Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy

Abstract

Background: COVID-19 mortality was associated with several reasons, including conspiracy theories and infodemic phenomena. However, little is known about the potential endogenous reasons for the increase in COVID-19 associated mortality in Italy. Objective: This study aimed to search the potential endogenous reasons for the increase in COVID-19 mortality recorded in Italy during the year 2020 and evaluate the statistical significance of the latter. Methods: We analyzed all the trends in the timelapse 2011-2019 related to deaths by age, sex, region, and cause of death in Italy and compared them with those of 2020. Ordinary least squares (OLS) linear regressions and ARIMA (p, d, q) models were applied to investigate the predictions of death in the year 2020 as compared to death reported in 2020. Grubbs and Iglewicz-Hoaglin tests were used to identify the statistical differences between the predictors and observed death during the year 2020. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Results: Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during the year 2020 is between 640,000 and 660,000 (95% confidence intervals range: 620,000 - 695,000) and these values were far from the observed deaths reported (n = 750,000). Significant difference in deaths at national level (P = 0.003), and higher male mortality than women (+18% versus +14%, P < 0.001 versus P = 0.01) was observed. Finally, higher mortality was strongly and positively correlated with latitude (R = 0.82, P < 0.001) Conclusions: Our findings suggest that the absence of historical endogenous reasons capable of justifying the increase in deaths and mortality observed in Italy in 2020. Together with the current knowledge on the novel coronavirus 2019, these findings provide decisive evidence on the devastating impact of COVID-19 in Italy. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy theorists. Moreover, given the marked concordance between the predictions of the ARIMA and OLS regression models, we suggest that these models be exploited to predict mortality trends.

Joinpoint Regression to Determine the Impact of COVID-19 on Mortality in Europe: A Longitudinal Analysis From 2000 to 2020 in 27 Countries

“…Also, the novel coronavirus 2019 has a higher contagion power than flu viruses [18]. Finally, there are valid molecular and biological rationales that explain the high virulence of COVID-19 [7,19]. We conclude that COVID-19 is the main reason for the dramatic scenario depicted in this study.…”

“…This research does not examine any non-linear subtrends capable of generating false and misleading statistical anomalies [7]. Besides, the models employed cannot explore the causal nature of the results found.…”

“…Although mortality increases with age and the infection severity is linked to a wide range of local factors and age-related comorbidities [7,[15][16][17][18][19], it is wrong to conclude that COVID-19 is not harmful to young people. In particular, i) long-COVID (long-term effects of COVID-19) is a condition not uncommon in the younger age groups [21], and ii) the uncontrolled spread favors the appearance of variants of concern, capable of being much more dangerous even for the youngest (e.g., Delta and Omicron) [22].…”

“…In addition, two different approaches are used to interpret the statistical significance of the results to allow the reader an independent evaluation and reduce the effect of the author's biases. Finally, Figure 1 has been conveniently constructed to be easy to interpret in order to provide an understandable reference for a lay public: specifically, simple linear scales and models have been adopted, thus making the 2020 statistical anomaly clear and evident [7].…”

“…The purpose of the manuscript is to provide epidemiologically relevant data and conclusive proof of the danger of COVID-19. Indeed, various conspiracy hypotheses have argued that a significant number of patients died from other causes, even if tested positive to the novel coronavirus 2019 [7]. Since risk perception is strongly influenced by how information and images are presented, a simple and intuitive figure has been developed to show the results to the lay public [8].…”