Demographic and socio-economic factors, and healthcare resource indicators associated with the rapid spread of COVID-19 in Northern Italy: An ecological study

Abstract

Background COVID-19 rapidly escalated into a pandemic, threatening 213 countries, areas, and territories the world over. We aimed to identify potential province-level socioeconomic determinants of the virus’s dissemination, and explain between-province differences in the speed of its spread, based on data from 36 provinces of Northern Italy. Methods This is an ecological study. We included all confirmed cases of SARS-CoV-2 reported between February 24th and March 30th, 2020. For each province, we calculated the trend of contagion as the relative increase in the number of individuals infected between two time endpoints, assuming an exponential growth. Pearson’s test was used to correlate the trend of contagion with a set of healthcare-associated, economic, and demographic parameters by province. The virus’s spread was input as a dependent variable in a stepwise OLS regression model to test the association between rate of spread and province-level indicators. Results Multivariate analysis showed that the spread of COVID-19 was correlated negatively with aging index (p-value = 0.003), and positively with public transportation per capita (p-value = 0.012), the % of private long-term care hospital beds and, to a lesser extent (p-value = 0.070), the % of private acute care hospital beds (p-value = 0.006). Conclusion Demographic and socioeconomic factors, and healthcare organization variables were found associated with a significant difference in the rate of COVID-19 spread in 36 provinces of Northern Italy. An aging population seemed to naturally contain social contacts. The availability of healthcare resources and their coordination could play an important part in spreading infection.

Health and Healthcare Variables Associated with Italy’s Excess Mortality during the First Wave of the COVID-19 pandemic: An Ecological Study

“…It is worth noting that, with both multivariate analyses (Models 1 and 2), GDP per capita loses its significance as a covariate positively correlated with excess mortality. This may stem from the fact that socioeconomic factors (capturing economic and social interactions and transportation flows) tend to be more significantly related to COVID-19 incidence than to excess mortality [11,16]. Simultaneously, health and healthcare variables become more important in explaining the excess mortality directly and indirectly caused by COVID-19 in the various provinces.…”

“…The copyright holder for this this version posted January 31, 2021. ; https://doi.org/10.1101/2021.01.28.21250669 doi: medRxiv preprint number of deaths during the same period for the years 2015 to (D 0 ), the excess mortality for the i-th province was computed as follows: Six variables regarding healthcare, health, and socioeconomic indicators were selected:  acquired immunodeficiency syndrome (AIDS) mortality rate: number of deaths due to AIDS per 1,000 population;  long-term care hospitalization rate: number of hospital admissions in long-term care wards per 1,000 population;  gross domestic product per capita based on purchasing power parity (GDP);  average density of general practitioners (GPs): number of GPs per 1,000 population;  average density of hospital physicians: number of hospital physicians per 10,000 population;  province's COVID-19 transmission factor: a measure of the rate at which COVID-19 spread in each province. This last variable, the transmission factor, measures the trend of contagion as the relative increase in the number of individuals infected between two time points, T0 and T1, and it was calculated under the hypothesis of exponential growth [11]. The data were drawn from the COVID-19 Italian Civil Protection Department's official database, which was updated daily [15].…”

“…This last variable, the transmission factor, measures the trend of contagion as the relative increase in the number of individuals infected between two time points, T0 and T1, and it was calculated under the hypothesis of exponential growth [11]. The data were drawn from the COVID-19 Italian Civil Protection Department’s official database, which was updated daily [15].…”

“…The pandemic's impact on different countries and regions varied [9], but the spread of COVID-19 followed much the same trend in 24 European nations early in the first wave [10]. Social, environmental, and economic factors have strongly influenced the transmission of the disease (especially in the complex scenario of the European continent [9,11]), with a far from a negligible impact on cumulative, all-causes excess deaths.…”

Opening schools and trends in SARS-CoV-2 transmission in European countries

“…A study analysing the relative rates of infection by age group identified higher rates among adolescents and young adults than among children during the first wave of the epidemic in Germany [RR (age group): 0.78 (10-14); 1.14 (15-19); 1.40 (20-24); 1.06 (25-29)] 17 . Another study conducted to identify sociodemographic factors behind the spread of COVID-19 found that provinces with lower aging indexes had higher rates of contagion, suggesting that younger people could be more responsible for spreading the virus at population level 18 . Opposite findings emerged for Hungary, however: a recent publication showed that the trend of contagion slowed beyond the change point after schools reopened.…”