Use of antibiotics in Paediatric Primary Health Care before and during the COVID-19 pandemic.

INTRODUCTION: Consumption of antibiotics is high in Spain, primarily in children. Excessive use of then contributes to the development of antimicrobial resistance. The aim of our study is to analyse the evolution of antibiotic consumption at the Primary Health Care in the paediatric population of Asturias, Spain, from 2014 to 2021, and to evaluate the impact of COVID-19 pandemic on it. METHODS: Retrospective and observational study using data about antibacterial agents for systemic use dispensed for official prescriptions to children under 14 years in Primary Care. Antibiotic consumption is expressed as defined daily dose (DDD) per 1000 inhabitants per day (DID). RESULTS: The antibiotic consumption rate dropped from 13.9 DID in 2014 to 4.0 in 2021 (ß=-1,42, p=0,002), with and inflection point in 2019. From 2019 to 2020 antibiotic use dropped by 47.1%. Antibiotic consumption remained very low from April 2020 to September 2021, and then moderately increased from October 2021. Prevalence of antibiotic use dropped from 39.9% in 2014 to 17.5% in 2021 (ß=-3,64, p=0,006). Relative consumption of amoxicillin/clavulanic acid decreased, while those of amoxiciline and third-generation cephalosporins increased. CONCLUSIONS: Paediatric antibiotic consumption collapsed in Asturias in 2020, coinciding with COVID-19 pandemic. Monitoring of antimicrobial usage indicators will allow to check if these changes are sustained over time.

[Antibiotic consumption in primary care in the adult population of Asturias during 2014-2020 period]. / Consumo de antibióticos en atención primaria en población adulta de Asturias durante el periodo 2014-2020.

Trend study of the consumption of systemic antibiotics in the adult population in of Primary Care of the Health Service of the Principality of Asturias (SESPA) during the period 2014̶2020. Retrospective observational study. SESPA, Primary Care. Population from the Individual Health Card database. Data were collected on the prescription of antibiotics, carried out in the family medicine consultations, dispensed in the pharmacy offices with charge of SESPA. Antibiotic use and consumption variables were analyzed using linear regression models. Prevalence of antibiotic use (population percentage); consumption rate of systemic antibiotics (DTD), relative consumption of narrow-spectrum antibiotics (percentage DDD). The average prevalence of the use of antibiotics for the 2014̶2019 period was 32.2% and 23.9% in 2020. The rate of consumption of systemic antibiotics decreased from 21.4 DTD in 2014 to 12.7 DTD in 2020. The consumption of narrow-spectrum antibiotics remained stable (19.4% DDD in 2014 and 19.3% DDD in 2020) (CI95: -0.10, 0.26). In the period from March to December 2020, the consumption of antibiotics decreased by 28.6% compared to the same period in 2019. In 2014̶2020, the consumption of antibiotics decreased, especially since the COVID-19 pandemic, with stabilization of the consumption of narrow-spectrum antibiotics compared to the total. There is variability in consumption by therapeutic subgroups.

Automated detection of frog calls and choruses by pulse repetition rate.

Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts. Analyzing such data sets will require robust and efficient tools that can automatically identify the presence of a species in audio recordings. Like bats and birds, many anuran species produce distinct vocalizations that can be captured by autonomous acoustic recorders and represent excellent candidates for automated recognition. However, in contrast to birds and bats, effective automated acoustic recognition tools for anurans are not yet widely available. An effective automated call-recognition method for anurans must be robust to the challenges of real-world field data and should not require extensive labeled data sets. We devised a vocalization identification tool that classifies anuran vocalizations in audio recordings based on their periodic structure: the repeat interval-based bioacoustic identification tool (RIBBIT). We applied RIBBIT to field recordings to study the boreal chorus frog (Pseudacris maculata) of temperate North American grasslands and the critically endangered variable harlequin frog (Atelopus varius) of tropical Central American rainforests. The tool accurately identified boreal chorus frogs, even when they vocalized in heavily overlapping choruses and identified variable harlequin frog vocalizations at a field site where it had been very rarely encountered in visual surveys. Using a few simple parameters, RIBBIT can detect any vocalization with a periodic structure, including those of many anurans, insects, birds, and mammals. We provide open-source implementations of RIBBIT in Python and R to support its use for other taxa and communities.

Los anuros (ranas y sapos) se encuentran dentro de los grupos taxonómicos más amenazados a nivel mundial. La conservación exitosa de los anuros dependerá de información mejorada sobre el estado y los cambios en las poblaciones locales, particularmente para las especies raras y amenazadas. Los sensores automatizados, como las grabadoras acústicas, tienen el potencial para proporcionar dicha información al incrementar masivamente la escala espacial y temporal de los esfuerzos de muestreo poblacional. El análisis de dicha información requerirá herramientas robustas y eficientes que puedan identificar automáticamente la presencia de una especie en las grabaciones de audio. Como las aves y los murciélagos, muchas especies de anuros producen vocalizaciones distintivas que pueden ser capturadas por las grabadoras acústicas autónomas y también son excelentes candidatas para el reconocimiento automatizado. Sin embargo, a diferencia de las aves y los murciélagos, todavía no se cuenta con una disponibilidad extensa de herramientas para el reconocimiento acústico automatizado de los anuros. Un método efectivo para el reconocimiento automatizado del canto de los anuros debe ser firme ante los retos de los datos reales de campo y no debería requerir conjuntos extensos de datos etiquetados. Diseñamos una herramienta de identificación de las vocalizaciones: la herramienta de identificación bioacústica basada en el intervalo de repetición (RIBBIT), el cual clasifica las vocalizaciones de los anuros en las grabaciones de audio con base en su estructura periódica. Aplicamos la RIBBIT a las grabaciones de campo para estudiar a dos especies: la rana coral boreal (Pseudacris maculata) de los pastizales templados de América del Norte y la rana arlequín variable (Atelopus varius), críticamente en peligro de extinción, de las selvas tropicales de América Central. Mostramos que RIBBIT puede identificar correctamente a las ranas corales boreales, incluso cuando vocalizan en coros con mucha superposición, y puede identificar las vocalizaciones de la rana arlequín variable en un sitio de campo en donde rara vez se le ha visto durante censos visuales. Mediante relativamente unos cuantos parámetros simples, RIBBIT puede detectar cualquier vocalización con una estructura periódica, incluyendo aquellas de muchos anuros, insectos, aves y mamíferos. Proporcionamos implementaciones de fuente abierta de RIBBIT en Python y en R para fomentar su uso para otros taxones y comunidades.

[Factors associated with the incidence and mortality from COVID-19 in the autonomous communities of Spain]. / Factores asociados a la incidencia y la mortalidad por COVID-19 en las comunidades autónomas.

OBJECTIVE: Analyze the evolution of the epidemic of COVID-19 after the alarm state and identify factors associated with the differences between the autonomous communities. METHOD: Ecological study that used epidemiological, demographic, environmental and variables on the structure of health services as explanatory variables. The analysis period was from March 15th (the start of the alarm state) until April 22nd, 2020. Incidence and mortality rates were the main response variables. The magnitude of the associations has been estimated using the Spearman correlation coefficient and multiple regression analysis. RESULTS: Incidence and mortality rates at the time of decree of alarm status are associated with current incidence, mortality and hospital demand rates. Higher mean temperatures are significantly associated with a lower current incidence of COVID-19 in the autonomous communities. Likewise, a higher proportion of older people in nursing homes is significantly associated with a higher current mortality in the autonomous communities. CONCLUSION: It is possible to predict the evolution of the epidemic through the analysis of incidence and mortality. Lower temperatures and the proportion of older people in residences are factors associated with a worse prognosis. These parameters must be considered in decisions about the timing and intensity of the implementation of containment measures. In this sense, strengthening epidemiological surveillance is essential to improve predictions.

Vulnerability to severe forms of COVID-19: an intra-municipal analysis in the city of Rio de Janeiro, Brazil / Vulnerabilidade a formas graves de COVID-19: uma análise intramunicipal na cidade do Rio de Janeiro, Brasil / Vulnerabilidad socioespacial y formas graves de COVID-19: un análisis intramunicipal en la ciudad de Río de Janeiro, Brasil

Diante da pandemia de COVID-19 e da escassez de ferramentas para orientar as ações de vigilância, controle e assistência de pessoas infectadas, o presente artigo tem por objetivo evidenciar áreas de maior vulnerabilidade aos casos graves da doença na cidade do Rio de Janeiro, Brasil, caracterizada por grande heterogeneidade socioespacial. Para o estabelecimento dessas áreas foi elaborado um índice de vulnerabilidade aos casos graves de COVID-19 com base na construção, ponderação e integração de três planos de informação: a densidade intradomiciliar média, a densidade de pessoas com 60 anos ou mais (ambas por setor censitário) e a incidência de tuberculose por bairros no ano de 2018. Os dados referentes à densidade intradomiciliar e de pessoas com 60 anos ou mais provêm do Censo Demográfico de 2010 e os de incidência de tuberculose do Sistema de Informação de Agravos de Notificação (SINAN). A ponderação dos indicadores que compuseram o índice foi realizada por meio da Análise Hierárquica de Processos (AHP), e os planos de informação foram integrados pela Combinação Linear Ponderada por álgebra de mapas. A espacialização do índice de vulnerabilidade aos casos graves na cidade do Rio de Janeiro evidencia a existência de áreas mais vulneráveis em diferentes porções do território, refletindo a sua complexidade urbana. Contudo, é possível observar que as áreas de maior vulnerabilidade estão nas regiões Norte e Oeste da cidade e em comunidades carentes encrustadas nas áreas nobres como as zonas Sul e Oeste. A compreensão dessas condições de vulnerabilidade pode auxiliar no desenvolvimento de estratégias de monitoramento da evolução da doença, bem como para o direcionamento das ações de prevenção e promoção da saúde.

Ante la pandemia de COVID-19, y la escasez de instrumentos para orientar las acciones de vigilancia, control y asistencia a las personas infectadas, el objetivo de este artículo persigue resaltar las áreas de mayor vulnerabilidad, donde se producen los casos graves de la enfermedad en la ciudad de Río de Janeiro, Brasil, caracterizada por una gran heterogeneidad socioespacial. Para el establecimiento de esas áreas se elaboró un índice de vulnerabilidad con los casos graves de COVID-19, a partir de la creación, ponderación e integración de tres planos de información: el de densidad intradomiciliaria media, el de densidad de personas con 60 años o más (ambas por sector de censo), y la incidencia de tuberculosis por barrios en el año 2018. Los datos referentes a la densidad intradomiciliaria y de personas con 60 años o más proceden del Censo Demográfico de 2010 y los de incidencia de tuberculosis del Sistema de Información para Enfermedades de Notificación (SINAN). La ponderación de los indicadores que formaron parte del índice se realizó mediante el Proceso Analítico Jerárquico (AHP por sus siglas en inglés) y los planos de información se integraron a través de la Combinación Lineal Ponderada por álgebra de mapas. La espacialización del índice de vulnerabilidad en lo que se refiere a los casos graves, en la ciudad de Río de Janeiro, pone en evidencia la existencia de áreas más vulnerables en diferentes áreas del territorio, reflejando su complejidad urbana. Por ello, es posible observar que las áreas de mayor vulnerabilidad se encuentran en las Regiones Norte y Oeste de la ciudad, así como en comunidades sin recursos insertadas en áreas pudientes como las Zonas Sur y Oeste. La comprensión de estas condiciones de vulnerabilidad puede apoyar el desarrollo de estrategias de supervisión de la evolución de la enfermedad, así como la dirección de acciones de prevención y promoción de la salud.

Given the characteristics of the COVID-19 pandemic and the limited tools for orienting interventions in surveillance, control, and clinical care, the current article aims to identify areas with greater vulnerability to severe cases of the disease in Rio de Janeiro, Brazil, a city characterized by huge social and spatial heterogeneity. In order to identify these areas, the authors prepared an index of vulnerability to severe cases of COVID-19 based on the construction, weighting, and integration of three levels of information: mean number of residents per household and density of persons 60 years or older (both per census tract) and neighborhood tuberculosis incidence rate in the year 2018. The data on residents per household and density of persons 60 years or older were obtained from the 2010 Population Census, and data on tuberculosis incidence were taken from the Brazilian Information System for Notificable Diseases (SINAN). Weighting of the indicators comprising the index used analytic hierarchy process (AHP), and the levels of information were integrated via weighted linear combination with map algebra. Spatialization of the index of vulnerability to severe COVID-19 in the city of Rio de Janeiro reveals the existence of more vulnerable areas in different parts of the city's territory, reflecting its urban complexity. The areas with greatest vulnerability are located in the North and West Zones of the city and in poor neighborhoods nested within upper-income parts of the South and West Zones. Understanding these conditions of vulnerability can facilitate the development of strategies to monitor the evolution of COVID-19 and orient measures for prevention and health promotion.