Abstract

Background

Approximately 30% of US children aged 24 months have not received all recommended vaccines. This study aimed to develop a prediction model to identify newborns at high risk for missing early childhood vaccines.

Methods

A retrospective cohort included 9080 infants born weighing ≥2000 g at an academic medical center between 2008 and 2013. Electronic medical record data were linked to vaccine data from the Washington State Immunization Information System. Risk models were constructed using derivation and validation samples. K-fold cross-validation identified risk factors for model inclusion based on alpha = 0.01. For each patient in the derivation set, the total number of weighted adverse risk factors was calculated and used to establish groups at low, medium, or high risk for undervaccination. Logistic regression evaluated the likelihood of not completing the 7-vaccine series by age 19 months. The final model was tested using the validation sample.

Results

Overall, 53.6% failed to complete the 7-vaccine series by 19 months. Six risk factors were identified: race/ethnicity, maternal language, insurance status, birth hospitalization length of stay, medical service, and hepatitis B vaccine receipt. Likelihood of non-completion was greater in the high (77.1%; adjusted odds ratio [AOR] 5.6; 99% confidence interval [CI]: 4.2, 7.4) and medium (52.7%; AOR 1.9; 99% CI: 1.6, 2.2) vs low (38.7%) risk groups in the derivation sample. Similar results were observed in the validation sample.

Conclusions

Our prediction model using information readily available in birth hospitalization records consistently identified newborns at high risk for undervaccination. Early identification of high-risk families could be useful for initiating timely, tailored vaccine interventions.We developed a prediction model using demographic and birth hospitalization information from electronic medical records that consistently identified newborns at risk for missing future childhood vaccines. Prediction models may aide providers as they initiate early, tailored vaccine interventions.

中文翻译：

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一种风险预测模型，用于识别有缺失儿童早期疫苗接种风险的新生儿

摘要

背景

大约 30% 的 24 个月大的美国儿童没有接种所有推荐的疫苗。本研究旨在开发一种预测模型，以识别缺少儿童早期疫苗的高风险新生儿。

方法

回顾性队列包括 2008 年至 2013 年间在学术医疗中心出生体重≥2000 克的 9080 名婴儿。电子病历数据与华盛顿州免疫信息系统的疫苗数据相关联。使用推导和验证样本构建风险模型。K 折交叉验证基于 alpha = 0.01 确定了模型包含的风险因素。对于推导集中的每位患者，计算加权不良风险因素的总数并用于建立疫苗接种不足风险的低、中或高风险组。逻辑回归评估了在 19 个月大之前未完成 7 种疫苗系列的可能性。最终模型使用验证样本进行了测试。

结果

总体而言，53.6% 未能在 19 个月内完成 7 种疫苗系列。确定了六个风险因素：种族/民族、母语、保险状况、出生住院时间、医疗服务和乙肝疫苗接种情况。在高（77.1%；调整优势比 [AOR] 5.6；99% 置信区间 [CI]：4.2, 7.4）和中（52.7%；AOR 1.9；99% CI：1.6, 2.2）中，未完成的可能性更大) 与衍生样本中的低 (38.7%) 风险组。在验证样本中观察到类似的结果。

结论

我们的预测模型使用出生住院记录中现成的信息，一致地确定了处于疫苗接种不足高风险的新生儿。早期识别高风险家庭可能有助于及时启动量身定制的疫苗干预措施。我们使用来自电子病历的人口统计和出生住院信息开发了一个预测模型，该模型始终如一地识别出有可能错过未来儿童疫苗的新生儿。预测模型可以帮助提供者启动早期的、量身定制的疫苗干预措施。