Background: Complete and accurate data on maternal smoking prevalence during pregnancy are not available at a local geographical scale in England. We employ a synthetic estimation approach to predict the expected prevalence of smoking during pregnancy and smoking at delivery by Primary Care Trust (PCT).
Methods: Multilevel logistic regression models were used with data from the 2010 Infant Feeding Survey and 2011 Census to predict the probability of mothers (a) smoking at any point during pregnancy and (b) smoking at delivery, according to age, deprivation, and the ethnic profile of the home area. These probabilities were applied to demographic information on mothers giving birth from 2010/11 Hospital Episode Statistics data to produce expected counts, and prevalence figures, of smokers by PCT, with Bayesian 95 % credible intervals. The expected prevalence of smoking at delivery by PCT was compared with midwife-collected Smoking at the Time of Delivery (SATOD) data using a Bland-Altman plot.
Results: The expected prevalence of smoking during pregnancy by PCT ranged from 8.1 % (95 % CI 5.6-1.0) to 31.6 % (27.5-34.8). The expected prevalence of smoking at delivery ranged from 2.5 % (1.4-4.0) to 17.1 % (13.7-20.4). Figures for expected smoking prevalence at delivery showed some agreement with SATOD, though SATOD data were generally higher than the synthetic estimates (mean difference 2.99 %).
Conclusions: It is possible to derive good estimates of expected smoking prevalence during pregnancy for small areas, potentially at much lower cost than conducting large surveys. Such data may be useful to help plan and commission smoking cessation services and monitor their effectiveness.
Keywords: Pregnancy; Smoking; Synthetic estimation.