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Factors Associated with Tobacco Cessation Services Request Among Users of an Online Self-Screening Questionnaire

Datos Bibliográficos

ID21645924
AutoresNorberto Francisco Hernández-Llanes (0000-0001-5918-3137, Equipo de Ciencia de Datos, Centros de Integración Juvenil AC, autor de correspondencia), Ricardo Sánchez-Domínguez (0000-0001-6221-6091, Departamento de Investigación Psicosocial y Documental, Centros de Integración Juvenil AC), Sofía Álvarez-Reza (0000-0001-7380-5297, Departamento de Investigación Psicosocial y Documental, Centros de Integración Juvenil AC), Carmen Fernández-Cáceres (0009-0000-1851-194X, Dirección General, Centros de Integración Juvenil AC), Rodrigo Marín-Navarrete (0000-0002-6084-8199, Dirección de Investigación y Enseñanza, Centros de Integración Juvenil AC)
Año2025
Volumen60
Número4
Páginas604-610
Fecha de publicación2025-03-21
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaSubstance Use & Misuse (JOURNAL)
Identificadores de la revistaISSN: 1082-6084 • E-ISSN: 1532-2491
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10826084.2024.2445851
PMID39731740
OpenAlexW4405862622
IdiomaEN
Referencias citadas46

OBJECTIVES: Tobacco smoking remains a major public health risk, responsible for millions of deaths worldwide. While smoking patterns in Mexico differ from those in countries with higher rates, comorbidities such as diabetes pose a health risk. Although many smokers want to quit, access to cessation services is limited. Internet-based cessation (I-BC) services are a promising modality that offers accessibility and machine learning (ML) has been successfully used to predict tobacco outcomes. This study uses ML to identify characteristics associated with requesting I-BC through an online self-assessment questionnaire in Mexico. METHODS: This was a retrospective, predictive, secondary analysis of 14,182 records of individuals aged 18 years and older who completed an online screening for nicotine dependence and their request for tobacco cessation services. Random forest algorithm with four oversampling methods was compared to select the best predictive model. The relative importance of predictor variables was measured as well. RESULTS: The algorithm had a sensitivity of 78.6% and a specificity of 68.8%. Specifically, age, sex, dependence severity indicators, locations such as the state of Mexico or Sinaloa, and even occasions such as World No Tobacco Day were identified as key factors influencing cessation service requests. CONCLUSIONS: These results suggest the random forest algorithm's effectiveness in predicting potential cessation service users. Furthermore, the predictor variables provide valuable insights for designing targeted prevention and awareness campaigns, potentially leading to improved campaign effectiveness and more individuals receiving cessation support

Environmental health · Family medicine · Population · Public health · Smoking cessation · The Internet · Tobacco use · World Wide Web · Diabetes Management and Education · Global Public Health Policies and Epidemiology · Medicine · Nursing · Smoking Behavior and Cessation

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