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1.
Emergencias ; 31(2): 107-110, 2019.
Artigo em Inglês, Espanhol | MEDLINE | ID: mdl-30963738

RESUMO

OBJECTIVES: To review changes occurring over time in cases of medication overdose attended by an emergency department. MATERIAL AND METHODS: Retrospective review of epidemiologic and care variables related to drug poisonings in a university teaching hospital in 2007 and 2017. We used multivariate analysis to compare the 2 years. RESULTS: A total of 750 cases were included; 438 (58.4%) were from 2007. Fewer cases were seen in 2017 even though the total numbers of emergencies and poisonings had risen (P<.001). Fewer cases were suicides or suicide attempts in 2017 (P<.001), and digestive tract decontamination and antidotes were used less often (P<.001 and P=.007, respectively). Admissions (P=.004) and voluntary self-discharges or patient losses were also down in 2017 (P=.03). However, multidrug poisonings increased (P=.001), especially in the context of recreational drug use by men. Benzodiazepine overdoses accounted for most of such cases (65.1%). CONCLUSION: Medication overdoses seem to be decreasing, although the proportion of men overdosing is rising. Suicide attempts, the abuse of specific medications, and admissions also seem to be decreasing.


Assuntos
Overdose de Drogas/epidemiologia , Adolescente , Adulto , Idoso , Overdose de Drogas/diagnóstico , Overdose de Drogas/etiologia , Serviço Hospitalar de Emergência , Feminino , Hospitais de Ensino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Espanha/epidemiologia , Tentativa de Suicídio/estatística & dados numéricos , Adulto Jovem
2.
Emergencias (Sant Vicenç dels Horts) ; 31(2): 107-110, abr. 2019. graf, tab
Artigo em Espanhol | IBECS | ID: ibc-182527

RESUMO

Objetivo: Análisis de la evolución temporal de las intoxicaciones medicamentosas (IM) atendidas en urgencias hospitalarias. Método: Estudio retrospectivo, con análisis multivariante de variables epidemiológicas y asistenciales de IM, comparando la casuística de los años 2007 con 2017, en un hospital universitario. Resultados: Se incluyeron 750 casos, 58,4% del 2007. En 2017, disminuyeron la causa suicida (p < 0,001), el empleo de descontaminación digestiva (p < 0,001) y antídotos (p = 0,007), y los ingresos (p = 0,004), altas voluntaria o fugas (p = 0,03). Se incrementó por el contrario la intoxicación múltiple (p = 0,001), especialmente en varones y en contexto recreativo. Las benzodiacepinas fueron los fármacos más implicados en las IM (65,1%). Conclusiones: Existe una tendencia al descenso de las IM atendidas, con incremento en varones, menos intencionalidad suicida, menos uso de terapéuticas específicas y menos admisiones hospitalarias


Objective: To review changes occurring over time in cases of medication overdose attended by an emergency department. Methods: Retrospective review of epidemiologic and care variables related to drug poisonings in a university teaching hospital in 2007 and 2017. We used multivariate analysis to compare the 2 years. Results: A total of 750 cases were included; 438 (58.4%) were from 2007. Fewer cases were seen in 2017 even though the total numbers of emergencies and poisonings had risen (P<.001). Fewer cases were suicides or suicide attempts in 2017 (P<.001), and digestive tract decontamination and antidotes were used less often (P<.001 and P=.007, respectively). Admissions (P=.004) and voluntary self-discharges or patient losses were also down in 2017 (P=.03). However, multidrug poisonings increased (P=.001), especially in the context of recreational drug use by men. Benzodiazepine overdoses accounted for most of such cases (65.1%). Conclusions: Medication overdoses seem to be decreasing, although the proportion of men overdosing is rising. Suicide attempts, the abuse of specific medications, and admissions also seem to be decreasing


Assuntos
Humanos , Masculino , Feminino , Adulto , Overdose de Drogas/epidemiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/epidemiologia , Serviço Hospitalar de Emergência/estatística & dados numéricos , Envenenamento/epidemiologia , Registros/estatística & dados numéricos , Estudos Retrospectivos , Análise Multivariada , Hospitais Universitários/estatística & dados numéricos
3.
Adicciones (Palma de Mallorca) ; 22(4): 293-300, oct.-dic. 2010. tab
Artigo em Inglês | IBECS | ID: ibc-84249

RESUMO

El objetivo del presente estudio es analizar los factores relacionados con el uso de sustancias adictivas en la adolescencia mediante reglas de asociación, herramientas descriptivas incluidas en Data Mining. Para ello se cuenta con una base de datos referidos al consumo de sustancias adictivas en la adolescencia y se utiliza el paquete arules, integrado en el programa de libre distribución R (versión 2.10.1). La muestra está formada por 9.300 estudiantes de edades comprendidas entre los 14 y los 18 años (47,1% chicos y 52,9% chicas) con una edad media de 15,6 años (SE=1,2). Los adolescentes contestaron un cuestionario anónimo que incluía preguntas sobre factores de riesgo personales, familiares y ambientales para el consumo desustancias. Las mejores reglas obtenidas en relación al consumo de sustancias relacionan el consumo de alcohol con la educación paterna percibida y el consumo de los amigos (confianza= 0.8528), el consumo de tabaco, cannabis y cocaína con la actuación paterna percibida y la realización de conductas ilegales (confianzas de 0.8032, 0.8718 y 1.0000, respectivamente)y el uso de éxtasis con el consumo de los iguales (confianza = 1.0000).En general, las reglas de asociación muestran de forma sencilla la relación existente entre ciertas pautas de actuación paterna percibida, la emisión de conductas desviadas de las normas de comportamiento social, el consumo por parte del grupo de iguales y el abuso de drogas, legales e ilegales, en la adolescencia. Se describen las implicaciones de los resultados obtenidos así como la utilidad de esta nueva metodología de análisis (AU)


The aim of this study is to analyse the factors related to the use of addictive substances in adolescence using association rules, descriptive tools included in Data Mining. Thus, we have a database referring to the consumption of addictive substances in adolescence, and use the free distribution program in the R arules package (version 2.10.0). The sample was made up of 9,300 students between the ages of 14 and 18 (47.1% boys and 52.9% girls) with an average age of 15.6 (SE=1.2). The adolescents answered an anonymous questionnaire on personal, family and environmental risk factors related to substance use. The best rules obtained with regard to substance use relate the consumption of alcohol to perceived parenting style and peer consumption (confidence = 0.8528), the use of tobacco (smoking), cannabis and cocaine to perceived parental action and illegal behaviour (confidence= 0.8032, 0.8718 and 1.0000, respectively), and the use of ecstasy to peer consumption (confidence = 1.0000). In general, the association rules show in a simple manner the relationship between certain patterns of perceived parental action, behaviours that deviate from social behavioural norms, peer consumption and the use of different legal and illegal drugs of abuse in adolescence. The implications of the results obtained are described, together with the usefulness of this new methodology of analysis (AU)


Assuntos
Humanos , Masculino , Feminino , Adolescente , Comportamento Aditivo/psicologia , Transtornos Relacionados ao Uso de Substâncias/psicologia , Associação , Comportamento do Adolescente/psicologia , Psicometria/instrumentação , Fumar Maconha/psicologia
4.
Adicciones ; 21(1): 65-80, 2009.
Artigo em Inglês, Espanhol | MEDLINE | ID: mdl-19333526

RESUMO

This paper is aimed mainly at making researchers in the field of drug addictions aware of a methodology of data analysis aimed at knowledge discovery in databases (KDD). KDD is a process consisting of a series of phases, the most characteristic of which is called data mining (DM), whereby different modelling techniques are applied in order to detect patterns and relationships among the data. Common and differentiating factors between the most widely used DM techniques are analysed, mainly from a methodological viewpoint, and their use is exemplified using data related to alcohol consumption in teenagers and its possible relationship with personality variables (N=7030). Although the overall accuracy obtained (% correct predictions) is very similar in the three models analyzed, the Artificial Neural Network (ANN) technique generates the most accurate model (64.1%), followed by Decision Trees (DT) (62.3%) and Naïve Bayes (NB) (59.9%).


Assuntos
Consumo de Bebidas Alcoólicas/psicologia , Adolescente , Algoritmos , Árvores de Decisões , Humanos
5.
Adicciones (Palma de Mallorca) ; 21(1): 65-80, ene.-mar. 2009. ilus, tab
Artigo em Espanhol | IBECS | ID: ibc-61389

RESUMO

El presente trabajo pretende principalmente acercar a los investigadores del campo de las drogodependencias una metodología de análisis de datos orientada al descubrimiento de conocimiento en bases de datos (KDD). El KDD es un proceso que consta de una serie de fases, la más característica de las cuales se denomina Data Mining (DM), en la que se aplican diferentes técnicas de modelado para detectar patrones y relaciones en los datos. Se analizan los factores comunes y diferenciadores de las técnicas DM más ampliamente utilizadas, desde una visión principalmente metodológica, y ejemplificando su uso con datos provenientes del consumo de alcohol en adolescentes y su posible relación con variables de personalidad (N=7030). Aunque la precisión global obtenida (% de predicciones correctas) es muy similar en los tres modelos analizados, las redes neuronales generan el modelo más preciso (64.1%), seguidas de los árboles de decisión (62.3%) y Naive Bayes (59.9%) (AU)


This paper is aimed mainly at making researchers in the field of drug addictions aware of a methodology of data analysis aimed at knowledge discovery in databases (KDD). KDD is a process consisting of a series of phases, the most characteristic of which is called data mining (DM), where by different model ling techniques are applied in order to detect patterns and relationships among the data. Common and differentiating factors between the most widely used DM techniques are analysed, mainly from a methodological viewpoint, and their use is exemplified using data related to alcohol consumption in teenagers and its possible relationship with personality variables (N=7030). Although the overall accuracy obtained (% correct predictions) is very similar in the three models analyzed, the Artificial Neural Network (ANN) technique generates the most accurate model (64.1%), followed by Decision Trees (DT) (62.3%) and Naïve Bayes (NB) (59.9%) (AU)


Assuntos
Humanos , Alcoolismo/epidemiologia , Redes Neurais (Computação) , Comportamento do Adolescente , Bases de Dados Estatísticos , Previsões , Árvores de Decisões
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