Factors affecting the causality assessment of adverse events following immunisation in paediatric clinical trials: An online survey

BackgroundSerious adverse events (SAEs) in clinical trials require reporting within 24 h, including a judgment of whether the SAE was related to the investigational product(s). Such assessments are an important component of pharmacovigilance, however classification systems for assigning relatedness vary across study protocols. This on-line survey evaluated the consistency of SAE causality assessment among professionals with vaccine clinical trial experience.MethodsMembers of the clinical advisory forum of experts (CAFÉ), a Brighton Collaboration online-forum, were emailed a survey containing SAEs from hypothetical vaccine trials which they were asked to classify. Participants were randomised to either two classification options (related/not related to study immunisation) or three options (possibly/probably/unrelated). The clinical scenarios, were (i) leukaemia diagnosed 5 months post-immunisation with a live RSV vaccine, (ii) juvenile idiopathic arthritis (JIA) 3 months post-immunisation with a group A streptococcal vaccine, (iii) developmental delay diagnosed at age 10 months after infant capsular group B meningococcal vaccine, (iv) developmental delay diagnosed at age 10 months after maternal immunisation with a group B streptococcal vaccine.ResultsThere were 140 respondents (72 two options, 68 three options). Across all respondents, SAEs were considered related to study immunisation by 28% (leukaemia), 74% (JIA), 29% (developmental delay after infant immunisation) and 42% (developmental delay after maternal immunisation). Having only two options made respondents significantly less likely to classify the SAE as immunisation-related for two scenarios (JIA p = 0.0075; and maternal immunisation p = 0.045). Amongst study investigators (n = 43) this phenomenon was observed for three of the four scenarios: (JIA p = 0.0236; developmental delay following infant immunisation p = 0.0266; and developmental delay after maternal immunisation p = 0.0495).ConclusionsSAE causality assessment is inconsistent amongst study investigators and can be influenced by the classification systems available to them. There is a pressing need for SAE classification systems to be standardised across study protocols.     

Severe immune-mediated drug-induced liver injury linked to ibandronate: A case report

Ibandronate is a widely used bisphosphonate with no previously well documented hepatotoxicity. We report the first case of ibandronate-related hepatotoxicity and, to our knowledge, the first case of immune-mediated drug-induced liver injury (DILI) related to bisphosphonates.     

Epidemiology and causation

Epidemiologists’ discussions on causation are not always very enlightening with regard to the notion of ‘cause’ in epidemiology. Epidemiologists rightly work from a science-based approach to causation in epidemiology, but largely disagree about the matter. Disagreement may be partly due to confusion of the question of useful concepts for causal inference in epidemiological practice with the question of the metaphysical presuppositions of causal concepts used in epidemiology. In other words, epidemiologists seem to confuse the practical results of epidemiological research at the population level with the metaphysical views about the reality of disease causation at the individual level in their writings on causation.

The history of confounding

Summary Confounding is a basic problem of comparability-and therefore has always been present in science. Originally a plain English word, it acquired more specific meanings in epidemiologic thinking about experimental and non-experimental research. The use of the word can be traced to Fisher. The concept was developed more fully in social science research, among others by Kish. Landmark developments in epidemiology in the second half of the 20^th century were by Cornfield and by Miettinen. These developments emphasised that reasoning about confounding is almost entirely an a priori process that we have to impose upon the data and the data-analysis to arrive at a meaningful interpretation. The problems of confounding present their old challenges again in recent applications to genetic epidemiology.

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Zusammenfassung Confounding ist ein grundlegendes Problem der Vergleichbarkeit und somit schon immer Teil der Wissenschaft. Ursprünglich ein einfaches englisches Wort, hat es in der experimentellen und nicht experimentellen epidemiologischen Forschung spezifischere Bedeutungen angenommen. Der Gebrauch des Wortes geht auf Fischer zurück. Das Konzept wurde in den Sozialwissenschaften unter anderen von Kish noch umfassender entwickelt. Historische Entwicklungen in der Epidemiologie der zweiten Hälfte des 20. Jahrhunderts sind auf Cornfield und Miettinen zurückzuführen. Diese Entwicklungen verdeutlichten, dass die Argumentation mit Störfaktoren/Confounding ein fast ausschliesslich deduktiver Prozess ist, den wir für die Daten und Datenanalyse anwenden müssen, um zu einer aussagekräftigen Interpretation zu gelangen. Die Probleme des Confounding sind auch in der neueren Anwendung auf dem Gebiet der genetischen Epidemiologie dieselben geblieben.

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Résumé L'effet de confusion est un problème élémentaire de comparabilité et a donc toujours été présent en science. C'était à l'origine un simple mot d'anglais, mais il a acquis une signification spécifique dans la pensée épidémiologique par rapport à la recherche expérimentale et non expérimentale. L'utilisation du mot remonte à Fisher. Le concept a été approfondi dans la recherche en science sociale, entre autres par Kish. Le développement du concept en épidémiologie dans la deuxième moitié du 20^ème siècle a été assuré par Cornfield et Miettinen. Ces développements ont mis l'accent sur le fait que le raisonnement sur l'effet de confusion est presque entièrement un processus a priori que nous devons imposer aux données et à l'analyse afin d'aboutir à une interprétation qui ait du sens. Les vieux défis liés à l'effet de confusion se représentent dans leurs applications récentes en épidémiologie génétique.

     

Causal relationship between heart rate and arterial blood pressure variability signals

A method is described which allows the determination of the causal relationship existing between two biological signals (heart rate and arterial blood pressure variability signals) which carry information about the role of control elicited by the autonomic nervous system. This method assumes an autoregressive (AR) model for the two signals to check the cross-correlation of the two residuals after AR identification. This information, together with the classical parameters of the spectral analysis (mean, variance, frequency and power in two typical bands, gain, phase and coherence) may provide a more precise evaluation of the complex mechanisms involved in the control of heart rate and blood pressure in numerous physiopathological situations.     

Estimation of the signal generating value of spontaneous reports of adverse reactions

The main function of drug adverse reaction spontaneous reporting systems is the generation of early signals of previously undocumented serious reactions to recently marketed products. To quantify the signal generating value of case reports we first score them on a 6-level causality/completeness scale combining the French and the European community assessment methods; prior documentation is scored on a 5-level scale inspired from the French method. Then the prior documentation value is subtracted from the imputability value and the resulting difference is considered as a signal generating value. The result is a scale of ranks ranging from 1 to 10, averaging 4.8 in the database of our regional program. It may serve to compare the usefulness of spontaneous reports originating from different sources and it may encourage clinicans to submit more informative case reports.     

Causally speaking: Challenges in measuring gentrification for population health research in the United States and Canada

The planet is rapidly urbanizing, the need for actionable evidence to guide the design of cities that help (not hinder) our health has never felt more urgent. One essential component of healthy city design is improving neighborhood conditions in previously disinvested areas. To ensure equitable city design, policy makers, city planners, health practitioners, and researchers are interested in understanding the complex relationship between urban change, gentrification, and population health. Yet, the causal link between gentrification and health outcomes remain unclear. Without clear and consistent gentrification measures, researchers struggle to identify populations who are exposed to gentrification, and to compare health outcomes between exposed and unexposed populations. To move the science forward, this paper summarizes the challenges related to gentrification measurement in the United States and Canada when aspiring to conduct studies to analyze causal relationships between gentrification and health. The paper concludes with a series of recommendations for studies aimed at examining both causes and consequences of gentrification and health.     

Field studies versus database studies on the risks and benefits of medication use during pregnancy: Distinct pieces of the same puzzle

Over the past two decades, findings on medication use during pregnancy have been accumulating from observational data. Generally, field studies with prospective recruitment of subjects have better outcome ascertainment, and more control on the longitudinal collection of data, but have lower sample sizes and thus they often lack statistical power to detect increased risks for rare events such as major congenital malformations. In addition, given the rarity of specific drug exposures in a population, even relatively common outcomes, such as low birth weight, may become rare in combination with the specific exposure. On the other hand, administrative databases usually provide larger samples and thus increased statistical power, decrease the probability of selection and recall bias, but often have missing data on potential confounders. Hence, debate amongst researchers, regulators and public health officials has been ongoing with regard to the most appropriate study populations for perinatal epidemiologic research. With this commentary, we aim to highlight the importance of both study populations, which can make complementary and crucial contributions to the iterative determination of causality as well as discuss basic epidemiologic principles that need to be applied in the field of perinatal pharmacoepidemiology for the purpose of causality assessment. This is relevant at present given that the United States Food and Drug Administration (US FDA) has modified their medication label requirements, especially given the international importance of these modifications.     

中青年人群超声骨密度异常与生活方式的多因素分析

目的:探讨中青年人群生活方式引起骨密度异常的危险因素。方法:选取年龄20⁴5岁体检者690例进行超声骨密度测定,其中骨密度正常者571例,低骨量者97例,骨质疏松者22例。通过问卷调查收集数据资料,使用SPSS 13.0从生活方式上进行多元Logistic回归分析。结果:Logistic回归分析显示,超声骨密度结果异常与长期膳食不合理、缺乏运动、肥胖、饮酒、吸烟呈正相关。结论:中青年人骨密度异常的危险因素包括长期善食不合理、缺乏运动、肥胖、饮酒、吸烟等。