A Risk Prediction Model to Identify Newborns at Risk for Missing Early Childhood Vaccinations. (17th August 2021)

• Record Type: Journal Article
• Authors: Oster, Natalia V; Williams, Emily C; Unger, Joseph M; Newcomb, Polly A; deHart, M Patricia; Englund, Janet A; Hofstetter, Annika M
• Is Part Of: Journal of the Pediatric Infectious Diseases Society. Volume 10:Number 12(2021)
• Page Start: 1080
• Page End: 1086
• Publication Date: 2021-08-17
• Subjects: immunization -- infectious disease -- newborn -- risk prediction -- vaccines; Communicable diseases in children -- Periodicals; Children -- Diseases -- Periodicals; 618.929
• DOI: 10.1093/jpids/piab073
• Languages: English
• ISSNs: 2048-7193

Abstract

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. Abstract : We developed a prediction model using demographic and birth hospitalization information from electronic medical records that consistently identified newborns at risk for missing future vaccines. Prediction models may aid providers as they initiate early, tailored vaccine interventions.