Smoking Cessation and Financial Stress

Mohammad Siahpush, Professor; Matt Spittal, Dr; Gopal K. Singh, Dr


Journal of Public Health. 2007;29(4):338-342. 

In This Article


We used data from Waves 1 to 3 (2001-04) of the Household Income and Labour Dynamics in Australia (HILDA) survey, a national longitudinal study based on a multi-stage area sample of households. The main focus of HILDA is on household structure, income, economic well-being and employment participation. However, respondents are also asked several questions about health and health-related behaviour. The first wave of the survey was conducted between August 2001 and January 2002, and involved face-to-face interviews with all household members aged 15 and over. Interviews were obtained from 7982 households, representing 66% of all households that were identified as in-scope. This in turn generated a sample of 15 127 people eligible for interview, 13 969 of whom were interviewed. In Wave 2 (between August 2002 and March 2003), 11 993 of respondents from Wave 1, and in Wave 3 (between August 2003 and March 2004), 11 190 of respondents from Wave 2 were successfully interviewed. The survey is described in more detail elsewhere.[9] The study was approved by the University of Melbourne Human Research Ethics.

We used data from a sub-sample of 1747 respondents who were aged 18 and over and reported being current smokers in Wave 1, and were either smokers in Waves 2 and Waves 3 (smokers) or were ex-smokers in Waves 2 and 3 (long-term quitters, i.e. quit for about 1 year or longer).[10] In Wave 1, respondents were asked: 'Do you smoke cigarettes or any other tobacco products?' They were provided with three options: 'No, I have never smoked', 'No, I have given up smoking' and 'Yes', distinguishing never-smokers, ex-smokers and current smokers, respectively. In Waves 2 and 3, respondents were asked the same question on smoking with the following response options: 'No, I have never smoked', 'No, I no longer smoke', 'Yes, I smoke daily', 'Yes, I smoke at least weekly (but not daily)' and 'Yes, I smoke less often than weekly'. The last three options identify current smokers.

Financial stress was measured with the items: '[In the past 6 months] did any of the following happen to you because of a shortage of money?... Could not pay electricity, gas or telephone bills on time, ... Could not pay the mortgage or rent on time, ... Pawned or sold something, ... Went without meals, ... Was unable to heat home, ... Asked for financial help from friends or family, ... Asked for help from a welfare/community organization?' These were binary (yes/no) items. Respondents were also asked whether they could raise, within a week, AU$2000 for an emergency. We created a binary financial stress indicator identifying people who experienced a financially stressful event or could not easily raise emergency money. The above items were previously used by Siahpush et al.[11] and Siahpush and Carlin[8] to construct a scale that was used as a predictor of smoking behaviour. These items were also employed by La Cava and Simon[12] to construct an index of financial stress that was used in an analysis of household debt in Australia.

Education was categorized into three groups: year 12 (final year of high school) or below; trade certificate or diploma; and university degree. Occupation was coded based on the Australian Standard Classification of Occupations[13] and divided into: blue-collar, including tradespeople, production and transport workers and labourers; white-collar, including clerical, service and sales workers; and professionals, including managers, administrators, professional and associated professionals. Income was divided into four categories, as shown in the Table 1 , Table 2 and Table 3 .

Statistical analyses were performed with Stata 8.214 on cases with complete data for all covariates. We used logistic regression to examine the association of cessation with financial stress measured at Wave 3, adjusting for baseline financial stress and other covariates. The 'svy' commands in Stata were used to compute robust standard errors and account for the effects of the complex sample design.


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