Koning Nynke R, Büchner Frederike L, Vermeiren Robert R J M, Crone Mathilde R, Numans Mattijs E
Department of Public Health and Primary Care, Leiden University Medical Centre, PO Box 9600 Postzone V0-P/V6-68, 2300 RC Leiden, The Netherlands.
Department of Child and Adolescent Psychiatry, Leiden University Medical Centre, Curium-LUMC, The Netherlands.
EClinicalMedicine. 2019 Oct 17;15:89-97. doi: 10.1016/j.eclinm.2019.09.007. eCollection 2019 Oct.
Despite being common and having long lasting effects, mental health problems in children are often under-recognised and under-treated. Improving early identification is important in order to provide adequate, timely treatment. We aimed to develop prediction models for the one-year risk of a first recorded mental health problem in children attending primary care.
We carried out a population-based cohort study based on readily available routine healthcare data anonymously extracted from electronic medical records of 76 general practice centers in the Leiden area, the Netherlands. We included all patients aged 1-19 years on 31 December 2016 without prior mental health problems. Multilevel logistic regression analyses were used to predict the one-year risk of a first recorded mental health problem. Potential predictors were characteristics related to the child, family and healthcare use. Model performance was assessed by examining measures of discrimination and calibration.
Data from 70,000 children were available. A mental health problem was recorded in 27•7% of patients during the period 2007-2017. Age independent predictors were somatic complaints, more than two GP visits in the previous year, one or more laboratory test and one or more referral/contact with other healthcare professional in the previous year. Other predictors and their effects differed between age groups. Model performance was moderate (-statistic 0.62-0.63), while model calibration was good.
This study is a first promising step towards developing prediction models for identifying children at risk of a first mental health problem to support primary care practice by using routine healthcare data. Data enrichment from other available sources regarding e.g. school performance and family history could improve model performance. Further research is needed to externally validate our models and to establish whether we are able to improve under-recognition of mental health problems.
尽管儿童心理健康问题很常见且具有长期影响，但往往未得到充分认识和治疗。改善早期识别对于提供充分、及时的治疗很重要。我们旨在为在初级保健机构就诊的儿童首次记录心理健康问题的一年风险建立预测模型。
我们基于从荷兰莱顿地区76个全科医疗中心的电子病历中匿名提取的现成常规医疗数据，开展了一项基于人群的队列研究。我们纳入了2016年12月31日年龄在1 - 19岁且无既往心理健康问题的所有患者。采用多水平逻辑回归分析来预测首次记录心理健康问题的一年风险。潜在预测因素包括与儿童、家庭及医疗使用相关的特征。通过检查区分度和校准度指标来评估模型性能。
有70000名儿童的数据可用。在2007 - 2017年期间，27.7%的患者记录了心理健康问题。与年龄无关的预测因素有躯体不适、前一年看全科医生超过两次、前一年进行过一次或多次实验室检查以及前一年与其他医疗专业人员有过一次或多次转诊/接触。其他预测因素及其影响在不同年龄组之间有所不同。模型性能中等（C统计量为0.62 - 0.63），而模型校准良好。
本研究是朝着利用常规医疗数据开发预测模型迈出的首个有前景的步骤，该模型用于识别有首次心理健康问题风险的儿童，以支持初级保健实践。来自其他可用来源（如学校表现和家族史）的数据丰富可能会提高模型性能。需要进一步研究以对我们的模型进行外部验证，并确定我们是否能够改善心理健康问题识别不足的情况。
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