Introducción: La pandemia de COVID-19 ha supuesto medidas de salud pública tales como el cierre de los centros educativos y el confinamiento domiciliario de la población.Métodos: Revisión bibliográfica de los efectos psicológicos en la población infanto-juvenil de las pandemias y del confinamiento, su impacto en el desarrollo, los factores de riesgo asociados y las posibles estrategias de prevención.Resultados: Las pandemias infecciosas se asocian a un aumento de la sintomatología ansiosa, depresiva y postraumática en la población infanto-juvenil. El confinamiento tiene repercusiones negativas sobre su salud mental y física. El desarrollo de los menores se puede ver afectado por el cierre de las escuelas, la limitación de las relaciones con iguales, la imposibilidad de realizar actividad física en el exterior y la pérdida de hábitos saludables de vida. La pandemia de COVID-19 se asocia con un incremento de factores de riesgo psicosociales, como son el aislamiento y la violencia intrafamiliar, la pobreza, el hacinamiento y el abuso de nuevas tecnologías. Se proponen medidas de prevención en el ámbito familiar, como la comunicación positiva, la promoción de hábitos saludables y el parenting. Se hace imprescindible reforzar la accesibilidad a la red de salud mental. Se deben diseñar estrategias de protección de la población infanto-juvenil en el contexto de la actual crisis sanitaria.Conclusiones: Preservar los derechos de las niñas y los niños, su salud mental y su desarrollo integral, sin poner en riesgo la salud de la comunidad, es un reto al que deben enfrentarse las autoridades competentes.
Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.
Massive molecular testing for COVID-19 has been pointed as fundamental to moderate the spread of the disease. Pooling methods can enhance testing efficiency, but they are viable only at very low prevalences of the disease. We propose Smart Pooling, a machine learning method that uses sociodemographic data from patients to increase the efficiency of pooled molecular testing for COVID-19 by arranging samples into all-negative pools. We show efficiency gains of 42% with respect to individual testing at disease prevalence of up to 25%, a regime in which two-step pooling offers marginal efficiency gains. Additionally, we calculate the possible efficiency gains of one- and two-dimensional two-step pooling strategies and present the optimal strategies for disease prevalences up to 25%. We discuss practical limitations to conduct pooling in the laboratory.
Introduction:Ramón y Cajal Hospital is the reference medical centre for Madrid-Barajas airport. Passengers arriving at the airport who need medical assistance are brought to this hospital. A percentage of these passengers require psychiatric evaluation and frequently need to be admitted into the ward for a certain length of time.Objective:Perform a descriptive analysis of the socio-demographic and clinical variables of inward psychiatric patients referred from Madrid-Barajas airport.Methods:Revise retrospectively clinical histories of inward psychiatric patients referred from Madrid-Barajas airport in the last 5 years.Data is analyzed using the SPSS software 15.0 version.Results:We collected 99 patients, 54 of them (54.5%) are males. The 38.4% of the sample is in the age range between 25 and 34 years, and the 26.3% are between 35 and 44 years old. The most frequent countries of origin are European countries (57.6% of the sample). 28 patients of that group (49.1%) are Spanish citizens. Other 26% percent of the patients are from Centre or South-America. The most common syndromic diagnosis at discharge is psychotic disorder (62.6%) followed by affective disorder (22.2%).Conclusion:We can deduce from these data that the usual patient referred from Madrid-Barajas airport to the emergencies department for psychiatric attention is a male, between 25 and 34 years of age, from a European country, with a psychotic disorder that usually ends up in an admission into the psychiatric hospitalization unit.
Introduction and Aim: Insight in schizophrenia shows critical implications for adherence. Non-adherence is particularly relevant in first-episode patients. Few studies have examined insight in early schizophrenia. The aim of this study is to examine relationship between insight, adherence and outcome in patients with early schizophrenia. Methods: Observational study in patients diagnosed for schizophrenia, schizophreniform, or schizoaffective disorder for less than 5 years. Data are collected retrospectively from first psychotic episode to study start, and prospectively (1 year). Association of demographic data, clinical measures, remission, relapses, and adherence with level of insight (Scale to Assess Unawareness of Mental Disorder and G12 item of PANSS) was evaluated. Adherence was assessed interviewing patients and family. Remission was defined according to Remission in Schizophrenia Working Group criteria. Preliminary data are shown. Results: 575 patients have been analyzed. Duration of illness was 3.9AE1.6 years. According to G12 item of PANSS, almost 50% of patients had moderate to extreme impairment in baseline insight, while this percentage was 15.8% at 12 mo. (N¼291). At baseline, 50% of patients showed good adherence to medication (>80%), and adherence rose to 78% at 12 mo. (N¼291). Remission (severity criteria) significantly increased from baseline (23.9%, N¼574) to 12 mo. (59.5%, N¼291; p<0.0001). A significant relationship between insight and remission at baseline (p<0.001) was found. Among patients who reached 12 mo. visit (N¼289), hospitalization was more frequent in those with poor baseline insight. Conclusions: Lack of insight is common in early schizophrenia and may be a relevant predictor of poor outcome.
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