Predicting population-level vulnerability among pregnant women using routinely collected data and the added relevance of self-reported data

European Journal of Public Health, vol. 34, pp. 1210–1217

Abstract

Recognizing and addressing vulnerability during the first thousand days of life can prevent health inequities. It is necessary to determine the best data for predicting multidimensional vulnerability (i.e. risk factors to vulnerability across different domains and a lack of protective factors) at population level to understand national prevalence and trends. This study aimed to (1) assess the feasibility of predicting multidimensional vulnerability during pregnancy using routinely collected data, (2) explore potential improvement of these predictions by adding self-reported data on health, well-being, and lifestyle, and (3) identify the most relevant predictors. The study was conducted using Dutch nationwide routinely collected data and self-reported Public Health Monitor data. First, to predict multidimensional vulnerability using routinely collected data, we used random forest (RF) and considered the area under the curve (AUC) and F1 measure to assess RF model performance. To validate results, sensitivity analyses (XGBoost and Lasso) were done. Second, we gradually added self-reported data to predictions. Third, we explored the RF model's variable importance. The initial RF model could distinguish between those with and without multidimensional vulnerability (AUC = 0.98). The model was able to correctly predict multidimensional vulnerability in most cases, but there was also misclassification (F1 measure = 0.70). Adding self-reported data improved RF model performance (e.g. F1 measure = 0.80 after adding perceived health). The strongest predictors concerned self-reported health, socioeconomic characteristics, and healthcare expenditures and utilization. It seems possible to predict multidimensional vulnerability using routinely collected data that is readily available. However, adding self-reported data can improve predictions.

Authors 6

1. Joyce M. Molenaar corresponding Aachen Department of Public Health and Primary Care/Health Campus The Hague

   Leiden University · Leiden University Medical Center · National Institute for Public Health and the Environment

   Affiliation as printed

   Department of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre , The Hague,

   Population Health and Health Services Research, Centre for Public Health, Healthcare and Society, National Institute for Public Health and the Environment (RIVM) , Bilthoven,

   Department of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands

   Population Health and Health Services Research, Centre for Public Health, Healthcare and Society, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands

2. Ka Yin Leung

   National Institute for Public Health and the Environment

   Affiliation as printed

   Department of Statistics, Data Science and Modelling, National Institute for Public Health and the Environment (RIVM) , Bilthoven,

   Department of Statistics, Data Science and Modelling, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands

3. Lindsey van der Meer

   Erasmus MC

   Affiliation as printed

   D , Rotterdam,

   epartment of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre , Rotterdam,

   Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands

4. Peter Paul F Klein

   National Institute for Public Health and the Environment

   Affiliation as printed

   Population Health and Health Services Research, Centre for Public Health, Healthcare and Society, National Institute for Public Health and the Environment (RIVM) , Bilthoven,

   Population Health and Health Services Research, Centre for Public Health, Healthcare and Society, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands

5. Jeroen Nathan Struijs Aachen Department of Public Health and Primary Care/Health Campus The Hague

   Leiden University · Leiden University Medical Center · National Institute for Public Health and the Environment

   Affiliation as printed

   Department of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre , The Hague,

   Population Health and Health Services Research, Centre for Public Health, Healthcare and Society, National Institute for Public Health and the Environment (RIVM) , Bilthoven,

   Department of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands

   Population Health and Health Services Research, Centre for Public Health, Healthcare and Society, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands

6. Jessica C. Kiefte–de Jong Aachen Department of Public Health and Primary Care/Health Campus The Hague

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre , The Hague,

   Department of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands