There is a need for more precise models to assess the determinants of health crises like COVID-19

Abstract

The COVID-19 pandemic has had a significant impact on global mortality. While the causal relationship between SARS-CoV-2 and the anomalous increase in deaths is established, more precise and complex models are needed to determine the exact weight of epidemiological factors involved. Indeed, COVID-19 behavior is influenced by a wide range of variables, including demographic characteristics, population habits and behavior, healthcare performance, and environmental and seasonal risk factors. The bidirectional causality between impacted and impacting aspects, as well as confounding variables, complicates efforts to draw clear, generalizable conclusions regarding the effectiveness and cost-benefit ratio of non-pharmaceutical health countermeasures. Thus, it is imperative that the scientific community and health authorities worldwide develop comprehensive models not only for the current pandemic but also for future health crises. These models should be implemented locally to account for micro-differences in epidemiological characteristics that may have relevant effects. It is important to note that the lack of a universal model does not imply that local decisions have been unjustified, and the request to decrease scientific uncertainty does not mean denying the evidence of the effectiveness of the countermeasures adopted. Therefore, this paper must not be exploited to denigrate either the scientific community or the health authorities.

Statistical significance misuse in public health research: an investigation of the current situation and possible solutions

“…In this regard, it must be emphasized that the P-value refers only to the test result and not the phenomenon under investigation in itself. Finally, academic journals should explicitly and compulsorily require that public health recommendations must be provided only after an analysis of data sensitivity, biases, confounding factors, risks, costs, and benefits, and not on the P-value or any other statistical indicator [21][22][23]. In this regard, guidelines and checklists previously discussed in the literature (e.g., SAMBR) can be helpful [24][25][26].…”

Research procedure for the paper “The use of inferential statistics in epidemiology and public health: A practical example for the retrospective analysis of youth mortality in Italy.”

“…For this reason, it is good to reiterate that the aforementioned investigation cannot provide causal evidence. (Rovetta, 2023). Firstly, in general, the result is strongly dependent on the selected compression model, a choice that inevitably involves a certain degree of subjectivity (Kowall & Stang, 2023).…”

Abbiamo riscontrato un aumento di mortalità giovanile in Italia durante il 2021 e il 2022?

“…Quindi, tutto ciò costituisce un'incertezza di base ineliminabile: infatti, la pandemia COVID-19 ha stravolto lo scenario epidemiologico, cambiando contemporaneamente molti fattori (e.g., il comportamento delle persone, lo scenario demografico, l'assistenza sanitaria, etc.) [2]. Tale aumento di entropia ci fa perdere precisione nell'elaborazione degli scenari controfattuali (e.g., cosa sarebbe accaduto se non ci fosse stata la COVID-19).…”