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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.

Migrants are likely to experience mental health conditions, being one of the most vulnerable groups during the COVID-19 pandemic. The present study aims to: (1) estimate the prevalence of depressive and anxious symptoms and (2) examine the impact of risk and protective factors on this symptomatology. A sample of 129 migrants living in Spain during the COVID-19 pandemic completed an anonymous online survey, including information on sociodemographic and individual characteristics, migration, basic needs, social environment and perceived health domains. Multiple Poisson regression models analysed the effects of risk and protective factors on depression and anxiety symptoms. The prevalence of depressive and anxiety symptoms was 22.3% and 21.4%, respectively. Risk factors such as living in a rented house and previous mental health conditions were associated with higher depression symptoms, whereas unemployment was related to anxiety symptoms. Conversely, older age, better self-esteem, and higher levels of social support were associated with fewer depression symptoms. Older age and better quality of life were related to fewer anxiety symptoms. These findings addressing risk and protective factors (e.g., social support, self-esteem) help to design culturally effective programs, particularly in migrants with pre-existing mental health conditions, adjusting the organisation of mental healthcare services in difficult times in Spain.

Aims This study aimed at evaluating the age, sex, and country-income patterns in aortic aneurysm disease burden, analysing trends in mortality and years of life lost (YLLs), as well as their causal drivers and risk factors, using the 2017 Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2017). Methods and results We described the temporal, global, and regional (195 countries) patterns of aortic aneurysm (thoracic and abdominal) mortality, YLLs, their drivers [sociodemographic index (SDI), healthcare access and quality index (HAQ index)] and risk factors using the GBD 1990–2017. Correlation and mixed multilevel modelling between aortic aneurysm mortality, YLLs, HAQ index and other variables were applied. From 1990 to 2017, a global declining trend in age-standardized aortic aneurysm mortality was found [2.88 deaths/100 000 (95% uncertainty intervals, UI 2.79 to 3.03) in 1990 and 2.19 deaths/100 000 (95% UI 2.09 to 2.28) in 2017]. Among high-income countries (HICs) a consistent declining Spearman’s correlation between age-standardised aortic aneurysm mortality, SDI (HICs; 1990 rho: 0.57, P ≤ 0.001; 2017 rho: 0.41, P = 0.001) and HAQ index was observed (HICs; 1990 rho: 0.50, P Conclusions Age-standardized aortic aneurysm mortality declined globally between 1990 and 2017. Globally, age-standardized aortic aneurysm mortality and YLLs were related to changes in SDI and HAQ index levels, while country-level income-related variations were also observed.

Sarcopenia and sarcopenic obesity research in low- and middle- income countries (LMICs) is limited. We investigated sarcopenia and sarcopenic obesity prevalence and sociodemographic, bio-clinical and lifestyle factors in LMICs settings. For the purposes of this study, the 10/66 Dementia Research Group follow-up wave information from individuals aged 65 and over in Cuba, Dominican Republic, Peru, Mexico, Puerto Rico, China, was employed and analysed (n = 8.694). Based on indirect population formulas, we calculated body fat percentage (%BF) and skeletal muscle mass index (SMI). Sarcopenia prevalence ranged from 12.4% (Dominican Republic) to 24.6% (rural Peru); sarcopenic obesity prevalence ranged from 3.0% (rural China) to 10.2% (rural Peru). Odds ratios (OR) with 95% confidence intervals (CI) for sarcopenia were higher for men 2.82 (2.22–3.57) and those with higher %BF 1.08 (1.07–1.09), whereas higher number of assets was associated with a decreased likelihood 0.93 (0.87–1.00). OR of sarcopenic obesity were higher for men 2.17 (1.70–2.76), those reporting moderate alcohol drinking 1.76 (1.21–2.57), and those with increased number of limiting impairments 1.54 (1.11–2.14). We observed heterogeneity in the prevalence of sarcopenia and sarcopenic obesity in the 10/66 settings. We also found a variety of factors to be associated with those. Our results reveal the need for more research among the older population of LMICs.

Background: Research suggests that changes in social support and loneliness have affected mental disorder symptoms during the COVID-19 pandemic. However, there are a lack of studies comparing the robustness of these associations. Aims: The aims were to estimate the strength of the associations of loneliness and social support with symptoms of depression, anxiety, and posttraumatic stress during the COVID-19 pandemic (2020–2022) in the general population. Method: The method entailed a systematic review and random-effects meta-analysis of quantitative studies. Results: Seventy-three studies were included in the meta-analysis. The pooled correlations of the effect size of the association of loneliness with symptoms of depression, anxiety, and posttraumatic stress were 0.49, 0.40, and 0.38, respectively. The corresponding figures for social support were 0.29, 0.19, and 0.18, respectively. Subgroup analyses revealed that the strength of some associations could be influenced by the sociodemographic characteristics of the study samples, such as age, gender, region, and COVID-19 stringency index, and by methodological moderators, such as sample size, collection date, methodological quality, and the measurement scales. Conclusions: Social support had a weak association with mental disorder symptoms during the COVID-19 pandemic while the association with loneliness was moderate. Strategies to address loneliness could be highly effective in reducing the impact of the pandemic on social relationships and mental health.

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.

The present article aims to highlight methodological aspects related to understanding and conceptualising social capital for the purposes of population research as well as describing the key challenges in the harmonisation process of indicators of social capital. The study was conducted in the frame of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. After a review of social capital theories developed in social science and a subsequent review of the documentation of 18 international cohorts, decision trees of the harmonisation of social variables were developed. The known-group validity was verified. The results focused on generalised trust, civic engagement and social participation are presented. The summary of the availability of any indicators of these concepts is classified in seven domains (generalised trust, political participation, religious participation, senior-specific participation, participation in sport groups, participation in volunteer/charity group activities, any participation) across surveys. The results of the analysis for known-group validity support the construct validity of the harmonised variables.

Although coronavirus disease 2019 (COVID-19) emerged in January 2020, there is no quantified effect size for non-pharmaceutical interventions (NPI) to control the outbreak in the continental US. Objective. To quantify national and sub-national effect sizes of NPIs in the US. Design. This is an observational study for which we obtained daily county level COVID-19 cases and deaths from January 22, 2020 through the phased removal of social distancing protections. A stepped-wedge cluster-randomized trial (SW-CRT) analytical approach is used, leveraging the phased implementation of policies. Data include 3142 counties from all 50 US states and the District of Columbia. Exposures. County-level NPIs were obtained from online county and state policy databases, then classified into four intervention levels: Level 1 (low) – declaration of a State of Emergency; Level 2 (moderate) – school closures, restricting nursing home access, or closing restaurants and bars; Level 3 (high) – non-essential business closures, suspending non-violent arrests, suspending elective medical procedures, suspending evictions, or restricting mass gatherings of at least 10 people; and Level 4 (aggressive) – sheltering in place / stay-at-home, public mask requirements, or travel restrictions. Additional county-level data were obtained to record racial (Black, Hispanic), economic (educational level, poverty), demographic (rural/urban) and climate factors (temperature, specific humidity, solar radiation). Main Outcomes. The primary outcomes are rates of COVID-19 cases, deaths and case doubling times. NPI effects are measured separately for nine US Census Region (Pacific, Mountain, West North Central, East North Central, West South Central, East South Central, South Atlantic, Middle Atlantic, New England). Results. Aggressive NPIs (level 4) significantly reduced COVID-19 case and death rates in all US Census Regions, with effect sizes ranging from 4.1% to 25.7% and 5.5% to 25.5%, respectively, for each day they were active. No other intervention level achieved significance across all US Regions. Intervention levels 3 and 4 both increased COVID-19 doubling times, with effects peaking at 25 and 40 days after initiation of each policy, respectively. The effectiveness of level 3 NPIs varied, reducing case rates in all regions except North Central states, but associated with significantly higher death rates in all regions except Pacific states. Intervention levels 1 and 2 did not indicate any effect on COVID-19 propagation and, in some regions, these interventions were associated with increased COVID-19 cases and deaths. Heterogeneity of NPI effects are associated with racial composition, poverty, urban-rural environment, and climate factors. Conclusion. Aggressive NPIs are effective tools to reduce COVID-19 propagation and mortality. Reducing social and environmental disparities may improve NPI effects in regions where less strict policies are in place.

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.

Background: The aim of the present study was to use the extensive Global Burden ofDiseases, Injuries, and Risk Factors Study (GBD) database from 1990–2017 to evaluate the levels andtemporal correlation trends between disability adjusted life years (DALYs) attributed tomusculoskeletal (MSK) disorders, all mental disorders collectively and by mental disorder subcategory.Methods: We utilized results of the GBD 2017 to describe the correlation patterns betweenDALYs due to MSK disorders, mental disorders and other diseases among 195 countries. Mixedmodel analysis was also applied. Results: A consistent relation was reported between age-adjustedDALYs attributed to MSK and mental disorders (in total) among the 195 countries, in both sexes,for 1990 to 2017 (1990 Rho = 0.487; 2017 Rho =0.439 p < 0.05). Distinct regional and gender correlationpatterns between age-adjusted DALYs due to MSK and mental disorders were reported. Nocorrelation was reported between DALYs due to MSK and all mental disorders collectively, amongLow- or Middle-income countries. However, in High-income countries (HICs), the correlation wasstrong and consistent between 1990 and 2017 (1990 Rho = 0.735; 2017 Rho = 0.727, p < 0.05).Conclusions: The reported correlation patterns call for targeted preventive strategies andintervention policies for mental and MSK disorders internationally. Special attention is neededamong HICs.