tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2022 Mar 3.
doi: 10.2196/36022. Online ahead of print.
The Effects of COVID-19 First Waves in Italy: An Answer Through a Retrospective Analysis of Mortality
Alessandro Rovetta[SUP] 1 [/SUP], Akshaya Srikanth Bhagavathula[SUP] 2 [/SUP]
Affiliations
Abstract
Background: Despite the available evidence on its severity, COVID-19 has often been compared to seasonal flu by some conspirators and even scientists. Various public discussions arose about the non-causal correlation between COVID-19 and the observed deaths during the pandemic period in Italy.
Objective: This paper aims to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test the above 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 regressions and ARIMA were used to predict the best value for 2020. Grubbs one-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a one 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 the multi-comparison adjustment and false negatives imply great epidemiological risk, Siegel less conservative approach and Holm-Bonferroni more conservative approach were employed. By doing so, we have 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 (95% CIs range: 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 (t=7.2, df=21, Adj. P<.001). Male and female national mortality excesses were 18.4% (P <.001, Adj. P=.006) and 14.1% (P=.005, Adj. P=.124), respectively. However, we found limited significance when comparing male and female mortality residuals' using the Mann - Whitney U test (P=.267, Adj. P>.999). Finally, mortality was strongly and positively correlated with latitude (R=0.82, Adj. 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 males, Adj. P <.001, 31% for females, P <.001, Adj. 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 the novel coronavirus 2019, 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.
. 2022 Mar 3.
doi: 10.2196/36022. Online ahead of print.
The Effects of COVID-19 First Waves in Italy: An Answer Through a Retrospective Analysis of Mortality
Alessandro Rovetta[SUP] 1 [/SUP], Akshaya Srikanth Bhagavathula[SUP] 2 [/SUP]
Affiliations
- PMID: 35238784
- DOI: 10.2196/36022
Abstract
Background: Despite the available evidence on its severity, COVID-19 has often been compared to seasonal flu by some conspirators and even scientists. Various public discussions arose about the non-causal correlation between COVID-19 and the observed deaths during the pandemic period in Italy.
Objective: This paper aims to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test the above 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 regressions and ARIMA were used to predict the best value for 2020. Grubbs one-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a one 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 the multi-comparison adjustment and false negatives imply great epidemiological risk, Siegel less conservative approach and Holm-Bonferroni more conservative approach were employed. By doing so, we have 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 (95% CIs range: 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 (t=7.2, df=21, Adj. P<.001). Male and female national mortality excesses were 18.4% (P <.001, Adj. P=.006) and 14.1% (P=.005, Adj. P=.124), respectively. However, we found limited significance when comparing male and female mortality residuals' using the Mann - Whitney U test (P=.267, Adj. P>.999). Finally, mortality was strongly and positively correlated with latitude (R=0.82, Adj. 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 males, Adj. P <.001, 31% for females, P <.001, Adj. 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 the novel coronavirus 2019, 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.