A municipality-level analysis of excess mortality in Italy in the period January-April 2020.

BACKGROUND: the first confirmed cases of COVID-19 in WHO European Region was reported at the end of January 2020 and, from that moment, the epidemic has been speeding up and rapidly spreading across Europe. The health, social, and economic consequences of the pandemic are difficult to evaluate, since there are many scientific uncertainties and unknowns. OBJECTIVES: the main focus of this paper is on statistical methods for profiling municipalities by excess mortality, directly or indirectly caused by COVID-19. METHODS: the use of excess mortality for all causes has been advocated as a measure of impact less vulnerable to biases. In this paper, observed mortality for all causes at municipality level in Italy in the period January-April 2020 was compared to the mortality observed in the corresponding period in the previous 5 years (2015-2019). Mortality data were made available by the Ministry of Internal Affairs Italian National Resident Population Demographic Archive and the Italian National Institute of Statistics (Istat). For each municipality, the posterior predictive distribution under a hierarchical null model was obtained. From the posterior predictive distribution, we obtained excess death counts, attributable community rates and q-values. Full Bayesian models implemented via MCMC simulations were used. RESULTS: absolute number of excess deaths highlights the burden paid by major cities to the pandemic. The Attributable Community Rate provides a detailed picture of the spread of the pandemic among the municipalities of Lombardy, Piedmont, and Emilia-Romagna Regions. Using Q-values, it is clearly recognizable evidence of an excess of mortality from late February to April 2020 in a very geographically scattered number of municipalities. A trade-off between false discoveries and false non-discoveries shows the different values of public health actions. CONCLUSIONS: despite the variety of approaches to calculate excess mortality, this study provides an original methodological approach to profile municipalities with excess deaths accounting for spatial and temporal uncertainty.

Strategia vaccinale in un contesto incerto e in costante evoluzione.

Politics is facing the need to make important decisions about anti-COVID-19 vaccination campaign in uncertain and changing contexts. With reference to the time frame between the administration of the first and second dose, the scientific evidence is still weak and comes from different contexts. New ways to collect and synthesize expert knowledge and opinions are needed with the direct involvement of the citizens in order to explain the uncertainties and maintain trust in institutions and their decisions.

The risk of over-diagnosis in serological testing. Implications for communications strategies.

BACKGROUND: since the beginning of the COVID-19 pandemic, the importance of developing a serological test has emerged and a debate on test accuracy and reliability become an issue widely discussed in the media. The importance of communication during this pandemic has been strongly underlined by public health experts, epidemiologists, media expert, psychologists, sociologists. In the case of serological tests, there are several aspects that have to be considered: why we perform the test, what population is tested, which are the parameters conditioning the results and their interpretation. OBJECTIVES: to show how to quantify the uncertainty related to the validity of the serological test with respect to its predictive value and in particular the positive predictive value. METHODS: the evaluation of a qualitative diagnostic test includes four distinct assessments: accuracy, empirical evidence, practical importance, and prevalence of the pathology. Accuracy is measured by the sensitivity and specificity of the test; empirical evidence is quantified by the likelihood ratio, respectively for a positive and negative test result; the practical importance of the result of a diagnostic test is assessed by the positive or negative predictive value. Prevalence of COVID-19 is substantial uncertainty and it is possible to estimate the apparent prevalence starting from the results obtained with a diagnostic test. RESULTS: at the moment, the knowledge about the accuracy of serological tests is limited and little attention is paid to confidence interval on point estimates. In terms of practical importance of testing at individual level, while negative predictive values are high whatever the level of sensitivity of the test, the interpretation of a positive results is very cumbersome. Positive predictive values above 90% can be reached only by tests with specificity above 99% at the expected prevalence rate of 5%. There is a linear relationship between apparent - testing positive - prevalence and real prevalence. The apparent prevalence in the context of serological test for COVID-19 is always larger than real prevalence. The level of specificity is crucial. CONCLUSIONS: the main applications of the serological test in the epidemic contest are: to study the seroprevalence of the virus antibodies in the general population; to screen the healthcare workers for the early identification of contagious subjects' health care settings and to screen the general population in order to identify new incident cases. In the first two cases, seroprevalence study and screening of a high-risk population, the consequences of the uncertainty associated to the statistics are already accounted for in the first situation, or are overcome by repeating the screening on the healthcare workers, and using the molecular test to verify the presence of the virus in those tested positive. The case of screening of general population is more complex and of major interest for the implication it may have on individual behaviours and on the implementation of public health interventions by the political decision makers. A positive result has, per se, no practical value for individuals since the probability of being really infected by the virus is low. The uncertainty associated with the different estimates (sensitivity, specificity and disease prevalence) play a double role: it is a key factor in defining the informative content of the test result and it might guide the individual actions and the public policy decisions.