Antonio, Rodríguez-Barranco, Miguel, Espín Balbino, Jaime, Pollán, Marina, and Sánchez, María-José

Published
2021
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16. The Role of Early Pregnancy Maternal pGCD59 Levels in Predicting Neonatal Hypoglycemia—Subanalysis of the DALI Study

Author

Bogdanet, Delia, Luque-Fernandez, Miguel Angel, Toth-Castillo, Michelle, Desoye, Gernot, O'Shea, Paula M, Dunne, Fidelma P, Halperin, Jose, A, Lapolla, A, and DALI Core Investigator Group

Subjects
Blood Glucose, Glycation, Epidemiology, Endocrinology, Diabetes and Metabolism, Biochemistry (medical), Clinical Biochemistry, Infant, Newborn, Infant, Glucose Tolerance Test, Biochemistry, Hypoglycemia, Infant, Newborn, Diseases, Biomarkers, Neonatal hypoglycaemia, Prediction, Diabetes, Gestational, Fetal Diseases, Endocrinology, Pregnancy, Humans, Female
Abstract

Context Neonatal hypoglycaemia (NH) is the most common metabolic problem in infants born of mothers with gestational diabetes. Plasma glycated CD59 (pGCD59) is an emerging biomarker that has shown potential in identifying women at risk of developing gestational diabetes. The aim of this study was to assess the association between early maternal levels of pGCD59 and NH. Objective The aim of this study was to assess the association between early pregnancy maternal levels of plasma glycated CD59 (pGCD59) and neonatal hypoglycemia (NH). Methods This is an observational study of pregnant women with a prepregnancy body mass index (BMI) greater than or equal to 29 screened for eligibility to participate in the Vitamin D and Lifestyle Intervention for Gestational Diabetes (DALI) trial. This analysis included 399 pregnancies. Levels of pGCD59 were measured in fasting maternal samples taken at the time of a 75-g, 2-hour oral glucose tolerance test performed in early pregnancy ( Results We identified 30 infants with NH. Maternal levels of pGCD59 in early pregnancy were positively associated with the prevalence of NH (one-way analysis of variance, P Conclusion Although pGCD59 levels in early pregnancy in women with BMI greater than or equal to 29 are associated with NH, our results indicate that this biomarker by itself is only a fair predictor of NH.

Published
2022

17. Protocolo del estudio sobre el efecto de la crisis económica en la mortalidad, la salud reproductiva y las desigualdades en salud en España

Author

Pérez, Glòria, Gotsens, Mercè, Palència, Laia, Marí-Dell’Olmo, Marc, Domínguez-Berjón, M. Felicitas, Rodríguez-Sanz, Maica, Puig, Vanessa, Bartoll, Xavier, Gandarillas, Ana, Martín, Unai, Bacigalupe, Amaia, Díez, Elia, Ruiz, Miguel, Esnaola, Santiago, Calvo, Montserrat, Sánchez, Pablo, Luque Fernández, Miguel Ángel, and Borrell, Carme

Published
2016
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20. The Diagnostic Accuracy of Second Trimester Plasma Glycated CD59 (pGCD59) to Identify Women with Gestational Diabetes Mellitus Based on the 75 g OGTT Using the WHO Criteria: A Prospective Study of Non-Diabetic Pregnant Women in Ireland

Author

Bogdanet, Delia, primary, Toth Castillo, Michelle, additional, Doheny, Helen, additional, Dervan, Louise, additional, Luque-Fernandez, Miguel-Angel, additional, Halperin, Jose A., additional, O’Shea, Paula M., additional, and Dunne, Fidelma P., additional

Published
2022
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24. Excess Mortality by Multimorbidity, Socioeconomic, and Healthcare Factors, amongst Patients Diagnosed with Diffuse Large B-Cell or Follicular Lymphoma in England

Author

Smith, Matthew James, primary, Belot, Aurélien, additional, Quartagno, Matteo, additional, Luque Fernandez, Miguel Angel, additional, Bonaventure, Audrey, additional, Gachau, Susan, additional, Benitez Majano, Sara, additional, Rachet, Bernard, additional, and Njagi, Edmund Njeru, additional

Published
2021
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25. Association between multimorbidity and socioeconomic deprivation on short-term mortality among patients with diffuse large B-cell or follicular lymphoma in England: a nationwide cohort study

Author

Smith, Matthew James, primary, Njagi, Edmund Njeru, additional, Belot, Aurelien, additional, Leyrat, Clémence, additional, Bonaventure, Audrey, additional, Benitez Majano, Sara, additional, Rachet, Bernard, additional, and Luque Fernandez, Miguel Angel, additional

Published
2021
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29. Estimating the Marginal Causal Effect and Potential Impact of Waterpipe Smoking on Risk of Multiple Sclerosis Using the Targeted Maximum Likelihood Estimation Method: A Large, Population-Based Incident Case-Control Study

Author

Abdollahpour, Ibrahim, Nedjat, Saharnaz, Almasi-Hashiani, Amir, Nazemipour, Maryam, Mansournia, Mohammad Ali, and Luque-Fernandez, Miguel Angel

Abstract

There are few if any reports regarding the role of lifetime waterpipe smoking in the etiology of multiple sclerosis (MS). In a population-based incident case-control study conducted in Tehran, Iran, we investigated the association between waterpipe smoking and MS, adjusted for confounders. Cases (n = 547) were patients aged 15-50 years identified from the Iranian Multiple Sclerosis Society between 2013 and 2015. Population-based controls (n = 1,057) were persons aged 15-50 years recruited through random digit telephone dialing. A doubly robust estimation method, the targeted maximum likelihood estimator (TMLE), was used to estimate the marginal risk ratio and odds ratio for the association between waterpipe smoking and MS. The estimated risk ratio and odds ratio were both 1.70 (95% confidence interval: 1.34, 2.17). The population attributable fraction was 21.4% (95% confidence interval: 4.0, 38.8). Subject to the limitations of case-control studies in interpreting associations causally, these results suggest that waterpipe use, or strongly related but undetermined factors, increases the risk of MS. Further epidemiologic studies, including nested case-control studies, are needed to confirm these findings.

Published
2021

30. Additional file 2 of The role of multimorbidity in short-term mortality of lung cancer patients in Spain: a population-based cohort study

Author

Niksic, Maja, Redondo-Sanchez, Daniel, Chang, Yoe-Ling, Rodriguez-Barranco, Miguel, Exposito-Hernandez, Jose, Marcos-Gragera, Rafael, Oliva-Poch, Ester, Bosch-Barrera, Joaquim, Sanchez, Maria-Jose, and Luque-Fernandez, Miguel Angel

Abstract

Additional file 2 Supplementary Table 5. Short-term (six-months) comorbidity status mortality risk adjusted for sex, age, province of residence, smoking status, cancer surgery, histology, TNM stage, and BMI among lung cancer patients in Girona and Granada, Spain in 2011 (n = 1259 lung cancer patients and 581 deaths at six-months after cancer diagnosis).

Published
2021
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31. Additional file 1 of The role of multimorbidity in short-term mortality of lung cancer patients in Spain: a population-based cohort study

Author

Niksic, Maja, Redondo-Sanchez, Daniel, Chang, Yoe-Ling, Rodriguez-Barranco, Miguel, Exposito-Hernandez, Jose, Marcos-Gragera, Rafael, Oliva-Poch, Ester, Bosch-Barrera, Joaquim, Sanchez, Maria-Jose, and Luque-Fernandez, Miguel Angel

Abstract

Additional file 1 Supplementary Table 1. Histology group classification. Supplementary Table 2. Comorbidities and their diagnostic ICD-10 codes. Supplementary Table 3. Comorbidity status, vital status at six-months, sociodemographic characteristics: smoking status, province of residence, BMI, cancer surgery, morphology and TNM stage among lung cancer patients diagnosed between 2010 and 2012, in two population-based Spanish cancer registries: Girona and Granada (n = 1259 lung cancer patients and 581 deaths at six-months after cancer diagnosis). Supplementary Table 4. Top 5 most frequent multimorbidity patterns with 2 or 3 comorbidities and 6-month mortality risk among lung cancer patients diagnosed between 2011 and 2012, in two population-based Spanish cancer registries: Girona and Granada (n = 1259 and 581 deaths at six-months after cancer diagnosis).

Published
2021
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32. Lung, Breast and Colorectal Cancer Incidence by Socioeconomic Status in Spain: A Population-Based Multilevel Study

Author

Redondo-Sánchez, Daniel, primary, Marcos-Gragera, Rafael, additional, Carulla, Marià, additional, Lopez de Munain, Arantza, additional, Sabater Gregori, Consol, additional, Jimenez Chillarón, Rosario, additional, Guevara, Marcela, additional, Nuñez, Olivier, additional, Fernández-Navarro, Pablo, additional, Sánchez, María-José, additional, and Luque-Fernandez, Miguel Angel, additional

Published
2021
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35. Sleep-disordered breathing and gestational diabetes mellitus: a meta-analysis of 9,795 participants enrolled in epidemiological observational studies

Author

Luque-Fernandez, Miguel Angel, Bain, Paul A., Gelaye, Bizu, Redline, Susan, and Williams, Michelle A.

Subjects
Diabetes in pregnancy -- Analysis, Epidemiology -- Analysis, Health
Abstract

OBJECTIVE-Recently, sleep-disordered breathing (SDB) has been reported to be associated with the development of gestational diabetes mellitus (GDM). Accordingly, as this is emergent area of research that has significant clinical [...]

Published
2013
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39. Multimorbidity by Patient and Tumor Factors and Time-to-Surgery Among Colorectal Cancer Patients in Spain: A Population-Based Study

Author

Luque-Fernandez, Miguel Angel, Redondo-Sanchez, Daniel, Lee, Shing Fung, Rodríguez-Barranco, Miguel, Carmona-García, Ma Carmen, Marcos-Gragera, Rafael, and Sánchez, María-José

Subjects
comorbidity, multimorbidity, colorectal cancer epidemiology, Clinical Epidemiology, elderly
Abstract

Miguel Angel Luque-Fernandez, 1–3 Daniel Redondo-Sanchez, 1, 3, 4 Shing Fung Lee, 5 Miguel Rodríguez-Barranco, 1, 3, 4 Ma Carmen Carmona-García, 6–8 Rafael Marcos-Gragera, 3, 6, 7, 9María-José Sánchez 1, 3, 4 1Non-Communicable Disease and Cancer Epidemiology Group, Instituto de Investigación Biosanitaria de Granada ibs. GRANADA, University of Granada, Granada, Spain; 2Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; 3Biomedical Network Research Centers of Epidemiology and Public Health (CIBERESP), Madrid, Spain; 4Andalusian School of Public Health, Granada, Spain; 5Department of Clinical Oncology, Tuen Mun Hospital, New Territories West Cluster, Hospital Authority, Hong Kong; 6Catalan Institute of Oncology, Epidemiology Unit and Girona Cancer Registry, Oncology Coordination Plan, Department of Health, Autonomous Government of Catalonia, Catalan Institute of Oncology, Girona, Spain; 7Descriptive Epidemiology, Genetics and Cancer Prevention Group, Biomedical Research Institute (IDIBGI), Girona, Spain; 8Department of Medical Oncology, Institut Català d’Oncologia Hospital Universitari de Girona Dr. Josep Trueta, Girona, Spain; 9Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, SpainCorrespondence: Miguel Angel Luque-FernandezAndalusian School of Public Health, Cuesta Del Observatorio, 4, Granada 18080, SpainEmail miguel-angel.luque@lshtm.ac.ukBackground: Cancer treatment and outcomes can be influenced by tumor characteristics, patient overall health status, and comorbidities. While previous studies have analyzed the influence of comorbidity on cancer outcomes, limited information is available regarding factors associated with the increased prevalence of comorbidities and multimorbidity among patients with colorectal cancer in Spain.Patients and Methods: This cross-sectional study obtained data from all colorectal cancer cases diagnosed in two Spanish provinces in 2011 from two population-based cancer registries and electronic health records. We calculated the prevalence of comorbidities according to patient and tumor factors, identified factors associated with an increased prevalence of comorbidity and multimorbidity, analyzed the association between comorbidities and time-to-surgery, and developed an interactive web application (https://comcor.netlify.com/).Results: The most common comorbidities were diabetes (23.6%), chronic obstructive pulmonary disease (17.2%), and congestive heart failure (14.5%). Among all comorbidities, 52% of patients were diagnosed at more advanced stages (stage III/IV). Patients with advanced age, restricted performance status or who were disabled, obese, and smokers had a higher prevalence of multimorbidity. Patients with multimorbidity had a longer time-to-surgery than those without comorbidity (17 days, 95% confidence interval: 3– 29 days).Conclusion: We identified a consistent pattern of factors associated with a higher prevalence of comorbidities and multimorbidity at diagnosis and an increased time-to-surgery among patients with colorectal cancer with multimorbidity in Spain. This pattern may provide insights for further etiological and preventive research and help to identify patients at a higher risk for poorer cancer outcomes and suboptimal treatment.Keywords: colorectal cancer epidemiology, comorbidity, multimorbidity, elderly

Published
2020

40. Socioeconomic Inequalities in Colorectal Cancer Survival in Southern Spain: A Multilevel Population-Based Cohort Study

Author

Luque-Fernandez, Miguel Angel, Redondo-Sánchez, Daniel, Rodríguez-Barranco, Miguel, Chang-Chan, Yoe-Ling, Salamanca-Fernández, Elena, Núñez, Olivier, Fernandez-Navarro, Pablo, Pollán, Marina, Sánchez, María-José, Instituto de Salud Carlos III, Centro de Investigación Biomedica en Red - CIBER, Gobierno de Andalucía, and Instituto de Salud Carlos III - ISCIII

Subjects
multilevel, socioeconomic inequalities, Socioeconomic Factors, Survival, epidemiological methods, population-based epidemiology, Epidemiological Monitoring, Multilevel Analysis, Clinical Epidemiology, colorectal cancer, Colorectal Neoplasms, survival, Original Research
Abstract

Miguel Angel Luque-Fernandez,1– 3 Daniel Redondo-Sánchez,1,2 Miguel Rodríguez-Barranco,1,2,4 Yoe-Ling Chang-Chan,1,4 Elena Salamanca-Fernández,1,2 Olivier Núñez,2,5 Pablo Fernandez-Navarro,2,5 Marina Pollán,2,5 María-José Sánchez1,2,4,6 1Instituto de Investigación Biosanitaria de Granada, Non-Communicable Disease and Cancer Epidemiology Group, ibs.GRANADA, University of Granada, Granada, Spain; 2Biomedical Network Research Centers of Epidemiology and Public Health (CIBERESP), Madrid, Spain; 3London School of Hygiene and Tropical Medicine, Non-Communicable Disease Epidemiology, London, UK; 4Andalusian School of Public Health, Granada, Spain; 5National Centre of Epidemiology, Health Institute Carlos III (CNE-ISCIII), Madrid, Spain; 6Department of Preventive Medicine and Public Health, University of Granada, Granada, SpainCorrespondence: Miguel Angel Luque-FernandezAndalusian School of Public Health, Cuesta Del Observatorio, 4, Granada 18080, SpainEmail miguel-angel.luque@lshtm.ac.ukBackground: Colorectal cancer (CRC) is the most frequently diagnosed cancer in Spain. Socioeconomic inequalities in cancer survival are not documented in Spain. We aim to study the association of socioeconomic inequalities with overall mortality and survival among CRC patients in southern Spain.Methods: We conducted a multilevel population-based cohort study, including CRC cases for the period 2011– 2013. The study time-to-event outcome was death, and the primary exposure was CRC patients’ socioeconomic status assessed by the Spanish deprivation index at the census tract level. We used a mixed-effects flexible hazard model, including census tract as a random intercept, to derive overall survival estimates by deprivation.Results: Among 3589 CRC patients and 12,148 person-years at risk (pyr), 964 patients died before the end of the follow-up. Mortality by deprivation showed the highest mortality rate for the most deprived group (96.2 per 1000 pyr, 95% CI: 84.0– 110.2). After adjusting for sex, age, cancer stage, and the area of residence, the most deprived had a 60% higher excess mortality risk than the less deprived group (excess mortality risk ratio: 1.6, 95% CI: 1.1– 2.3).Conclusions: We found a consistent association between deprivation and CRC excess mortality and survival. The reasons behind these inequalities need further investigation in order to improve equality cancer outcomes in all social groups.Keywords: socioeconomic inequalities, colorectal cancer, survival, population-based epidemiology, epidemiological methods, multilevel

Published
2020

42. Additional file 2 of Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers

Author

Fowler, Helen, Belot, Aurelien, Ellis, Libby, Maringe, Camille, Luque-Fernandez, Miguel, Njagi, Edmund, Navani, Neal, Sarfati, Diana, and Rachet, Bernard

Subjects
respiratory system
Abstract

Additional file 2. Probability (%) of condition present as single or multiple comorbidity, by deprivation group (lung cancer). Additional results in complement to those presented in Fig. 3: graphs representing the probability of having any of nine comorbid conditions in lung cancer patients.

Published
2020
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43. Additional file 3 of Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers

Author

Fowler, Helen, Belot, Aurelien, Ellis, Libby, Maringe, Camille, Luque-Fernandez, Miguel, Njagi, Edmund, Navani, Neal, Sarfati, Diana, and Rachet, Bernard

Abstract

Additional file 3. Probability (%) of condition present as single or multiple comorbidity, by deprivation group (rectal cancer). Additional results in complement to those presented in Fig. 3: graphs representing the probability of having any of nine comorbid conditions in rectal cancer patients.

Published
2020
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50. Summarizing and communicating on survival data according to the audience: a tutorial on different measures illustrated with population-based cancer registry data

Author

Belot, Aurélien, Ndiaye, Aminata, Luque-Fernandez, Miguel-Angel, Kipourou, Dimitra-Kleio, Maringe, Camille, Rubio, Francisco Javier, and Rachet, Bernard

Subjects
restricted mean survival time, number of life years lost, conditional survival, crude probability of death, Methodology, Clinical Epidemiology, net survival, relative survival setting, Corrigendum, survival, competing risks
Abstract

Aurélien Belot, Aminata Ndiaye, Miguel-Angel Luque-Fernandez, Dimitra-Kleio Kipourou, Camille Maringe, Francisco Javier Rubio, Bernard Rachet Cancer Survival Group, Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK Abstract: Survival data analysis results are usually communicated through the overall survival probability. Alternative measures provide additional insights and may help in communicating the results to a wider audience. We describe these alternative measures in two data settings, the overall survival setting and the relative survival setting, the latter corresponding to the particular competing risk setting in which the cause of death is unavailable or unreliable. In the overall survival setting, we describe the overall survival probability, the conditional survival probability and the restricted mean survival time (restricted to a prespecified time window). In the relative survival setting, we describe the net survival probability, the conditional net survival probability, the restricted mean net survival time, the crude probability of death due to each cause and the number of life years lost due to each cause over a prespecified time window. These measures describe survival data either on a probability scale or on a timescale. The clinical or population health purpose of each measure is detailed, and their advantages and drawbacks are discussed. We then illustrate their use analyzing England population-based registry data of men 15–80years old diagnosed with colon cancer in 2001–2003, aiming to describe the deprivation disparities in survival. We believe that both the provision of a detailed example of the interpretation of each measure and the software implementation will help in generalizing their use. Keywords: survival, competing risks, relative survival setting, conditional survival, restricted mean survival time, net survival, crude probability of death, number of life years lost

Published
2019

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