{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T20:09:37Z","timestamp":1788898177636,"version":"build-2803163510"},"reference-count":36,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"\n Background<\/jats:title>\n Telehealth 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A 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\u2013level 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The 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).<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Telehealth use surged in Virginia during the COVID-19 pandemic, particularly in rural areas. However, the findings indicate that disparities persist in the post\u2013COVID-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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/73271","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T11:05:06Z","timestamp":1768907106000},"page":"e73271","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian Spatial Analysis of Trends and Disparities in Telehealth Use During the COVID-19 Pandemic: Retrospective Observational Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2907-1296","authenticated-orcid":false,"given":"Jong Hyung","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9159-4876","authenticated-orcid":false,"given":"Roy T","family":"Sabo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5868-3711","authenticated-orcid":false,"given":"Jacqueline","family":"Britz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9879-3608","authenticated-orcid":false,"given":"Benjamin","family":"Webel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9497-3310","authenticated-orcid":false,"given":"Kelly M","family":"Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5809-5810","authenticated-orcid":false,"given":"Evan","family":"French","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4060-9155","authenticated-orcid":false,"given":"Alex H","family":"Krist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/1357633X20916567"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2015.0206"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2016.1461"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/20552076241233148"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2018.05125"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10112293"},{"key":"ref7","unstructured":"MehrotraAChernewMELinetskyDHatchHCutlerDMThe impact of the COVID-19 pandemic on outpatient visits: a rebound emergesCommonwealth Fund202005192024-05-05https:\/\/www.commonwealthfund.org\/publications\/2020\/apr\/impact-covid-19-outpatient-visits"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2196\/29575"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.nurpra.2020.09.013"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/pr.2015.192"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1186\/s12875-021-01543-4"},{"key":"ref12","unstructured":"MehrotraAChernewMELinetskyDHatchHCutlerDMSchneiderECThe impact of COVID-19 on outpatient visits in 2020: visits remained stable, despite a late surge in casesCommonwealth Fund202102222025-05-29https:\/\/www.commonwealthfund.org\/publications\/2021\/feb\/impact-covid-19-outpatient-visits-2020-visits-stable-despite-late-surge"},{"key":"ref13","unstructured":"SarfoLSilva GordonBKurowskiDTelehealth use increased more than 50-fold for primary care visits and behavioral health services in early months of the COVID-19 pandemicHealth Care Cost Institute202211162024-05-06https:\/\/tinyurl.com\/36pdu46e"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2020.00823"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2023.0260"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1089\/tmr.2023.0065"},{"key":"ref17","unstructured":"Virginia telehealth spend and utilization report: June 2023Virginia Health Innovation Network2024-12-01https:\/\/vahealthinnovation.org\/wp-content\/uploads\/2023\/07\/VA-Telehealth-Report_June2023.pdf"},{"key":"ref18","unstructured":"Social Deprivation Index (SDI)Robert Graham Center20182024-06-04https:\/\/www.graham-center.org\/maps-data-tools\/social-deprivation-index.html"},{"key":"ref19","unstructured":"GeverdtDEducation Demographic and Geographic Estimates program (EDGE): ZIP Code Tabulation Area (ZCTA) locale assignments file documentation. 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