RESUMEN
BACKGROUND: Tobacco use is a leading preventable cause of morbidity and mortality worldwide. Little is known about recent prevalence and trends in tobacco use among adolescents globally. We aimed to assess the recent global prevalence of tobacco use in young adolescents and the secular trends in prevalence between 1999 and 2018. METHODS: We used the most recent Global Youth Tobacco Surveys data on adolescents aged 13-15 years from 143 countries or territories that had done at least one survey between Jan 1, 2010, and Dec 31, 2018, to assess the recent prevalence of tobacco use; and data from 140 countries that had done two or more surveys between Jan 1, 1999, and Dec 31, 2018, to assess the trends in the prevalence of tobacco use. FINDINGS: 530 234 adolescents were included from the 143 countries that had done at least one survey between 2010 and 2018. 1â192â312 adolescents were included from the 140 countries that had done two or more surveys between 1999 and 2018. The most recent global prevalence of cigarette smoking was 11·3% (95% CI 10·3-12·3) in boys and 6·1% (5·6-6·6) in girls, based on cigarette smoking on at least 1 day during the past 30 days, 6·0% (5·5-6·6) and 2·6% (2·4-2·9) based on smoking on at least 3 days, and 4·2% (3·8-4·6) and 1·6% (1·4-1·8) based on smoking on at least 6 days. The most recent prevalence of the use of tobacco products other than cigarettes (eg, chewing tobacco, snuff, dip, cigars, cigarillos, pipe, electronic cigarettes) on at least 1 day during the past 30 days was 11·2% (9·9-12·6) in boys and 7·0% (6·4-7·7) in girls. The most recent prevalence of any tobacco use on at least 1 day during the past 30 days was 17·9% (16·1-19·6) in boys and 11·5% (10·5-12·4) in girls. The prevalence of cigarette smoking on at least 1 day during the past 30 days decreased between the first and last surveys in 80 (57·1%) of 140 countries, was unchanged in 39 countries (27·9%), and increased in 21 countries (15·0%). However, the prevalence of the use of tobacco products other than cigarettes was unchanged or increased in 81 (59·1%) of 137 countries. INTERPRETATION: The global prevalence of tobacco use among adolescents aged 13-15 years was substantial. Although the prevalence of cigarette smoking decreased over time in the majority of countries, the prevalence of the use of other tobacco products increased or did not change in the majority of countries during the past two decades. These findings re-emphasise the need to strengthen tobacco control efforts among young adolescents globally. FUNDING: Shandong University.
Asunto(s)
Fumar Cigarrillos/epidemiología , Uso de Tabaco/epidemiología , Tabaco sin Humo , Adolescente , África/epidemiología , Asia/epidemiología , América Central/epidemiología , Fumar Cigarrillos/tendencias , Europa (Continente)/epidemiología , Femenino , Humanos , Masculino , América del Norte/epidemiología , Prevalencia , Distribución por Sexo , América del Sur/epidemiología , Fumar Tabaco/epidemiología , Fumar Tabaco/tendencias , Uso de Tabaco/tendenciasAsunto(s)
Ebolavirus , Fiebre Hemorrágica Ebola , Niño , Humanos , Proyectos de Investigación , Estudios RetrospectivosAsunto(s)
Infección por el Virus Zika , Virus Zika , Brasil , Femenino , Humanos , Placenta , Embarazo , Complicaciones Infecciosas del EmbarazoRESUMEN
Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM's constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs.