PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Machine Learning Analysis of Factors Influencing Pediatric Telehealth Visits During COVID-19: A State-Level Comparison Using 2021-22 National Survey of Children’s Health Data
Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (15:38:19 CEST)
How to cite:
Lee, Y.-S.; Shrestha, J.; Sprong, M. E.; Huang, X.; Tuladhar, S.; Chuang, M. Y. Machine Learning Analysis of Factors Influencing Pediatric Telehealth Visits During COVID-19: A State-Level Comparison Using 2021-22 National Survey of Children’s Health Data. Preprints2024, 2024090986. https://doi.org/10.20944/preprints202409.0986.v1
Lee, Y.-S.; Shrestha, J.; Sprong, M. E.; Huang, X.; Tuladhar, S.; Chuang, M. Y. Machine Learning Analysis of Factors Influencing Pediatric Telehealth Visits During COVID-19: A State-Level Comparison Using 2021-22 National Survey of Children’s Health Data. Preprints 2024, 2024090986. https://doi.org/10.20944/preprints202409.0986.v1
Lee, Y.-S.; Shrestha, J.; Sprong, M. E.; Huang, X.; Tuladhar, S.; Chuang, M. Y. Machine Learning Analysis of Factors Influencing Pediatric Telehealth Visits During COVID-19: A State-Level Comparison Using 2021-22 National Survey of Children’s Health Data. Preprints2024, 2024090986. https://doi.org/10.20944/preprints202409.0986.v1
APA Style
Lee, Y. S., Shrestha, J., Sprong, M. E., Huang, X., Tuladhar, S., & Chuang, M. Y. (2024). Machine Learning Analysis of Factors Influencing Pediatric Telehealth Visits During COVID-19: A State-Level Comparison Using 2021-22 National Survey of Children’s Health Data. Preprints. https://doi.org/10.20944/preprints202409.0986.v1
Chicago/Turabian Style
Lee, Y., Sushil Tuladhar and Michael Y Chuang. 2024 "Machine Learning Analysis of Factors Influencing Pediatric Telehealth Visits During COVID-19: A State-Level Comparison Using 2021-22 National Survey of Children’s Health Data" Preprints. https://doi.org/10.20944/preprints202409.0986.v1
Abstract
Background/Objectives:
The COVID-19 pandemic reduced in-person pediatric visits in the United States by over 50%, while telehealth visits increased significantly. The national use of telehealth for children and the factors influencing its use have been rarely studied. This study aimed to investigate the prevalence of telehealth use during the COVID-19 pandemic and explore the potential factors linked to its use at the state level.
Methods:
A cross-sectional study of the National Survey of Children’s Health (2021-22) sponsored by the federal Maternal and Child Health Bureau was performed. We used the least absolute shrinkage and selection operator (LASSO) regression to predict telehealth use during the pandemic. A bar map showing the significant factors from the multivariable regression was created.
Results:
Of the 101,136 children, 15.25% reported using telehealth visits due to COVID-19, and 3.67% reported using telehealth visits due to other health conditions. The Northeast states showed the highest telehealth use due to COVID-19. In the Midwest and Southern states, children had a lower prevalence of telehealth usage unrelated to COVID-19. The LASSO regressions demonstrated that telehealth usage (due to and not due to) was associated with age, insurance type, household income, usual source of pediatric preventive care, perceived child health, blood disorders, allergy, brain injury, seizure, ADHD, anxiety, depression, and special needs.
Conclusions:
This study demonstrated significant variability in the use of telehealth among states during the COVID-19 pandemic. Understanding who uses telehealth and why, as well as identifying access barriers, helps maximize telehealth potential and improve healthcare outcomes for all.
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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