Evidence map›Paper›PMID 39439712›Full record

ArticleTelemedicine reports2024

Poor Representation of Rural Counties of the United States in Some Measures of Consumer Broadband.

Cari A Bogulski, Maysam Rabbani, Corey J Hayes, Aysenur Betul Cengil, Catherine C Shoults, Hari Eswaran

Abstract read
In one paragraph

Article in Telemedicine reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Cari A BogulskiDepartment of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.ORCID https://orcid.org/0000-0001-6002-5945
Maysam RabbaniFeliciano School of Business, Montclair State University, Montclair, New Jersey, USA.
Corey J HayesDepartment of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.ORCID https://orcid.org/0000-0002-7776-6157
Aysenur Betul CengilDepartment of Industrial Engineering, College of Engineering, University of Arkansas, Fayetteville, Arkansas, USA.
Catherine C ShoultsDepartment of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.ORCID https://orcid.org/0000-0003-1960-9062
Hari EswaranDepartment of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: 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. Methods: 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. Results: 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. Conclusions: 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.

Indexed as

broadbanddigital divideequityinternetruralspeed tests

Identifiers

PMID39439712
PMCPMC11491573

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.