Rigdon Joseph, Montez Kimberly, Palakshappa Deepak, Brown Callie, Downs Stephen M, Albertini Laurie W, Taxter Alysha
Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, USA.
Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, USA.
J Clin Transl Sci. 2024 Oct 28;8(1):e195. doi: 10.1017/cts.2024.645. eCollection 2024.
More than 5 million children in the United States experience food insecurity (FI), yet little guidance exists regarding screening for FI. A prediction model of FI could be useful for healthcare systems and practices working to identify and address children with FI. Our objective was to predict FI using demographic, geographic, medical, and historic unmet health-related social needs data available within most electronic health records.
This was a retrospective longitudinal cohort study of children evaluated in an academic pediatric primary care clinic and screened at least once for FI between January 2017 and August 2021. American Community Survey Data provided additional insight into neighborhood-level information such as home ownership and poverty level. Household FI was screened using two validated questions. Various combinations of predictor variables and modeling approaches, including logistic regression, random forest, and gradient-boosted machine, were used to build and validate prediction models.
A total of 25,214 encounters from 8521 unique patients were included, with FI present in 3820 (15%) encounters. Logistic regression with a 12-month look-back using census block group neighborhood variables showed the best performance in the test set (C-statistic 0.70, positive predictive value 0.92), had superior C-statistics to both random forest (0.65, < 0.01) and gradient boosted machine (0.68, = 0.01), and showed the best calibration. Results were nearly unchanged when coding missing data as a category.
Although our models could predict FI, further work is needed to develop a more robust prediction model for pediatric FI.
美国有超过500万儿童面临粮食不安全（FI）问题，但关于FI筛查的指导却很少。FI预测模型对于致力于识别和解决有FI问题儿童的医疗保健系统和医疗机构可能会有所帮助。我们的目标是利用大多数电子健康记录中可用的人口统计学、地理、医疗和历史未满足的与健康相关的社会需求数据来预测FI。
这是一项回顾性纵向队列研究，研究对象为在一家学术性儿科初级保健诊所接受评估且在2017年1月至2021年8月期间至少接受过一次FI筛查的儿童。美国社区调查数据提供了关于邻里层面信息（如房屋所有权和贫困水平）的更多见解。使用两个经过验证的问题对家庭FI进行筛查。采用多种预测变量组合和建模方法，包括逻辑回归、随机森林和梯度提升机，来构建和验证预测模型。
共纳入了来自8521名独特患者的25214次就诊记录，其中3820次（15%）就诊存在FI。使用人口普查街区组邻里变量进行12个月回顾的逻辑回归在测试集中表现最佳（C统计量为0.70，阳性预测值为0.92），其C统计量优于随机森林（0.65，<0.01）和梯度提升机（0.68，=0.01），并且校准效果最佳。将缺失数据编码为一个类别时，结果几乎没有变化。
尽管我们的模型可以预测FI，但仍需要进一步开展工作来开发一个更强大的儿科FI预测模型。
