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 speed-ing up and rapidly spreading across Europe. The health, so-cial, and economic consequences of the pandemic are diffi-cult 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 bi-ases. 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 municipal-ity, the posterior predictive distribution under a hierarchical null model was obtained. From the posterior predictive distri-bution, 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 calcu-late excess mortality, this study provides an original method-ological approach to profile municipalities with excess deaths accounting for spatial and temporal uncertainty.