157 General discussion stable-class. Women in the multidimensional vulnerability-class shared multiple risk factors in various domains and a lack of protective factors. These women more often had adverse outcomes, including premature birth and caesarean section, as compared to the healthy and socioeconomically stable-class. The three classes with risk factors in one domain and protective factors in others did not. These results show the importance of considering the co-existence of multiple risk factors and protective factors that may act as positive exposures or buffering mechanisms promoting resilience. The results also suggest that early detection of vulnerability and strategies to improve parental health and well-being might benefit from focusing on different domains and combining medical and social care and support, with attention to the systemic causes of vulnerability. The next study, described in Chapter 3, further explored which data to use to predict multidimensional vulnerability at population-level. Our previous study was conducted in a non-representative subset of pregnant women, meaning that the prevalence of multidimensional vulnerability among all pregnant women in the Netherlands was unknown. It was unclear whether the prevalence could be assessed using routinely collected nationwide data as readily available in DIAPER. Hence, we studied the feasibility of using solely routinely collected data for predictions, the relevance of adding self-reported data, and the most important predictors. The results showed that it is feasible to use solely routinely collected data to predict multidimensional vulnerability. This data is readily available for the entire population and can provide a robust foundation for longitudinal monitoring and policy formulation at population-level. Nevertheless, results also showed that self-reported data was of added value in the predictions. Moreover, self-reported health variables were found to be important predictors to multidimensional vulnerability, next to socioeconomic characteristics and healthcare utilization. Hence, the results offer the opportunity to explore how self-reported health can be systematically included (e.g. in screening and care registries) to enhance the provision of personalized care and support while further improving population-level predictions in the future. Which indicators can be used to monitor the action program Solid Start on a local level? Chapter 4 describes the development of an indicator set to monitor the action program Solid Start at local level, using a modified Delphi study with several rounds of questionnaires and online meetings. For local monitoring, experts desired an indicator set covering both processes and outcomes, both parents and children, and both risk and protective factors. The final indicator set comprised nineteen indicators within the three phases of the action program Solid Start: preconception, pregnancy and after birth. Topics included poverty, psychological/psychiatric problems, stress, smoking, vulnerability, preconception care, low literacy and premature birth. The prioritized indicators primarily related to social determinants of health rather than specific clinical aspects. Additionally, a development agenda was set with topics and indicators lacking nationwide data or clear operationalization (e.g. stress, unintended pregnancy, loneliness). We identified both similarities and differences in the selected indicators for monitoring the action program Solid Start at local level compared to national level. These variations can reasonably be attributed to differing purposes and informational needs: monitoring and evaluating 6
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