Beeckman et al. BMC Public Health 2010, 10:527 http://www.biomedcentral.com/1471-2458/10/527 into three categories. The lowest income group was defined at 60% or less than the median equivalent national income for Belgium [27]. This threshold of 60% is the at-risk-of-poverty threshold [28]. Equivalent incomes between 60% and 120% of that threshold were considered as moderate incomes. An income above 120% of the median national equivalent income was defined as high income. Data on the net monthly income of thirty one women (9.3%) were missing. This was more often the case in women active in the labour market and those more highly educated (Chi2 analyses; available upon request). Within the group of more highly educated, active women, missings occurred at random. Multiple imputation methodology was applied to estimate missing data. Imputation was performed multiple times to create 5 complete data sets. In con- ducting our analyses, we used the Poisson regression techniques in the 5 imputed data sets. We then com- bined the results via the MIANALYZE procedure in SAS version 9.1 (SAS Institute, Cary, NC) to obtain the overall parameter estimates [29,30]. Educational level, activity status and health insurance coverage were the predictors used for the imputation. A third group of variables referred to pregnancy- related determinants. Parity, the pregnancy being planned, the pregnancy being desired, medically assisted pregnancy, history of obstetric risk (previous preterm birth, low birth weight, miscarriage, still birth, admission to neonatal care unit), high risk status during antenatal care (hypertension, pre-eclampsia, gestational diabetes mellitus, preterm contractions, hospital admission or anaemia), gestational age at initiation of care, and conti- nuity of carer, were included in the analysis. Continuity of care was measured by the COC index [31,32]. This index counts the number of visits to each different health care provider compared to the total number of visits. Statistical analysis First, the number of antenatal visits was analysed bivar- iatly using Mann-Withney or Kruskall-Wallis tests. Sec- ond, a multivariate Poisson regression model was used to examine the determinants for differences in the num- ber of antenatal visits. A Poisson model is standard when analyzing count data, especially when skewed dis- tributions are observed [33,34], such as the number of antenatal visits. This multivariate model is used to quan- tify the independent effects of predisposing, enabling and pregnancy-related factors after adjusting for con- founding factors. As this was an exploratory study, we aimed to include all variables and choose for a back- wards model as the best option. Corresponding to the health behavior model of Andersen and Newman [17,24], all predisposing variables were considered first. Page 3 of 9 Variables that were not significant at p < 0.05 were sub- sequently omitted from the model, starting with the variable with the highest p-value. This cycle was repeated until all variables were significant at the p < 0.05 level. In the next round, the enabling factors were considered, with the selected predisposing variables fixed in the model. Again the least significant variable was left out and the model rerun to select a set of sig- nificant variables. In the third round, the pregnancy- related factors were studied, controlling for the variables in the first two rounds. Unadjusted and adjusted differ- entials in antenatal visits and 95% confidence intervals (CIs) were calculated for all women in the study. The distribution of residuals and their relationship with the linear predictor, as well as the deviance reduction, were all examined in order to test goodness-of-fit of the model. We used SAS 9.1 for all the analyses. Results Characteristics of the study sample Four hundred and thirty-two women corresponded to the inclusion criteria (figure 2); there were thirty eight refusals. The main reasons not to participate in the study were lack of interest and disapproval by the part- ner. The women who refused did not differ from the women in the study regarding age and origin (Fisher Exact, p = 0.60 & p = 0.43 respectively). In total, three hundred ninety-four women were included in the study and sixty one dropped out. Ten women stopped their participation after the first telephone call, thirty three because of miscarriage; eighteen were lost to follow-up, despite additional verifications with the clinical centres and reminders being sent. Compared with the final sam- ple, significantly more Maghreb, Turkish and women of another origins dropped out (p = 0.007). The proportion of women active in the labor market was significantly lower in the drop-out group compared with the study sample (p = 0.035). (Results not shown but available on request). Figure 2 recruitment scheme.