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.

IF 2.4 4区 医学 Q2 HEALTH CARE SCIENCES & SERVICES Healthcare Pub Date : 2024-10-31 DOI:10.3390/healthcare12212170
Yu-Sheng Lee, Junu Shrestha, Matthew Evan Sprong, Xueli Huang, Sushil Tuladhar, Michael Y Chuang
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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 their 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 reasons. 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 visits due to other health reasons. The LASSO regressions demonstrated that telehealth visits were 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.

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对 COVID-19 期间儿科远程医疗就诊影响因素的机器学习分析:使用 2021-22 年全国儿童健康调查数据进行州级比较。
背景/目标:COVID-19 大流行使美国儿科亲自就诊人数减少了 50%以上,而远程保健就诊人数却大幅增加。很少有人研究过全国儿童远程保健的使用情况以及影响其使用的因素。本研究旨在调查 COVID-19 大流行期间远程医疗的使用率,并探索与各州使用远程医疗相关的潜在因素。研究方法对联邦妇幼保健局赞助的全国儿童健康调查(2021-22 年)进行了横截面研究。我们使用最小绝对收缩和选择算子(LASSO)回归法来预测大流行期间远程保健的使用情况。我们绘制了一张条形图,显示了多元回归中的重要因素。结果:在 101,136 名儿童中,15.25% 因 COVID-19 而使用远程医疗,3.67% 因其他健康原因而使用远程医疗。东北部各州因 COVID-19 而使用远程保健的比例最高。在中西部和南部各州,儿童因其他健康原因进行远程保健就诊的比例较低。LASSO 回归表明,远程保健就诊与年龄、保险类型、家庭收入、儿科预防保健的通常来源、儿童健康感知、血液疾病、过敏、脑损伤、癫痫发作、多动症、焦虑、抑郁和特殊需求有关。结论这项研究表明,在 COVID-19 大流行期间,各州在使用远程保健方面存在很大差异。了解谁在使用远程医疗及其原因,以及识别使用障碍,有助于最大限度地发挥远程医疗的潜力并改善所有人的医疗保健结果。
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来源期刊
Healthcare
Healthcare Medicine-Health Policy
CiteScore
3.50
自引率
7.10%
发文量
0
审稿时长
47 days
期刊介绍: Healthcare (ISSN 2227-9032) is an international, peer-reviewed, open access journal (free for readers), which publishes original theoretical and empirical work in the interdisciplinary area of all aspects of medicine and health care research. Healthcare publishes Original Research Articles, Reviews, Case Reports, Research Notes and Short Communications. We encourage researchers to publish their experimental and theoretical results in as much detail as possible. For theoretical papers, full details of proofs must be provided so that the results can be checked; for experimental papers, full experimental details must be provided so that the results can be reproduced. Additionally, electronic files or software regarding the full details of the calculations, experimental procedure, etc., can be deposited along with the publication as “Supplementary Material”.
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