Evidence map›Paper›PMID 34224882›Full record

ArticleWorld neurosurgery2021

Predictors of Telemedicine Utilization in a Pediatric Neurosurgical Population During the COVID-19 Pandemic.

William A Lambert, Nathan K Leclair, Joshua Knopf, Maua H Mosha, Markus J Bookland, Jonathan E Martin, David S Hersh

Abstract read
In one paragraph

Article in World neurosurgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
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.

William A LambertUConn School of Medicine, Farmington, Connecticut, USA.
Nathan K LeclairUConn School of Medicine, Farmington, Connecticut, USA.
Joshua KnopfUConn School of Medicine, Farmington, Connecticut, USA.
Maua H MoshaDepartment of Research, Connecticut Children's, Hartford, Connecticut, USA.
Markus J BooklandDepartment of Surgery, UConn School of Medicine, Farmington, Connecticut, USA; Department of Pediatrics, UConn School of Medicine, Farmington, Connecticut, USA; Division of Neurosurgery, Connecticut Children's, Hartford, Connecticut, USA.
Jonathan E MartinDepartment of Surgery, UConn School of Medicine, Farmington, Connecticut, USA; Division of Neurosurgery, Connecticut Children's, Hartford, Connecticut, USA.
David S HershDepartment of Surgery, UConn School of Medicine, Farmington, Connecticut, USA; Department of Pediatrics, UConn School of Medicine, Farmington, Connecticut, USA; Division of Neurosurgery, Connecticut Children's, Hartford, Connecticut, USA. Electronic address: dhersh@connecticutchildrens.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn the wake of the COVID-19 pandemic, telemedicine has become rapidly adopted by the neurosurgical community; however, few studies have examined predictors of telemedicine utilization. Here, we analyze patient variables associated with the acceptance of a telemedicine encounter by a pediatric neurosurgical population during the early phases of the COVID-19 pandemic.

methodsAll patients seen in a single institution's outpatient pediatric neurosurgery clinic between April 1, 2020 and July 31, 2020 were retrospectively reviewed. Demographic variables were collected for each patient's first completed encounter. Patients participating in telemedicine were compared with those seen in person. Univariate analysis was performed using the Wilcoxon rank sum test for continuous variables and Fischer exact test for categorical variables. A logistic regression multivariable analysis was then performed.

resultsWe included 682 patients (374 telemedicine and 308 in person). Univariate analysis demonstrated that telemedicine visits were more likely to occur at earlier study dates (P < 0.001) and that patients participating in telemedicine visits were more likely to be established rather than new patients (P < 0.001), White or Caucasian (P < 0.001), not Hispanic or Latino (P < 0.001), English-speaking (P < 0.001), non-Medicare/Medicaid recipients (P < 0.001), have lower no-show rates (P = 0.006), and live farther from the hospital (P = 0.005). Multivariable analysis demonstrated older age (P = 0.031), earlier appointment date (P < 0.01), established patient status (P < 0.001), English-speaking (P < 0.02), and non-Medicare/Medicaid insurance (P < 0.05) were significant predictors of telemedicine utilization.

conclusionsSignificant demographic differences exist among pediatric patients who participated in telemedicine versus those who requested an in-person visit at our institution. Addressing barriers to access will be crucial for promoting health equity in continued utilization of telemedicine.

Indexed as

TelemedicineAgedAmbulatory CareChildCOVID-19HumansMaleNeurosurgeryPatientsRetrospective StudiesSARS-CoV-2COVID-19Pediatric neurosurgeryTelehealthTelemedicine

Identifiers

PMID34224882
PMCPMC9760262

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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.