The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends

Abstract

Background Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy. Objective This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series. Methods We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis. Results Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals’ using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006). Conclusions Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19–related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

Common Statistical and Methodological Errors in Scientific Investigations: A Simple Guide to Avoid Invalid Conclusions and Unfounded Decisions

“…For instance, the superposition of non-linear trends could give rise to a linear trend up to a certain date (threshold) and a non-linear trend beyond the latter (Figure 6). If the external event occurs in conjunction with the threshold, an improper causal association could be asserted (this is not the case of COVID-19 impact on mortality [7]). Moreover, the external factors could be multiple, which makes it difficult to identify which of these was predominant for the change in trend.…”

“…Finally, it is possible to obtain different results by observing different temporal windows to make the comparisons. For example, the statistics on excess deaths in Italy during 2020 are slightly different considering the previous five years as a comparison time lapse instead of the previous ten years [7]. This happens because all the real models have a margin of uncertainty and it is not always easy to understand the correct width of the temporal window.…”

“…One of the most common errors is to believe that the adjustment for the multi-comparison should only be applied when the results or data are directly compared with each other (e.g., as done in 2.14). Indeed, the adjustment for the multi-comparison -when deemed appropriate -should be made when the final decision (e.g., to apply a lockdown based on the abnormal increase in flu symptoms among a population) is based on the result of multiple tests, regardless of whether these tests are mutually comparative or independent [7]. As a matter of fact, the increased likelihood of false positives is due to the high number of tests used and not to the nature of the latter [13].…”

A New Paper Framework to Increase Reproducibility: Example Relating to Web Pharmacovigilance During COVID-19 in Italy

“…COVID-19 pandemic in Italy [7-9], infodemiology and infoveillance [10], seasonal flu in Italy [11, 12].…”

Retrospective Longitudinal Analysis of Deaths and Mortality in the Over 50 Population Due to COVID-19 in Italy

“…Ciononostante, svariate analisi comparative sull'eccesso di mortalità hanno fornito forti evidenze sul fatto che i decessi durante la COVID-19 (la relativa patologia) siano stati largamente sottostimati (2-5). Le motivazioni più plausibili risiedono nell'insufficiente capacità di testing (specialmente nelle prime fasi dell'epidemia), il non trascurabile rate di falsi negativi dei tamponi diagnostici e cause laterali di mortalità come il sovraccarico delle strutture sanitarie (4,5). A tal proposito, è doveroso considerare che il decesso di un individuo è determinato da una lunga e complessa serie di fattori di rischio, tra cui il suo stato di salute fisica e psicologica, il suo stile di vita ed eventuali comorbilità (6)(7)(8)(9).…”