A Bayesian spatio-temporal analysis of mortality rates in Spain: application to the COVID-19 2020 outbreak

Abstract

Background The number of deaths attributable to COVID-19 in Spain has been highly controversial since it is problematic to tell apart deaths having COVID as the main cause from those provoked by the aggravation by the viral infection of other underlying health problems. In addition, overburdening of health system led to an increase in mortality due to the scarcity of adequate medical care, at the same time confinement measures could have contributed to the decrease in mortality from certain causes. Our aim is to compare the number of deaths observed in 2020 with the projection for the same period obtained from a sequence of previous years. Thus, this computed mortality excess could be considered as the real impact of the COVID-19 on the mortality rates. Methods The population was split into four age groups, namely: (< 50; 50–64; 65–74; 75 and over). For each one, a projection of the death numbers for the year 2020, based on the interval 2008–2020, was estimated using a Bayesian spatio-temporal model. In each one, spatial, sex, and year effects were included. In addition, a specific effect of the year 2020 was added ("outbreak"). Finally, the excess deaths in year 2020 were estimated as the count of observed deaths minus those projected. Results The projected death number for 2020 was 426,970 people, the actual count being 499,104; thus, the total excess of deaths was 72,134. However, this increase was very unequally distributed over the Spanish regions. Conclusion Bayesian spatio-temporal models have proved to be a useful tool for estimating the impact of COVID-19 on mortality in Spain in 2020, making it possible to assess how the disease has affected different age groups accounting for effects of sex, spatial variation between regions and time trend over the last few years.

Heterogeneity in subnational mortality in the context of the COVID-19 pandemic: the case of Belgian districts in 2020

“…However, when set-ting the interval threshold at 80% and not accounting for uncertainty around life table estimates for small areas, 33 districts out of 43 experienced a significant drop. Other research found capital cities to be areas within countries that were the most affected in 2020 [12,13,16]. Stockholm, the capital city of Sweden, experienced a drop of 1.5 year in its life expectancy between 2019 and 2020 [17].…”

“…Considering the difficulties associated with assessing the mortality shock nationally, relatively few studies have evaluated the heterogeneity of the mortality shock at a small-area level [12,13,16,17]. Yet national-level analyses may conceal substantial heterogeneity in how various small areas have been affected.…”

Spatial Statistical Models: An Overview under the Bayesian Approach

“…Moreover, it is useful for disease mapping to determine the onset of an epidemic disease. A recent application includes the analysis of the mortality rate of COVID-19 in Spain and Italy using a Gaussian process to explain the spatial pattern [47,48]; the spatial mapping of schistosomiasis in Tanzania to determine the prevalence and spatial pattern [49]. It is also considered to be a powerful tool for image analysis in the medical field.…”

COVID-19 Secondary Infections in ICU Patients and Prevention Control Measures: A Preliminary Prospective Multicenter Study

“…We sought to assess differences in the incidence of ICU-acquired secondary and MDR infections in critically ill mechanically ventilated COVID-19 patients that were routinely placed in well-established ICUs in Spanish national infection control programs according to the use or non-use of SDD based on the hypothesis that fewer secondary and MDR infections would be associated with the addition of SDD. This study was conducted in four ICUs in the Canary Islands, where the excess of deaths for 2020 was smaller than for mainland Spain [ 36 , 37 ], similar to the lower impact of the pandemic on other islands [ 38 ].…”