Population Older than 69 Had Similar Fatality Rates Independently If They Were Admitted in Nursing Homes or Lived in the Community: A Retrospective Observational Study during COVID-19 First Wave.

The aim of this study is to assess the influence of living in nursing homes on COVID-19-related mortality, and to calculate the real specific mortality rate caused by COVID-19 among people older than 20 years of age in the Balaguer Primary Care Centre Health Area during the first wave of the pandemic. We conducted an observational study based on a database generated between March and May 2020, analysing COVID-19-related mortality as a dependent variable, and including different independent variables, such as living in a nursing home or in the community (outside nursing homes), age, sex, symptoms, pre-existing conditions, and hospital admission. To evaluate the associations between the independent variables and mortality, we calculated the absolute and relative frequencies, and performed a chi-square test. To avoid the impact of the age variable on mortality and to assess the influence of the "living in a nursing home" variable, we established comparisons between infected population groups over 69 years of age (in nursing homes and outside nursing homes). Living in a nursing home was associated with a higher incidence of COVID-19 infection, but not with higher mortality in patients over 69 years of age (p = 0.614). The real specific mortality rate caused by COVID-19 was 2.270/00. In the study of the entire sample, all the comorbidities studied were associated with higher mortality; however, the comorbidities were not associated with higher mortality in the infected nursing home patients group, nor in the infected community patients over 69 years of age group (except for neoplasm history in this last group). Finally, hospital admission was not associated with lower mortality in nursing home patients, nor in community patients over 69 years of age.

The Risk of Hospitalization in COVID-19 Patients Can Be Predicted by Lung Ultrasound in Primary Care.

BACKGROUND: The usefulness of Lung Ultrasound (LUS) for the diagnosis of interstitial syndrome caused by COVID-19 has been broadly described. The aim of this study was to evaluate if LUS may predict the complications (hospital admission) of COVID-19 pneumonia in primary care patients. METHODS: This observational study collects data from a cohort of 279 patients with clinical symptoms of COVID-19 pneumonia who attended the Balaguer Primary Health Care Area between 16 March 2020 and 30 September 2020. We collected the results of LUS scans reported by one general practitioner. We created a database and analysed the absolute and relative frequencies of LUS findings and their association with hospital admission. We found that different LUS patterns (diffuse, attenuated diffuse, and predominantly unilateral) were risk factors for hospital admission (p < 0.05). Additionally, an evolutionary pattern during the acute phase represented a risk factor (p = 0.0019). On the contrary, a normal ultrasound pattern was a protective factor (p = 0.0037). Finally, the presence of focal interstitial pattern was not associated with hospital admission (p = 0.4918). CONCLUSION: The lung ultrasound was useful to predict complications in COVID-19 pneumonia and to diagnose other lung diseases such as cancer, tuberculosis, pulmonary embolism, chronic interstitial pneumopathy, pleuropericarditis, pneumonia or heart failure.

Spatial stratification of wildfire drivers towards enhanced definition of large-fire regime zoning and fire seasons.

The area affected by wildfires is experiencing an overall decrease in the Mediterranean European region. However, there is no clear trend associated to the incidence of large fire events, which continue to pose an important threat to assets-at-risk, while debates on control by meteorological or fuel drivers are ongoing. Understanding the underlying spatial and temporal patterns of large-fire drivers is of critical importance for a more efficient and science-based management, and specifically for improving wildfire season definition and informing fuel management. Taking advantage of the reliable wildfire data available in Spain, we analyzed large fires (>100 ha) in the period 2010-2015 to outline homogenous spatial-temporal regions in terms of the influence of the main drivers of large-fire activity: temperature, wind speed, slope, distance to populated places and roads, and proximity to agricultural lands. We combined Geographically Weighted Logit Regression (GWLR) models to parameterize the marginal influence of the drivers, with optimized hierarchical clustering to define uniform regions in terms of the underlying driving factors. These regions were subsequently analyzed for monthly distribution of fire occurrence and associated fuel models. We identified four different zones in terms of drivers' features, capturing dissimilar intra-annual patterns of fire activity and affected fuels: one covering the Mediterranean and two along the northern coast, and a fourth aggregation in the hinterlands that seems to act as transition area. The Mediterranean and hinterland were linked to weather-related summer ignitions, late and early summer respectively. The northern cluster gathers most winter fires starting in remote locations under steep slopes and strong wind conditions. The northwestern cluster accounts for most of the fire activity in Spain, related to complex relief and shrub-type fuels.

Time, geography and weather provide insights into the ecological strategy of a migrant species.

Farmland and migratory bird populations are in decline. The Common quail (Coturnix coturnix) provides an exception to this trend and its populations have remained stable over the last two decades. However, some basic facts regarding quail biology and ecology, such as the geographic distribution of age and sex classes during the summer, remain poorly understood. We analyzed 43,194 Spanish quail ringing records from 1961 to 2014 to assess the effects of geography and weather conditions on the probability that individuals will be ringed during the various stages of their annual cycle (arrival -spring migration-, stationary breeding period, departure -autumn migration- and winter) for the different quail age-sex classes over time. We found that spatial distribution of the age and sex classes can be explained by date, latitude, longitude, altitude, rainfall, and temperature. Our results suggest that date accounts for most of the variation in the distribution of quail age classes, followed by the weather variables, and then latitude, and altitude. Similarly, date also accounts for most of the variation in the distribution of the two sexes. These results could partially explain why this species has avoided population decline, since its ecological strategy is based on its temporal and spatial distribution combined with the segregation of age and sex groups. We hypothesize that the distribution of quail age and sex classes follows variations in weather and habitat suitability to exploit seasonal and geographic variations in resource availability. The migratory and nomadic movements of quail, combined with the occurrence of multiple breeding attempts within a single season, may also allow these birds to overcome the impacts of predators and anthropogenic environmental change. Conservation and management efforts should therefore take account of these age and sex related temporal and spatial patterns.

Intra-specific association between carbon isotope composition and productivity in woody plants: A meta-analysis.

The study of intra-specific variations in growth and plant physiological response to drought is crucial to understand the potential for plant adaptation to global change. Carbon isotope composition (δ(13)C) in plant tissues offers an integrated measure of intrinsic water-use efficiency (WUEi). The intra-specific association between δ(13)C and productivity has been extensively studied in herbaceous crops, but species-specific information on woody plants is still limited and has so far provided contradictory results. In this work we explored the general patterns of the relationship between δ(13)C and growth traits (height, diameter and biomass) using a meta-analysis. We compiled information from 49 articles, including 176 studies performed on 34 species from 16 genera. We found a positive global intra-specific correlation between δ(13)C and growth (Gr=0.28, P<0.0001), stronger for biomass than for height, and non-significant for diameter. The extent of this intra-specific association increased from Mediterranean to subtropical, temperate and boreal biomes, i.e. from water-limited to energy-limited environments. Conifers and shrubs, but not broadleaves, showed consistent positive intra-specific correlations. The meta-analysis also revealed that the relationship between δ(13)C and growth is better characterized at juvenile stages, under near-optimal and controlled conditions, and by analyzing δ(13)C in leaves rather than in wood.