Natalia V Oster1, Emily C Williams1,2, Joseph M Unger1,3, Polly A Newcomb3,4, M Patricia deHart5, Janet A Englund6,7, Annika M Hofstetter6,7. 1. Department of Health Systems and Population Health, University of Washington, Seattle, Washington, USA. 2. Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Administration Puget Sound, Seattle, Washington, USA. 3. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. 4. Department of Epidemiology, University of Washington, Seattle, Washington, USA. 5. Office of Immunization and Child Profile, Washington State Department of Health, Tumwater, Washington, USA. 6. Department of Pediatrics, University of Washington, Seattle, Washington, USA. 7. Center for Clinical and Translational Research, Seattle Children's Research Institute, Seattle, Washington, USA.
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.
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.
Authors: Natalia V Oster; Emily C Williams; Joseph M Unger; Polly A Newcomb; Elizabeth N Jacobson; M Patricia deHart; Janet A Englund; Annika M Hofstetter Journal: Vaccine Date: 2019-03-28 Impact factor: 3.641
Authors: Douglas J Opel; John Heritage; James A Taylor; Rita Mangione-Smith; Halle Showalter Salas; Victoria Devere; Chuan Zhou; Jeffrey D Robinson Journal: Pediatrics Date: 2013-11-04 Impact factor: 7.124
Authors: Amanda F Dempsey; Jennifer Pyrznawoski; Steven Lockhart; Juliana Barnard; Elizabeth J Campagna; Kathleen Garrett; Allison Fisher; L Miriam Dickinson; Sean T O'Leary Journal: JAMA Pediatr Date: 2018-05-07 Impact factor: 16.193