Development of a risk classification model in early pregnancy to screen for suboptimal postnatal mother-to-infant bonding: A prospective cohort study.

BACKGROUND

Previous studies identified demographic, reproduction-related and psychosocial correlates of suboptimal mother-to-infant bonding. Their joint informative value was still unknown. This study aimed to develop a multivariable model to screen early in pregnancy for suboptimal postnatal mother-to-infant bonding and to transform it into a risk classification model.

METHODS

Prospective cohort study conducted at 116 midwifery centers between 2010-2014. 634 women reported on the Mother-to-Infant Bonding questionnaire in 2015-2016. A broad range of determinants before 13 weeks of gestation were considered. Missing data were described, analyzed and imputed by multiple imputation. Multivariable logistic regression with backward elimination was used to develop a screening model. The explained variance, the Area Under the Curve of the final model were calculated and a Hosmer and Lemeshow test performed. Finally, we designed a risk classification model.

RESULTS

The prevalence of suboptimal mother-to-infant bonding was 11%. The estimated probability of suboptimal mother-to-infant can be calculated: P(MIBS≥4) = 1/(1+exp(-(-4.391+(parity× 0.519)+(Adult attachment avoidance score× 0.040))). The explained variance was 14% and the Area Under the Curve was 0.750 (95%CI 0.690-0.809). The Hosmer and Lemeshow test had a p-value of 0.21. This resulted in a risk classification model.

CONCLUSION

Parity and adult attachment avoidance were the strongest independent determinants. Higher parity and higher levels of adult attachment avoidance are associated with an increased risk of suboptimal mother-to-infant bonding. The model and risk classification model should be externally validated and optimized before use in daily practice. Future research should include an external validation study, a study into the additional value of non-included determinants and finally a study on the impact and feasibility of the screening model.