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      Prevalence of psychotropic and antidepressant use in a Brazilian Amazon city: analysis of two cross-sectional studies Translated title: Prevalência de uso de psicotrópicos e antidepressivos em uma cidade da Amazônia brasileira: análise de dois estudos transversais

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          Abstract

          Abstract This article aims to assess the prevalence of psychotropic and antidepressant use and associated factors in a Brazilian Amazon city. Two cross-sectional studies conducted in Manaus in 2015 and 2019 with adults selected by probabilistic sampling. Prevalence ratios (PR) and 95% confidence intervals (95%CI) were calculated by Poisson regression with robust variance. 3,479 participants were included in 2015 and 2,321 in 2019; 2.0% used psychotropics in 2015 and 2.7% in 2019. Antidepressants were used by 0.4% (2015) and 1.4% (2019). Psychotropic use was lower in younger (PR = 0.41; 95%CI: 0.19-0.90), partnerless (PR = 0.64; 95%CI: 0.44-0.93), and informal workers (PR=0.47; 95%CI: 0.25-0.86), but higher in people with poor health (PR=2.86; 95%CI: 1.71-4.80), multimorbidity (PR = 3.24; 95%CI: 1.87-5.60), and who visited doctors (PR = 3.04; 95%CI: 1.45-6.38) or dentists (PR = 1.50; 95%CI: 1.08-2.10). Antidepressant use was higher in 2019 (PR = 2.90; 95%CI: 1.52-5.54), people with poor health (PR = 2.77; 95%CI: 1.16-6.62), and multimorbidity (PR = 8.72; 95%CI: 2.71-28.00), while lower in informal workers (PR = 0.33; 95%CI: 0.12-0.87) and unemployed (PR = 0.26; 95%CI: 0.08-0.81). Use of psychotropics remained stable in Manaus from 2015 to 2019, while antidepressant use more than tripled, which was marked by social inequalities.

          Translated abstract

          Resumo O objetivo deste artigo é avaliar a prevalência do uso de psicotrópicos e antidepressivos e fatores associados em uma cidade da Amazônia. Dois estudos transversais foram realizados em Manaus, em 2015 e 2019, com adultos selecionados por amostragem probabilística. Razões de prevalência (RP) e intervalos de confiança de 95% (IC95%) foram calculados por regressão de Poisson. Foram incluídos 3.479 participantes em 2015 e 2.321 em 2019; 2,0% usaram psicotrópicos em 2015 e 2,7% em 2019. Antidepressivos foram usados por 0,4% (2015) e 1,4% (2019). O uso de psicotrópicos foi menor em jovens (RP = 0,41; IC95%: 0,19-0,90), sem companheiros (RP = 0,64; IC95%: 0,44-0,93) e trabalhadores informais (RP = 0,47; IC95%: 0,25-0,86), mas maior em pessoas com saúde ruim (RP = 2,86; IC95%: 1,71-4,80), multimorbidade (RP = 3,24; IC95%: 1,87-5,60) e que visitaram médico (RP = 3,04; IC95%: 1,45-6,38) ou dentista (RP = 1,50; IC95%: 1,08-2,10). O uso de antidepressivos foi maior em 2019 (RP = 2,90; IC95%: 1,52-5,54), e pessoas com saúde ruim (RP = 2,77; IC95%: 1,16-6,62) e multimorbidade (RP = 8,72; IC95%: 2,71-28,00), mas menor em trabalhadores informais (RP = 0,33; IC95%: 0,12-0,87) e desempregados (RP = 0,26; IC95%: 0,08-0,81). O uso de psicotrópicos permaneceu estável em Manaus de 2015 a 2019, enquanto o de antidepressivos triplicou, sendo marcados por desigualdades sociais.

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          Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

          (2022)
          Summary Background The mental disorders included in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 were depressive disorders, anxiety disorders, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, eating disorders, idiopathic developmental intellectual disability, and a residual category of other mental disorders. We aimed to measure the global, regional, and national prevalence, disability-adjusted life-years (DALYS), years lived with disability (YLDs), and years of life lost (YLLs) for mental disorders from 1990 to 2019. Methods In this study, we assessed prevalence and burden estimates from GBD 2019 for 12 mental disorders, males and females, 23 age groups, 204 countries and territories, between 1990 and 2019. DALYs were estimated as the sum of YLDs and YLLs to premature mortality. We systematically reviewed PsycINFO, Embase, PubMed, and the Global Health Data Exchange to obtain data on prevalence, incidence, remission, duration, severity, and excess mortality for each mental disorder. These data informed a Bayesian meta-regression analysis to estimate prevalence by disorder, age, sex, year, and location. Prevalence was multiplied by corresponding disability weights to estimate YLDs. Cause-specific deaths were compiled from mortality surveillance databases. The Cause of Death Ensemble modelling strategy was used to estimate death rate by age, sex, year, and location. The death rates were multiplied by the years of life expected to be remaining at death based on a normative life expectancy to estimate YLLs. Deaths and YLLs could be calculated only for anorexia nervosa and bulimia nervosa, since these were the only mental disorders identified as underlying causes of death in GBD 2019. Findings Between 1990 and 2019, the global number of DALYs due to mental disorders increased from 80·8 million (95% uncertainty interval [UI] 59·5–105·9) to 125·3 million (93·0–163·2), and the proportion of global DALYs attributed to mental disorders increased from 3·1% (95% UI 2·4–3·9) to 4·9% (3·9–6·1). Age-standardised DALY rates remained largely consistent between 1990 (1581·2 DALYs [1170·9–2061·4] per 100 000 people) and 2019 (1566·2 DALYs [1160·1–2042·8] per 100 000 people). YLDs contributed to most of the mental disorder burden, with 125·3 million YLDs (95% UI 93·0–163·2; 14·6% [12·2–16·8] of global YLDs) in 2019 attributable to mental disorders. Eating disorders accounted for 17 361·5 YLLs (95% UI 15 518·5–21 459·8). Globally, the age-standardised DALY rate for mental disorders was 1426·5 (95% UI 1056·4–1869·5) per 100 000 population among males and 1703·3 (1261·5–2237·8) per 100 000 population among females. Age-standardised DALY rates were highest in Australasia, Tropical Latin America, and high-income North America. Interpretation GBD 2019 showed that mental disorders remained among the top ten leading causes of burden worldwide, with no evidence of global reduction in the burden since 1990. The estimated YLLs for mental disorders were extremely low and do not reflect premature mortality in individuals with mental disorders. Research to establish causal pathways between mental disorders and other fatal health outcomes is recommended so that this may be addressed within the GBD study. To reduce the burden of mental disorders, coordinated delivery of effective prevention and treatment programmes by governments and the global health community is imperative. Funding Bill & Melinda Gates Foundation, Australian National Health and Medical Research Council, Queensland Department of Health, Australia.
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            Variable selection – A review and recommendations for the practicing statistician

            Abstract Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10–30. This number is often too large to be considered in a statistical model. We provide an overview of various available variable selection methods that are based on significance or information criteria, penalized likelihood, the change‐in‐estimate criterion, background knowledge, or combinations thereof. These methods were usually developed in the context of a linear regression model and then transferred to more generalized linear models or models for censored survival data. Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p‐values or confidence intervals. Therefore, we give pragmatic recommendations for the practicing statistician on application of variable selection methods in general (low‐dimensional) modeling problems and on performing stability investigations and inference. We also propose some quantities based on resampling the entire variable selection process to be routinely reported by software packages offering automated variable selection algorithms.
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              Mental Health Service Provision in Low- and Middle-Income Countries

              This article discusses the provision of mental health services in low- and middle-income countries (LMICs) with a view to understanding the cultural dynamics–how the challenges they pose can be addressed and the opportunities harnessed in specific cultural contexts. The article highlights the need for prioritisation of mental health services by incorporating local population and cultural needs. This can be achieved only through political will and strengthened legislation, improved resource allocation and strategic organisation, integrated packages of care underpinned by professional communication and training, and involvement of patients, informal carers, and the wider community in a therapeutic capacity.
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                Author and article information

                Journal
                csc
                Ciência & Saúde Coletiva
                Ciênc. saúde coletiva
                ABRASCO - Associação Brasileira de Saúde Coletiva (Rio de Janeiro, RJ, Brazil )
                1413-8123
                1678-4561
                January 2023
                : 28
                : 1
                : 83-92
                Affiliations
                [2] Sorocaba SP orgnameUniversity of Sorocaba orgdiv1Post-Graduate Program of Pharmaceutical Sciences Brazil
                [1] Campinas São Paulo orgnameUniversidade Estadual de Campinas orgdiv1Faculty of Pharmaceutical Sciences Brazil gustavo.tiguman@ 123456gmail.com
                Article
                S1413-81232023000100083 S1413-8123(23)02800100083
                10.1590/1413-81232023281.10152022
                36629583
                71ce90ed-cad4-499f-8b4a-776659089ce2

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 09 March 2022
                : 23 July 2022
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 17, Pages: 10
                Product

                SciELO Brazil

                Categories
                Article

                Psychotropic drugs,Uso de medicamentos,Antidepressivos,Psicotrópicos,Drug utilization,Antidepressive agents

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