A Predictive Model and Risk Factors for Case Fatality of COVID-19

This study aimed to create an individualized analysis model of the risk of intensive care unit (ICU) admission or death for coronavirus disease 2019 (COVID-19) patients as a tool for the rapid clinical management of hospitalized patients in order to achieve a resilience of medical resources. This is an observational, analytical, retrospective cohort study with longitudinal follow-up. Data were collected from the medical records of 3489 patients diagnosed with COVID-19 using RT-qPCR in the period of highest community transmission recorded in Europe to date: February-June 2020. The study was carried out in in two health areas of hospital care in the Madrid region: the central area of the Madrid capital (Hospitales de Madrid del Grupo HM Hospitales (CH-HM), n = 1931) and the metropolitan area of Madrid (Hospital Universitario Príncipe de Asturias (MH-HUPA) n = 1558). By using a regression model, we observed how the different patient variables had unequal importance. Among all the analyzed variables, basal oxygen saturation was found to have the highest relative importance with a value of 20.3%, followed by age (17.7%), lymphocyte/leukocyte ratio (14.4%), CRP value (12.5%), comorbidities (12.5%), and leukocyte count (8.9%). Three levels of risk of ICU/death were established: low-risk level (20%). At the high-risk level, 13% needed ICU admission, 29% died, and 37% had an ICU-death outcome. This predictive model allowed us to individualize the risk for worse outcome for hospitalized patients affected by COVID-19

A Genomic Snapshot of the SARS-CoV-2 Pandemic in the Balearic Islands

7 páginas, 3 figurasObjective: To analyze the SARS-CoV-2 genomic epidemiology in the Balearic Islands, a unique setting in which the course of the pandemic has been influenced by a complex interplay between insularity, severe social restrictions and tourism travels. Methods: Since the onset of the pandemic, more than 2,700 SARS-CoV-2 positive respiratory samples have been randomly selected and sequenced in the Balearic Islands. Genetic diversity of circulating variants was assessed by lineage assignment of consensus whole genome sequences with PANGOLIN and investigation of additional spike mutations. Results: Consensus sequences were assigned to 46 different PANGO lineages and 75% of genomes were classified within a VOC, VUI, or VUM variant according to the WHO definitions. Highest genetic diversity was documented in the island of Majorca (42 different lineages detected). Globally, lineages B.1.1.7 and B.1.617.2/AY.X were identified as the 2 major lineages circulating in the Balearic Islands during the pandemic, distantly followed by lineages B.1.177/B.1.177.X. However, in Ibiza/Formentera lineage distribution was slightly different and lineage B.1.221 was the third most prevalent. Temporal distribution analysis showed that B.1 and B.1.5 lineages dominated the first epidemic wave, lineage B.1.177 dominated the second and third, and lineage B.1.617.2 the fourth. Of note, lineage B.1.1.7 became the most prevalent circulating lineage during first half of 2021; however, it was not associated with an increased in COVID-19 cases likely due to severe social restrictions and limited travels. Additional spike mutations were rarely documented with the exception of mutation S:Q613H which has been detected in several genomes (n = 25) since July 2021. Conclusion: Virus evolution, mainly driven by the acquisition and selection of spike substitutions conferring biological advantages, social restrictions, and size population are apparently key factors for explaining the epidemic patterns registered in the Balearic Islands.This work has been supported by the Instituto de Salud Carlos III of Spain through the project COV20/00140: Addressing unknowns of COVID-19 transmission and infection combining pathogen genomics and epidemiology to inform public health interventions and European Union HERA Incubator program through grant ECDC/HERA/2021/024 ECD.12241. CL-C was supported by a Juan Rodés contract (JR19/00003) from Instituto de Salud Carlos III.Peer reviewe

El perfecto ciudadano : ensayo de adaptación para las escuelas hispanoamericanas del hermoso libro francés de Pablo Doumer "Le livre de mes fils"

Retr. de Alfonso XII

A spatio-temporal Poisson hurdle point process to model wildfires

Wildfires have been studied in many ways, for instance as a spatial point pattern or through modeling the size of fires or the relative risk of big fires. Lately a large variety of complex statistical models can be fitted routinely to complex data sets, in particular wildfires, as a result of widely accessible high-level statistical software, such as R. The objective in this paper is to model the occurrence of big wildfires (greater than a given extension of hectares) using an adapted two-part econometric model, specifically a hurdle model. The methodology used in this paper is useful to determine those factors that help any fire to become a big wildfire. Our proposal and methodology can be routinely used to contribute to the management of big wildfire

Spatio-temporal log-Gaussian Cox processes for modelling wildfire occurrence: the case of Catalonia, 1994-2008

Wildfires have become one of the principal environmental problems in the Mediterranean basin. While fire plays an important role in most terrestrial plant ecosystems, the potential hazard that it represents for human lives and property has led to the application of fire exclusion policies that, in the long term, have caused severe damage, mainly due to the increase of fuel loadings in forested areas, in some forest systems. The lack of an easy solution to forest fire management highlights the importance of preventive tasks. The observed spatio-temporal pattern of wildfire occurrences may be idealized as a realization of some stochastic process. In particular, we may use a space–time point pattern approach for the analysis and inference process. We studied wildfires in Catalonia, a region in the north-east of the Iberian Peninsula, and we analyzed the spatio-temporal patterns produced by those wildfire incidences by considering the influence of covariates on trends in the intensity of wildfire locations. A total of 3,166 wildfires from 1994–2008 have been recorded. We specified spatio-temporal log-Gaussian Cox process models. Models were estimated using Bayesian inference for Gaussian Markov Random Field through the integrated nested Laplace approximation algorithm. The results of our analysis have provided statistical evidence that areas closer to humans have more human induced wildfires, areas farther have more naturally occurring wildfires. We believe the methods presented in this paper may contribute to the prevention and management of those wildfires which are not random in space or tim

Diabetes mellitus mortality in Spanish cities: Trends and geographical inequalities

Aim: To analyze the geographical pattern of diabetes mellitus (DM) mortality and its association with socioeconomic factors in 26 Spanish cities. Methods: We conducted an ecological study of DM mortality trends with two cross-sectional cuts (1996–2001; 2002–2007) using census tract (CT) as the unit of analysis. Smoothed standardized mortality rates (sSMR) were calculated using Bayesian models, and a socioeconomic deprivation score was calculated for each CT. Results: In total, 27,757 deaths by DM were recorded, with higher mortality rates observed in men and in the period 1996–2001. For men, a significant association between CT deprivation score and DM mortality was observed in 6 cities in the first study period and in 7 cities in the second period. The highest relative risk was observed in Pamplona (RR, 5.13; 95% credible interval (95%CI), 1.32–15.16). For women, a significant association between CT deprivation score and DM mortality was observed in 13 cities in the first period and 8 in the second. The strongest association was observed in San Sebastián (RR, 3.44; 95%CI, 1.25–7.36). DM mortality remained stable in the majority of cities, although a marked decrease was observed in some cities, including Madrid (RR, 0.67 and 0.64 for men and women, respectively). Conclusions: Our findings demonstrate clear inequalities in DM mortality in Spain. These inequalities remained constant over time are were more marked in women. Detection of high-risk areas is crucial for the implementation of specific interventions.This work was partially supported by FIS (PI0426, PI081488, PI080330, PI081017, PI081713), the DGA (PI126/08), FUNCIS (PI84/07), Fundación Caja Murcia (FFIS/CM10/27), and by CIBER Epidemiología y Salud Pública (CIBERESP)

Methods to smooth mortality indicators: application to analysis of inequalities in mortality in Spanish cities (the MEDEA Project)

Aunque la experiencia en el estudio de las desigualdades en la mortalidad en las ciudades españolas es amplia, quedan grandes núcleos urbanos que no han sido investigados utilizando la sección censal como unidad de análisis territorial. En este contexto se sitúa el proyecto coordinado «Desigualdades socioeconómicas y medioambientales en la mortalidad en ciudades de España. Proyecto MEDEA», en el cual participan 10 grupos de investigadores de Andalucía, Aragón, Cataluña, Galicia, Madrid, Comunitat Valenciana y País Vasco. Cabe señalar cuatro particularidades: a) se utiliza como área geográfica básica la sección censal; b) se emplean métodos estadísticos que tienen en cuenta la estructura geográfica de la región de estudio para la estimación de riesgos; c) se aprovechan las oportunidades que ofrecen 3 fuentes de datos complementarias (información sobre contaminación atmosférica, información sobre contaminación industrial y registros de mortalidad), y d) se emprende un análisis coordinado de gran alcance, favorecido por la implantación de la redes temáticas de investigación. El objetivo de este trabajo es explicar los métodos para la suavización de indicadores de mortalidad en el proyecto MEDEA. El artículo se centra en la metodología y los resultados del modelo de mapa de enfermedades de Besag, York y Mollié (BYM). Aunque en el proyecto se han suavizado, mediante el modelo BYM, las rezones de mortalidad estandarizadas (RME) correspondientes a 17 grandes grupos de causas de defunción y 28 causas específicas, aquí se aplica esta metodología a la mortalidad por cáncer de tráquea, de bronquios y de pulmón en ambos sexos en la ciudad de Barcelona durante el período 1996-2003. Como resultado se aprecia un diferente patrón geográfico en las RME suavizadas en ambos sexos. En los hombres se observan unas RME mayores que la unidad en los barrios con mayor privación socioeconómica. En las mujeres este patron se observa en las zonas con un mayor nivel socioeconómico. Although there is some experience in the study of mortality inequalities in Spanish cities, there are large urban centers that have not yet been investigated using the census tract as the unit of territorial analysis. The coordinated project was designed to fill this gap, with the participation of 10 groups of researchers in Andalusia, Aragon, Catalonia, Galicia, Madrid, Valencia, and the Basque Country. The MEDEA project has four distinguishing features: a) the census tract is used as the basic geographical area; b) statistical methods that include the geographical structure of the region under study are employed for risk estimation; c) data are drawn from three complementary data sources (information on air pollution, information on industrial pollution, and the records of mortality registrars), and d) a coordinated, large-scale analysis, favored by the implantation of coordinated research networks, is carried out. The main objective of the present study was to explain the methods for smoothing mortality indicators in the context of the MEDEA project. This study focusses on the methodology and the results of the Besag, York and Mollié model (BYM) in disease mapping. In the MEDEA project, standardized mortality ratios (SMR), corresponding to 17 large groups of causes of death and 28 specific causes, were smoothed by means of the BYM model; however, in the present study this methodology was applied to mortality due to cancer of the trachea, bronchi and lung in men and women in the city of Barcelona from 1996 to 2003. As a result of smoothing, a different geographical pattern for SMR in both genders was observed. In men, a SMR higher than unity was found in highly deprived areas. In contrast, in women, this pattern was observed in more affluent areas.Estudio parcialmente financiado por el proyecto «Mortalidad en áreas pequeñas Españolas y Desigualdades socioEconómicas y Ambientales (MEDEA)»: PI04/0399 (GRECS, Universitat de Girona), PI04/2013 (ASPB, Barcelona), PI04/0388 (Departamento de Sanidad y Servicio Vasco de Salud, Gobierno Vasco), PI04/0041 (CNE, ISCIII), PI04/0170 (Conselleria de Sanitat, Valencia); por la Red de Centros de Epidemiología y Salud Pública (FISS C03/09); y por el CIBER de Epidemiología y Salud Pública (CIBERESP).S

Métodos para la suavización de indicadores de mortalidad: aplicación al análisis de desigualdades en mortalidad en ciudades del Estado español (Proyecto MEDEA) Methods to smooth mortality indicators: application to analysis of inequalities in mortality in Spanish cities (the MEDEA Project)

Aunque la experiencia en el estudio de las desigualdades en la mortalidad en las ciudades españolas es amplia, quedan grandes núcleos urbanos que no han sido investigados utilizando la sección censal como unidad de análisis territorial. En este contexto se sitúa el proyecto coordinado «Desigualdades socioeconómicas y medioambientales en la mortalidad en ciudades de España. Proyecto MEDEA», en el cual participan 10 grupos de investigadores de Andalucía, Aragón, Cataluña, Galicia, Madrid, Comunitat Valenciana y País Vasco. Cabe señalar cuatro particularidades: a) se utiliza como área geográfica básica la sección censal; b) se emplean métodos estadísticos que tienen en cuenta la estructura geográfica de la región de estudio para la estimación de riesgos; c) se aprovechan las oportunidades que ofrecen 3 fuentes de datos complementarias (información sobre contaminación atmosférica, información sobre contaminación industrial y registros de mortalidad), y d) se emprende un análisis coordinado de gran alcance, favorecido por la implantación de la redes temáticas de investigación. El objetivo de este trabajo es explicar los métodos para la suavización de indicadores de mortalidad en el proyecto MEDEA. El artículo se centra en la metodología y los resultados del modelo de mapa de enfermedades de Besag, York y Mollié (BYM). Aunque en el proyecto se han suavizado, mediante el modelo BYM, las razones de mortalidad estandarizadas (RME) correspondientes a 17 grandes grupos de causas de defunción y 28 causas específicas, aquí se aplica esta metodología a la mortalidad por cáncer de tráquea, de bronquios y de pulmón en ambos sexos en la ciudad de Barcelona durante el período 1996-2003. Como resultado se aprecia un diferente patrón geográfico en las RME suavizadas en ambos sexos. En los hombres se observan unas RME mayores que la unidad en los barrios con mayor privación socioeconómica. En las mujeres este patrón se observa en las zonas con un mayor nivel socioeconómico.Although there is some experience in the study of mortality inequalities in Spanish cities, there are large urban centers that have not yet been investigated using the census tract as the unit of territorial analysis. The coordinated project «Socioeconomic and environmental inequalities in mortality in Spanish cities. The MEDEA project» was designed to fill this gap, with the participation of 10 groups of researchers in Andalusia, Aragon, Catalonia, Galicia, Madrid, Valencia, and the Basque Country. The MEDEA project has four distinguishing features: a) the census tract is used as the basic geographical area; b) statistical methods that include the geographical structure of the region under study are employed for risk estimation; c) data are drawn from three complementary data sources (information on air pollution, information on industrial pollution, and the records of mortality registrars), and d) a coordinated, large-scale analysis, favored by the implantation of coordinated research networks, is carried out. The main objective of the present study was to explain the methods for smoothing mortality indicators in the context of the MEDEA project. This study focusses on the methodology and the results of the Besag, York and Mollié model (BYM) in disease mapping. In the MEDEA project, standardized mortality ratios (SMR), corresponding to 17 large groups of causes of death and 28 specific causes, were smoothed by means of the BYM model; however, in the present study this methodology was applied to mortality due to cancer of the trachea, bronchi and lung in men and women in the city of Barcelona from 1996 to 2003. As a result of smoothing, a different geographical pattern for SMR in both genders was observed. In men, a SMR higher than unity was found in highly deprived areas. In contrast, in women, this pattern was observed in more affluent areas