“Look at Me, I Plan to Quit Smoking”: Bayesian Hierarchical Analysis of Adolescent Smokers’ Intention to Quit Smoking

Healthcare

JohnN. Ng’ombe¹  NovanF. P. Tembo²  N.Rodgers Nedson³ 

[1] Department of Agricultural Economics and Rural Sociology, Auburn University, Auburn, AL 36830, USA;Department of Public Health, University of Lusaka, Lusaka 10101, Zambia;School of Medicine, University of Zambia, Lusaka 10101, Zambia;

关键词: tobacco smoking;    smoking cessation;    Hamiltonian Monte Carlo;    Bayesian analysis;    Zambia;   

DOI  :  10.3390/healthcare8020076

来源: DOAJ

【 摘 要 】

The tobacco epidemic is one of the most prominent public health threats the world has ever faced. Public health policy that seeks to limit the problem may have to target not only the price of tobacco but also the initiation stage in a smoker’s life—the adolescent stage. Most research on teen smoking focuses on initiation and other stories. Moreover, what determines the desire to quit smoking among teens is not well understood, even though planning to quit smoking is an important stage toward successful cessation. This research contributes to healthcare literature by using Bayesian hierarchical techniques, estimated using Hamiltonian Monte Carlo (HMC) and its extension, the No-U-Turn Sampler (NUTS), to empirically identify what drives the intention to quit smoking among teen smokers in Zambia. Results suggest that, among the junior secondary school-going adolescent smokers in Zambia, about 63% have plans to quit smoking. We find socio-demographic characteristics and several tobacco-smoking-related factors as salient drivers of adolescent smokers’ plans to quit smoking. For policymaking, we recommend that school-going teen smokers should have access to smoking cessation aids to help them quit smoking. Most importantly, increased awareness of dangers of smoking, advice by health professionals, stringent public policies on smoking, as well as parental guidance could be useful to help adolescent smokers realize their quitting plans.

【 授权许可】

Unknown