2026-07-21T20:00:00Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/164962024-11-28T18:35:33Zcom_20.500.12105_2052com_20.500.12105_2051com_20.500.12105_15322col_20.500.12105_19608col_20.500.12105_16963col_20.500.12105_16966

00925njm 22002777a 4500 dc Santiago-Pérez, María I author López-Vizcaíno, Esther author Pérez-Ríos, Mónica author Guerra-Tort, Carla author Rey-Brandariz, Julia author Varela-Lema, Leonor author Martín-Gisbert, Lucía author Ruano-Ravina, Alberto author Schiaffino, Anna author Galan, Iñaki author Candal-Pedreira, Cristina author Montes, Agustín author Ahluwalia, Jasjit author 2023-05 Introduction: Complete and accurate data on smoking prevalence at a local level would enable health authorities to plan context-dependent smoking interventions. However, national health surveys do not generally provide direct estimates of smoking prevalence by sex and age groups at the subnational level. This study uses a small-area model-based methodology to obtain precise estimations of smoking prevalence by sex, age group and region, from a population-based survey. Methods: The areas targeted for analysis consisted of 180 groups based on a combination of sex, age group (15-34, 35-54, 55-64, 65-74, and ≥75 years), and Autonomous Region. Data on tobacco use came from the 2017 Spanish National Health Survey (2017 SNHS). In each of the 180 groups, we estimated the prevalence of smokers (S), ex-smokers (ExS) and never smokers (NS), as well as their coefficients of variation (CV), using a weighted ratio estimator (direct estimator) and a multinomial logistic model with random area effects. Results: When smoking prevalence was estimated using the small-area model, the precision of direct estimates improved; the CV of S and ExS decreased on average by 26%, and those of NS by 25%. The range of S prevalence was 11-46% in men and 4-37% in women, excluding the group aged ≥75 years. Conclusions: This study proposes a methodology for obtaining reliable estimates of smoking prevalence in groups or areas not covered in the survey design. The model applied is a good alternative for enhancing the precision of estimates at a detailed level, at a much lower cost than that involved in conducting large-scale surveys. This method could be easily integrated into routine data processing of population health surveys. Having such estimates directly after completing a health survey would help characterize the tobacco epidemic and/or any other risk factor more precisely. Tob Induc Dis. 2023 May 18;21:63. 10.18332/tid/162379 1617-9625 Tobacco induced diseases 37215189 http://hdl.handle.net/20.500.12105/16496 Behavioral risk factor surveillance system Health surveys Prevalence Small-area analysis Smoking Small-area models to assess the geographical distribution of tobacco consumption by sex and age in Spain