Statistical methods Descriptive statistics were used to characterise the intervention and the control group. The effectiveness of an early oral health intervention was estimated by comparing intervention and control groups on clinical oral health outcomes at age five years using a multiple logistic regression model for each outcome variable. The model was fitted unadjusted and adjusted for one of the following independent variables at a time: brushing habit, start age of tooth brushing, diet habit, parent's origin, parent's education, bottle-feeding, night meals, sugar water, sex, heart problems, cyanosis, birth weight, heart medication and syndrome.The various dependent dichotomised outcome variables for evaluating the effect of the intervention program were caries experience, dental erosion, PI and, GBI. The independent variables used in the logistic regression were selected in relation to known caries risk determinants and demographic background factors, which could influence the oral outcomes. In the final model, maximally adjustments were included to guarantee sufficient power. The adjustments with the largest deviation from odds ratio (OR) were selected in the unadjusted model if the deviation was at least 10% of the unadjusted OR. For other comparisons chi-square and independent sample t-test were used. The general significance level was set to 0.05. The Bonferroni correction was used to address multiple comparisons leading to the marginal significance levels of 0.0028 when comparing characteristics between the intervention and control group, and 0.01 for the logistic models. All analyses were performed using SPSS version 23 (Inc. Chicago, IL, USA) and Matlab 9.0 (Mathworks Inc.).