Teresa Arechavala1, Xavier Continente2, Mónica Pérez-Ríos3, Esteve Fernández4, Núria Cortés-Francisco5, Anna Schiaffino6, Francesc Centrich7, Glòria Muñoz7, María José López8. 1. Agència de Salut Pública de Barcelona, Servei d'Avaluació i Mètodes d'Intervenció, Barcelona, Spain; CIBER de Epidemiología y Salud Pública, Madrid, Spain; Universitat Pompeu Fabra (UPF), Department of Experimental and Health Science, Barcelona, Spain. 2. Agència de Salut Pública de Barcelona, Servei d'Avaluació i Mètodes d'Intervenció, Barcelona, Spain; CIBER de Epidemiología y Salud Pública, Madrid, Spain; Institut d'investigació Biomèdica Sant Pau (IIB St. Pau), Barcelona, Spain. 3. CIBER de Epidemiología y Salud Pública, Madrid, Spain; Epidemiology Unit, Galician Directorate for Public Health, Galician Health Authority, Xunta de Galicia, Santiago de Compostela, Spain; Department of Preventive Medicine and Public Health, School of Medicine, University of Santiago de Compostela, Spain. 4. Tobacco Control Unit, Cancer Control and Prevention Program, Institut Català d'Oncologia (ICO), L'Hospitalet de Llobregat, Barcelona, Spain; Cancer Prevention and Control Group, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Department of Clinical Sciences, Campus de Bellvitge, School of Medicine and Health Sciences, Universitat de Barcelona, L'Hospitalet de llobregat, Barcelona, Spain. 5. Agència de Salut Pública de Barcelona, Servei d'Avaluació i Mètodes d'Intervenció, Barcelona, Spain; Institut d'investigació Biomèdica Sant Pau (IIB St. Pau), Barcelona, Spain. 6. Cancer Prevention and Control Group, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Direcció General de Planificació en Salut, Departament de Salut, Generalitat de Catalunya, Spain. 7. Agència de Salut Pública de Barcelona, Servei d'Avaluació i Mètodes d'Intervenció, Barcelona, Spain. 8. Agència de Salut Pública de Barcelona, Servei d'Avaluació i Mètodes d'Intervenció, Barcelona, Spain; CIBER de Epidemiología y Salud Pública, Madrid, Spain; Universitat Pompeu Fabra (UPF), Department of Experimental and Health Science, Barcelona, Spain; Institut d'investigació Biomèdica Sant Pau (IIB St. Pau), Barcelona, Spain. Electronic address: mjlopez@aspb.cat.
Abstract
INTRODUCTION: Questionnaires are widely used to assess secondhand smoke (SHS) exposure. However, the validity of self-reported SHS exposure indicators has been rarely assessed. We aimed to assess correlations, sensitivity, specificity, and predictive values between self-reported SHS exposure indicators and airborne nicotine concentrations. METHODS: We performed a cross-sectional study with a convenience sample of 175 homes in Barcelona and Santiago de Compostela, Spain. Airborne nicotine samples were collected from participants' homes and a self-administered questionnaire was completed on SHS exposure in the home. Spearman correlations coefficients and sensitivity, specificity and predictive values were assessed between self-reported SHS exposure indicators and nicotine concentrations in the home. RESULTS: All self-reported SHS exposure indicators correlated moderately strongly with airborne nicotine concentrations (Spearman correlations coefficient ranging from 0.58 to 0.65). Moreover, sensitivities and negative predictive values between self-reported indicators and the presence of nicotine in the home were below 66.4% while specificities and positive predictive values were over 78.4%. The "number of people usually smoking in the home" showed the best results (rs = 0.65, p < 0.001; sensitivity = 50.4%, specificity = 95.2%, PPV = 95.0, NPV = 51.3). CONCLUSIONS: The self-reported SHS indicators assessed in this study showed moderate and strong correlations, low sensitivities, and high specificities. Among them, the best results were obtained with the "number of people usually smoking in the home".
INTRODUCTION: Questionnaires are widely used to assess secondhand smoke (SHS) exposure. However, the validity of self-reported SHS exposure indicators has been rarely assessed. We aimed to assess correlations, sensitivity, specificity, and predictive values between self-reported SHS exposure indicators and airborne nicotine concentrations. METHODS: We performed a cross-sectional study with a convenience sample of 175 homes in Barcelona and Santiago de Compostela, Spain. Airborne nicotine samples were collected from participants' homes and a self-administered questionnaire was completed on SHS exposure in the home. Spearman correlations coefficients and sensitivity, specificity and predictive values were assessed between self-reported SHS exposure indicators and nicotine concentrations in the home. RESULTS: All self-reported SHS exposure indicators correlated moderately strongly with airborne nicotine concentrations (Spearman correlations coefficient ranging from 0.58 to 0.65). Moreover, sensitivities and negative predictive values between self-reported indicators and the presence of nicotine in the home were below 66.4% while specificities and positive predictive values were over 78.4%. The "number of people usually smoking in the home" showed the best results (rs = 0.65, p < 0.001; sensitivity = 50.4%, specificity = 95.2%, PPV = 95.0, NPV = 51.3). CONCLUSIONS: The self-reported SHS indicators assessed in this study showed moderate and strong correlations, low sensitivities, and high specificities. Among them, the best results were obtained with the "number of people usually smoking in the home".
Authors: Vincent Berardi; Georgiana Bostean; Lydia Q Ong; Britney S Wong; Bradley N Collins; Melbourne F Hovell Journal: J Immigr Minor Health Date: 2021-11-27
Authors: Teresa DeAtley; Suzanne M Colby; Melissa A Clark; Alexander Sokolovsky; Rachel L Denlinger-Apte; Patricia A Cioe; Rachel Cassidy; Eric C Donny; Jennifer W Tidey Journal: Int J Environ Res Public Health Date: 2021-04-03 Impact factor: 4.614