Evidence map›Paper›PMID 41556554›Full record

Observational studyJMIR formative research2026

Bayesian Spatial Analysis of Trends and Disparities in Telehealth Use During the COVID-19 Pandemic: Retrospective Observational Study.

Jong Hyung Lee, Roy T Sabo, Jacqueline Britz, Benjamin Webel, Kelly M Rodriguez, Evan French, Alex H Krist

Abstract readObservational Study
In one paragraph

Observational study in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Jong Hyung LeeDepartment of Family Medicine and Population Health, School of Medicine, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0009-0005-2907-1296
Roy T SaboDepartment of Biostatistics, School of Public Health, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0001-9159-4876
Jacqueline BritzDepartment of Family Medicine and Population Health, School of Medicine, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0001-5868-3711
Benjamin WebelDepartment of Family Medicine and Population Health, School of Medicine, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0001-9879-3608
Kelly M RodriguezBroadlands Family Practice/Inova, Ashburn, VA, United States.ORCID 0009-0000-9497-3310
Evan FrenchC Kenneth and Dianne Wright Center for Clinical and Translational Research, Office of the Vice President for Research and Innovation, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0002-5809-5810
Alex H KristDepartment of Family Medicine and Population Health, School of Medicine, Virginia Commonwealth University, Richmond, VA, United States.ORCID 0000-0003-4060-9155

Funding

Wright Regional Center for Clinical and Translational ScienceUM1TR004360 · NCATS · VIRGINIA COMMONWEALTH UNIVERSITY · PI FREDERICK Gerard MOELLER · 2023 to 2026
$16.3M
Virginia Accountable Health Equity and Action Dashboard (VA-AHEAD): Community Framing of Equity Data to Support Clinical and Translational ScienceRC2TR005115 · NCATS · VIRGINIA COMMONWEALTH UNIVERSITY · PI Alexander H Krist · 2024 to 2026
$3.7M
NCATS NIH HHS RC2 TR005115NCATS NIH HHS UM1 TR004360
6 · The paper itself

Abstract

backgroundTelehealth has emerged as an essential health care tool, particularly during the COVID-19 pandemic, when in-person medical services were significantly restricted. While telehealth adoption surged during the pandemic, disparities in its access and use have been observed, especially among vulnerable populations. Understanding these trends and identifying barriers is crucial for promoting equitable health care delivery.

objectiveThis study aims to assess disparities in telehealth use across Virginia, focusing on demographic, socioeconomic, and geographic factors influencing access. Using spatial modeling, we evaluate the association between community-level characteristics and telehealth use. Our findings can highlight areas where telehealth remains underused, informing targeted interventions to improve equitable access.

methodsA retrospective observational analysis was conducted from 2016 to 2021 using data from the Virginia All-Payer Claims Database (APCD) and demographic data from the American Community Survey. Annual telehealth use rates were calculated at the zip code tabulation area level during the study period. Demographic and socioeconomic variables, such as educational attainment, poverty, and broadband internet access, along with geographic factors, including population density and rurality, were incorporated. A Bayesian spatial regression with conditional autoregressive priors on zip code tabulation area-level random effects was used to assess the relationship between telehealth use and community-level characteristics. The deviance information criterion was used to select the final model. Results were presented as relative risks (RRs) with 95% credible intervals.

resultsThe trends showed an increase in telehealth use during the pandemic, with rural areas showing the most notable rise in 2020 (41.2% of all the visits), up from 14.2% in 2016, representing a statistically significant upward trend (P<.001). However, by 2021, telehealth use shifted, with suburban areas leading (43.1% of the visits), while rural areas followed (37.7%), indicating evolving patterns of adoption over time. Some sociodemographic factors exhibited temporal shifts in their association with telehealth use. Disparities in telehealth use among older adults improved, as the adjusted RR increased from 0.74 in 2019 to 0.95 in 2020, though a slight decline was observed in 2021 (RR 0.92, 95% credible interval 0.89-0.96). Conversely, disparities among non-Hispanic Black populations widened, with adjusted RR declining from 0.96 in 2020 to 0.93 in 2021 (95% credible interval 0.90-0.97), signaling persistent disparities. Higher telehealth use was associated with better broadband access (adjusted RR 1.06, 95% credible interval 1.01-1.11) and increased population density (adjusted RR 1.07, 95% credible interval 1.02-1.12).

conclusionsTelehealth use surged in Virginia during the COVID-19 pandemic, particularly in rural areas. However, the findings indicate that disparities persist in the post-COVID-19 pandemic period, especially among minority population groups and older adults. Addressing these gaps requires targeted interventions, including expanding broadband infrastructure and improving telehealth literacy. These efforts are crucial to ensuring equitable access to telehealth services, especially for underserved communities.

Indexed as

COVID-19Healthcare DisparitiesHealth Services AccessibilityTelemedicineAdolescentAdultAgedBayes TheoremFemaleHumansMaleMiddle AgedPandemicsRetrospective StudiesSARS-CoV-2Socioeconomic FactorsBayesian analysisCOVID-19health disparitiesrural healthtelehealthtelehealth use over timeVirginia

Identifiers

PMID41556554
PMCPMC12869146

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.