Dobson, Annette

Gartner, Coral

Hall, Wayne

Leung, Janni

Lucke, Jayne

Psychological Medicine

University of Queensland eSpace

  
 
 
Reply: Smoking and mental health in young women – challenges in 
interpretation: a reply 
 
JANNI LEUNGa1 c1, CORAL GARTNERa2, WAYNE HALLa2, JAYNE LUCKEa2 and ANNETTE DOBSONa1 
a1 School of Population Health, University of Queensland, Brisbane, Australia  
a2 UQ Centre for Clinical Research, University of Queensland, Brisbane, Australia  
Correspondence: 
c1 Address for correspondence: J. Leung, University of Queensland, School of Population 
Health, Herston Road, Herston, QLD 4066, Australia. (Email: j.leung1@uq.edu.au) 
 
 
POST-PRINT 
This is a post-print (post-refereed, final accepted) version of the manuscript that has been 
published in Psychological Medicine. The citation details and the link to the final publisher 
version are below. 
Leung, Janni, Gartner, Coral, Hall, Wayne, Lucke, Jayne and Dobson, Annette (2013) Smoking and 
mental health in young women - challenges in interpretation: a reply. Psychological Medicine, 432: 445-
447. doi:10.1017/S0033291712002371  
Published online: :22 Oct 2012 
http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8803035&fileId=S0033291712002371 
We welcome the opportunity to discuss the concerns raised by Gariépy et al. (2012) on the 
interpretation of our data on the relationships between smoking and mental health in young 
women. Gariépy et al. suggest that by excluding women who have ever been pregnant in our 
analysis, we limited the generalizability of our findings. The results were much the same 
when we re-ran the analyses on the full sample of women. It was still the case that young 
women who smoked at earlier waves had significantly higher odds of poor mental health at 
later waves (see Appendix 1), and young women with poor mental health at previous waves 
had significantly higher odds of smoking at later waves (see Appendix 2). As in our previous 
analyses, the strength of the association increased with the number of cigarettes smoked per 
day (CPD). 
Second, Gariépy et al. argued that it was difficult to assess the potential for bias in our study 
because we did not provide data on the women with missing data on smoking or mental 
health status. Only a very small number of women had this missing data (Leung et al. 2012, 
Fig. 1) and their exclusion is unlikely to have had a substantial impact on our results. These 
excluded women were more likely to have lower education, to have been born in a non-
English-speaking country, and to have more difficulties managing their income (Young et al. 
2006). These variables have previously been shown to be associated with both smoking and 
poor mental health. In addition, the women with missing data were more likely to be smokers 
and have poor mental health. Therefore, as we argued in our discussion, any missing data are 
more likely to have biased our findings in the direction of underestimating the strength of the 
association between smoking and poor mental health. 
Third, Gariépy et al. questioned our treatment of an ordinal measure of smoking as an 
interval variable in the structural equation models. They suggested that a categorical ordinal 
definition of smoker types would have been better. We can confirm that smoker type was 
analysed as an ordinal categorical variable in our structural equation models. Using Amos 
17.0 software, we coded the smoking status variables as an ordered-categorical variable and 
fitted the model using Bayesian estimation. In addition, we presented the results from the 
generalized estimated equation models to show that the relationship between smoking and 
poor mental health increased with increasing level of smoking. When all paths were entered 
simultaneously in a single model, smoking was associated with poor mental health, and poor 
mental health was associated with smoking. 
Fourth, Gariépy et al. also commented on the challenges in untangling the temporal order of 
the relationship between prior mental health problems and the risk of being a former smoker. 
We concur with the comment that this is a limitation of our data. We have attempted to 
address this issue in model 4 in each of Tables 2 and 3. The findings support our 
interpretation that the association is bi-directional. 
Last, Gariépy et al. correctly identified several misprints in Table 2, where some odds ratios 
appeared incorrectly and the reference value was 10.00 instead of 1.00. It appears that in the 
first eight rows, ‘1.**’ has been misprinted as ‘10.**’. For example, the odds ratio for poor 
mental health (according to the Mental Health Index from the SF-36) for ex-smokers should 
be 1.21 (not 10.21). None of the odds ratios presented in Table 2 should be over 10.00. Please 
see Appendix 3 for the corrected values. 
Appendix 1 
Smoking status (predictor) at Poor mental health (outcome) at waves 2.3.4.5 (good as 
waves 1,2,3,4 (never as 
reference) 
reference) 
OR 95% CI 
Never 1.00  
Ex-smoker 1.19 1.09-1.30 
Smoke <10 cpd 1.25 1.13-1.39 
Smoke 10-19 cpd 1.54 1.37-1.73 
Smoke 20+ cpd 1.97 1.70-2.27 
 
Longitudinal analysis of smoking status predicting subsequent mental health status using 
generalized estimated equation analysis for all young women participating in the Australian 
Longitudinal Study on Women's Health with and without any experience of pregnancy 
OR, Odds ratio; CI, confidence interval; CPD, cigarettes per day. 
Mental health status was measured by the SF-36 Mental Health Index, ⩽52 as poor. 
Appendix 2 
 
Longitudinal analysis of mental health status predicting subsequent smoking status using 
generalized estimated equation analysis for all young women participating in the Australian 
Longitudinal Study on Women's Health with and without any experience of pregnancy 
OR, Odds ratio; CI, confidence intervals; CPD, cigarettes per day. 
Mental health status was measured by the SF-36 Mental Health Index (MHI), ⩽52 as poor. 
Appendix 3 
 Longitudinal analysis of smoking status predicting subsequent mental health status using 
generalized estimated equation models 
MHI, Mental Health Index; CESD, Center for Epidemiologic Studies Depression Scale; OR, 
Odds ratio; CI, confidence intervals; CPD, cigarettes per day. 
Covariates included marital status, education level, and employment status. 
References 
• G Gariepy, KJ Smith, M Clyde, N Schmitz (2012). Smoking and mental health in young women 
– challenges in interpretation. Psychological Medicine. doi:.  
• J Leung, C Gartner, W Hall, J Lucke, A Dobson (2012). A longitudinal study of the bidirectional 
relationship between tobacco smoking and psychological distress in a community sample of 
young Australian women. Psychological Medicine 42, 1273–1282.  
• A Young, J Powers, SL Bell (2006). Attrition in longitudinal studies: who do you 
lose? Australian & New Zealand Journal of Public Health 30, 353–361.  
 


Smoking and mental health in young women - challenges in interpretation: a reply

JANNI LEUNG

CORAL GARTNER

WAYNE HALL

JAYNE LUCKE

ANNETTE DOBSON

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Smoking and mental health in young women – challenges in interpretation: a reply

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