Influence of meteorological factors on the severity of COVID-19 in Spain: Observational ecological study. / Influencia de los factores meteorológicos en la gravedad de la COVID-19 en España: estudio observacional ecológico.

OBJECTIVES: Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. METHODS: An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the CarlosIII Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). RESULTS: Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7±2.6% vs. 11.5±2.6%; P=9.9×10-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0±0.6% vs. 3.1±0.8%; P=1.7×10-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; P=3.0×10-6) and mortality (Rho: -0.70; P=5.3×10-9). In those provinces with a mean air temperature <10°C mortality by COVID-19 was twice that of those with >16°C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non-standardized ß coeff.: -0.24; 95%CI: -0.31 to -0.16; P=2.38×10-8). CONCLUSIONS: The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.

Influence of meteorological factors on the severity of COVID-19 in Spain: Observational ecological study Influencia de los factores meteorologicos en la gravedad de la COVID-19 en España: estudio observacional ecologico

Objectives Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. Methods An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the Carlos III Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). Results Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7 ± 2.6% vs. 11.5 ± 2.6%;p = 9.9 × 10−5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0 ± 0.6% vs. 3.1 ± 0.8%;p = 1.7 × 10−5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: −0.59;p = 3.0 × 10-6) and mortality (Rho: −0.70;p = 5.3 × 10−9). In those provinces with a mean air temperature <10 °C mortality by COVID-19 was twice that of those with >16 °C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature;the latter was inversely and independently correlated with mortality (non standardised B coeff.: −0.24;IC 95%: −0.31 to −0.16;p = 2.38 × 10−8). Conclusions The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.

Influence of meteorological factors on the severity of COVID-19 in Spain: Observational ecological study.

Objectives: Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. Methods: An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the Carlos III Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). Results: Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7 ± 2.6% vs. 11.5 ± 2.6%; p = 9.9 × 10-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0 ± 0.6% vs. 3.1 ± 0.8%; p = 1.7 × 10-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; p = 3.0 × 10-6) and mortality (Rho: -0.70; p = 5.3 × 10-9). In those provinces with a mean air temperature <10 °C mortality by COVID-19 was twice that of those with >16 °C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non standardised B coeff.: -0.24; IC 95%: -0.31 to -0.16; p = 2.38 × 10-8). Conclusions: The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.

Objetivos: Evaluar si factores meteorológicos y geográficos pudieron relacionarse con la gravedad de la COVID-19 en España. Métodos: Estudio ecológico, a escala provincial, que analiza la influencia de factores meteorológicos y geográficos en la hospitalización y mortalidad por COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior), durante las tres primeras olas. Los datos de hospitalizaciones y mortalidad se obtuvieron del Instituto de Salud Carlos III (ISCIII). Los datos epidemiológicos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE). Las variables meteorológicas de la Agencia estatal de meteorología (AEMET). Resultados: El porcentaje de pacientes hospitalizados por COVID-19, del total de personas infectadas, fue inferior en las provincias costeras que en las del interior peninsular (8,7 ± 2,6% vs. 11,5 ± 2,6%; p = 9,9 × 10−5). De igual manera la costa registró menor porcentaje de mortalidad que el interior peninsular (2,0 ± 0,6% vs. 3,1 ± 0,8%; p = 1,7 × 10−5). La temperatura media correlacionó negativamente con la hospitalización (Rho: −0,59; p = 3,0 × 10−6) y la mortalidad por COVID-19 (Rho: −0,70; p = 5,3 × 10−9). Las provincias con una temperatura media <10 °C duplicaron la mortalidad por COVID respecto a las de >16 °C. La mortalidad se relacionó con la localización provincial (costa/interior), la altitud, la edad de la población y la temperatura media, siendo esta última la variable asociada de manera independiente (Coef. B no estandarizado: −0,24; IC 95%: −0,31 a −0,16; p = 2,38 × 10−8). Conclusiones: La mortalidad por COVID-19 durante las tres primeras olas de la pandemia en nuestro país se asoció inversamente con la temperatura media.

Cross-Sectional Analysis of Risk Factors for Outbreak of COVID-19 in Nursing Homes for Older Adults in the Community of Madrid.

INTRODUCTION: Nursing homes for older adults have been hot spots for SARS-CoV-2 infections and mortality. Factors that facilitate COVID-19 outbreaks in these settings need to be assessed. METHODS: A retrospective cross-sectional study of a cohort of residents and workers in nursing homes taking occasion of a point seroprevalence survey was done in the Community of Madrid. Factors related to outbreaks in these facilities were analyzed. RESULTS: A total of 369 nursing homes for older adults, making a population of 23,756 residents and 20,795 staff members, were followed from July to December 2020. There were 54.2% SARS-CoV-2 IgG+ results in residents and in 32.2% of workers. Sixty-two nursing homes (16.8%) had an outbreak during the follow-up. Nursing homes with outbreaks had more residents than those without (median number of 81 [IQR, 74] vs. 50 [IQR, 56], p < 0.001). Seropositivity for SARS-CoV-2 was lower in facilities with versus without outbreaks, for residents (42.2% [IQR, 55.7] vs. 58.7% [IQR, 43.4], p = 0.002) and for workers (23.9% [IQR, 26.4] vs. 32.8% [IQR, 26.3], p = 0.01). For both residents and staff, the number of infections in outbreaks was larger in centers with lower, as compared with intermediate or high seroprevalence. The size of the facility did not correlate with the number of cases in the outbreak. Taking the incidence of cases in the community as a time-dependent variable (p = 0.03), a Cox analysis (HR [95% CI], p) showed that intermediate or high seroprevalence among residents in the facility was related to a reduction of 55% (0.45 [0.25-0.80], p = 0.007) and 78% (0.22 [0.10-0.48], p < 0.001) in the risk of outbreaks, respectively, as compared with low sero-prevalence. Also, as compared with smaller, medium (1.91 [1.00-3.65], p = 0.05) or large centers (4.57 [2.38-8.75], p < 0.001) had more respective risk of outbreaks. CONCLUSIONS: The size of the facility and the seroprevalence among residents in nursing homes, and the incidence of infections in the community, are associated with the risk of outbreaks of COVID-19. Facilities with greater proportion of seropositives had smaller number of cases. Monitoring of immunity in nursing homes may help detect those at a greater risk of future cases.

Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic.

Tools to predict surges in cases and hospitalizations during the COVID-19 pandemic may help guide public health decisions. Low cycle threshold (CT) counts may indicate greater SARS-CoV-2 concentrations in the respiratory tract, and thereby may be used as a surrogate marker of enhanced viral transmission. Several population studies have found an association between the oscillations in the mean CT over time and the evolution of the pandemic. For the first time, we applied temporal series analysis (Granger-type causality) to validate the CT counts as an epidemiological marker of forthcoming pandemic waves using samples and analyzing cases and hospital admissions during the third pandemic wave (October 2020 to May 2021) in Madrid. A total of 22,906 SARS-CoV-2 RT-PCR-positive nasopharyngeal swabs were evaluated; the mean CT value was 27.4 (SD: 2.1) (22.2% below 20 cycles). During this period, 422,110 cases and 36,727 hospital admissions were also recorded. A temporal association was found between the CT counts and the cases of COVID-19 with a lag of 9-10 days (p ≤ 0.01) and hospital admissions by COVID-19 (p < 0.04) with a lag of 2-6 days. According to a validated method to prove associations between variables that change over time, the short-term evolution of average CT counts in the population may forecast the evolution of the COVID-19 pandemic.

INFLUENCIA DE LOS FACTORES METEOROLOGICOS EN LA GRAVEDAD DE LA COVID-19 EN ESPAÑA: ESTUDIO OBSERVACIONAL ECOLOGICO INFLUENCE OF METEOROLOGICAL FACTORS ON THESEVERITY OF COVID-19 IN SPAIN: OBSERVATIONAL ECOLOGICAL STUDY

Objetivos: Evaluar si factores meteorológicos y geográficos pudieron relacionarse con la gravedad de la COVID-19 en España. Métodos: Estudio ecológico, a escala provincial, que analiza la influencia de factores meteorológicos y geográficos en la hospitalización y mortalidad por COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior), durante las tres primeras olas. Los datos de hospitalizaciones y mortalidad se obtuvieron del Instituto de Salud Carlos III (ISCIII). Los datos epidemiológicos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE). Las variables meteorológicas de la Agencia estatal de meteorología (AEMET). Resultados: El porcentaje de pacientes hospitalizados por COVID-19, del total de personas infectadas, fue inferior en las provincias costeras que en las del interior peninsular (8,7±2,6% vs. 11,5 ±2,6%;p=9,9x10-5). De igual manera la costa registró menor porcentaje de mortalidad que el interior peninsular (2,0±0,6% vs. 3,1±0,8%;p=1,7x10-5). La temperatura media correlacionó negativamente con la hospitalización (Rho: -0,59;p=3,0x10-6) y la mortalidad por COVID-19 (Rho: -0,70;p=5,3x10-9). Las provincias con una temperatura media <10ºC duplicaron la mortalidad por COVID respecto a las de >16ºC. La mortalidad se relacionó con la localización provincial (costa/interior), la altitud, la edad de la población y la temperatura media, siendo esta última la variable asociada de manera independiente (Coef. B no estandarizado: -0,24;IC 95%: -0,31 a -0,16;p=2,38x10-8). Conclusiones: La mortalidad por COVID-19 durante las tres primeras olas de la pandemia en nuestro país se asoció inversamente con la temperatura media.

Influence of weather factors on the incidence of COVID-19 in Spain Influencia de los factores meteorológicos en la incidencia de COVID-19 en España

Introduction Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables. Methods A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET). Results The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566 ± 181 vs. 782 ± 154;P = 2.5 × 10−5). The cumulative incidence correlated negatively with mean air temperature (r = −0.49;P = 2.2 × 10−4) and rainfall (r = −0.33;P = .01), and positively with altitude (r = 0.56;P = 1.4 × 10−5). The Spanish provinces with an average temperature <10 °C had almost twice the cumulative incidence than the provinces with temperatures >16 °C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of −0.62;P = 3.7 × 10−7 and −0.47;P = 4.2 × 10−5 respectively) Conclusions Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections

Influence of weather factors on the incidence of COVID-19 in Spain.

Introduction: Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables. Methods: A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET). Results: The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566 ± 181 vs. 782 ± 154; P = 2.5 × 10-5). The cumulative incidence correlated negatively with mean air temperature (r = -0.49; P = 2.2 × 10-4) and rainfall (r = -0.33; P = .01), and positively with altitude (r = 0.56; P = 1.4 × 10-5). The Spanish provinces with an average temperature <10 °C had almost twice the cumulative incidence than the provinces with temperatures >16 °C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of -0.62; P = 3.7 × 10-7 and -0.47; P = 4.2 × 10-5 respectively). Conclusions: Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections.

Introducción: Varios estudios han analizado la influencia de factores meteorológicos y geográficos en la incidencia de COVID-19. La estacionalidad podría tener importancia en la transmisión de SARS-CoV-2. Nuestro estudio evalúa el patrón geográfico de la COVID-19 en España y su relación con las distintas variables meteorológicas. Métodos: Estudio ecológico a escala provincial que analiza la influencia de factores meteorológicos y geográficos en la incidencia acumulada de COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior) durante las tres primeras olas. La incidencia acumulada se calculó con los datos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE), las variables meteorológicas se obtuvieron de la Agencia estatal de meteorología (AEMET). Resultados: La incidencia acumulada total, en los tres periodos, fue menor en las provincias costeras que en las del interior (566 ± 181 vs. 782 ± 154; P = 2,5 × 10−5). La incidencia acumulada correlacionó negativamente con la temperatura media (r = −0,49; P = 2,2 × 10−4) y las precipitaciones (r = −0,33; P = ,01), y positivamente con la altitud (r = 0,56; P = 1,4 × 10−5). Las provincias españolas con una temperatura media <10 °C tuvieron casi el doble de incidencia acumulada que las provincias con temperaturas >16 °C. La temperatura media y las precipitaciones fueron las variables asociadas con la incidencia acumulada provincial de COVID-19, con independencia de otros factores (Coeficiente Beta de −0,62; P = 3,7 × 10−7 y −0,47; P = 4,2 × 10−5 respectivamente). Conclusiones: Los factores meteorológicos y geográficos podrían influir en la evolución de la pandemia en España. El reconocimiento de la estacionalidad del COVID-19 ayudaría a predecir nuevas olas.

Humoral and Cellular Response after mRNA Vaccination in Nursing Homes: Influence of Age and of History of COVID-19.

BACKGROUND: Most residents and staff in nursing homes have received full vaccination. Factors related to the immune response to vaccination might be related to the risk of future severe COVID-19 and may guide the need for vaccine boosters. DESIGN: Nursing homes that were tested in a point survey in July-October 2020 were again analyzed after a vaccination campaign in June-July 2021. Immune responses according to IgG against nucleocapsid and spike antigens, and CD4 and CD8 interferon-gamma release assay against spike antigens, were evaluated. RESULTS: A total of 1973 subjects were tested (61.7% residents, 48.3% staff), with a mean (SD) follow-up of 46.4 (3.6) weeks between assessments. More than half of residents and more than a third of staff had evidence of COVID-19 before vaccination; 26.9% and 22.7% had seroreversion of IgG-N, and 8.9% and 4.6% had IgG-N seroconversion at second assessment, respectively. Up to 96.8% of residents and 98.1% of workers had positive IgG-S after a mean of 19.9 (2.1) weeks after vaccination. In residents with vs without a history of COVID-19, IgG-S titers were 4.11 (0.54) vs. 2.73 (0.74) logAU/mL (p < 0.001); in workers these titers were 3.89 (0.61) vs. 3.15 (0.64) logAU/mL (p < 0.001). Linear regression analysis showed that younger age (OR: -0.03 per 10 years-older [95% CI, -0.04 to -0.02], p < 0.001) and evidence of COVID-19 (OR: 1.14 [95% CI, 1.08 to 1.20], p < 0.001) are associated with greater IgG-S titers after vaccination. A direct association was found between IgG-S titers and the intensity of IFN-gamma response against spike antigens. CONCLUSIONS: Waning of humoral response and reinfection seems to be more frequent in older as compared to younger adults, although cellular responses shortly after vaccination are comparable between these groups. Younger age and prior COVID-19 are related to greater humoral response after vaccination against SARS-CoV-2.

Venous Thrombosis within 30 Days after Vaccination against SARS-CoV-2 in a Multinational Venous Thromboembolism Registry.

BACKGROUND: Venous thromboembolism (VTE)-including deep vein thrombosis, pulmonary embolism, and cerebral venous sinus thrombosis (CVST)-may occur early after vaccination against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We sought to describe the site, clinical characteristics, and outcomes of VTE after vaccination against SARS-CoV-2. METHODS: In a prospective study using the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) platform, patients with VTE 4-30 days after vaccination against SARS-CoV-2 (1 February 2021 through 30 April 2021) were included. VTE patients recruited from the same centers into RIETE in the same months in 2018-2019 were selected as the reference group. All-cause mortality and major bleeding were the main study outcomes. RESULTS: As of 30 April 2020, 102 patients with post-vaccination VTEs had been identified (28 after adenovirus-based vaccination [ChAdOx1 nCov-19; AstraZeneca] and 74 after mRNA-based vaccination [mRNA-1273; Moderna, and BNT162b2; Pfizer]). Compared with 911 historical controls, patients with VTE after adenovirus-based vaccination more frequently had CVST (10.7% vs. 0.4%, p < 0.001) or thrombosis at multiple sites (17.9% vs. 1.3%, p < 0.001), more frequently had thrombocytopenia (40.7% vs. 14.7%, p < 0.001), and had higher 14-day mortality (14.3% vs. 0.7%; odds ratio [OR]: 25.1; 95% confidence interval [CI]: 6.7-94.9) and major bleeding rates (10.3% vs. 1.0%, OR: 12.03, 95% CI: 3.07-47.13). The site of thrombosis, accompanying thrombocytopenia, and 14-day mortality rates were not significantly different for patients with VTE after mRNA-based vaccination, compared with historical controls. CONCLUSIONS: Compared with historical controls, VTE after adenovirus-based vaccination against SARS-CoV-2 is accompanied by thrombocytopenia, occurs in unusual sites, and is associated with worse clinical outcomes.

A Pilot Study for the Evaluation of an Interferon Gamma Release Assay (IGRA) To Measure T-Cell Immune Responses after SARS-CoV-2 Infection or Vaccination in a Unique Cloistered Cohort.

Assessment of T-cell responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens may be of value to determine long-lasting protection to breakthrough infections or reinfections. Interferon gamma release assay is a validated method to test cellular immunity in mycobacterial infections and has been proposed for patients with SARS-CoV-2 infection or vaccination. Quantitative IgG to spike and qualitative IgG to nucleocapsid antigens were determined by chemiluminescence microparticle immunoassay using the Architect platform (Abbott), and interferon gamma release assays against two Qiagen proprietary mixes of SARS-CoV-2 spike protein (antigen 1 and antigen 2) were performed for a selected group of subjects. A total of 121 subjects in a cloistered institution after a COVID-19 outbreak was studied. IgG spike levels and interferon gamma concentrations were highest among subjects after two doses of vaccine, followed by patients with a longer history of past COVID-19 and no vaccination. The best cutoff for the interferon gamma assay was 25 IU/L for all subgroups of individuals and the two sets of SARS-CoV-2 antigens studied. Testing T-cell response may be of clinical utility to determine immunity after exposure to SARS-CoV-2 antigens, with the interferon gamma concentration of 25 IU/L as the best cutoff either after infection or vaccination.

Development and validation of a predictive model of in-hospital mortality in COVID-19 patients.

We retrospectively evaluated 2879 hospitalized COVID-19 patients from four hospitals to evaluate the ability of demographic data, medical history, and on-admission laboratory parameters to predict in-hospital mortality. Association of previously published risk factors (age, gender, arterial hypertension, diabetes mellitus, smoking habit, obesity, renal failure, cardiovascular/ pulmonary diseases, serum ferritin, lymphocyte count, APTT, PT, fibrinogen, D-dimer, and platelet count) with death was tested by a multivariate logistic regression, and a predictive model was created, with further validation in an independent sample. A total of 2070 hospitalized COVID-19 patients were finally included in the multivariable analysis. Age 61-70 years (p<0.001; OR: 7.69; 95%CI: 2.93 to 20.14), age 71-80 years (p<0.001; OR: 14.99; 95%CI: 5.88 to 38.22), age >80 years (p<0.001; OR: 36.78; 95%CI: 14.42 to 93.85), male gender (p<0.001; OR: 1.84; 95%CI: 1.31 to 2.58), D-dimer levels >2 ULN (p = 0.003; OR: 1.79; 95%CI: 1.22 to 2.62), and prolonged PT (p<0.001; OR: 2.18; 95%CI: 1.49 to 3.18) were independently associated with increased in-hospital mortality. A predictive model performed with these parameters showed an AUC of 0.81 in the development cohort (n = 1270) [sensitivity of 95.83%, specificity of 41.46%, negative predictive value of 98.01%, and positive predictive value of 24.85%]. These results were then validated in an independent data sample (n = 800). Our predictive model of in-hospital mortality of COVID-19 patients has been developed, calibrated and validated. The model (MRS-COVID) included age, male gender, and on-admission coagulopathy markers as positively correlated factors with fatal outcome.

Influence of weather factors on the incidence of COVID-19 in Spain. / Influencia de los factores meteorológicos en la incidencia de COVID-19 en España.

INTRODUCTION: Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables. METHODS: A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET). RESULTS: The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566±181 vs. 782±154; p=2.5×10-5). The cumulative incidence correlated negatively with mean air temperature (r=-0.49; p=2.2×10-4) and rainfall (r=-0.33; p=0.01), and positively with altitude (r=0.56; p=1. 4×10-5). The Spanish provinces with an average temperature <10°C had almost twice the cumulative incidence than the provinces with temperatures >16°C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of -0.62; p=3.7×10-7 and -0.47; p=4.2×10-5 respectively). CONCLUSIONS: Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections.

Influencia De Los Factores Meteorologicos En La Incidencia De COVID-19 En España Influence Of Weather Factors On The Incidence Of COVID-19 In Spain

Introducción: Varios estudios han analizado la influencia de factores meteorológicos y geográficos en la incidencia de COVID-19. La estacionalidad podría tener importancia en la transmisión de SARS-CoV-2. Nuestro estudio evalúa el patrón geográfico de la COVID-19 en España y su relación con las distintas variables meteorológicas. Métodos: Estudio ecológico a escala provincial que analiza la influencia de factores meteorológicos y geográficos en la incidencia acumulada de COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior) durante las tres primeras olas. La incidencia acumulada se calculó con los datos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE), las variables meteorológicas se obtuvieron de la Agencia estatal de meteorología (AEMET). Resultados: La incidencia acumulada total, en los tres periodos, fue menor en las provincias costeras que en las del interior (566±181 vs. 782±154;p=2,5x10 -5 ). La incidencia acumulada correlacionó negativamente con la temperatura media (r=-0,49;p=2,2x10 -4 ) y  las precipitaciones (r=-0,33;p=0,01), y positivamente con la altitud (r=0,56;p=1,4x10 -5 ). Las provincias españolas con una temperatura media < 10ºC tuvieron casi el doble de incidencia acumulada que las provincias con temperaturas >16ºC. La temperatura media y las precipitaciones fueron las variables asociadas con la incidencia acumulada provincial de COVID-19, con independencia de otros factores (Coeficiente Beta de -0,62;p=3,7x10 -7 y -0,47;p=4,2x10 -5 respectivamente). Conclusiones: Los factores meteorológicos y geográficos podrían influir en la evolución de la pandemia en España. El reconocimiento de la estacionalidad del COVID-19 ayudaría a predecir nuevas olas.

The demography and characteristics of SARS-CoV-2 seropositive residents and staff of nursing homes for older adults in the Community of Madrid: the SeroSOS study.

BACKGROUND: Nursing homes for older adults have concentrated large numbers of severe cases and deaths for coronavirus disease 2019 (COVID-19). METHODS: Point seroprevalence study of nursing homes to describe the demography and characteristic of severe acute respiratory syndrome by coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG)-positive residents and staff. RESULTS: Clinical information and blood samples were available for 9,332 residents (mean age 86.7 ± 8.1 years, 76.4% women) and 10,614 staff (mean age 45.6 ± 11.5, 86.2% women). Up to 84.4% of residents had frailty, 84.9% co-morbidity and 69.3% cognitive impairment; 65.2% of workers were health-aides.COVID-19 seroprevalence was 55.4% (95% confidence interval (CI), 54.4-56.4) for older adults and 31.5% (30.6-32.4) for staff. In multivariable analysis, frailty of residents was related with seropositivity (odds ratio (OR): 1.19, P = 0.02). In the case of staff, age > 50 years (2.10, P < 0.001), obesity (1.19, P = 0.01), being a health-aide (1.94, P < 0.001), working in a center with high seroprevalence in residents (3.49, P < 0.001) and contact with external cases of COVID-19 (1.52, P < 0.001) were factors associated with seropositivity. Past symptoms of COVID-19 were good predictors of seropositivity for residents (5.41, P < 0.001) and staff (2.52, P < 0.001). CONCLUSIONS: Level of dependency influences risk of COVID-19 among residents. Individual and work factors, contacts outside the nursing home are associated with COVID-19 exposure in staff members. It is key to strengthen control measures to prevent the introduction of COVID-19 into care facilities from the community.

Gestión hospitalaria en la pandemia

OBJETIVOS: Describir la organización de un hospital durante la pandemia por COVID-19, prestando atención a aspectos tanto organizativos como de liderazgo, y considerando todas las áreas hospitalarias, incluido el quirófano. MATERIAL Y MÉTODOS: Revisión de la literatura en relación con los consejos organizativos para la gestión hospitalaria dentro de la pandemia. Además, se han tenido en consideración las recomendaciones de sociedades, de instituciones como la OMS, el CDC, el ECDC, Ministerio de Sanidad y Consejería de Sanidad de Madrid y la propia experiencia del centro. RESULTADOS: Descripción de los elementos claves para la organización, así como de los diferentes ámbitos de actuación dentro de un hospital: urgencias, consultas,hospitalización y quirófanos. CONCLUSIONES: La gestión durante una pandemia requiere un alto grado de agilidad en la respuesta y plasticidad en las personas. Todas las estructuras hospitalarias deben adecuarse a una situación para la que no han sido concebidas y todo el personal debe ponerse al servicio de una enfermedad que condiciona todas las decisiones. Ser capaz de adaptarse y de intentar adelantarse a lo que va a ocurrir son las claves del éxito OBJECTIVES: To describe the organization of a hospital during the COVID-19 pandemic, paying attention to both organizational and leadership aspects, and considering all hospital areas, including the operating room. MATERIAL AND METHODS: Review of the literature regarding the organizational councils for hospital management within the pandemic. In addition, the recommendations of societies, institutions such as the WHO, the CDC, the ECDC, the National Ministry of Health and the Ministry of Health of Madrid and the center’s own experience have been taken into account. RESULTS: Description of the key elements for the organization, as well as the different areas of action within a hospital: emergencies, consultations, hospitalization and operating rooms. CONCLUSIONS: Management during a pandemic requires a high degree of agility in response and plasticity in people. All hospital structures must adapt to a situation for which they have not been conceived and all staff must place themselves at the service of a disease that conditions all decisions. Being able to adapt and try to anticipate what is going to happen are the keys to success

Presenting Characteristics, Treatment Patterns, and Outcomes among Patients with Venous Thromboembolism during Hospitalization for COVID-19.

Venous thromboembolism (VTE) is common in patients with coronavirus disease-2019 (COVID-19). However, limited data exist on patient characteristics, treatments, and outcomes. To describe the clinical characteristics, treatment patterns, and short-term outcomes of patients diagnosed with VTE during hospitalization for COVID-19. This is a prospective multinational study of patients with incident VTE during the course of hospitalization for COVID-19. Data were obtained from the Registro Informatizado de la Enfermedad TromboEmbólica (RIETE) registry. All-cause mortality, VTE recurrences, and major bleeding during the first 10 days were separately investigated for patients in hospital wards versus those in intensive care units (ICUs). As of May 03, 2020, a total number of 455 patients were diagnosed with VTE (83% pulmonary embolism, 17% isolated deep vein thrombosis) during their hospital stay; 71% were male, the median age was 65 (interquartile range, 55-74) years. Most patients (68%) were hospitalized in medical wards, and 145 in ICUs. Three hundred and seventeen (88%; 95% confidence interval [CI]: 84-91%) patients were receiving thromboprophylaxis at the time of VTE diagnosis. Most patients (88%) received therapeutic low-molecular-weight heparin, and 15 (3.6%) received reperfusion therapies. Among 420 patients with complete 10-day follow-up, 51 (12%; 95% CI: 9.3-15%) died, no patient recurred, and 12 (2.9%; 95% CI: 1.6-4.8%) experienced major bleeding. The 10-day mortality rate was 9.1% (95% CI: 6.1-13%) among patients in hospital wards and 19% (95% CI: 13-26%) among those in ICUs. This study provides characteristics and early outcomes of patients diagnosed with acute VTE during hospitalization for COVID-19. Additional studies are needed to identify the optimal strategies to prevent VTE and to mitigate adverse outcomes associated.

Nosocomial SARS-CoV-2 infection in urology departments: Results of a prospective multicentric study.

OBJECTIVE: To evaluate the coronavirus disease 2019 perioperative infection rate and mortality rate of patients undergoing urological surgeries during the early pandemic period in Spain. METHODS: This was a non-interventional multicenter prospective study carried out from 9 March to 3 May 2020 in two urology departments in Madrid, Spain. Clinical, microbiological and radiological data of patients who underwent surgery were collected from computerized medical records. RESULTS: A total of 148 patients were included in the study, and 141 were analyzed for nosocomial infection risk, after excluding previous and concomitant severe acute respiratory syndrome coronavirus type 2 infections. Elective surgeries represented 76.6% of the procedures, whereas emergent surgeries represented 23.4%. Preoperative screening was carried out with polymerase chain reaction test in 34 patients, all were negative. A total of 14 patients also had chest X-ray (not suspicious in all cases). Three patients (2.1%) developed severe acute respiratory syndrome coronavirus type 2 nosocomial infection (symptoms developed between the third day after surgery to the 14th day after hospital discharge). Time from admission to a compatible clinical case was 5.5 days (4-12 days). Two patients underwent surgery with concomitant diagnosis of coronavirus disease. The mortality rate due to severe acute respiratory syndrome coronavirus type 2 infection is 0.7%, and the specific mortality rate in patients undergoing surgery with community-acquired coronavirus disease 2019 infection was 50% (1/2). CONCLUSIONS: The nosocomial severe acute respiratory syndrome coronavirus type 2 infection rate was low in patients undergoing urological surgical procedures during the peak of the pandemic in Madrid. With appropriate perioperative screening, urological surgical activity can be carried out in safety conditions.

Healthcare management during COVID-19 pandemia. / Gestion hospitalaria en la pandemia

OBJECTIVES: To describe the organization of a hospital during the COVID-19 pandemic, paying attention to both organizational and leadership aspects, and considering all hospital areas, including the operating room.MATERIAL ANDMETHODS: Review of the literatureregarding the organizational councils for hospital management within the pandemic. In addition, the recommendations of societies, institutions such as the WHO, the CDC, the ECDC, the National Ministry of Health and the Ministry of Health of Madrid and the center's own experience have been taken into account. RESULTS: Description of the key elements for the organization,as well as the different areas of action within a hospital: emergencies, consultations, hospitalization and operating rooms. CONCLUSIONS: Management during a pandemic requires a high degree of agility in response and plasticity in people. All hospitalstructures must adapt to a situationfor which they have not been conceived and all staff must place themselves at the service of a disease that conditions all decisions. Being able to adapt and try to anticipate what is going to happen are the keys to success. OBJETIVOS: Describir la organizacion de un hospital durante la pandemia por COVID-19, prestando atencion a aspectos tanto organizativos como de liderazgo, y considerando todas las areas hospitalarias, incluido el quirofano.MATERIAL YMETODOS: Revision de la literatura en relacion con los consejos organizativos para la gestion hospitalaria dentro de la pandemia. Ademas, se han tenido en consideracion las recomendaciones de sociedades, de instituciones como la OMS, el CDC, el ECDC, Ministerio de Sanidad y Consejeria de Sanidad de Madrid y la propia experiencia del centro.RESULTADOS: Descripcion de los elementos claves para la organizacion, asi como de los diferentes ambitos de actuacion dentro de un hospital: urgencias, consultas,hospitalizacion y quirofanos.CONCLUSIONES: La gestion durante una pandemia requiere un alto grado de agilidad en la respuesta y plasticidad en las personas. Todas las estructuras hospitalarias deben adecuarse a una situacion para la que no han sido concebidas y todo el personal debe ponerse al servicio de una enfermedad que condiciona todas las decisiones. Ser capaz de adaptarse y de intentar adelantarse a lo que va a ocurrir son las claves del exito.

[Healthcare management during COVID-19 pandemia.]

OBJECTIVES: To describe the organization of a hospital during the COVID-19 pandemic, paying attention to both organizational and leadership aspects, and considering all hospital areas, including the operating room.MATERIAL AND METHODS: Review of the literatureregarding the organizational councils for hospital management within the pandemic. In addition, the recommendations of societies, institutions such as the WHO, the CDC, the ECDC, the National Ministry of Health and the Ministry of Health of Madrid and the center's own experience have been taken into account. RESULTS: Description of the key elements for the organization,as well as the different areas of action within a hospital: emergencies, consultations, hospitalization and operating rooms. CONCLUSIONS: Management during a pandemic requires a high degree of agility in response and plasticity in people. All hospital structures must adapt to a situationfor which they have not been conceived and all staff must place themselves at the service of a disease that conditions all decisions. Being able to adapt and try to anticipate what is going to happen are the keys to success.