Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project.

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

Lessons from COVID-19 mortality data across countries.

OBJECTIVE: Several online sources provide up-to-date open-access data on numbers, rates and proportions of COVID-19 deaths. Our article aims of comparing and interpreting between-country trends of mortality rate, case-fatality and all-cause excess mortality. METHODS: We used data from open databases (Our World in Data mostly) for comparing mortality of eleven western countries (Austria, Belgium, Canada, France, Germany, Italy, Netherlands, Spain, Sweden, UK, USA). Between-country trends in mortality rate and case-fatality (both including deaths for COVID-19 as numerator and therefore labelled as COVID-19 mortality metrics) and all-cause excess mortality (i.e. observed deaths during the epidemic compared with those expected based on mortality in the same periods of previous years) were compared. RESULTS: Although Belgium ranks first in mortality from COVID-19 (possibly due to the broadest criterion for attributing a death to COVID-19), it does not rank first for all-cause excess mortality. Conversely, compared with Belgium, the UK, Italy and Spain have reported lower COVID-19 mortality (possibly due to the narrower definitions for a COVID-19 death) but higher all-cause excess mortality. Germany and Austria are the unique countries for which COVID-19 mortality, case-fatality and all-cause excess mortality consistently exhibited the lowest rates. CONCLUSION: Between-country heterogeneity of COVID-19 mortality metrics could be largely explained by differences of criteria for attributing a death to COVID-19; in age/comorbidity structures; in policies for identifying asymptomatic people affected from SARS-CoV-2 infection. All-cause excess mortality is recommended as a more reliable metric for comparing countries.

Surveying immigrants in Southern Europe: Spanish and Italian strategies in comparative perspective.

Italy and Spain, as countries of recent immigration and high irregularity rates, have struggled to adapt their statistical system, especially their population registers, to adequately reflect the presence of an increasing number of immigrants in their territory. The population registers of the two countries have adapted differently to these changing realities: Spain introduced significant improvements in Padrón which have increased its coverage and accuracy. This is still not the case in Italy, making it necessary to resort to non-random sampling methods. The paper discusses the methodological implications of these differences and evaluates different methodological solutions based on both random and non-random sampling methods in both countries.