Resumo Objetivo: Analisar o efeito do transtorno depressivo maior (TDM) como fator de risco para a ideação suicida em indivíduos com idades entre 11 e 24 anos que frequentam instituições de ensino. Fontes de dados: Revisão sistemática realizada até agosto de 2020 por meio de buscas nas bases United States National Library of Medicine (PubMed) e Biblioteca Virtual em Saúde (BVS), sem limitações quanto à data de publicação. Síntese dos dados: Oito estudos foram selecionados para a metanálise, incluindo 35.443 jovens com idade média de 16,8 anos, predominantemente do sexo feminino (51,2%) e de países asiáticos de renda média (91,6%). Foi encontrado odds ratio (OR) de TDM de 3,89 (intervalo de confiança — IC95% 2,46–6,17) para a ideação suicida em jovens. A análise de subgrupo mostrou efeitos maiores na Ásia (OR=4,71; IC95% 3,22–6,89) do que nas Américas (OR=1,71; IC95% 1,44–2,03). O modelo de metarregressão demonstrou que adolescentes mais jovens (coef=-0,63; IC95% -1,09–-0,18; p＜0,01) e estudos mais antigos (coef=-0,23; IC95% -0,039–-0,08; p＜0,01) apresentaram maiores efeitos do TDM sobre a ideação suicida. Conclusões: Observou-se relação entre TDM e ideação suicida no continente asiático, entre adolescentes mais jovens e em estudos mais antigos. Objetivo (TDM 1 2 ensino dados 202 PubMed (PubMed BVS, BVS , (BVS) publicação metanálise 35443 35 443 35.44 168 16 8 16, 51,2% 512 51 (51,2% 91,6%. 916 91,6% . 91 6 (91,6%) OR (OR 389 3 89 3,8 intervalo IC95 IC 2,46–6,17 246617 46 17 OR=4,71 OR471 4 71 (OR=4,71 3,22–6,89 322689 22 OR=1,71 OR171 (OR=1,71 1,44–2,03. 144203 1,44–2,03 44 03 1,44–2,03) coef=0,63 coef063 coef coef= 0,63 0 63 (coef=-0,63 1,09–0,18 109018 1,09– 0,18 09 18 -1,09–-0,18 p＜0,01 p001 p 01 coef=0,23 coef023 0,23 23 (coef=-0,23 0,039–0,08 0039008 0,039– 0,08 039 08 -0,039–-0,08 Conclusões Observouse Observou se asiático 20 (BVS 3544 35.4 51,2 5 (51,2 91,6 9 (91,6% 38 3, IC9 2,46–6,1 24661 OR=4,7 OR47 7 (OR=4,7 3,22–6,8 32268 OR=1,7 OR17 (OR=1,7 14420 1,44–2,0 coef=0,6 coef06 063 0,6 (coef=-0,6 1,09–0,1 10901 109 1,09 018 0,1 -1,09–-0,1 p＜0,0 p00 coef=0,2 coef02 023 0,2 (coef=-0,2 0,039–0,0 003900 0039 0,039 008 0,0 -0,039–-0,0 354 35. 51, (51, 91, (91,6 2,46–6, 2466 OR=4, OR4 (OR=4, 3,22–6, 3226 OR=1, OR1 (OR=1, 1442 1,44–2, coef=0, coef0 06 0, (coef=-0, 1,09–0, 1090 10 1,0 -1,09–-0, p＜0, p0 02 0,039–0, 00390 003 0,03 00 -0,039–-0, (51 (91, 2,46–6 246 OR=4 (OR=4 3,22–6 322 OR=1 (OR=1 144 1,44–2 coef=0 (coef=-0 1,09–0 1, -1,09–-0 p＜0 0,039–0 -0,039–-0 (5 (91 2,46– OR= (OR= 3,22– 32 14 1,44– (coef=- -1,09–- p＜ -0,039–- ( (9 2,46 3,22 1,44 (coef= -1,09– -0,039– 2,4 3,2 1,4 (coef -1,09 -0,039 2, -1,0 -0,03 -1, -0,0 -1 -0, - -0

Abstract Objective: This study aimed to analyze the effect of major depressive disorder (MDD) as a risk factor for suicidal ideation in individuals whose ages varied from 11 to 24 years and who were attending educational institutions. Data source: A systematic review was carried out by searching in PubMed and Biblioteca Virtual em Saúde (BVS). Original studies conducted in educational institutions, including individuals whose age varied from 11 to 24 years, in English, Spanish, or Portuguese were included. Data synthesis: Eight studies were selected for the meta-analysis, including 35,443 youths, with an average age of 16.8 years, predominantly female (51.2%), and from middle-income Asian countries (91.6%). An odds ratio of MDD of 3.89 (95%CI 2.46–6.17) for suicide ideation in youth was found. Subgroup analysis showed higher effects in Asia (OR=4.71; 95%CI 3.22–6.89) than Americas (OR=1.71; 95%CI 1.44–2.03). The meta-regression model indicated that younger adolescents (coef=-0.63; 95%CI 1.09–-0.18; p<0.01) and older studies (coef=-0.23; 95%CI 0.039–-0.08; p<0.01) presented higher effects of MDD on suicidal ideation. Conclusions: Early detection and treatment of MDD in youth patients are of utmost importance for preventing suicidal ideation. Educational institutions could play an important role in the early detection and intervention. Objective (MDD 1 2 source BVS. BVS . (BVS) English Spanish included synthesis metaanalysis, metaanalysis meta analysis, meta-analysis 35443 35 443 35,44 youths 168 16 8 16. 51.2%, 512 51.2% , 51 (51.2%) middleincome middle income 91.6%. 916 91.6% 91 6 (91.6%) 389 3 89 3.8 95CI CI 95 2.46–6.17 246617 46 17 found OR=4.71 OR471 OR 4 71 (OR=4.71 3.22–6.89 322689 22 OR=1.71 OR171 (OR=1.71 1.44–2.03. 144203 1.44–2.03 44 03 1.44–2.03) metaregression regression coef=0.63 coef063 coef coef= 0.63 0 63 (coef=-0.63 1.09–0.18 109018 1.09– 0.18 09 18 1.09–-0.18 p<0.01 p001 p 01 coef=0.23 coef023 0.23 23 (coef=-0.23 0.039–0.08 0039008 0.039– 0.08 039 08 0.039–-0.08 Conclusions intervention (BVS 3544 35,4 51.2 5 (51.2% 91.6 9 (91.6% 38 3. 2.46–6.1 24661 OR=4.7 OR47 7 (OR=4.7 3.22–6.8 32268 OR=1.7 OR17 (OR=1.7 14420 1.44–2.0 coef=0.6 coef06 063 0.6 (coef=-0.6 1.09–0.1 10901 109 1.09 018 0.1 1.09–-0.1 p<0.0 p00 coef=0.2 coef02 023 0.2 (coef=-0.2 0.039–0.0 003900 0039 0.039 008 0.0 0.039–-0.0 354 35, 51. (51.2 91. (91.6 2.46–6. 2466 OR=4. OR4 (OR=4. 3.22–6. 3226 OR=1. OR1 (OR=1. 1442 1.44–2. coef=0. coef0 06 0. (coef=-0. 1.09–0. 1090 10 1.0 1.09–-0. p<0. p0 02 0.039–0. 00390 003 0.03 00 0.039–-0. (51. (91. 2.46–6 246 OR=4 (OR=4 3.22–6 322 OR=1 (OR=1 144 1.44–2 coef=0 (coef=-0 1.09–0 1. 1.09–-0 p<0 0.039–0 0.039–-0 (51 (91 2.46– OR= (OR= 3.22– 32 14 1.44– (coef=- 1.09–- p< 0.039–- (5 (9 2.46 (OR 3.22 1.44 (coef= ( 2.4 3.2 1.4 (coef 2.

Objective: To analyze the predictors of smoking cessation treatment outcomes in a sample with a high rate of medical and psychiatric disorders and addictions. Methods: Analysis of predictors of success of a 6-week treatment provided by an addiction care unit (CAPS-AD) to 367 smokers in Brazil from 2007 to 2010. Forty variables were collected at baseline. Success was defined as abstinence from smoking for a period of at least 14 consecutive days, including the last day of treatment. Twenty variables were selected for the logistic regression model. Results: The only condition correlated with successful treatment after logistic regression was smoking one's first cigarette 5 minutes or more after waking (beta = 1.85, 95% confidence interval [95%CI] = 1.11-3.10, p = 0.018). Subjects with hypertension and alcohol use disorders and those who were undergoing psychiatric treatment showed success rates comparable to or greater than the average success rate of the sample (34.2-44.4%). Conclusions: These findings support the importance of the variable time to first cigarette in treatment outcomes for a sample with a high rate of clinical and psychiatric disorders. Good success rates were observed for pharmacological treatment, which was combined with group therapy based on cognitive-behavioral concepts and integrated into ongoing treatment of other addictions and psychiatric disorders.

CONTEXTO: A prevalência do uso prescrito de metilfenidato (MPH) e correlatos ainda é pouco conhecida no Brasil. OBJETIVO: Estimar a prevalência do uso prescrito de MPH e correlatos em uma amostra populacional de universitários brasileiros. MÉTODOS: Doze mil setecentos e onze universitários foram solicitados a responder a um questionário sobre o uso de drogas. Em relação ao uso na vida de MPH, os universitários foram divididos em dois grupos: usuários de MPH (MPHU) e não usuários de MPH (MPHNU). Um modelo de regressão quasibinomial foi usado para avaliar a correlação desse uso com as demais variáveis. RESULTADOS: 0,9% dos universitários relataram ter feito uso de MPH na vida (MPHU). Ser da região Centro-Oeste (RP = 4,8; p < 0,01) e Sul (RP = 5,2; p < 0,05), morar em repúblicas (RP = 5,8; p < 0,001), já ter feito uso prescrito de anfetaminas (RP = 8,9; p < 0,001) e benzodiazepínicos (< 3 semanas: RP = 4,4; p < 0,001; ≥ 3 semanas: RP = 6,7; p < 0,001) e fazer uso recente e de risco de álcool (RP = 4,0; p < 0,05) foram variáveis associadas ao MPHU. CONCLUSÃO: A associação do uso de álcool e de outras drogas com o uso prescrito de MPH entre universitários sugere a importância da triagem do padrão do uso de álcool e de outras drogas entre estudantes com sintomas de transtorno de déficit de atenção e hiperatividade (TDAH).

BACKGROUND: The prevalence of prescribed use of methylphenidate (MPH) and its correlates are not well-known in Brazil. OBJECTIVE: To estimate the prevalence of prescribed use of MPH and its correlates in a sample of Brazilian college students. METHODS: Twelve-thousand seven hundred and eleven college students filled out a drug use questionnaire. They were divided into two groups based on the lifetime use of MPH: MPH users (MPHU) and MPH non-users (MPHNU). Quasi-binomial regression models were carried out in order to evaluate the correlation among MPHU and other variables. RESULTS: A lifetime use of MPH was reported from 0.9% of college students (MPHU). Being from the Midwest (PR = 4.8, p < 0.01) and South (PR = 5.2, p < 0.05), living in students housing (PR = 5.8, p < 0.001), prescribed use of amphetamines (PR = 8.9, p < 0.001) and benzodiazepines (< 3 weeks: PR = 4.4, p < 0.001; ≥ 3 weeks: PR = 6.7, p < 0.001), and harmful use of alcohol (PR = 4.0, p < 0.05) were correlated with MPHU. DISCUSSION: The association of alcohol and drug use with prescribed use of MPH among college students suggests the importance of screening drinking patterns and use of other drugs among students with ADHD symptoms.