人口统计学、知识和吸烟:工具变量方法

Y. Cheah, K. Lim, Muhammad Fadhli Mohd Yusoff
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引用次数: 1

摘要

吸烟是导致各种疾病的主要因素之一。本研究的目的是调查决定成年人吸烟行为的因素。本研究采用工具变量(IV)回归估计人口统计学和知识因素对吸烟参与决策和数量决策的影响。使用了一个发展中国家(马来西亚)的具有全国代表性的数据。与普遍看法相反,研究发现知识与吸烟倾向和吸烟量呈正相关。年龄、性别、财富指数、教育程度、种族、婚姻状况和居住地与吸烟显著相关。男性吸烟的可能性更大,吸烟的次数也比女性多。受教育程度降低了吸烟的可能性和数量。马来人和已婚人士吸烟的可能性低于非马来人和未婚人士。城市居民比农村居民吸烟更多。在政策影响方面,建议旨在减少吸烟率的干预措施不应过分注重提高马来西亚成年人对吸烟对健康影响的认识。禁烟政策的制定必须考虑到与吸烟的可能性和数量相关的人口因素。本研究首次将知识作为独立变量进行分析,并使用IV回归分析吸烟的参与决策和数量决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DEMOGRAPHICS, KNOWLEDGE AND SMOKING: AN INSTRUMENTAL VARIABLE APPROACH
Smoking is one of the main factors that causes various diseases. The objective of the present study is to investigate factors determining smoking behaviour among adults. The present study used instrumental variable (IV) regressions to estimate the effects of demographic and knowledge factors on participation decision and amount decision of smoking. Nationally representative data of a developing country (Malaysia) was used. Contrary to popular belief, knowledge was found to be positively associated with the propensity to smoke, as well as the amount of smoking. Age, gender, wealth index, educational level, ethnicity, marital status and house locality were significantly associated with smoking. Males were more likely to smoke and smoked more than females. Educational level reduced the likelihood and amount of smoking. Malays and married individuals were less likely to smoke than non-Malays and unmarried individuals. Urban dwellers smoked more cigarette relative to rural dwellers. In terms of policy implication, it is suggested that intervention measures directed toward reducing the prevalence of smoking should not pay too much attention to improving the knowledge of health effects of smoking among Malaysian adults. Anti-smoking policies must be designed carefully by taking into account of the demographic factors which are correlated with the likelihood and amount of smoking. The present study is the first of its kind that includes knowledge as a separate variable for analyses and uses IV regressions to analyse participation decision and amount decision of smoking.
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