Modeling for the Stringency of Lock-Down Policies: Effects of Macroeconomic and Healthcare Variables in Response to the COVID-19 Pandemic.

Background: The spread of COVID-19 has been characterized by unprecedented global lock-downs. Although, the extent of containment policies cannot be explained only through epidemic data. Previous studies already focused on the relationship between the economy and healthcare, focusing on the impact of diseases in countries with a precarious economic situation. However, the pandemic caused by SARS-CoV-2 drew most countries of the world into a precarious economic situation mostly caused by the global and local lock-downs policies. Methods: A discriminant analysis performed via partial least squares procedure was applied to evaluate the impact of economic and healthcare variables on the containment measures adopted by 39 countries. To collect the input variables (macroeconomic, healthcare, and medical services), we relied on official databases of international organizations, such as The World Bank and WHO. Results: The stringency lock-down policies could not only be influenced by the epidemical data, but also by previous features of the selected countries, such as economic and healthcare conditions. Conclusions: Indeed, economic and healthcare variables also contributed to shaping the implemented lock-down policies.

Early predictors of clinical deterioration in a cohort of outpatients with COVID-19 in southern Italy: A multicenter observational study.

Data regarding early predictors of clinical deterioration in patients with infection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is still scarce. The aim of the study is to identify early symptoms or signs that may be associated with severe coronavirus disease 2019 (COVID-19). We conducted a multicentre prospective cohort study on a cohort of patients with COVID-19 in home isolation from March 2020 to April 2021. We assessed longitudinal clinical data (fever, dyspnea, need for hospitalization) through video calls at three specific time points: the beginning of symptoms or the day of the first positivity of the nasopharyngeal swab for SARS-CoV-2-RNA (t0 ), and 3 (t3 ) and 7 (t7 ) days after the onset of symptoms. We included 329 patients with COVID-19: 182 (55.3%) males, mean age 53.4 ± 17.4 years, median Charlson comorbidity index (CCI) of 1 (0-3). Of the 329 patients enrolled, 171 (51.98%) had a mild, 81 (24.6%) a moderate, and 77 (23.4%) a severe illness; 151 (45.9%) were hospitalized. Compared to patients with mild COVID-19, moderate and severe patients were older (p < 0.001) and had more comorbidities, especially hypertension (p < 0.001) and cardiovascular diseases (p = 0.01). At t3 and t7 , we found a significant higher rate of persisting fever (≥37°C) among patients with moderate (91.4% and 58.0% at t3 and t7 , respectively; p < 0.001) and severe outcome (75.3% and 63.6%, respectively; p < 0.001) compared to mild COVID-19 outcome (27.5% and 11.7%, respectively; p < 0.001). Factors independently associated with a more severe outcome were persisting fever at t3 and t7 , increasing age, and CCI above 2 points. Persisting fever at t3 and t7 seems to be related to a more severe COVID-19. This data may be useful to assess hospitalization criteria and optimize the use of resources in the outpatient setting.

Modeling for the Stringency of Lock-Down Policies: Effects of Macroeconomic and Healthcare Variables in Response to the COVID-19 Pandemic

Background The spread of COVID-19 has been characterized by unprecedented global lock-downs. Although, the extent of containment policies cannot be explained only through epidemic data. Previous studies already focused on the relationship between the economy and healthcare, focusing on the impact of diseases in countries with a precarious economic situation. However, the pandemic caused by SARS-CoV-2 drew most countries of the world into a precarious economic situation mostly caused by the global and local lock-downs policies. Methods A discriminant analysis performed via partial least squares procedure was applied to evaluate the impact of economic and healthcare variables on the containment measures adopted by 39 countries. To collect the input variables (macroeconomic, healthcare, and medical services), we relied on official databases of international organizations, such as The World Bank and WHO. Results The stringency lock-down policies could not only be influenced by the epidemical data, but also by previous features of the selected countries, such as economic and healthcare conditions. Conclusions Indeed, economic and healthcare variables also contributed to shaping the implemented lock-down policies.