Bogulski Cari A, Rabbani Maysam, Hayes Corey J, Cengil Aysenur Betul, Shoults Catherine C, Eswaran Hari
Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
Institute for Digital Health and Innovation, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
Telemed Rep. 2024 Oct 8;5(1):290-303. doi: 10.1089/tmr.2024.0048. eCollection 2024.
Telehealth has the potential to mitigate the lack of health care access in rural and underserved communities; however, telehealth is only viable where sufficiently high-speed internet broadband is available to patients. Existing broadband data sets may not accurately reflect the state of broadband, particularly in rural communities. We examined consumer internet speed test data from two organizations to see if the number of tests per 1,000 residents varied across county-level rurality.
We analyzed county-level data from Measurement Labs (M-Lab) and Ookla for Good (Ookla fixed and mobile) across the calendar years 2020 and 2021. We used the number of tests conducted per 1,000 residents within United States counties as the outcome variable, and Rural-Urban Continuum Codes (RUCC) as the main independent variable of interest.
Using negative binomial models with robust standard errors, we found that the number of fixed speed tests conducted per 1,000 residents was generally lower in rural counties relative to counties with over one million residents. However, we found no associations between any categories of county-level rurality for the number of mobile tests conducted per 1,000 residents. Patterns of association with other covariates emerged as significant in some models and not in others, suggesting key differences among users generating speed tests among these data sources.
Our findings demonstrate the poor representation of residents from very rural counties in M-Lab and Ookla fixed data sets of user-generated internet speed tests. Additional data are needed to inform broadband infrastructure investment to identify those communities most left behind by broadband expansion efforts.
远程医疗有潜力缓解农村及医疗服务不足社区医疗服务可及性差的问题；然而，远程医疗仅在患者能够使用足够高速的互联网宽带时才可行。现有的宽带数据集可能无法准确反映宽带状况，尤其是在农村社区。我们研究了来自两个组织的消费者互联网速度测试数据，以了解每1000名居民的测试次数是否因县级农村地区差异而有所不同。
我们分析了2020年和2021年全年来自测量实验室（M-Lab）和Ookla for Good（Ookla固定和移动）的县级数据。我们将美国各县内每1000名居民进行的测试次数作为结果变量，并将城乡连续体代码（RUCC）作为主要的感兴趣自变量。
使用具有稳健标准误差的负二项式模型，我们发现，与居民超过100万的县相比，农村县每1000名居民进行的固定速度测试次数通常较低。然而，我们发现每1000名居民进行的移动测试次数与任何县级农村地区类别之间均无关联。在一些模型中，与其他协变量的关联模式显著，而在另一些模型中则不显著，这表明这些数据源中生成速度测试的用户之间存在关键差异。
我们的研究结果表明，在用户生成的互联网速度测试的M-Lab和Ookla固定数据集中，来自非常偏远农村县的居民代表性不足。需要更多数据来为宽带基础设施投资提供信息，以确定那些在宽带扩展努力中最被忽视的社区。
