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Journal of Public Health Medicine 25:318-324 (2003)
© Faculty of Public Health 2003; all rights reserved.

A model to predict the results of changes in smoking behaviour on smoking prevalence


John Kemm, Honorary Clinical Lecturer

Department of Public Health and Epidemiology, University of Birmingham, Birmingham B15 2TT


Address correspondence to Dr Kemm. E-mail: john.kemm{at}doh.gsi.gov.uk

Background Data are available on the prevalence of smoking states (never, current and ex). However, data on behaviour change rates (starting – never to current, quitting – current to ex and lapsing – ex to current) are not readily available and cannot be simply derived from or related to prevalence data.

Method A model was constructed to relate prevalence of smoking states to behaviour change rates. It was populated with prevalence of smoking status taken from the General Household Survey together with population structure, age- and sex-specific death rates, and birth rates for England and Wales. This model could be used to calculate past behaviour change given observed prevalence of smoking states or future prevalence of smoking given predicted rates of behaviour change.

Results To fit data it was necessary to assume that as they age some ex smokers reclassify themselves as never smokers. In the age band 16–19 years about 9 per cent of never smokers start smoking, and about 5 per cent of current smokers quit. In the age band 20–24 years the corresponding figures for starting are about 4 per cent in males and 2 per cent in females, and for quitting about 2 per cent in both. In older age bands the percentages starting are zero or less than zero (indicating reclassifying), and the percentage quitting rises with age. Net lapsing (shift from ex to current) occurred very infrequently and is quantitatively unimportant. If the current starting, quitting and lapsing rates are maintained the Smoking kills target will not be met. Future prevalence of smoking under different scenarios is examined.

Conclusion The model is useful in calculating the proportions changing smoking state from serial cross-sectional data on prevalence and for predicting future prevalence.

Keywords: smoking, modelling


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