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 (of 218 countries) 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 are 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 (PM 10, PM 2.5 and O 3 ). 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 significant 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.
■ Abstract AIM:The aim of the present work was to evaluate the relationships between sociodemographic, clinical, and lifestyle characteristics and the presence of metabolic syndrome, among high and low altitude living elderly individuals without known CVD. METHODS: During 2005During -2011During , 1959 elderly (aged 65 to 100 years) individuals from 13 Mediterranean islands were enrolled. Sociodemographic, clinical, and lifestyle factors were assessed using standard procedures. Metabolic syndrome was defined according to the (Adult Treatment Panel) ATP III criteria. Mountainous areas were defined those more than 400 meters in height. RESULTS: For the present analysis 713 men and 596 women were studied; the prevalence of the metabolic syndrome was 29% (24% in men, 35% in women, p < 0.001). Furthermore, the prevalence of metabolic syndrome was 55% in the elders living in mountainous areas, as compared with 26% among those living at sea-level (p = 0.01). Similarly, the prevalence of hypertension, hypercholesterolemia, and obesity were higher in high altitude as compared with low altitude areas (all pvalues < 0.01). After adjusting for various confounders, elders living in high altitude areas were 3.06-times more likely to have the metabolic syndrome than those living at sea-level (OR = 3.06, 95%CI 2.02-4.65). However, when the annual number of visits to health care centers was taken into account, the effect of altitude of living was not associated with the presence of the syndrome. CONCLUSIONS: A considerable proportion of mountainous living elderly had the metabolic syndrome. Public health actions need to be taken to reduce the burden of cardiometabolic disorders by enabling better access to health care, especially in remote mountainous rural areas.
BackgroundClimate variation has long been studied in relation to human health. The aim of the present work was to evaluate the relationship between environmental humidity, and air temperature with the prevalence of diabetes, among elderly islanders.MethodsDuring 2005–2011, 1959 elderly (aged 65 to 100 years) individuals from 13 Mediterranean islands were enrolled. Socio-demographic, clinical and lifestyle factors were assessed using standard procedures. Diabetes was defined as fasting blood glucose levels > 125 mg/dl. Relative environmental humidity was measured as a percentage of air moisture and mean daily temperature in degrees Celsius.ResultsFor the present analysis 713 men (74 ± 7 years) and 596 women (73 ± 7 years) with complete data were studied; 27% of both men and women had diabetes. The prevalence of diabetes was 42% in the elders living in high relative humidity areas (i.e., >70%) as compared with 24% among those living at low relative humidity residential areas (p < 0.001). After adjusting for age, sex and mean temperature, an increase in the area’s relative humidity by 1 degree, increased the likelihood of having diabetes by 12% (OR = 1.12, 95% CI 1.05 to 1.20). No significant association was observed between mean temperature and diabetes (OR = 0.97, 95% CI 0.74, 1.26).ConclusionsA considerable proportion of elderly, especially those living in high relative humidity areas, had diabetes. Further research is needed to confirm this observation and to understand the underlying mechanisms.
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.
In conclusion, the activity of car use seems to be an indicator of quality of life among older adults, as measured through successful aging.
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