Government interventions and control policies to contain the first COVID-19 outbreak: An analysis of evidence.

BACKGROUND: The overarching aim of this study was to evaluate the effectiveness over time of government interventions and policy restrictions and the impact of determinants on spread and mortality during the first-wave of the COVID-19 pandemic, globally, regionally and by country-income level, up to 18 May 2020. METHODS: We created a global database merging World Health Organization daily case reports (from 218 countries/territories) with other socio-demographic and population health measures from 21 January to 18 May 2020. A four-level government policy interventions score (low to very high) was created based on the Oxford Stringency Index. RESULTS: Our results support the use of very high government interventions to suppress both COVID-19 spread and mortality effectively during wave one globally compared to other policy levels of control. Similar trends in virus propagation and mortality were observed in all country-income levels and specific regions. CONCLUSIONS: Rapid implementation of government interventions was needed to contain the first wave of the COVID-19 outbreak and to reduce COVID-19-related mortality.

A GBD 2019 study of health and Sustainable Development Goal gains and forecasts to 2030 in Spain.

This study aimed to report mortality, risk factors, and burden of diseases in Spain. The Global Burden of Disease, Injuries, and Risk Factors 2019 estimates the burden due to 369 diseases, injuries, and impairments and 87 risk factors and risk factor combinations. Here, we detail the updated Spain 1990-2019 burden of disease estimates and project certain metrics up to 2030. In 2019, leading causes of death were ischaemic heart disease, stroke, chronic obstructive pulmonary disease, Alzheimer's disease, and lung cancer. Main causes of disability adjusted life years (DALYs) were ischaemic heart disease, diabetes, lung cancer, low back pain, and stroke. Leading DALYs risk factors included smoking, high body mass index, and high fasting plasma glucose. Spain scored 74/100 among all health-related Sustainable Development Goals (SDGs) indicators, ranking 20 of 195 countries and territories. We forecasted that by 2030, Spain would outpace Japan, the United States, and the European Union. Behavioural risk factors, such as smoking and poor diet, and environmental factors added a significant burden to the Spanish population's health in 2019. Monitoring these trends, particularly in light of COVID-19, is essential to prioritise interventions that will reduce the future burden of disease to meet population health and SDG commitments.

COVID-19 Mortality in Europe, by Latitude and Obesity Status: A Geo-Spatial Analysis in 40 Countries.

On 30 January 2020, the World Health Organization (WHO) declared the current novel coronavirus disease 2019 (COVID-19) as a public health emergency of international concern and later characterized it as a pandemic. New data show that excess body mass and vitamin D deficiency might be related to the disease severity and mortality. The aim of this study was to evaluate whether latitude, as a proxy of sunlight exposure and Vitamin D synthesis, and prevalent obesity among European populations, is related to COVID-19 spread and severity. European COVID-19 data (incidence and fatality), including information on the prevalence of obesity, social distancing, and others were obtained by the "Our World in Data" website on 17 April 2021. Adjusted analysis showed that higher COVID-19 incidence and fatality were pictured in countries being in higher latitude, both during the whole period, as well as, during the time period 1 November 2020-31 March 2021. Higher incidence and fatality of COVID-19 were observed where the prevalence of overweight/obesity was higher during the whole time period, whereas during the time period 1 November 2020-31 March 2021, only COVID-19 incidence was higher but not a fatality. The present results provide insights for targeted interventions and preventive strategies against COVID-19.

Heterogeneity in the Effectiveness of Non-pharmaceutical Interventions During the First SARS-CoV2 Wave in the United States.

Background: Attempts to quantify effect sizes of non-pharmaceutical interventions (NPI) to control COVID-19 in the US have not accounted for heterogeneity in social or environmental factors that may influence NPI effectiveness. This study quantifies national and sub-national effect sizes of NPIs during the early months of the pandemic in the US. Methods: Daily county-level COVID-19 cases and deaths during the first wave (January 2020 through phased removal of interventions) were obtained. County-level cases, doubling times, and death rates were compared to four increasingly restrictive NPI levels. Socio-demographic, climate and mobility factors were analyzed to explain and evaluate NPI heterogeneity, with mobility used to approximate NPI compliance. Analyses were conducted separately for the US and for each Census regions (Pacific, Mountain, east/West North Central, East/West South Central, South Atlantic, Middle Atlantic and New England). A stepped-wedge cluster-randomized trial analysis was used, leveraging the phased implementation of policies. Results: Aggressive (level 4) NPIs were associated with slower COVID-19 propagation, particularly in high compliance counties. Longer duration of level 4 NPIs was associated with lower case rates (log beta -0.028, 95% CI -0.04 to -0.02) and longer doubling times (log beta 0.02, 95% CI 0.01-0.03). Effects varied by Census region, for example, level 4 effects on doubling time in Pacific states were opposite to those in Middle Atlantic and New England states. NPI heterogeneity can be explained by differential timing of policy initiation and by variable socio-demographic county characteristics that predict compliance, particularly poverty and racial/ethnic population. Climate exhibits relatively consistent relationships across Census regions, for example, higher minimum temperature and specific humidity were associated with lower doubling times and higher death rates for this period of analysis in South Central, South Atlantic, Middle Atlantic, and New England states. Conclusion and Relevance: Heterogeneity exists in both the effectiveness of NPIs across US Census regions and policy compliance. This county-level variability indicates that control strategies are best designed at community-levels where policies can be tuned based on knowledge of local disparities and compliance with public health ordinances.

Estimating the COVID-19 Spread Through Real-time Population Mobility Patterns: Surveillance in Low- and Middle-Income Countries.

BACKGROUND: On January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries. OBJECTIVE: The first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space-time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide. METHODS: We created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population's mobility patterns at the country level were obtained from Google community mobility reports. The prospective space-time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used. RESULTS: We found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread. CONCLUSIONS: The prospective space-time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level.

Are environmental pollution and biodiversity levels associated to the spread and mortality of COVID-19? A four-month global analysis.

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM10, PM2.5, and O3). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country's biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.