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1 ISSN São Paulo Medical Journal/Evidence for Health Care, 2013 July 4; 131(4): S ã o P a u l o E v i d e n c e f o r H e a l t h C a r e July 4 - Volume Number 4 Reproducibility study: The IDEAL classification system: a new method for classifying fractures of the distal extremity of the radius description and reproducibility Cross-sectional study: Obstructive sleep apnea syndrome: complaints and housing characteristics in a population in the United States Validation study: Translation, cross-cultural adaptation and validation of the Brazilian version of the Nonarthritic Hip Score Retrospective study: Clinical and hematological effects of hydroxyurea therapy in sickle cell patients: a singlecenter experience in Brazil Medline, Lilacs, SciELO, Science Citation Index Expanded, Journal Citation Reports/Sciences Edition (impact factor 0.588) and EBSCO Publishing Museu do Ipiranga - Visão interna Rubens Chiri - Federada da São Paulo Medical Journal does not charge authors for publication.

2 Sabe qual é a melhor palavra que define o que é ser pai? CONFIANÇA! A mesma confiança que você compartilha diariamente com seus pacientes e filhos. E é por isso que você merece uma instituição que cuide de você, proteja suas finanças e mostre o melhor caminho. Seu Expedito A Unicred é uma instituição financeira cooperativa que tem como objetivo principal a saúde financeira e o bem estar dos cooperados. Afinal, esse é o jeito Unicred de ser: simples e próximo. Feliz Dia dos Pais. Uma homenagem da Unicred àqueles que são nossos heróis, exemplos e amigos. COOPERATIVA DE CRÉDITO Americana, Amparo, Araras, Barretos, Bebedouro, Caçapava, Campinas, Capivari, Campos do Jordão, Caraguatatuba, Conchal, Cubatão, Cruzeiro, Guaratinguetá, Guarujá, Guarulhos, Indaiatuba, Itapetininga, Itapeva, Itu, Jacareí, Leme, Limeira, Lorena, Mogi das Cruzes, Monte Azul Paulista, Nova Odessa, Osasco, Peruíbe, Pindamonhangaba, Piracicaba, Pirassununga, Praia Grande, Rio Claro, Salto, São Carlos, São Paulo, São Vicente, São José dos Campos, Santa Barbara D Oeste, Santos, Sorocaba, Sumaré, Tatuí, Taubaté, Tietê e Valinhos.

3 Index Correspondence to: Associação Paulista de Medicina Publicações Científicas Av. Brig. Luís Antônio, o andar São Paulo (SP) Brasil CEP Tel. (+55 11) ou (+55 11) Fax: (+55 11) serial&pid= &ing=en&nrm=iso Editorial 211 From classroom to bedside: integration of the basic science curriculum in medical teaching Alessandro Wasum Mariani, Paulo Manuel Pêgo-Fernandes Original article 213 Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population Adriano Marçal Pimenta, Mariana Santos Felisbino-Mendes, Gustavo Velasquez-Melendez 220 Obstructive sleep apnea syndrome: complaints and housing characteristics in a population in the United States Khalil Ansarin, Leyla Sahebi, Siamak Sabur 228 Circumstances and factors associated with accidental deaths among children, adolescents and young adults in Cuiabá, Brazil Christine Baccarat de Godoy Martins, Maria Helena Prado de Mello-Jorge 238 Clinical and hematological effects of hydroxyurea therapy in sickle cell patients: a single-center experience in Brazil Ana Cristina Silva-Pinto, Ivan Lucena Angulo, Denise Menezes Brunetta, Fabia Idalina Rodrigues Neves, Sarah Cristina Bassi, Gil Cunha De Santis, Dimas Tadeu Covas 244 Translation, cross-cultural adaptation and validation of the Brazilian version of the Nonarthritic Hip Score Letícia Nunes Carreras Del Castillo, Gustavo Leporace, Themis Moura Cardinot, Roger Abramino Levy, Liszt Palmeira de Oliveira 252 The IDEAL classification system: a new method for classifying fractures of the distal extremity of the radius description and reproducibility João Carlos Belloti, João Baptista Gomes dos Santos, Vinícius Ynoe de Moraes, Felipe Vitiello Wink, Marcel Jun Sugawara Tamaoki, Flávio Faloppa 257 Trends in treatment of anterior cruciate ligament injuries of the knee in the public and private healthcare systems of Brazil Diego Costa Astur, Rodrigo Ferreira Batista, Arliani Gustavo, Moises Cohen Review article 264 Diagnosis and treatment of mast cell disorders: practical recommendations Alex Freire Sandes, Raphael Salles Scortegagna Medeiros, Edgar Gil Rizzatti Case report 275 Bilateral tibial hemimelia type 1 (1a and 1b) with T9 and T10 hemivertebrae: a novel association Victor Michael Salinas-Torres, Leticia Oralia Barajas-Barajas, Nicolas Perez-Garcia, Guillermo Perez- Garcia Letter to the editor 279 Ginseng: potential for the antileishmanial arsenal? Nader Pazyar, Reza Yaghoobi 281 Medicine and creativity in medical psychology Décio Gilberto Natrielli Filho, Mailu Enokibara Silva, Décio Gilberto Natrielli 283 Should every adult patient in the hospital have an internist? Mine Durusu Tanriover, Goksel Guven, Cagin Buldukoglu, Omer Diker, Burcin Halacli, Gonul Yildirim, Arzu Topeli Cochrane highlights 285 Prophylactic drug management for febrile seizures in children Martin Offringa, Richard Newton Comments: Lívia Cunha Elkis 286 Phlebotonics for haemorrhoids Nirmal Pereira, Danae Liolitsa, Satheesh Iype, Anna Croxford, Muhhamed Yassin, Lang Peter, Obioha Ukaegbu, Christopher van Issum Comments: Sarhan Sydney Saad I Instructions for authors (www.scielo.br/spmj) Sao Paulo Med J. 2013; 131(4):i-ii i

4 Organization Founded in 1932, a bimonthly publication of the Associação Paulista de Medicina Editors: Paulo Manuel Pêgo-Fernandes and Álvaro Nagib Atallah. Editorial advisor: Rachel Riera. Editorial assistant: Marina de Britto. Scientific journalist and editor: Patrícia Logullo (MTB: ). Editorial auxiliary: Joyce de Fátima Silva Nakamura. Associate editors: Adriana Seber, Alexander Wagner Silva de Souza, Antonio José Gonçalves, Aytan Miranda Sipahi, Cristina Muccioli, Delcio Matos, Domingo Marcolino Braile, Edina Mariko Koga da Silva, Edmund Chada Baracat, Elcio dos Santos Oliveira Vianna, Emmanuel de Almeida Burdmann, Fernando Antonio de Almeida, Fernando Ferreira Costa, Flávio Faloppa, Heráclito Barbosa de Carvalho, José Antônio Rocha Gontijo, José Carlos Costa Baptista-Silva, José Roberto Lapa e Silva, Júlio César Rodrigues Pereira, Laércio Joel Franco, Marilza Vieira Cunha Rudge, Milton de Arruda Martins, Moacir Fernandes de Godoy, Olavo Pires de Camargo, Sergio Tufik, Soubhi Kahhale, Walter José Gomes. Proofreading: David Elliff. Desktop publishing: Zeppelini Editorial (www.zeppelini.com.br). Listed in: Medline, Lilacs, SciELO, Science Citation Index Expanded and Journal Citation Reports/Sciences Edition (impact factor 0.711) and EBSCO publishing. International Board: Alexandre Wagner Silva de Souza (University Medical Center Groningen, Groningen, Netherlands), Angeles R. Badell (Faculty of Medicine, University of Barcelona, Barcelona, Spain), Charles J. Menkes (Cochin Hospital, Paris, France), José Fragata (Hospital Cuf Infant Santo, Lisbon), Luiz Dratcu (Guy s Hospital, South London and Maudsley NHS Trust, York Clinic, London), Marcelo Cypel (University Health Network, Toronto, Canada), Karla Soares-Weiser (Enhance Reviews Ltd, Wantage, United Kingdom), Tirone E. David (Toronto General Hospital, Toronto, Canada), Mário Viana de Queiroz (Hospital de Santa Maria, Lisbon), Wadih Arap (MD Anderson Cancer Center, University of Texas, Houston, United States), Wellington Cardoso (Boston University, Boston, United States). All articles published, including editorials and letters, represent the opinions of the authors and do not reflect the official policy of the Associação Paulista de Medicina or the institution with which the authors are affiliated, unless this is clearly specified. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Copyright 2013 by Associação Paulista de Medicina. SPMJ website: access to the entire São Paulo Medical Journal/Revista Paulista de Medicina website is free to all. We will give at least six months notice of any change in policy. SPMJ printed version: six issues/year; 1 volume/year, beginning on first Thursday in January. One-year subscription for the year 2013: individual US$ 165; institutional US$ 230. Scientific Council Abrão Rapoport Hospital Heliópolis, São Paulo Adriana Costa e Forti Faculdade de Medicina, Universidade Federal do Ceará Alexandre Fogaça Cristante Faculdade de Medicina da Universidade de São Paulo Álvaro Nagib Atallah Escola Paulista de Medicina, Universidade Federal de São Paulo Auro del Giglio Faculdade de Medicina da Fundação ABC Carlos Alberto Morais Sá Universidade do Rio de Janeiro - UNIRIO Carmen Cabanelas Pazos de Moura Instituto Carlos Chagas Filho, Universidade Federal do Rio de Janeiro Cármino Antonio De Souza Faculdade de Ciências Médicas, Universidade Estadual de Campinas Dario Birolini Faculdade de Medicina, Universidade de São Paulo Eduardo Katchburian Escola Paulista de Medicina, Universidade Federal de São Paulo Eduardo Maia Freese de Carvalho Faculdade de Medicina, Universidade Federal de Pernambuco, Centro de Pesquisas Aggeu Magalhães - CpqAM/FIOCRUZ. Egberto Gaspar de Moura Instituto de Biologia Roberto Alcantara Gomes, Universidade Estadual do Rio de Janeiro Eliézer Silva Hospital Israelita Albert Einstein, São Paulo Emílio Antonio Francischetti - Faculdade de Medicina da Universidade Estadual do Rio de Janeiro Emmanuel de Almeida Burdmann Faculdade de Medicina de São José do Rio Preto Fabio Bessa Lima Instituto de Ciências Biomédicas, Universidade de São Paulo Florence Kerr-Corrêa Faculdade de Medicina de Botucatu, Universidade Estadual de São Paulo Francisco José Penna Faculdade de Medicina Universidade Federal de Minas Gerais Geraldo Rodrigues de Lima Escola Paulista de Medicina, Universidade Federal de São Paulo Irineu Tadeu Velasco Faculdade de Medicina da Universidade de São Paulo João Renato Rebello Pinho Instituto Adolfo Lutz, Secretaria de Estado da Saúde de São Paulo Joel Spadaro Faculdade de Ciências Médicas de Botucatu, Universidade Estadual de São Paulo Jorge Pinto Ribeiro Faculdade de Medicina, Universidade Federal do Rio Grande do Sul Jorge Sabbaga Hospital Alemão Oswaldo Cruz, São Paulo José Antonio Marin-Neto Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo José Carlos Nicolau Instituto do Coração, Universidade de São Paulo José Geraldo Mill Faculdade de Medicina, Universidade Federal do Espírito Santo José Mendes Aldrighi Faculdade de Saúde Pública, Universidade de São Paulo José Roberto Lapa e Silva Instituto de Doenças do Tórax, Universidade Federal do Rio de Janeiro Leopoldo Soares Piegas Instituto Dante Pazzanese de Cardiologia, São Paulo Luiz Jacintho da Silva Faculdade de Ciências Médicas, Universidade Estadual de Campinas Luiz Paulo Kowalski Hospital AC Camargo, São Paulo Márcio Abrahão Escola Paulista de Medicina, Universidade Federal de São Paulo Maria Inês Schmidt Faculdade de Medicina, Universidade Federal do Rio Grande do Sul Maurício Mota de Avelar Alchorne Escola Paulista de Medicina, Universidade Federal de São Paulo Mauro Schechter Hospital Universitário Clementino Fraga Filho, Universidade Federal do Rio de Janeiro Milton de Arruda Martins Faculdade de Medicina, Universidade de São Paulo Moysés Mincis Faculdade de Ciências Médicas de Santos Nelson Hamerschlak Hospital Israelita Albert Einstein, São Paulo Noedir Antônio Groppo Stolf Faculdade de Medicina, Universidade de São Paulo Pérsio Roxo Júnior Faculdade de Medicina de Ribeirão Preto Raul Cutait Hospital Sírio-Libanês, São Paulo Raul Negrão Fleury Instituto Lauro de Souza Lima, Coordenadoria dos Institutos de Pesquisa da Secretaria de Saúde de São Paulo Raul Marino Junior Faculdade de Medicina, Universidade de São Paulo Ricardo Brandt de Oliveira Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo Roberto A. Franken Faculdade de Ciências Médicas da Santa Casa de Misericórdia de São Paulo Ruy Laurenti Faculdade de Saúde Pública, Universidade de São Paulo Soubhi Kahhale Faculdade de Medicina, Universidade de São Paulo Wilson Roberto Catapani Faculdade de Medicina do ABC, Santo André Wilson Cossermelli Reclin Reumatologia Clínica, São Paulo Diretoria Executiva da Associação Paulista de Medicina (Triênio ) Presidente: Florisval Meinão 1 º Vice-Presidente: Roberto Lotfi Júnior 2 º Vice-Presidente: Donaldo Cerci da Cunha 3 º Vice-Presidente: Paulo de Conti 4 º Vice-Presidente: Akira Ishida Secretário Geral: Paulo Cezar Mariani 1 º Secretário: Ruy Y. Tanigawa Diretor Administrativo: Lacildes Rovella Júnior Diretor Administrativo Adjunto: Roberto De Mello 1 º Diretor de Patrimônio e Finanças: Murilo Rezende Melo 2 º Diretor de Patrimônio e Finanças: João Márcio Garcia Diretor Científico: Paulo Manuel Pêgo Fernandes Diretor Científico Adjunto: Álvaro Nagib Atallah Diretor de Defesa Profissional: João Sobreira de Moura Neto Diretor de Defesa Profissional Adjunto: Marun David Cury Diretor de Comunicações: Renato Françoso Filho Diretor de Comunicações Adjunto: Leonardo da Silva Diretor de Marketing: Nicolau D Amico Filho Diretor de Marketing Adjunto: Ademar Anzai Diretor de Eventos: Mara Edwirges Rocha Gândara Diretor de Eventos Adjunto: Regina Maria Volpato Bedone Diretor de Tecnologia de Informação: Marcelo Rosenfeld Levites Diretor de Tecnologia de Informação Adj.: Desiré Carlos Callegari Diretor de Previdência e Mutualismo: Paulo Tadeu Falanghe Diretor de Previdência e Mutualismo Adj.: Clóvis Francisco Constantino Diretor Social: Alfredo de Freitas Santos Filho Diretor Social Adjunto: Nelson Álvares Cruz Filho Diretora de Ações Comunitárias: Denise Barbosa Diretora de Ações Comunitárias Adjunta: Yvonne Capuano Diretor Cultural: Guido Arturo Palomba Diretor Cultural Adjunto: Carlos Alberto Monte Gobbo Diretor de Serviços aos Associados: José Luiz Bonamigo Filho Diretor de Serviços aos Associados Adjunto: João Carlos Sanches Anéas Diretor de Economia Médica: Tomás Patrício Smith-Howard Diretor de Economia Médica Adjunto: Jarbas Simas 1 º Diretor Distrital: Airton Gomes 2 º Diretor Distrital: Arnaldo Duarte Lourenço 3 º Diretor Distrital: Lauro Mascarenhas Pinto 4 º Diretor Distrital: Wilson Olegário Campagnone 5 º Diretor Distrital: José Renato dos Santos 6 º Diretor Distrital: José Eduardo Paciência Rodrigues 7 º Diretor Distrital: Eduardo Curvello Tolentino 8 º Diretor Distrital: Helencar Ignácio 9 º Diretor Distrital: José do Carmo Gaspar Sartori 10 º Diretor Distrital: Paulo Roberto Mazaro 11 º Diretor Distrital: José de Freitas Guimarães Neto 12 º Diretor Distrital: Marco Antônio Caetano 13 º Diretor Distrital: Marcio Aguilar Padovani 14 º Diretor Distrital: Wagner de Matos Rezende ii Sao Paulo Med J. 2013; 131(4):i-ii

5 DOI: / Editorial From classroom to bedside: integration of the basic science curriculum in medical teaching Da sala de aula para a beira do leito: a integração da ciência básica curricular no ensino da medicina Alessandro Wasum Mariani I, Paulo Manuel Pêgo-Fernandes II Instituto do Coração (InCor), Hospital das Clínicas (HC), Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil I MD. Thoracic Surgeon, Instituto do Coração (InCor), Hospital das Clínicas (HC), Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil. II I MD, PhD. Associate Professor, Discipline of Thoracic Surgery, Instituto do Coração (InCor), Hospital das Clínicas (HC), Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil. Around the world, universities have been rethinking not only the medical curriculum but also the entire way of teaching medicine. The enormous torrent of new knowledge, medical specialization, ever-faster entry of technology and even the concepts of evidence-based medicine have made it essential to discuss again and reorganize the medical curriculum. In many medical schools, the undergraduate medical curriculum can classically be divided into basic sciences, clinical sciences and clerkship (internship). The increases in the length of time spent on clerkship that have been instituted in many medical schools is a sign of change in teaching and a clear example of a significant curricular modification. The problem-based learning (PBL) approach can be considered to be an effort towards making learning more dynamic. There are obviously pros and cons to this, with advocates and critics of this approach. Here, we do not wish to discuss the complete reformulation of the curriculum, or to go into the issue of what curricular proposal would be most appropriate, if only because we take the view that different realities should have different teaching proposals. Rather, we would like to discuss how to better integrate basic sciences and to highlight their importance. In this regard, several authors have drawn attention to the need for better integration of teaching. In 2000, Harden published an interesting article that proposed 11 consecutive and progressively correlated steps that he named the integration ladder, which can be used for assessing and planning the medical curriculum. 1 A very interesting solution was produced by the University of California. In rethinking its medical teaching, it considered that full integration of basic, clinical and social sciences was important. The idea was that the final application of knowledge, which is dependent both on practice and on deepening of the theory, could be better explored by schools through introducing multimodal teaching tools. This thinking resulted in implementation of a program named Human Biology and Disease, which basically aimed to unify basic sciences and clinical sciences. This methodology was studied and described in a paper published in Another interesting experience at Harvard Medical School was described in This consisted of a new proposal that sought better integration of the curriculum through not dividing the material into blocks over the academic year, thus ensuring that students were in contact with their different subjects continuously and unceasingly throughout the year. 3 There is a worldwide trend towards this curricular integration: both horizontally, between subjects, and vertically, between basic sciences and clinical sciences. It is taking place through the argument that it provides teaching that is more complete and effective in terms of knowledge and applicability. In this regard, article three of the national curricular directives for undergraduate medical courses that have been issued by the Higher Education Chamber of the Brazilian National Education Council states the following: 4 Sao Paulo Med J. 2013; 131(4):

6 Editorial Mariani AW, Pêgo-Fernandes PM The undergraduate medical course provides entry/professional training for physicians with a profile of generalist, humanist, critical and reflective training, who have the capacity to act, based on ethical principles, on the health-illness process at its different levels of care, with actions to promote health, prevent disease, recover health and rehabilitate the individual, from a perspective of comprehensiveness of care, with a sense of social responsibility and commitment towards active citizenship, as a promoter of full health for human beings. To fulfill this target, medical schools and educators should make every effort to constantly improve teaching. This should, without any doubt, include adaptation of the basic sciences program, thereby making it dynamic, efficient and (why not?) more attractive to students. All of this has the aim that basic sciences should serve as a firm foundation for clinical knowledge and for development of research. REFERENCES 1. Harden RM. The integration ladder: a tool for curriculum planning and evaluation. Med Educ. 2000;34(7): Wilkerson L, Stevens CM, Krasne S. No content without context: integrating basic, clinical, and social sciences in a pre-clerkship curriculum. Med Teach. 2009;31(9): Ogur B, Hirsh D, Krupat E, Bor D. The Harvard Medical School- Cambridge integrated clerkship: an innovative model of clinical education. Acad Med. 2007;82(4): Brasil. Ministério da Educação. Conselho Nacional de Educação. Câmara de Educação Superior. Resolução CNE/CES No. 4, de 7 de novembro de Institui Diretrizes Curriculares Nacionais do Curso de Graduação em Medicina. Diário Oficial da União, Brasília, 9 de novembro de Seção 1, p. 38. Available from: gov.br/cne/arquivos/pdf/ces04.pdf. Accessed in 2013 (Jun 13). Sources of funding: None Conflict of interest: None Date of first submission: June 6, 2013 Last received: June 6, 2013 Accepted: June 19, 2013 Address for correspondence: Alessandro Wasum Mariani Rua Treze de Maio, 1217 apto. 31 Bela Vista São Paulo (SP) Brasil CEP Sao Paulo Med J. 2013; 131(4):211-2

7 DOI: / ORIGINAL ARTICLE Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population Agregação e padrão de combinação dos componentes da síndrome metabólica em uma população rural adulta brasileira Adriano Marçal Pimenta I, Mariana Santos Felisbino-Mendes II, Gustavo Velasquez-Melendez I Department of Maternal and Child Nursing and Public Health, School of Nursing, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil I PhD. Professor in the Department of Maternal and Child Nursing and Public Health, School of Nursing, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil. II MSc. Doctoral Student in the Department of Maternal and Child Nursing and Public Health, School of Nursing, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil. KEY WORDS: Metabolic syndrome x. Risk factors. Rural population. Cluster analysis. Obesity, abdominal. PALAVRAS-CHAVE: Síndrome x metabólica. Fatores de risco. População rural. Análise por conglomerados. Obesidade abdominal. ABSTRACT CONTEXT AND OBJECTIVE: Metabolic syndrome is characterized by clustering of cardiovascular risk factors such as obesity, dyslipidemia, insulin resistance, hyperinsulinemia, glucose intolerance and arterial hypertension. The aim of this study was to estimate the probability of clustering and the combination pattern of three or more metabolic syndrome components in a rural Brazilian adult population. DESIGN AND SETTING: This was a cross-sectional study conducted in two rural communities located in the Jequitinhonha Valley, Minas Gerais, Brazil. METHODS: The sample was composed of 534 adults (both sexes). Waist circumference, blood pressure and demographic, lifestyle and biochemical characteristics were assessed. The prevalences of metabolic syndrome and its components were estimated using the definitions of the National Cholesterol Education Program Adult Treatment Panel III. A binomial distribution equation was used to evaluate the probability of clustering of metabolic syndrome components. The statistical significance level was set at 5% (P < 0.05). RESULTS: Metabolic syndrome was more frequent among women (23.3%) than among men (6.5%). Clustering of three or more metabolic syndrome components was greater than expected by chance. The commonest combinations of three metabolic syndrome components were: hypertriglyceridemia + low levels of HDL-c + arterial hypertension and abdominal obesity + low levels of HDL-c + arterial hypertension; and of four metabolic syndrome components: abdominal obesity + hypertriglyceridemia + low levels of HDL-c + arterial hypertension. CONCLUSION: The population studied presented high prevalence of metabolic syndrome among women and clustering of its components greater than expected by chance, suggesting that the combination pattern was non-random. RESUMO CONTEXTO E OBJETIVO: A síndrome metabólica é caracterizada pela agregação de fatores de risco cardiovasculares como obesidade, dislipidemia, resistência à insulina, hiperinsulinemia, intolerância à glicose e hipertensão arterial. Este estudo objetivou estimar a probabilidade de agregação e o padrão de combinação de três ou mais componentes da síndrome metabólica em população rural adulta brasileira. TIPO DE ESTUDO E LOCAL: Estudo transversal, conduzido em duas comunidades rurais da região do Vale do Jequitinhonha, Minas Gerais. MÉTODOS: A amostra foi constituída de 534 adultos, de ambos os sexos, dos quais foram aferidas a circunferência da cintura, a pressão arterial e características demográficas, do estilo de vida e bioquímicas. Prevalências da síndrome metabólica e seus componentes foram estimados usando a definição da National Cholesterol Education Program Adult Treatment Panel III. A equação da distribuição binomial foi utilizada para avaliar a probabilidade de agregação dos componentes da síndrome metabólica. O nível de significância estatística estabelecido foi 5% (P < 0,05). RESULTADOS: Síndrome metabólica foi mais frequente em mulheres (23,3%) que homens (6,5%). A agregação de três ou mais componentes da síndrome metabólica foi maior do que esperada ao acaso. Combinações mais comuns para três componentes da síndrome metabólica foram hipertrigliceridemia + baixos níveis de HDL-c + hipertensão arterial, obesidade abdominal + baixos níveis de HDL-c + hipertensão arterial. Para quatro componentes, obesidade abdominal + hipertrigliceridemia + baixos níveis de HDL-c + hipertensão arterial. CONCLUSÃO: Na população estudada, a prevalência da síndrome metabólica foi alta entre mulheres e houve agregação dos seus componentes acima do esperado que ocorra ao acaso, sugerindo padrão não aleatório de combinação. Sao Paulo Med J. 2013; 131(4):

8 ORIGINAL ARTICLE Pimenta AM, Felisbino-Mendes MS, Velasquez-Melendez G INTRODUCTION Metabolic syndrome is characterized by clustering of cardiovascular risk factors such as obesity, dyslipidemia, insulin resistance, hyperinsulinemia, glucose intolerance and hypertension. 1 This syndrome is recognized as an important public health problem worldwide, due to prevalence greater than 20.0% in adult populations living both in urban and in rural areas, 2-7 and also to its strong association with cardiovascular diseases and type 2 diabetes, which are both major causes of death worldwide. 1,8-9 Despite this epidemiological context, the management of metabolic syndrome in clinical practice remains controversial, 10,11 primarily because of the random clustering of its components. Moreover, metabolic syndrome is diagnosed based upon the presence of three or more components out of a total of five, 12 which could lead to a plethora of combination patterns. 13 These various combinations require different interventions and therapeutic approaches, and this is often neglected in clinical practice. 13 Nonetheless, accurate management of metabolic syndrome, in order to control the current global epidemics of cardiovascular disease and diabetes mellitus, is of fundamental importance. 14 In Brazil, these issues have been poorly investigated, particularly in rural areas, given that studies have usually focused on the prevalence of metabolic syndrome and associated factors in urban populations. Furthermore, there may be a need for different approaches towards evaluating metabolic syndrome in rural populations, which are also highly affected by this pathological condition 4,5,7 and thus would benefit from interventions such as establishing preventive strategies and adequate treatment. OBJECTIVE The objective of the present study was to estimate the probability of clustering and the combining pattern of three or more metabolic syndrome components in a rural Brazilian adult population. METHODS Study population and design A cross-sectional population-based study was conducted between November 2004 and March 2005 in two communities, Virgem das Graças and Caju, in the rural areas of the municipalities of Ponto dos Volantes and Jequitinhonha, respectively. These communities are located in the Jequitinhonha Valley, in the northeast of the state of Minas Gerais, Brazil. In other projects conducted in these areas, a census performed by our research group in 2001 showed that 1216 individuals were living in these communities. For the present study, 621 of these subjects were excluded due to the following criteria: age less than 18 years (n = 522); emigration (n = 33); death (n = 6); pregnancy (n = 14); diabetes diagnosis (n = 12); polymerase chain reaction values above 10 mg/l [which could indicate acute infection or inflammation] (n = 31); 15 and physical impossibilities that compromised anthropometric measurements (n = 3). Moreover, 61 individuals were also lost because of their absence at the time of the survey (n = 47) or refusal to participate (n = 14). Finally, data from 534 participants remained available for analysis. Ethics committee approval This study was approved by the Research Ethics Committee of Universidade Federal de Minas Gerais (UFMG), in accordance with National Health Council Resolution 196/96. All of the subjects who took part in the study were informed about the objectives of the research and their rights as participants, and then were asked to sign a consent form. Data collection An interview was conducted by nurses, in which the participants answered a survey questionnaire covering various aspects of their demographic characteristics (sex, age, skin color, marital status and schooling) and lifestyle characteristics (smoking habits and alcohol consumption). At the conclusion of the interview, a clini cal evaluation was performed on the participants, which included waist circumference and blood pressure measurements, carried out in triplicate by well-trained staff in accordance with standard procedures. 16 Blood samples were collected from each participant by means of venous puncture following a fasting period of 12 hours. Serum and plasma aliquots were obtained by centrifugation of each sample, and were appropriately treated and stored in vials maintained at 4 C until arrival at the laboratory for biochemical analysis, in accordance with the recommended technical specifications for avoiding damage to biological material. Colorimetric enzymatic methods were used to determine glucose, triglyceride and total cholesterol values using a Roche Cobas Mira Plus analyzer (Roche Diagnostics, Switzerland). The highdensity lipoprotein cholesterol (HDL-c) concentration was also determined by means of colorimetric enzymatic assay, following precipitation of the low-density lipoprotein cholesterol (LDL-c) and very low-density lipoprotein cholesterol (VLDL-c) fractions, using phosphotungstic acid and magnesium chloride. The LDL-c concentrations were calculated by applying the Friedewald equation, 17 since there were no triglyceride values > 400 mg/dl: LDL-c = total cholesterol (HDL-c + triglycerides/5). Waist circumference was measured to the nearest millimeter, using a non-extendable measuring tape, and this was done exactly halfway between the margin of the lowest rib and the iliac crest, with participants in a standing posi tion (accurate to 0.1 cm). 16 Blood pressure was measured by means of an indirect method, using a sphygmomanometer (mercury manometer), in accordance with the Seventh Report of the Joint National Committee on 214 Sao Paulo Med J. 2013; 131(4):213-9

9 Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population ORIGINAL ARTICLE Prevention, Detection, Evaluation and Treatment of High Blood Pressure. 18 Measurements were made three times in each participant s right arm with two-minute intervals between measurements, after an initial resting period of at least five minutes. Definition of metabolic syndrome Metabolic syndrome was diagnosed in accordance with the definition of the National Cholesterol Education Program - Adult Treatment Panel III (NCEP-ATP III), which requires the presence of three or more of the following components: Abdominal obesity: waist circumference 102 cm for men and 88 cm for women. 2. Hypertriglyceridemia: triglycerides 150 mg/dl; 3. Low HDL-c: HDL-c < 40 mg/dl for men and < 50 mg/dl for women; 4. Hyperglycemia: fasting blood glucose 100 mg/dl Arterial hypertension: systolic blood pressure 130 mmhg and/or diastolic blood pressure (DBP) 85 mmhg and/or hypertension treatment. Statistical analyses The study population characteristics were presented in terms of the absolute and relative frequencies of the demographic and lifestyle variables, stratified by sex. These same procedures were used to present the prevalence of metabolic syndrome and its components. Statistical differences were evaluated by means of Pearson s chi-square test, and the significance level was set at 5% (P < 0.05). The analyses on clustering of three or more metabolic syndrome components, independently of their cutoff points, were performed based on the quintile distribution of each factor according to sex. Thus, a given component was determined to be present in an individual if he or she had values in the lowest quintile for HDL-c and the highest quintile for the other factors. The expected degree of clustering of three or more metabolic syndrome components was estimated by calculating the probability of d occurrences for n factors, where the probability of each occurrence was 0.20 (extreme quintile). Individual probabilities were calculated from the binomial formula presented below. 19 Finally, the expected probability was compared with the observed proportion of subjects in the highest quintile for three or more metabolic syndrome components, using the Pearson chisquare test. In addition, the proportions of three or more metabolic syndrome component combinations were also calculated stratified by sex. Statistical differences were evaluated by means of the Pearson chi-square test, and the significance level was set at 5% (P < 0.05). All the analyses were performed using the Statistical Package for the Social Sciences (SPSS) software package for Windows, version 15.0 (SPSS Inc., Chicago, IL, United States). RESULTS The study population was composed of 270 men (50.6%) and 264 women (49.4%). The major demographic and lifestyle characteristics of the subjects, according to sex, are shown in Table 1. The age intervals among the population presented homogenous distribution, with a slightly higher proportion of individuals with ages between 18 and 29 years. Most of the subjects lived with a spouse (69.3%) and were of mixed/black color (75.3%). This last characteristic was observed more frequently among men. The proportion of individuals with less than five years of schooling was high (76.3%), as was the proportion of illiterates (34.5%). The prevalences of alcohol consumption and smoking habit were 23.6% and 30.3%, respectively. These habits were also more frequent among men. The prevalence of metabolic syndrome and its components, according to sex, are shown in Table 2. Metabolic syndrome was diagnosed in 14.9% of the participants, and was four times more frequent among women than among men (P < 0.05). Concerning metabolic syndrome components, 11.6% of the population Table 1. Study population distribution according to demographic and lifestyle characteristics, stratified by sex. Virgem das Graças and Caju, Sex Variables Male Female Total n % n % n % Age (years) Skin color* White Mixed/black Marital status Living with partner Living without partner Schooling (years) Illiterate Smoking habits* Nonsmoker Former smoker Smoker Alcohol consumption (grams/day)* No consumption > *P < 0.05 for differences between sexes; Mixed/black includes all variations of mixed or black; Lowest ethanol consumption equals 3.1 grams/day. Sao Paulo Med J. 2013; 131(4):

10 ORIGINAL ARTICLE Pimenta AM, Felisbino-Mendes MS, Velasquez-Melendez G presented abdominal obesity, 15.2% hypertriglyceridemia, 44.1% low levels of HDL-c, 10.6% hyperglycemia and 59.7% hypertension. Abdominal obesity and low levels of HDL-c were proportionally higher among women (P < 0.05). Graph 1 presents the observed and expected frequencies of metabolic syndrome components, according to sex. Observed Table 2. Prevalence of metabolic syndrome and its components according to sex. Virgem das Graças and Caju, Sex Variables Male Female Total n % n % n % Metabolic syndrome Yes No Waist circumference* < 102 ( ); < 88 ( ) ( ); 88 ( ) Triglycerides (mg/l) < HDL-c (mg/dl)* < 40 ( ); < 50 ( ) ( ); 50 ( ) Fasting glucose (mg/dl) < Hypertension No Yes *P < 0.05 for differences between sexes; = men; = women; HDL-c = high density lipoprotein cholesterol; hypertension is defined as blood pressure 130/85 mmhg and/or hypertension due to drug treatment. % of individuals men women expected Number of components *P-value = (Pearson s chi-square test comparing observed and expected proportions of metabolic syndrome components). Graph 1. Observed (bar) and expected (line) proportions of individuals for each number of metabolic syndrome components, according to sex. Virgem das Graças and Caju, rates are represented by bars while expected rates are represented by lines. The observed frequencies were calculated based on the quintile distribution of metabolic syndrome components and are therefore not identical to the ones shown in Table 2. The expected frequencies were calculated in order to ascertain the nature of the co-occurrence of metabolic syndrome components and were based on the binomial distribution equation. Thus, for both sexes, the clustering of three or more metabolic syndrome components did not occur by chance, since the observed frequency (14.7% for men and 16.0% for women) was higher than the expected frequency (10.2%) (Table 2). Additionally, these proportions were statistically tested and, in fact, this confirmed the differences between the observed and expected proportions (P-value < 0.05). Table 3 shows the combinations of metabolic syndrome components among the individuals who presented this condition, according to sex. The most common combinations of three components in the population studied were hypertriglyceridemia + low levels of HDL-c + hypertension, and abdominal obesity + low levels of HDL-c + hypertension. The first pattern was more frequent among men and the second, among women. The most frequent combination of four components was abdominal obesity + hypertriglyceridemia + low levels of HDL-c + hypertension. This pattern was also more common among women. On the other hand, the most frequent four-component-pattern presented by men was hypertriglyceridemia + low levels of HDL-c + hyperglycemia + hypertension. DISCUSSION In this study, the prevalence of metabolic syndrome was 14.9%. The degree of clustering of its components was higher than what would be expected by chance and standard combinations between them. Although the magnitude of the metabolic syndrome was not as high as what was observed in other studies conducted in both urban and rural areas, which was over 20%, 2-7 metabolic syndrome is an important public health concern in the study population, since these two communities are located in one of the poorest regions of Brazil. Alternatively, in analyses stratified by sex, the data have demonstrated high prevalence of metabolic syndrome among women (23.3%) and low prevalence among men (6.5%). Similar findings have also been shown in studies developed in rural areas in Brazil 4 and other countries. 5,20 There seems to be a pattern of metabolic syndrome occurrence in rural areas that is characterized by higher prevalence among women. This phenomenon might be determined by people s occupations in this region, which differ greatly according to sex. Men still perform the field activities that require high energy expenditure, while women are devoted to housework. 5 Another investigation 216 Sao Paulo Med J. 2013; 131(4):213-9

11 Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population ORIGINAL ARTICLE developed using the same population as in this study showed that men were more active than women in relation to leisure, commuting and work, while women were more active than men in the household domain. 21 We observed in this study that there were occurrences of three or more metabolic syndrome components at rates greater than would be predicted by chance. This may be indicative of the existence of underlying mechanisms that contribute towards these cluster patterns. This is an important finding, since not all associations among the components have been fully elucidated, thus leading researchers to ask whether the proposed definitions used to diagnose metabolic syndrome might only be random cardiovascular risk factors. 10,11 Similar results were found in an investigation conducted on a sample of 4,975 subjects aged between 18 and 74 years from the Framingham Offspring Study, which was an urban population. In that study, it was also pointed out that clustering of three or more cardiovascular risk factors (high levels of total cholesterol, low levels of HDL-c, hypertriglyceridemia, overall obesity, elevated systolic blood pressure and hyperglycemia) occurred at a rate greater than what would be expected by chance and, hence, there ought to be a connection between them. 22 In other studies conducted on urban populations, it has also been demonstrated that combinations of three or more metabolic syndrome components occurred more frequently than the expected by chance Physicians and researchers have been recognizing that cardiovascular disease determinants tend to cluster, and therefore the risk of developing these illnesses rises in line with increases in their clustering abilities Therefore, our results corroborate those found in other investigations, thus providing greater consistency regarding the affirmation that metabolic syndrome is clinically useful as a diagnostic tool. Our results also show that, in the rural population studied, the occurrence of clustering of metabolic syndrome components was systematic and not random, thereby reinforcing the hypothesis that underlying pathophysiological mechanisms are involved in this process. One important criticism of the clinical importance of metabolic syndrome relates to the fact that it is diagnosed based on the presence of three or more components out of a total of five. This creates the possibility of 16 combinations, with different pathophysiological patterns and, consequently, multiple treatment options, 13 although we only found 13 among the studied population. Additionally, longitudinal studies have demonstrated variations in the risk of mortality according to the different combination patterns of metabolic syndrome components. 29,30 In our study, we observed that the most frequent combination of three components was hypertriglyceridemia + low levels of HDL-c + arterial hypertension among men and abdominal obesity + low levels of HDL-c + arterial hypertension Table 3. Combinations of metabolic syndrome components among the participants with this condition, according to sex. Virgem das Graças and Caju, Sex Combinations Male Female Total n % n % n % Three components HTG + LHDL-c + HBP AO + LHDL-c + HBP HTG + HGLY + HBP AO + HGLY + HBP LHDL-c + HGLY + HBP AO + HTG + HBP AO + HTG + LHDL-c Four components AO + HTG + LHDL-c + HBP AO + LHDL-c + HGLY + HBP HTG + LHDL-c + HGLY + HBP AO + HTG + HGLY + HBP AO + HTG + LHDL-c + HGLY Five components AO + HTG + LHDL-c + HGLY + HBP HTG = hypertriglyceridemia (triglycerides 150 mg/dl); LHDL-c = low levels of high density lipoprotein cholesterol (HDL-c < 40 mg/dl for men and < 50 mg/dl for women); HBP = hypertension (blood pressure 130/85 mmhg and/or hypertension due to drug treatment); HGLY = hyperglycemia; AO = abdominal obesity. among women, while the most common combination of four components was abdominal obesity + hypertriglyceridemia + low levels of HDL-c + arterial hypertension, for the whole study population. These combination patterns are similar to those found in other studies that also used the NCEP-ATP III metabolic syndrome definition. 13,20,31-33 As a result, it seems that the factors of real relevance in clinical practice are the combination patterns of metabolic syndrome components, according to sex. Consequently, healthcare professionals conduct could be guided by these characteristics. Furthermore, it could be seen that the most frequent combination patterns of metabolic syndrome components in most of the study population were abdominal obesity and dyslipidemia (hypertriglyceridemia and/or low levels of HDL-c). It was only in the pattern of hypertriglyceridemia + low levels of HDL-c + arterial hypertension that co-occurrence of abdominal obesity and dyslipidemia was not observed. On the other hand, this pattern was more frequent among men with an anthropometric profile consisting of low proportions of overall and abdominal obesity, thereby corroborating the findings of other studies. 13,20 Obesity, especially the abdominal or visceral type, plays a fundamental role in the pathophysiological mechanism of metabolic syndrome, given that it triggers the insulin resistance pathway as result of excessive free fatty acid accumulation in the blood circulation. 1,34 Sao Paulo Med J. 2013; 131(4):

12 ORIGINAL ARTICLE Pimenta AM, Felisbino-Mendes MS, Velasquez-Melendez G We believe that our findings have a social impact because the study population was still undergoing the process of epidemiological transition, i.e. high rates of morbidity and mortality due to infectious and parasitic diseases 35 were observed to coexist with increased occurrence of non-communicable illnesses. 36 Moreover, the local health services are poor, thus hindering the population s access to actions aimed at health promotion and disease prevention, control and treatment. 35 Thus, this evidence of clustering and combination patterns of metabolic syndrome components contributes towards the existing knowledge. It corroborates the usefulness of metabolic syndrome as a diagnostic tool with simple clinical-laboratory criteria that are easily applicable at the primary health care level, including isolated rural areas in one of the poorest regions of Brazil. This study has the following limitations: a) the sample was selected according to convenience, which means that caution is required in interpreting the external validity of our findings; b) the total number of individuals with a positive diagnosis of metabolic syndrome was small (n = 73), which needs to be taken into consideration in evaluating component combination patterns. CONCLUSION The rural population studied here presented high prevalence of metabolic syndrome among women. The metabolic syndrome components presented clustering at a rate greater than what would be expected by chance, suggesting that the combination patterns were non-random. The patterns that were most frequently observed were the following: hypertriglyceridemia + low levels of HDL-c + arterial hypertension; abdominal obesity + low levels of HDL-c + arterial hypertension; and abdominal obesity + hypertriglyceridemia + low levels of HDL-c + arterial hypertension. REFERENCES 1. Cornier MA, Dabelea D, Hernandez TL, et al. The metabolic syndrome. Endocr Rev. 2008;29(7): Ford ES. The metabolic syndrome and mortality from cardiovascular disease and all-causes: findings from the National Health and Nutrition Examination Survey II Mortality Study. Atherosclerosis. 2004;173(2): Dutra ES, de Carvalho KM, Miyazaki E, Hamann EM, Ito MK. Metabolic syndrome in central Brazil: prevalence and correlates in the adult population. Diabetol Metab Syndr. 2012;4(1): Oliveira EP, Souza MLA, Lima MDA. Prevalência de síndrome metabólica em uma área rural do semi-árido baiano [Prevalence of metabolic syndrome in a semi-arid rural area in Bahia]. Arq Bras Endocrinol Metabol. 2006;50(3): Gregory CO, Dai J, Ramirez-Zea M, Stein AD. Occupation is more important than rural or urban residence in explaining the prevalence of metabolic and cardiovascular disease risk in Guatemalan adults. J Nutr. 2007;137(5): Zubair N, Kuzawa CW, McDade TW, Adair LS. 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Circulation. 2003;107(3): Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign: Human Kinetics Books; Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6): Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19): Snedecor GW, Cochran WG. Statistical methods. 6 th ed. Ames: Iowa State University Press; Aekplakorn W, Kessomboon P, Sangthong R, et al. Urban and rural variation in clustering of metabolic syndrome components in the 218 Sao Paulo Med J. 2013; 131(4):213-9

13 Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population ORIGINAL ARTICLE Thai population: results from the fourth National Health Examination Survey BMC Public Health. 2011;11: Bicalho PG, Hallal PC, Gazzinelli A, Knuth AG, Velásquez-Meléndez G. Atividade física e fatores associados em adultos de área rural em Minas Gerais, Brasil [Adult physical activity levels and associated factors in rural communities of Minas Gerais State, Brazil]. Rev Saude Publica. 2010;44(5): Wilson PW, Kannel WB, Silbershatz H, D Agostino RB. Clustering of metabolic factors and coronary heart disease. Arch Intern Med. 1999;159(10): Stagnaro S. Epidemiological evidence for the non-random clustering of the components of the metabolic syndrome: multicentre study of the Mediterranean Group for the Study of Diabetes. Eur J Clin Nutr. 2007;61(9): Aizawa Y, Watanabe H, Ramadan MM, et al. Clustering trend of components of metabolic syndrome. Int J Cardiol. 2007;121(1): Ramachandran A, Snehalatha C, Latha E, Satyavani K, Vijay V. Clustering of cardiovascular risk factors in urban Asian Indians. Diabetes Care. 1998;21(6): Genest J Jr, Cohn JS. Clustering of cardiovascular risk factors: targeting high-risk individuals. Am J Cardiol. 1995;76(2):8A-20A. 27. Yusuf HR, Giles WH, Croft JB, Anda RF, Casper ML. Impact of multiple risk factor profiles on determining cardiovascular disease risk. Prev Med. 1998;27(1): Katakami N, Kaneto H, Matsuhisa M, et al. Clustering of several cardiovascular risk factors affects tissue characteristics of the carotid artery. Atherosclerosis. 2008;198(1): Guize L, Thomas F, Pannier B, et al. All-cause mortality associated with specific combinations of the metabolic syndrome according to recent definitions. Diabetes Care. 2007;30(9): Hong Y, Jin X, Mo J, et al. Metabolic syndrome, its preeminent clusters, incident coronary heart disease and all-cause mortality--results of prospective analysis for the Atherosclerosis Risk in Communities study. J Intern Med. 2007;262(1): Zaliūnas R, Slapikas R, Babarskiene R, et al. The prevalence of the metabolic syndrome components and their combinations in men and women with acute ischemic syndromes. Medicina (Kaunas). 2008;44(7): Chen W, Srinivasan SR, Elkasabany A, Berenson GS. Cardiovascular risk factors clustering features of insulin resistance syndrome (Syndrome X) in a biracial (Black-White) population of children, adolescents, and young adults: the Bogalusa Heart Study. Am J Epidemiol. 1999;150(7): Liu J, Zhao D, Wang W, et al. [Incidence risk of cardiovascular diseases associated with specific combinations regarding the metabolic syndrome components]. Zhonghua Liu Xing Bing Xue Za Zhi. 2008;29(7): Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2005;365(9468): Reis DC, Kloos H, King C, et al. Accessibility to and utilisation of schistosomiasis-related health services in a rural area of state of Minas Gerais, Brazil. Mem Inst Oswaldo Cruz. 2010;105(4): Silva DA, Felisbino-Mendes MS, Pimenta AM, et al. Distúrbios metabólicos e adiposidade em uma população rural [Metabolic disorders and adiposity in a rural population]. Arq Bras Endocrinol Metabol. 2008;52(3): This paper forms part of the thesis Fatores associados à síndrome metabólica em área rural de Minas Gerais, presented to the School of Nursing, Universidade Federal de Minas Gerais (UFMG), on December 15, 2008 Sources of funding: This study was supported by a grant from Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG: CDS 530/04) Conflict of interest: None Date of first submission: June 26, 2011 Last received: August 31, 2012 Accepted: November 26, 2012 Address for correspondence: Gustavo Velásquez-Meléndez Departamento de Enfermagem Materno-Infantil e Saúde Pública Universidade Federal de Minas Gerais, Escola de Enfermagem Avenida Alfredo Balena, 190 Santa Efigênia Belo Horizonte (MG) Brasil CEP Tel. (+55 31) Fax. (+55 31) Sao Paulo Med J. 2013; 131(4):

14 ORIGINAL ARTICLE DOI: / Obstructive sleep apnea syndrome: complaints and housing characteristics in a population in the United States Síndrome de apneia obstrutiva do sono: queixas e características da habitação em uma população dos Estados Unidos da América Khalil Ansarin I, Leyla Sahebi II, Siamak Sabur III Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran I MD. Internist, Pulmonologist and Chairman of Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. II MSc. Doctoral Student of Epidemiology, Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Science, Tabriz, Iran. III MD, PhD. Assistant Professor of Clinical Epidemiology and Medicine, Department of Clinical Epidemiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. KEY WORDS: Sleep apnea, obstructive. Housing. Nutrition surveys. Snoring. United States. PALAVRAS-CHAVE: Apnéia do sono tipo obstrutiva. Habitação. Inquéritos nutricionais. Ronco. Estados Unidos. ABSTRACT CONTEXT AND OBJECTIVE: Obstructive sleep apnea syndrome (OSAS) is one of the leading causes of morbidity and mortality in adults. Early detection of the disorder and discovery of risk factors through standardized questionnaires will lead to reduction of the OSAS burden. The main aim of this study was to estimate the prevalence of OSAS symptoms and examine their association with housing characteristics. DESIGN AND SETTING: Cross-sectional study at a medical school. METHODS: Demographic, housing and body measurement data on 5,545 individuals aged 16 years and over of various races were selected from the National Health and Nutrition Examination Survey. We analyzed the probability of OSAS based on habitual snoring combined with daytime sleepiness and/or witnessed apnea. Univariate and multiple linear regression were used. RESULTS: 9.8% of the men and 6.9% of the women reported symptoms suggestive of OSAS (habitual snoring, daytime sleepiness and/or apnea). The following prevalences of symptoms were found among males and females respectively: frequent snoring 35.1%, 22.3%, excessive daytime sleepiness 6.4%, 3.4% and frequent apnea 14.9%, 20.6%. Using multiple linear regression, OSAS symptoms were correlated with gender, age, body mass index (BMI), marital status and education. Regarding housing characteristics, mildew or musty smell and pets in the environment were associated with a high probability of OSAS. CONCLUSION: OSAS symptoms were more prevalent than in developing countries. The environment was an important risk factor, but environmental factors are easier to control and manage than other variables like BMI or socioeconomic status. RESUMO CONTEXTO E OBJETIVO: Síndrome de apneia obstrutiva do sono (SAOS) é uma das principais causas de morbidade e mortalidade em adultos. Detecção precoce da doença e descoberta de fatores de risco com questionários padronizados levarão a redução dos danos por SAOS. O principal objetivo deste estudo foi estimar a prevalência de sintomas de SAOS e examinar sua associação com características da habitação. TIPO DE ESTUDO E LOCAL: Estudo transversal em faculdade de medicina. MÉTODOS: Dados demográficos, habitacionais e de medidas corporais sobre indivíduos de 16 anos ou mais, de diversas raças, foram selecionados do National Health and Nutrition Examination Survey. Analisamos a probabilidade de SAOS com base no ronco habitual combinada com sonolência diurna e/ou apneia testemunhada. Análise univariada e regressão linear múltipla foram usadas. RESULTADOS: 9,8% dos homens e 6,9% das mulheres relataram sintomas sugestivos de SAOS (ronco habitual, sonolência diurna e/ou apneia). A prevalência de sintomas em homens e mulheres, respectivamente, foi: ronco frequente 35,1%, 22,3%, sonolência excessiva diurna 6,4%, 3,4% e apneia frequente 14,9%, 20,6%. Através de regressão linear múltipla, sintomas de SAOS foram relacionados com gênero, idade, índice de massa corpórea (IMC), estado civil e educação. Das características da habitação, mofo ou cheiro de mofo e animais de estimação no ambiente foram associados com alta probabilidade de SAOS. CONCLUSÃO: Sintomas de SAOS foram mais prevalentes do que nos países em desenvolvimento. O meio ambiente foi um fator de risco importante, porém é mais fácil controlar e manejar fatores ambientais do que outras variáveis, como IMC ou status socioeconômico. 220 Sao Paulo Med J. 2013; 131(4):220-7

15 Obstructive sleep apnea syndrome: complaints and housing characteristics in a population in the United States ORIGINAL ARTICLE INTRODUCTION Obstructive sleep apnea syndrome (OSAS) is a considerable issue in public health. It is a highly prevalent disorder among middleaged adults 1 and is independently associated with certain risk factors. It is also related to many medical conditions such as hypertension, cardiovascular morbidity and mortality, coronary insufficiency, stroke, type 2 diabetes and pulmonary disease. 2-5 Untreated OSAS can cause road accidents, 1 loss of work productivity, 5 occupational injuries 1 and even sudden death. 4 Therefore, this disorder may lead to a huge multilateral problem unless proper control and management is implemented. The prevalence of obstructive sleep apnea among adults in the Western world ranges from 3% to 28%, 5 and in the United States of America (USA), the prevalence is currently estimated to be 5% to 10%. 6,7 The prevalence of OSAS in some studies based on various questionnaires has been reported as follows: based on the Berlin Criteria, in New Zealand (2009) 2.8%, 2 Iran (2011) 4.9% 8 and USA (2006) 26%; 5 based on a self-reported questionnaire, in France (2007) 3.5% 1 and Hong Kong (2001) 2.1%; 9 and based on the Epworth Sleepiness Scale (ESS), in Nigeria (2008) 1.2% 10 and India (2004) 3.6%. 11 It is noteworthy that in some regions only 10% of the population has been adequately screened for appropriate diagnosis. 4 Discrepancies in observed prevalence and underreporting may be due to non-standardized definitions and variation between diagnostic methods. 1 OSAS screening, diagnosis and treatment entails some challenges. Polysomnography or respiratory polygraphy is a precise method for diagnosing OSAS, but this method has its disadvantages, such as expensiveness, inaccessibility and difficulty to perform. Thus, in some cases, these disorders are not diagnosed and only a few cases are properly treated. 1 Another diagnostic method is to screen by means of a questionnaire based on three symptoms: reported habitual snoring, daytime sleepiness and witnessed apnea. Early detection of OSAS not only reduces the morbidity risk but also leads to significant reduction in the cost of care for other conditions. 2,10 Many studies have been published in relation to weight and demographic variables, which all have strong relationships with OSAS. 1-3,8,11-13 Information on environmental factors affecting this outcome is unavailable. Housing characteristics are an important environmental variable; adverse conditions are preventable and can be dealt with cost-effectively, and thus may be a determinant in decreasing the burden of disease. OBJECTIVE The purpose of this study was to assess the prevalence of symptoms of OSAS in a population in the USA and analyze housing characteristic risks in relation to demographic and body mass index (BMI) variables, using the National Health and Nutrition Examination Survey (NHANES) Sleep Disorders Questionnaire and dataset. 14 METHODS We used the NHANES dataset (2005 to 2006), publicly available from: NHANES was a major program that was implemented by the United States National Center for Health Statistics (NCHS). NCHS is part of the Centers for Disease Control and Prevention (CDC) and is responsible for producing vital and health statistics for the United States. NHANES was conducted in all 50 states of the USA. NHANES data were not obtained using a simple random sample. Rather, a complex, multistage, probability sampling design was used to select participants such that they would be representative of the civilian of the civilian, non-institutionalized US population. Oversampling of certain population subgroups was done to increase the reliability and precision of the health status indicator estimates for these groups. The NCHS used four questionnaires: demographic variables, housing characteristics, body measurements and OSAS. After the datasets for demographic variables, housing characteristics, OSAS and body measurements had been merged, inconsistencies relating to 594 sequence numbers led these individuals to be excluded from the study. Thus, 5,545 individuals aged 16 years and over were selected, from several racial groups in the United States: Mexican American, Other Hispanic, Non-Hispanic White, Non- Hispanic Black and Other Race Including Multiracial. Demographic variables The population for the demographics questionnaire was interviewed directly in the subjects homes and a proxy was provided for individuals who could not answer the questions themselves. The variables selected for this evaluation included age, gender, marital status, education status, pregnancy status, household size and family size. Housing characteristics One study participant in each family responded for the entire family and these responses were released for all members of the same family. The housing characteristics provided family-level interview data on the type of home, number of apartments in the building, age of home, number of rooms in home, time lived in home, whether home was owned or rented, water source and treatment, and allergy component-related questions about the presence of furry animals. Sleeping characteristics This section included questions on sleep habits and disorders. A subscale of eight questions, relating to general productivity, Sao Paulo Med J. 2013; 131(4):

16 ORIGINAL ARTICLE Ansarin K, Sahebi L, Sabur S from the Functional Outcomes of Sleep Questionnaire, was also included. 14 Variables pertaining to OSAS were selected in order to analyze the probability of OSAS based on habitual snoring (often or almost every night), combined with daytime sleepiness (often or almost always) and/or witnessed apnea (often or almost every night, as confirmed by self-reports). Body mass index (BMI) All survey participants were eligible for the body measurement component. There were no medical, safety or other exclusions for the body measurement protocol. The body measurement data were collected by trained health technicians, who were accompanied by a recorder during each body measurement examination. The health technicians used their discretion to obtain as many measurements as practical for individuals who were using a wheelchair. Body weight data for individuals who had had limb amputations and also those from pregnant women were excluded from the analysis. Height was measured using a Seca electronic stadiometer, in an upright standing position, with head and heels against the stadiometer before taking the height, unless this position was anatomically impossible. Before the measurement, the participants took a deep breath and held it while the headboard was positioned. If the individuals were unable to stand with the head and heels against the stadiometer, the trunk needed to be vertical above the waist and the arms and shoulders needed to be relaxed. 14 Weight was measured on a Toledo digital scale, in pounds with automatic conversion to kilograms. The participants were weighed in their underwear. They were instructed to stand still at the center of the scale platform facing the recorder, with their hands at their sides, and to look straight ahead. After they had been properly positioned and the digital readout was stable, the recorder clicked on the capture button on the screen. The weight, length and height measurements were entered directly into the computer system by clicking on the Get buttons. 14 In accordance with the World Health Organization (WHO) definitions, BMI (kg/m 2 ) less than 18.5 was considered to be underweight, BMI greater than 25 was considered to be overweight and greater than 30 was considered to be obese. 15 Analysis After assessment to check that the data presented normal distribution, all the continuous variables were summarized in terms of mean ± standard deviation (SD) and categorical variables were expressed as percentages. All significance tests were two-sided, and P values less than 0.05 were considered to be statistically significant. Univariate and multiple linear regression were used for the analysis. Variables showing associations with P values less than 0.10 in univariate analyses were considered to be candidate risk factors to be used in multiple analysis. RESULTS The participants mean age was 41.8 ± 20 years. Among this sample of individuals 16 years of age from the USA, 2,826 (51%) were women and 2,719 were men (49%) and consisted of the following: Mexican Americans (1,248 cases, 22.5%), Other Hispanics (174 cases, 3.1%), Non-Hispanic Whites (2,468 cases, 44.5%), Non-Hispanic Blacks (1,428 cases, 22.5%) and Other Race Including Multiracial (227 cases, 4.1%). The frequency distribution among the study participants is shown in Table 1. The prevalence of habitual snoring ( 5 nights/week) was 28.7% (confidence interval, 95% CI: ); habitual apnea ( 5 nights/week) was 4.8% (95% CI: ) and habitual excessive daytime sleepiness (EDS) ( 5 times a month) was 17.9% (95% CI: ). The prevalences of habitual snoring, apnea and EDS were 35.1% (849 subjects; 95% CI: ), 6.4% (160 subjects; 95% CI: ) and 14.9% (405 cases; 95% CI: ) among the men and 22.3% (539 subjects; 95% CI: ), 3.4% (87 subjects; 95% CI: ) and 20.6% (584 subjects; 95% CI: ) among the women, respectively. The OSAS symptoms had strong correlations (snoring/apnea: P < and ß = 0.39; snoring/eds: P < and ß = 0.09; and EDS/apnea: P < and ß = 0.12). The estimated prevalence of OSAS was 8.3% (463 subjects; 95% CI: ). The prevalence of symptoms associated with OSAS is shown in Graph 1. The prevalence of symptoms associated with OSAS according to house characteristics is presented in Table 2. In order to investigate variables relating to OSAS, univariate and multiple linear regression tests were performed. Variables with more than two categories were analyzed in the form of a dummy variable. These variables included race, education status, marital status and type of house. In the univariate linear regression, BMI, age, gender, race, education status, marital status, type of house, age of home, rented or owned home, source of water, presence of mildew or musty smell, living or spending time with pets and ratio of population density in the home were variables that had significant relationships with OSAS. The details the of analysis were as follows: the probability of OSAS increased linearly with increasing age and BMI respectively (ß = 0.087, P < ; ß = 0.026, P < ); the probability among the women was less than among the men (ß = , P = ); the probability among the Mexicans was greater than among other races (ß = 0.43, P < ); the probability was less among Non-Hispanic Blacks (ß = , P < ). The probability of OSAS among school students (16-20 years) without delay (i.e. students who never failed in the end-of-year 222 Sao Paulo Med J. 2013; 131(4):220-7

17 Obstructive sleep apnea syndrome: complaints and housing characteristics in a population in the United States ORIGINAL ARTICLE Table 1. Distribution of participants according to demographic characteristics Name of variable Age (years), mean (standard deviation) Men, n (%) Body mass index (kg/m 2 ), n (%) Education status* n (%) Marital status n (%) Co-factors < missing missing missing Mexican American n = (17.8) 608 (47.7) 28 (2.3) 366 (30) 449 (36.8) 376 (30.8) (16) 31 (2.5) 356 (28.6) 189 (15.2) 234 (18.8) 190 (15.3) 46 (3.7) (45.5) 49 (3.9) 58 (4.6) 40 (3.2) 417 (33.4) 116 (9.3) - Other Hispanic n = (16.7) 81 (46.6) 4 (2.3) 61 (35.3) 57 (32.9) 51 (29.5) 1 20 (11.6) 3 (1.7) 37 (21.4) 33 (19.1) 22 (12.7) 41 (23.7) 17 (9.8) 1 82 (47.4) 4 (2.3) 9 (5.2) 2 (1.2) 57 (32.9) 19 (11.0) 1 Non-Hispanic (White) n = (20.6) 1224 (49.6) 68 (2.8) 829 (34.6) 759 (31.7) 738 (30.8) (6.5) 12 (0.5) 102 (4.2) 223 (9.1) 633 (25.8) 721 (29.4) 602 (24.6) (53.4) 174 (7.1) 230 (9.3) 41 (1.7) 530 (21.5) 172 (7.0) 5 Non-Hispanic (Black) n = (19.5) 712 (49.9) 32 (2.3) 427 (30.7) 377 (27.1) 555 (39.9) (15.7) 12 (0.8) 58 (4.1) 233 (16.4) 324 (22.9) 409 (28.9) 158 (11.2) (29.3) 86 (6.0) 118 (8.3) 65 (4.6) 629 (44.0) 111 (7.8) - Other n = (16.9) 94 (41.4) 10 (4.6) 95 (43.6) 57 (26.1) 56 (25.7) 9 29 (12.9) 2 (0.9) 19 (8.4) 8 (3.6) 48 (21.3) 52 (23.1) 67 (29.8) (46.3) 10 (4.4) 179 (7.5) 6 (2.6) 71 (31.3) 189(7.9) - *Education status. 1: school student for more than 4 years without any failure in end-of-year examinations and thus no repetition of the school year; 2: school student for more than 4 years with failure in end-of-year examinations and the need to repeat the school year; 3: less than 9 th grade, 4: 9-11 th grade; 5: high school graduate; 6: university student without a certificate; and 7: university graduate. Marital status. 1: married; 2: widowed; 3: divorced; 4: separated; 5: never married; and 6: living with parent. examinations and thus never had to repeat the school year) was less than among others (ß = 1.43, P < ), while university students without a certificate and university graduates had higher probability of disorders (ß = -0.36, P < ). Regarding marital status, married (ß = -0.3, P < ), widowed (ß = -0.59, P < , 95% CI: ), divorced (ß = -0.94, P < ) and separated participants (ß = -1.19, P < ) presented higher probability of OSAS, while participants who had never married (ß =1.03, P < ) had lower probability of OSAS. From examining the associations shown between the home environment and the disorder, the following results could be inferred: living in a mobile home or trailer produced a higher probability of OSAS than shown by other types of home (ß = 0.79, P = 0.005). In addition, a direct linear relationship was found between duration of living in a house and probability of the disease. Homes with a well as the water source (rather than from a water supply company) and those with mildew or a musty smell had higher probability of disorders (ß = 0.79, P = 0.005) and (ß = -0.34, P = 0.003). Living with pets was also associated with greater possibility of OSAS (ß = , P < ), but population density presented an inverse relationship with OSAS (ß = -0.63, P < ). The variables snoring apnea EDS Almostalways Frequency Occasionally Rarely Never Snoring/apnea: never, rarely (1-2 nights/week), occasionally (3-4 nights/week), frequently (5 or more nights/week); EDS: never, rarely (once a month), sometimes (2-4 times a month), frequently (5-15 time a month), almost always (16-30 times a month). EDS = excessive daytime sleepiness. Graph 1. Prevalence of obstructive sleep apnea syndrome-related symptoms based on intensity Sao Paulo Med J. 2013; 131(4):

18 ORIGINAL ARTICLE Ansarin K, Sahebi L, Sabur S Table 2. Prevalence of the symptoms of obstructive sleep apnea syndrome (OSAS) according to house characteristics Housing characteristics Sleep disorders Snoring Apnea EDS N (%) Mobile home or trailer 185 (50) 47 (12.3) 85 (20.5) One-family house, isolated 1350 (45) 315 (10) 586 (17.1) Type of house One-family house* 199 (40.9) 37 (7.2) 98 (17.7) Apartment 358 (41.9) 92 (9.9) 188 (18.8) Dormitory 27 (32.1) 5 (5.9) 27 (29.7) 1 or 2 24 (42.1) 5 (8.3) 10 (14.7) 3 or 4 49 (38.0) 20 (13.2) 31 (19.1) How many apartments in the building? 5 to (42.3) 27 (10.5) 45 (16.7) 10 to (44.1) 23 (9.7) 49 (19.8) 20 to (39.1) 3 (3.1) 20 (18.7) 50 or more 38 (42.7) 13 (13.1) 27 (22.9) 1990 to present 79 (45.7) 119 (11) 203 (17.5) 1978 to (42.8) 82 (10.7) 153 (18.7) When was home built? 1960 to (46.8) 71 (9.7) 160 (20.3) 1950 to (47.7) 52 (11.6) 79 (16.3) 1940 to (45.3) 27 (9.2) 52 (16.4) Before (44.5) 51 (9.5) 127 (21.4) Owned 1418 (45.5) 336 (10.4) 597 (17) Rented or owned Rented 653 (41.9) 152 (9) 348 (19.1) Other 48 (42.9) 8 (6.5) 38 (27.5) Company 1761 (43.8) 404 (9.5) 809 (17.5) Source of tap water Well 328 (47.1) 82 (11.6) 152 (19.8) Other 15 (46.9) 6 (19.4) 7 (20) Water treatment devices Yes 549 (45.1) 130 (10.3) 214 (15.6) No 1559 (44) 360 (9.6) 760 (18.7) Mildew or musty smell Yes 324 (43.7) 81 (10.3) 188 (21.9) No 1789 (44.3) 412 (9.7) 790 (17.1) Cockroaches seen in home Yes 389 (42.5) 85 (8.6) 195 (18.4) No 1730 (44.6) 411 (10.1) 788 (17.8) Living or spending time with animals Yes 916 (47.3) 210 (10.4) 449 (20.7) No 1203 (42.1) 286 (9.4) 535 (16.1) Dog in house now Yes 662 (47.2) 157 (10.8) 336 (21.4) Cat in house now Yes 408 (46.9) 98 (10.9) 202 (20.9) Small furry animal in house now Yes 69 (46.9) 12 (8.1) 42 (26.1) > (45.2) 434 (10.6) 812 (82.9) Population density in house (40.9) 58 (6.7) 150 (16.1) 2 21 (32.8) 2 (3) 16 (23.9) > 2 7 (41.2) 2 (10) 1 (5) Less than one year 402 (41.8) 96 (9.5) 193 (17.9) 1-2 years 375 (42.2) 92 (9.6) 201 (19.4) How many years has family lived in home? 3-5 years 371 (43.4) 82 (9.2) 169 (17.4) 6-10 years 326 (47.1) 66 (9.1) 134 (17) More than 10 years 645 (46.3) 160 (11) 286 (17.8) EDS = excessive daytime sleepiness; *Attached to one or more houses; Total number of people in the household divided by the number of rooms. of number of apartments in the building, home age and having cockroaches in the home did not show any association with OSAS. In multiple linear regression analysis, the variables of age, gender, BMI, education status, marital status, mildew or musty smell, and animals living or spending time in the home presented significant relationships with OSAS. The test results are shown in Table 3. DISCUSSION To the best of our knowledge, this study is the first broad study on data from the USA to contain valuable information about sleep disorders and risk factors relating to housing characteristics in the presence of important variables like weight and demographic variables. The prevalence of symptoms of OSAS was 8.3%. 224 Sao Paulo Med J. 2013; 131(4):220-7

19 Obstructive sleep apnea syndrome: complaints and housing characteristics in a population in the United States ORIGINAL ARTICLE We compared several studies conducted among adult in different countries using questionnaire instruments. The prevalence of OSAS was 1% in Nigeria 10 (0.5% among women and 1.9% among men), 3.6% in India, % in Hong Kong 9 and 5% in Iran; 8 however, it was 7.4% in France 1 and 26% in the USA. 5 OSAS in the United States, like in other Western countries, is more common than in developing countries. Obesity and aging are probably crucial factors in the United States, and the rising trend of these two factors requires more attention to this context. 17,18 Although the prevalence of risky apnea in the present study was similar to findings in Pakistan (10-12%), 19 it was more common in Malaysia (15.2%). 20 On the other hand, rates if 3.5% and 6.1% were reported in France and Turkey, respectively. 1,12 Nevertheless, it should be noted that 90% of individuals with sleep apnea are undiagnosed. 21 The prevalence of habitual snoring was similar to findings from studies conducted in the USA (46%) and Malaysia (47.3%). 5,20 The prevalence observed in our study was higher than in the following other regions: Nigeria 31.6%, 10 São Paulo 31% 22 and France 22%. 1 The rate of excessive daytime sleepiness in the present study (15%) was similar to findings from France (16%) and Malaysia (14.8%). 1,20 On the other hand, the reported prevalence was 6.5% in the USA (2005). 5 Consistent with a study conducted in France, 1 habitual snoring and apnea were more prevalent among men (35.1 and 6.4%) than among women (22.3 and 3.4%). However, EDS was more prevalent among women (20.6 versus 14.9%). The prevalence of symptoms in the French study was consistently lower (61%, 7% and 24%). 1 OSAS was less common among women, and this was similar to other studies; for instance in France, Japan, New Zealand 1,2,4 and also in Nigeria, India, and Hong Kong. 9,10 Epidemiological studies have confirmed that the gender ratio of OSAS is 2 to 3.1, 10,20,21 and this ratio was 1.42 in our study. In addition to cases of more prevalent OSAS disorders among men, these differences were statistically significant in our study and some other studies 1,2,4,8,23 although a study conducted in Iran did not reach any significant variation. 8 BMI has been an important modifiable risk factor in relation to occurrences of OSAS This has been confirmed in several studies. 1,8,10-13,17 For example, obese individuals presented a risk of OSAS that was 10 times higher in the study by Salvador et al. 27 There was also a strong relationship between these in the presence of confounding variables. On the basis of several studies, increased body weight can alter the normal upper airway mechanics during sleep through a variety of distinct mechanisms. 28 Table 3. Analytical statistics on the status of obstructive sleep apnea syndrome, from multiple linear regression test Variables* ß SE P-value Body mass index (kg/m 2 ) Age Gender: 1: male; 2: female Race (dummy) Race Race Race Education status (dummy) Education status Education status Education status Education status Education status Marital status (dummy) Marital status Marital status Marital status Marital status Marital status Type of house (dummy) Type of house Type of house When was home built? How many years has family lived in home? Water source: 1: Company 2: Well Has home had mildew or a musty smell? 1: Yes 2: No Have you seen cockroaches in your home? 1: Yes 2: No Do animals live or spend time in home? 1: Yes 2: No Ratio of population density *Variables with P < 0.1 are entered in the model; Education status 1: school student for more than 4 years without any failure in end-of-year examinations and thus no repetition of the school year; 2: school student for more than 4 years with failure in end-of-year examinations and the need to repeat the school year; 3: less than 9 th grade, 4: 9-11 grade; 5: high school graduate; 6: university student without certificate; 7: university graduate; Race: 1: Mexican American; 2: other Hispanic; 3: non-hispanic (white); 4: other; Marital status: 1: married; 2: widowed; 3: divorced; 4: separated; 5: never married; 6: living with parent; Type of house: 1: mobile home or trailer; 2: one-family house, detached from any other house; 3: one-family house, attached to one or more houses; 4: apartment; 5: dormitory. SE = standard error. There was a direct trend between age and OSAS in the present study. These results were consistent with reports from different countries 1,5 in which some of the results were analyzed from multiple tests, like in the present study. 4,8 Thus, the effect of age-related chronic diseases on OSAS needs to be investigated. Sao Paulo Med J. 2013; 131(4):

20 ORIGINAL ARTICLE Ansarin K, Sahebi L, Sabur S Education was another variable significantly associated with OSAS, with regard to confounding variables. It was found that high school students who never failed in the end-of-year examinations and thus never had to repeat the school year presented lower probability of OSAS. There was a direct association between education status and OSAS. 8 Participants whose marital status was separated showed less possibility of OSAS than shown by other marital statuses. In a report from Nigeria, being married was a risk factor for OSAS. 10 Although Mexican Americans presented a high risk in univariate regression, multiple regressions did not support this result. In other studies, Asian, African-American and Hispanic racial groups presented higher risk. 13 One main goal in the present study was to investigate the association between housing characteristics and OSAS. Although the home environment has been reported as a factor in people s health, 29 its role in relation to sleep status had not been particularly studied. Although the variables of type of house, population density in the home, water source and the age of the house were significant in the test, their relationship did not maintain significance in the multiple model. An odor of mildew or a musty smell and living or spending time with animals had strong relationships with OSAS both in univariate and in multiple linear regression. Our results are new in terms of exposure factors associated with OSAS. Whether the mechanisms through which housing characteristics correlate with OSAS are pathological mechanisms (e.g. through the activity of some fungi such as Aspergillum in damp environments or microorganisms found in the saliva of wool or animal), or are autoimmune mechanisms (e.g. through immune reactions against allergens such as wool and animal hair), or are physiological (e.g. through affecting concentration due to moisture and the air pressure in the home environment), these can be evaluated and thus, eventually, effective management of OSAS may be achieved. Housing characteristics should be taken into account for public health purposes for better management of OSAS in the US, as well as in clinical decision-making relating to this syndrome. CONCLUSION Symptoms of OSAS were more prevalent in the USA (8.3%) than in developing countries. Moreover, the environment was an important factor for OSAS. REFERENCES 1. Meslier N, Vol S, Balkau B, et al. Prévalence des symptômes du syndrome d apnées du sommeil. Etude dans une population française d âge moyen. [Prevalence of symptoms of sleep apnea syndrome. Study of a French middle-aged population]. Rev Mal Respire. 2007;24(3 Pt 1): Mihaere KM, Harris R, Gander PH, et al. Obstructive sleep apnea in New Zealand adults: prevalence and risk factors among Māori and non-māori. Sleep. 2009;32(7): Adewole O, Adeyemo H, Ayeni F, et al. Prevalence and correlates of snoring among adults in Nigeria. Afr Health Sci. 2008;8(2): Kayukawa Y, Shirakawa S, Hayakawa T, et al. Habitual snoring in an outpatient population in Japan. Psychiatr Clin Neurosci. 2000;54(4): Hiestand DM, Britz P, Goldman M, Phillips B. Prevalence of symptoms and risk of sleep apnea in the US population: Results from the national sleep foundation sleep in America 2005 poll. Chest. 2006;130(3): de la Hoz RE, Aurora RN, Landsbergis P, et al. Snoring and obstructive sleep apnea among former World Trade Center rescue workers and volunteers. J Occup Environ Med. 2010;52(1): Tishler PV, Larkin EK, Schluchter MD, Redline S. Incidence of sleepdisordered breathing in an urban adult population: the relative importance of risk factors in the development of sleep-disordered breathing. JAMA. 2003;289(17): Amra B, Farajzadegan Z, Golshan M, Fietze I, Penzel T. Prevalence of sleep apnea-related symptoms in a Persian population. Sleep Breath. 2011;15(3): Ip MS, Lam B, Lauder IJ, et al. A community study of sleep-disordered breathing in middle-aged Chinese men in Hong Kong. Chest. 2001;119(1): Adewole OO, Hakeem A, Fola A, et al. Obstructive sleep apnea among adults in Nigeria. J Natl Med Assoc. 2009;101(7): Sharma SK, Kumpawat S, Banga A, Goel A. Prevalence and risk factors of obstructive sleep apnea syndrome in a population of Delhi, India. Chest. 2006;130(1): Onat A, Hergenç G, Uyarel H, et al. Obstructive sleep apnea syndrome is associated with metabolic syndrome rather than insulin resistance. Sleep Breath. 2007;11(1): Nieto FJ, Young TB, Lind BK, et al. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large communitybased study. Sleep Heart Health Study. JAMA. 2000;283(14): Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey. NHANES U.S. department of health and human services centers for disease control and prevention national center for health statistics overview. Available from: nhanes05_06.htm. Accessed in 2012 (Aug 16). 15. World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. Technical Report Series No.854. Available from: childgrowth/publications/physical_status/en/index.html. Accessed in 2012 (Aug 16). 226 Sao Paulo Med J. 2013; 131(4):220-7

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