Measuring the Impact of Future Outbreaks? A Secondary Analysis of Routinely Available Data in Spain

Abstract

Background: As of 7 January 2022, it is estimated that 5.5 million people worldwide have died from COVID-19. Although the full impact of SARS-CoV-2 (COVID-19) on healthcare systems worldwide is still unknown, we must consider the socio-economic impact. For instance, it has resulted in an 11% decrease in the GDP (Gross Domestic Product) in the European Union. We aim to provide valuable information for policymakers by analysing widely available epidemiological and socioeconomic indicators using Spanish data. Methods: Secondary analysis of routinely available data from various official data sources covering the period from 1 March 2020 to 31 March 2021. To measure the impact of COVID-19 in the population, a set of epidemiological and socioeconomic indicators were used. The interrelationships between these socioeconomic and epidemiological indicators were analysed using Pearson's correlation. Their behaviour was grouped according to their greater capacity to measure the impact of the pandemic and was compared to identify those that are more appropriate to monitor future health crises (primary outcome) using multivariate analysis of canonical correlation for estimating the correlation between indicators using different units of analysis. Results: Data from different time points were analysed. The excess of mortality was negatively correlated with the number of new companies created during the pandemic. The increase in COVID-19 cases was associated with the rise of unemployed workers. Neither GDP nor per capita debt was related to any epidemiological indicators considered in the annual analysis. The canonical models of socioeconomic and epidemiological indicators of each of the time periods analysed were statistically significant (0.80-0.91 p < 0.05). Conclusions: In conclusion, during the COVID-19 pandemic in Spain, excess mortality, incidence, lethality, and unemployment constituted the best group of indicators to measure the impact of the pandemic. These indicators, widely available, could provide valuable information to policymakers and higher management in future outbreaks.

Keywords: COVID-19; epidemiologic factors; health management; health services; socioeconomic factors.

Figure 1

Graphical representation of the canonical correlations of the socioeconomic and epidemiological indicators during 2020 annual data. AN: Andalusia; AR: Aragon; AS: Asturias; IB: Balearic Islands; CN: Canary Islands; CB: Cantabria; CM: Castilla La-Mancha; CL: Castilla y León; CT: Catalonia; CEyML: Ceuta and Melilla; VC: Community of Valencia; EX: Extremadura; GA: Galicia; RI: La Rioja; MD: Community of Madrid; MC: Murcia; NC: Navarra; PV: Basque Country. Socioeconomic Indicators (PARO: unemployment rate; EMSS: percentage change in companies registered with Social Security; NEM: percentage change in new companies created). Epidemiological Indicators (IN: incidence; SAT: bed saturation; LET: lethality; ICU: rate of COVID-19 admissions among COVID-19 cases; EXMOR: excess mortality).