Understanding and monitoring vulnerability during pregnancy at population-level

Abstract Background Detecting and addressing vulnerability during pregnancy is important to optimize the first thousands days of children’s lives and prevent health inequities. We aimed to gain insight into vulnerability during pregnancy and determine the best data to predict vulnerability. Methods In the Netherlands, two studies were done using nationwide routinely collected data and self-reported Public Health Monitor-data. The first study utilized all data to identify classes of vulnerability among 4172 pregnant women through Latent Class Analysis. The second study predicted multidimensional vulnerability at population-level using solely routinely collected data first, and step-by-step incorporating self-reported data. Random Forest (RF) analysis was employed, and model performance was assessed using Area Under the Curve (AUC) and F1-measure. Results Five vulnerability-classes were found: ‘healthy and socioeconomically stable’, ‘high care utilization’, ‘socioeconomic vulnerability’, ‘psychosocial vulnerability’, and ‘multidimensional vulnerability’. Women in the ‘multidimensional vulnerability’-class shared multiple risk factors across domains and lacked protective factors. They more often had adverse outcomes such as premature birth. In the next study, the initial RF-model achieved an AUC of 0.98 in distinguishing multidimensional vulnerability, with an F1-measure of 0.70. Adding self-reported data improved model performance. Strong predictors were self-reported health, socioeconomic characteristics and healthcare utilization. Conclusions Several vulnerability classes can be distinguished among pregnant women. The co-existence of risk factors and lack of protective factors leads to more adverse outcomes. Effective public health strategies should include both medical and social care and support. Next, it seems possible to predict multidimensional vulnerability using data that is readily available, providing a foundation for longitudinal monitoring and policy formulation. Key messages • Considering the combination of both social risk and protective factors related to vulnerability during pregnancy is necessary. • Routinely collected data could provide insight in the prevalence, geographical distribution and trends in multidimensional vulnerability during pregnancy at population-level.