Molenaar Joyce M, Leung Ka Yin, van der Meer Lindsey, Klein Peter Paul F, Struijs Jeroen N, Kiefte-de Jong Jessica C
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.
Department of Public Health and Primary Care/Health Campus The Hague, Leiden University Medical Centre, The Hague, the Netherlands.
Eur J Public Health. 2024 Dec 1;34(6):1210-1217. doi: 10.1093/eurpub/ckae184.
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.
认识并解决生命最初一千天中的脆弱性问题可以预防健康不平等。有必要确定在人群层面预测多维脆弱性（即不同领域的脆弱性风险因素以及缺乏保护因素）的最佳数据，以了解全国范围内的患病率和趋势。本研究旨在：（1）评估使用常规收集的数据预测孕期多维脆弱性的可行性；（2）通过添加关于健康、幸福感和生活方式的自我报告数据来探索这些预测的潜在改进；（3）确定最相关的预测因素。该研究使用了荷兰全国常规收集的数据和自我报告的公共卫生监测数据。首先，为了使用常规收集的数据预测多维脆弱性，我们使用了随机森林（RF），并考虑曲线下面积（AUC）和F1度量来评估RF模型的性能。为了验证结果，进行了敏感性分析（XGBoost和套索回归）。其次，我们逐步将自我报告的数据添加到预测中。第三，我们探索了RF模型的变量重要性。初始RF模型能够区分有无多维脆弱性的人群（AUC = 0.98）。该模型在大多数情况下能够正确预测多维脆弱性，但也存在误分类（F1度量 = 0.70）。添加自我报告的数据提高了RF模型的性能（例如，添加自我感知健康后F1度量 = 0.80）。最强的预测因素涉及自我报告的健康状况、社会经济特征以及医疗保健支出和利用情况。使用现成的常规收集数据似乎有可能预测多维脆弱性。然而，添加自我报告的数据可以改善预测效果。
