Suchergebnisse

Zusammenfassung: Ziel dieses Beitrags ist, einen breiten Überblick darüber zu geben, wie die Evolution biomedizinischer Informationen – aus epidemiologischer und aus gesundheitspolitischer Sicht – zwangsläufig zu einer Veränderung der Analysewerkzeuge und -methoden geführt hat. Um den Herausforderungen zu begegnen, die mit der wechselnden Bevölkerungsdynamik, den neuen grossen Migrationsströmen, der Veränderung der Alterungsdynamik und der sehr schnellen Entwicklung der Medizin in Richtung «Precision Medicine» zusammenhängen, ist es notwendig, dass die gesellschaftlich-organisatorische Antwort des Sozial- und Gesundheitssystems den Veränderungen des Fortschritts und den neuen Bedürfnissen angepasst wird. Im Beitrag werden wir darlegen, wie mit dem Übergang von einer individuellen zu einer Perspektive von epidemiologisch typischer Population auch viele Paradigmen ändern, die mit einer Eigenschaft des Anhaltspunkts zusammenhängen. Wir positionieren uns in einem Modell-Typ «Global System», in dem die Integration zwischen verschiedenen Informationsquellen und Präventionsmassnahmen unerlässlich wird. ; This contribution is addressed to provide a large spectrum overview on how the evolution of biomedical information has brought unavoidably about a change of tools and modes of analysis both from the epidemiology and public health service point of view. In order to face the challenges connected to the change of population dynamics, to the large migration fluxes, the change of ageing dynamics and the very rapid evolution of medicine towards «precision medicine», it is necessary that the social-organizational response of social and health system adapts itself to the ongoing changes and new needs. In this article we will show how, moving from an individual perspective to the one of a population typical of epidemiology, a lot of paradigms connected to data also change and this positions us in a «global system» model type where the integration of different sources of information and prevention interventions become unavoidable.

Abstract: Questo contributo è rivolto a fornire una panoramica ad ampio spettro su come l'evoluzione dell'informazioni biomediche abbia inevitabilmente portato ad un cambio di strumenti e modalità di analisi sia dal punto di vista dell'epidemiologia che della sanità pubblica. Per affrontare le sfide legate al cambiamento delle dinamiche di popolazione, ai nuovi grandi flussi migratori, al cambiamento delle dinamiche di invecchiamento, e all'evoluzione rapidissima della medicina nella direzione della «precision medicine» è necessario che la risposta socio-organizzativa del sistema sociale e sanitario si adegui tempestivamente ai mutamenti in corso e alle nuove esigenze. Nel l'articolo mostreremo come passando da una prospettiva individuale a quella di popolazione tipica dell'epidemiologia, cambino anche molti paradigmi legati ad una natura del dato, e ci si collochi in un modello tipo «global-system» in cui l'integrazione tra fonti diverse di informazione e interventi di prevenzione diventano imprescindibili.

The COVID-19 pandemic cast a dramatic spotlight on the use of data as a fundamental component of good decision-making. Evaluating and comparing alternative policies required information on concurrent infection rates and insightful analysis to project them into the future. Statisticians in Israel were involved in these processes early in the pandemic in some silos as an ad-hoc unorganized effort. Informal discussions within the statistical community culminated in a roundtable, organized by three past presidents of the Israel Statistical Association, and hosted by the Samuel Neaman Institute in April 2021. The meeting was designed to provide a forum for exchange of views on the profession's role during the COVID-19 pandemic, and more generally, on its influence in promoting evidence-based public policy. This paper builds on the insights and discussions that emerged during the roundtable meeting and presents a general framework, with recommendations, for involving statisticians and statistics in decision-making.

Abstract
In the midst of the COVID-19 experience, we learned an important scientific lesson: knowledge acquisition and information quality in medicine depends more on "data quality" rather than "data quantity." The large number of COVID-19 reports, published in a very short time, demonstrated that the most advanced statistical and computational tools cannot properly overcome the poor quality of acquired data. The main evidence for this observation comes from the poor reproducibility of results. Indeed, understanding the data generation process is fundamental when investigating scientific questions such as prevalence, immunity, transmissibility, and susceptibility. Most of COVID-19 studies are case reports based on "non probability" sampling and do not adhere to the general principles of controlled experimental designs. Such collected data suffers from many limitations when used to derive clinical conclusions. These include confounding factors, measurement errors and bias selection effects. Each of these elements represents a source of uncertainty, which is often ignored or assumed to provide an unbiased random contribution. Inference retrieved from large data in medicine is also affected by data protection policies that, while protecting patients' privacy, are likely to reduce consistently usefulness of big data in achieving fundamental goals such as effective and efficient data-integration. This limits the degree of generalizability of scientific studies and leads to paradoxical and conflicting conclusions. We provide such examples from assessing the role of risks factors. In conclusion, new paradigms and new designs schemes are needed in order to reach inferential conclusions that are meaningful and informative when dealing with data collected during emergencies like COVID-19.