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Jan P Vandenbroucke,1– 3 Henrik Toft Sørensen,1,4 David H Rehkopf,5 Jaimie L Gradus,6 Johan P Mackenbach,7 M Maria Glymour,8 Sandro Galea,9 Victor W Henderson1,10 1Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark; 2Leiden University, Leiden, Netherlands; 3London School of Hygiene & Tropical Medicine, London, UK; 4Aarhus University Hospital, Aarhus, Denmark; 5Stanford Center for Population Health Sciences, Stanford University, CA, USA; 6Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA; 7Department of Public Health, Erasmus University Rotterdam, Rotterdam, Holland; 8Department of Epidemiology, Boston University School of Public Health, Boston University, Boston, MA, USA; 9School of Public Health, Boston University, Boston, MA, USA; 10Departments of Epidemiology & Population Health and of Neurology & Neurological Sciences, Stanford University, Stanford, CA, USAAbstract: This paper is a summary of key presentations from a workshop in Iceland on May 3– 4, 2023 arranged by Aarhus University and with participation of the below-mentioned scientists.Below you will find the key messages from the presentations made by:Professor Jan Vandenbroucke, Department of Clinical Epidemiology, Aarhus University, Emeritus Professor, Leiden University; Honorary Professor, London School of Hygiene & Tropical Medicine, UKProfessor, Chair Henrik Toft Sørensen, Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, DenmarkProfessor David H. Rehkopf, Director, the Stanford Center for Population Health Sciences, Stanford University, CA., USProfessor Jaimie Gradus, Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Johan Mackenbach, Emeritus Professor, Department of Public Health, Erasmus University Rotterdam, HollandProfessor, Chair M Maria Glymour, Department of Epidemiology, Boston University School of Public Health, Boston University, Boston, Massachusetts, USProfessor, Dean Sandro Galea, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Victor W. Henderson, Departments of Epidemiology & Population Health and of Neurology & Neurological Sciences, Stanford University, Stanford, CA, US; Department of Clinical Epidemiology, Aarhus University, Aarhus, DKKeywords: Ageing, Multimorbidity, Social inequality

Lau Caspar Thygesen,1 Kaare Christensen,2 Mikael Rørth,3,4 Henrik Toft Sørensen,3,4 Jan P Vandenbroucke,3,5,6 Rudi GJ Westendorp7 1National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark; 2The Danish Aging Research Center, Department of Public Health, University of Southern Denmark, Odense, Denmark; 3Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark; 4Aarhus University Hospital, Aarhus, Denmark; 5Department of Medical Statistics and Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; 6Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; 7Department of Public Health and Center for Healthy Aging, University of Copenhagen, Copenhagen, DenmarkCorrespondence: Lau Caspar ThygesenNational Institute of Public Health, University of Southern Denmark, Studiestræde 6, Copenhagen, DK-1455, DenmarkTel +45 65 50 77 71Email lct@sdu.dkAbstract: Aging of the population is a pressing challenge for healthcare systems and knowledge of a patient’s prognosis is a key to shaping effective interventions. As the prevalence of multimorbidity strongly increases with age, the prognostic value of multiple disease diagnoses for survival among older people may diminish, whereas other measures of health, such as functional status (defined as a measure of an individual’s ability to perform activities of daily living), may become more important. In this commentary, the impact of age on the prognostic value of multimorbidity is discussed, with the aim of identifying relevant alternative risk indicators for different age groups. The key question is to determine at what age the prognostic value of multimorbidity for meaningful clinical outcomes decreases and is overridden by the prognostic value of functional status. This tipping point likely depends on age, calendar time, and birth cohort. The public health and clinical implications of these tipping points are important. Among younger and middle-aged persons, interventions could be directed towards prevention and treatment of specific diseases, while among older persons efforts should focus more on improving functional levels that include physical, emotional, and social dimensions.Keywords: chronic disease, health services, public health, aged, multimorbidity

Jan P Vandenbroucke,1–3 Neil Pearce3 1Leiden University Medical Center, Leiden, the Netherlands; 2Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark; 3Department of Medical Statistics and Centre for Global NCDs, London School of Hygiene and Tropical Medicine, London, UK Abstract: Where do new research questions come from? This is at best only partially taught in courses or textbooks about clinical or epidemiological research. Methods are taught under the assumption that a researcher already knows the research question and knows which methods will fit that question. Similarly, the real complexity of the thought processes that lead to a scientific undertaking is almost never described in published papers. In this paper, we first discuss how to get an idea that is worth researching. We describe sources of new ideas and how to foster a creative attitude by “cultivating your thoughts”. Only a few of these ideas will make it into a study. Next, we describe how to sharpen and focus a research question so that a study becomes feasible and a valid test of the underlying idea. To do this, the idea needs to be “pruned”. Pruning a research question means cutting away anything that is unnecessary, so that only the essence remains. This includes determining both the latent and the stated objectives, specific pruning questions, and the use of specific schemes to structure reasoning. After this, the following steps include preparation of a brief protocol, conduct of a pilot study, and writing a draft of the paper including draft tables. Then you are ready to carry out your research. Keywords: study design, writing a paper, research questions

Priscila Corraini,1 Morten Olsen,1 Lars Pedersen,1 Olaf M Dekkers,1,2 Jan P Vandenbroucke1–3 1Department of Clinical Epidemiology, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark; 2Leiden University Medical Center, Leiden, the Netherlands; 3Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK Abstract: We revisited the three interrelated epidemiological concepts of effect modification, interaction and mediation for clinical investigators and examined their applicability when using research databases. The standard methods that are available to assess interaction, effect modification and mediation are explained and exemplified. For each concept, we first give a simple “best-case” example from a randomized controlled trial, followed by a structurally similar example from an observational study using research databases. Our explanation of the examples is based on recent theoretical developments and insights in the context of large health care databases. Terminology is sometimes ambiguous for what constitutes effect modification and interaction. The strong assumptions underlying the assessment of interaction, and particularly mediation, require clinicians and epidemiologists to take extra care when conducting observational studies in the context of health care databases. These strong assumptions may limit the applicability of interaction and mediation assessments, at least until the biases and limitations of these assessments when using large research databases are clarified. Keywords: methods, epidemiology, effect modifiers, stratified analyses, health care administrative claims

Mette Nørgaard,1 Vera Ehrenstein,1 Jan P Vandenbroucke1–3 1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; 2Department of Clinical Epidemiology, Leiden University Medical Center, The Netherlands; 3Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom Abstract: Population-based health care databases are a valuable tool for observational studies as they reflect daily medical practice for large and representative populations. A constant challenge in observational designs is, however, to rule out confounding, and the value of these databases for a given study question accordingly depends on completeness and validity of the information on confounding factors. In this article, we describe the types of potential confounding factors typically lacking in large health care databases and suggest strategies for confounding control when data on important confounders are unavailable. Using Danish health care databases as examples, we present the use of proxy measures for important confounders and the use of external adjustment. We also briefly discuss the potential value of active comparators, high-dimensional propensity scores, self-controlled designs, pseudorandomization, and the use of positive or negative controls. Keywords: observational studies, health care databases, confounding

Corraini P, Olsen M, Pedersen L, Dekkers OM, Vandenbroucke JP, Clin Epidemiol. 2017;9:331–338.For clarification, on page 334, Interaction section, left column, last paragraph, the interpretation of the relative risk due to interaction should read “This excess relative risk due to interaction indicates that the probability of serious upper gastrointestinal bleeding following combined use of aspirin and clopidogrel is much higher than the probability expected from the addition of the effects of the two drugs.” where it previously read “By obtaining this relative risk due to interaction, the likelihood of serious upper gastrointestinal bleeding from the combined use of aspirin and clopidogrel was eight times higher than the likelihood expected by the addition of the two drugs.”Read the original article

Corraini P, Olsen M, Pedersen L, Dekkers OM, Vandenbroucke JP, Clin Epidemiol. 2017;9:331–338.On pages 333–334, the interaction contrast appeared incorrectly as 1.5 per 1,000 person-years, where it should read –1.5 per 1,000 person-years. This occurred from an inversion between the result of the rate reduction of heart disease due to aspirin among those receiving warfarin (10.3–8.7=1.6) and the result of the rate reduction due to aspirin among those not receiving warfarin (13.3–10.2=3.1).The interpretation of the interaction contrast should then read in page 334 as: “As the interaction contrast was below zero, it could be concluded that the effect of combining warfarin and aspirin in reducing ischemic heart disease was lower than that expected by the sum of the effects of either agent on its own.”Read the original article

The purpose of the present article is to explain the calculation of incidence rates in dynamic populations with the use of simple mathematical and statistical concepts. The first part will consider incidence rates in dynamic populations, and how they can best be taught in basic, intermediate and advanced courses. The second part will briefly explain how and why incidence rates are calculated in cohorts. [ABSTRACT FROM AUTHOR]

In this paper, we discuss relationships between causal interactions within the counterfactual framework and interference in which the exposure of one person may affect the outcomes of another. We show that the empirical tests for causal interactions can, in fact, all be adapted to empirical tests for particular forms of interference. In the context of interference, by recoding the response as some function of the outcomes of the various persons within a cluster, a wide range of different forms of interference can potentially be detected. The correspondence between causal interactions and forms of interference extends to encompass n-way causal interactions, interference between n persons within a cluster, and multivalued exposures. The theory for causal interactions provides a complete conceptual apparatus for assessing interference as well. The results are illustrated using data from a hypothetical vaccine trial to reason about specific forms of interference and spillover effects that may be present in this vaccine setting. We discuss the implications of this correspondence for our conceptualizations of interaction and for application to vaccine trials and many other settings in which spillover effects may be present. [ABSTRACT FROM AUTHOR]

Rationale: In his 1978 article on tuberculosis (TB) in twins, Comstock concluded that the 2.5-fold higher concordance rate for TB among monozygotic versus dizygotic twins in the Prophit survey of the 1950s implicated inherited susceptibility as a major risk factor for TB in humans. His analysis did not take into account strong imbalance of variables within subgroups, underestimating possible confounding effects of environmental factors. Objectives: To reconsider the role of environmental versus hereditary factors in determining the concordance rate of TB among twin pairs. Methods: Reanalysis of the Prophit Survey. Measurements and Main Results: A known Mycobacterium tuberculosis-positive or M. tuberculosis-negative sputum in the index TB case markedly influenced the odds ratio (OR) of concordance in the twin pairs. In 87 pairs with co-twins exposed to a sputum-negative index case, monozygotic and dizygotic twins did not differ in concordance for TB (OR, 1.1; 95% confidence interval [95% CI], 0.4-2.8). A higher concordance rate for TB among monozygotic versus dizygotic twins was confined to 106 pairs with the co-twins exposed to a sputum-positive index case (OR, 3.4; 95% CI, 1.6-7.2), and was highest in adolescent twins living together. ORs of TB concordance were proportional to intensity of exposure (sputum smear positivity, physical proximity between twin pairs, contagiousness of disease, and living together) rather than to zygosity. Conclusions: In the Prophit survey of susceptibility to TB among twins, environmental factors (i.e., intensity of exposure to tubercle bacilli) outweigh the importance of hereditary factors. Environmental factors and the context of transmission should be given more emphasis when studying interindividual and population differences in susceptibility to infectious diseases such as TB. [ABSTRACT FROM AUTHOR]

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover 3 main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors, to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. Eighteen items are common to all 3 study designs and 4 are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available at http://www.annals.org and on the Web sites of PLoS Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies. [ABSTRACT FROM AUTHOR]