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The COVID-19 pandemic is causing a significant global health crisis. As the disease continues to spread worldwide, little is known about the countrylevel factors affecting the transmission in the early weeks. The present study objective was to explore the country-level factors, including government actions that explain the variation in the cumulative cases of COVID-19 within the first 15 days since the first case reported. Using publicly available sources, country socioeconomic, demographic and health-related risk factors, together with government measures to contain COVID-19 spread, were analysed as predictors of the cumulative number of COVID-19 cases at three time points (t = 5, 10 and 15) since the first case reported (n = 134 countries). Drawing on negative binomial multivariate regression models, HDI, healthcare expenditure and resources, and the variation in the measures taken by the governments, significantly predicted the incidence risk ratios of COVID-19 cases at the three time points. The estimates were robust to different modelling techniques and specifications. Although wealthier countries have elevated human development and healthcare capacity in respect to their counterparts (low-and middle-income countries) the early implementation of effective and incremental measures taken by the governments are crucial to controlling the spread of COVID-19 in the early weeks.

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Objective. To identify socioeconomic factors associated with antimicrobial resistance of Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli in Chilean hospitals (2008–2017). Methods. We reviewed the scientific literature on socioeconomic factors associated with the emergence and dissemination of antimicrobial resistance. Using multivariate regression, we tested findings from the literature drawing from a longitudinal dataset on antimicrobial resistance from 41 major private and public hospitals and a nationally representative household survey in Chile (2008–2017). We estimated resistance rates for three priority antibiotic–bacterium pairs, as defined by the Organisation for Economic Co-operation and Development; i.e., imipenem and meropenem resistant P. aeruginosa, cloxacillin resistant S. aureus, and cefotaxime and ciprofloxacin resistant E. coli. Results. Evidence from the literature review suggests poverty and material deprivation are important risk factors for the emergence and transmission of antimicrobial resistance. Most studies found that worse socioeconomic indicators were associated with higher rates of antimicrobial resistance. Our analysis showed an overall antimicrobial resistance rate of 32.5%, with the highest rates for S. aureus (40.6%) and the lowest for E. coli (25.7%). We found a small but consistent negative association between socioeconomic factors (income, education, and occupation) and overall antimicrobial resistance in univariate (p < 0.01) and multivariate analyses (p < 0.01), driven by resistant P. aeruginosa and S. aureus. Conclusion. Socioeconomic factors beyond health care and hospital settings may affect the emergence and dissemination of antimicrobial resistance. Preventing and controlling antimicrobial resistance requires efforts above and beyond reducing antibiotic consumption.

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