Evidence map›Paper›PMID 33769942›Full record

ArticleJournal of medical Internet research2021

Rural Telemedicine Use Before and During the COVID-19 Pandemic: Repeated Cross-sectional Study.

Cherry Chu, Peter Cram, Andrea Pang, Vess Stamenova, Mina Tadrous, R Sacha Bhatia

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 1 pooled it
–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

78 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  11. Improving forensic healthcare: ARMED, a new telemedical examination.International journal of legal medicine · 2025
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18 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Cherry ChuWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada.ORCID 0000-0002-4072-0083
Peter CramDivision of General Internal Medicine and Geriatrics, Sinai Health System and University Health Network, Toronto, ON, Canada.ORCID 0000-0002-1910-346X
Andrea PangInstitute for Clinical Evaluative Sciences, Toronto, ON, Canada.ORCID 0000-0003-3324-7168
Vess StamenovaWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada.ORCID 0000-0003-3252-7766
Mina TadrousWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada.ORCID 0000-0003-1911-6129
R Sacha BhatiaWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada.ORCID 0000-0001-6206-5318

Funding

Comparing hospitalization rates, outcomes, and treatment intensity for elderly patients across OECD countriesR01AG058878 · NIA · HARVARD MEDICAL SCHOOL · PI Peter M Cram, Bruce E. Landon · 2019 to 2026
$3.4M
NIA NIH HHS R01 AG058878
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has led to a notable increase in telemedicine adoption. However, the impact of the pandemic on telemedicine use at a population level in rural and remote settings remains unclear.

objectiveThis study aimed to evaluate changes in the rate of telemedicine use among rural populations and identify patient characteristics associated with telemedicine use prior to and during the pandemic.

methodsWe conducted a repeated cross-sectional study on all monthly and quarterly rural telemedicine visits from January 2012 to June 2020, using administrative data from Ontario, Canada. We compared the changes in telemedicine use among residents of rural and urban regions of Ontario prior to and during the pandemic.

resultsBefore the pandemic, telemedicine use was steadily low in 2012-2019 for both rural and urban populations but slightly higher overall for rural patients (11 visits per 1000 patients vs 7 visits per 1000 patients in December 2019, P<.001). The rate of telemedicine visits among rural patients significantly increased to 147 visits per 1000 patients in June 2020. A similar but steeper increase (P=.15) was observed among urban patients (220 visits per 1000 urban patients). Telemedicine use increased across all age groups, with the highest rates reported among older adults aged ≥65 years (77 visits per 100 patients in 2020). The proportions of patients with at least 1 telemedicine visit were similar across the adult age groups (n=82,246/290,401, 28.3% for patients aged 18-49 years, n=79,339/290,401, 27.3% for patients aged 50-64 years, and n=80,833/290,401, 27.8% for patients aged 65-79 years), but lower among younger patients <18 years (n=23,699/290,401, 8.2%) and older patients ≥80 years (n=24,284/290,401, 8.4%) in 2020 (P<.001). There were more female users than male users of telemedicine (n=158,643/290,401, 54.6% vs n=131,758/290,401, 45.4%, respectively, in 2020; P<.001). There was a significantly higher proportion of telemedicine users residing in relatively less rural than in more rural regions (n=261,814/290,401, 90.2% vs n=28,587/290,401, 9.8%, respectively, in 2020; P<.001).

conclusionsTelemedicine adoption increased in rural and remote areas during the COVID-19 pandemic, but its use increased in urban and less rural populations. Future studies should investigate the potential barriers to telemedicine use among rural patients and the impact of rural telemedicine on patient health care utilization and outcomes.

Indexed as

AgedCOVID-19Cross-Sectional StudiesFemaleHumansMaleOntarioPandemicsRural PopulationSARS-CoV-2Telemedicinechronic diseasechronic illnessCOVID-19health carehealth servicesolder adultspandemicpopulationremoteruraltelemedicinevirtual care

Identifiers

PMID33769942
PMCPMC8023379

What OpenQuestion holds

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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.