Evidence map›Paper›PMID 39361360›Full record

ArticleJournal of medical Internet research2024

Health Care Usage During the COVID-19 Pandemic and the Adoption of Telemedicine: Retrospective Study of Chronic Disease Cohorts.

Margrét Vilborg Bjarnadóttir, David Anderson, Kelley M Anderson, Omar Aljwfi, Alina Peluso, Adam Ghannoum, Gayle Balba, Nawar Shara

Abstract read
In one paragraph

Article in Journal of medical Internet research, 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. Observational
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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

8 authors.

Margrét Vilborg Bjarnadóttir *Decisions, Operations and Information Technology, University of Maryland, College Park, College Park, MD, United States.ORCID 0000-0003-2955-1992
David Anderson *Villanova School of Business, Villanova, PA, United States.ORCID 0000-0002-1128-6206
Kelley M AndersonSchool of Nursing, Georgetown University, Washington, DC, United States.ORCID 0000-0002-4405-2991
Omar AljwfiMedStar Health Research Institute, Hyattsville, MD, United States.ORCID 0000-0003-4243-7924
Alina PelusoOak Ridge National Laboratory, Oak Ridge, TN, United States.ORCID 0000-0003-2895-0406
Adam GhannoumUniversity of Maryland, College Park, College Park, MD, United States.ORCID 0009-0002-8039-2499
Gayle BalbaDepartment of Infectious Diseases, MedStar Georgetown University Hospital, Washington, DC, United States.ORCID 0009-0009-2702-8410
Nawar SharaMedStar Health Research Institute, Hyattsville, MD, United States.ORCID 0000-0002-9005-252X

Funding

Maternal Morbidity and Mortality: Risk Factors, Early Detection and Personalized InterventionUL1TR001409 · NCATS · GEORGETOWN UNIVERSITY · PI GONDRE-LEWIS, MARJORIE C, MELLMAN, THOMAS A · 2015 to 2024
$37.8M
NCATS NIH HHS UL1 TR001409
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic accelerated telehealth adoption across disease cohorts of patients. For many patients, routine medical care was no longer an option, and others chose not to visit medical offices in order to minimize COVID-19 exposure. In this study, we take a comprehensive multidisease approach in studying the impact of the COVID-19 pandemic on health care usage and the adoption of telemedicine through the first 12 months of the COVID-19 pandemic.

objectiveWe studied the impact of the COVID-19 pandemic on in-person health care usage and telehealth adoption across chronic diseases to understand differences in telehealth adoption across disease cohorts and patient demographics (such as the Social Vulnerability Index [SVI]).

methodsWe conducted a retrospective cohort study of 6 different disease cohorts (anxiety: n=67,578; depression: n=45,570; diabetes: n=81,885; kidney failure: n=29,284; heart failure: n=21,152; and cancer: n=35,460). We used summary statistics to characterize changes in usage and regression analysis to study how patient characteristics relate to in-person health care and telehealth adoption and usage during the first 12 months of the pandemic.

resultsWe observed a reduction in in-person health care usage across disease cohorts (ranging from 10% to 24%). For most diseases we study, telehealth appointments offset the reduction in in-person visits. Furthermore, for anxiety and depression, the increase in telehealth usage exceeds the reduction in in-person visits (by up to 5%). We observed that younger patients and men have higher telehealth usage after accounting for other covariates. Patients from higher SVI areas are less likely to use telehealth; however, if they do, they have a higher number of telehealth visits, after accounting for other covariates.

conclusionsThe COVID-19 pandemic affected health care usage across diseases, and the role of telehealth in replacing in-person visits varies by disease cohort. Understanding these differences can inform current practices and provides opportunities to further guide modalities of in-person and telehealth visits. Critically, further study is needed to understand barriers to telehealth service usage for patients in higher SVI areas. A better understanding of the role of social determinants of health may lead to more support for patients and help individual health care providers improve access to care for patients with chronic conditions.

Indexed as

COVID-19PandemicsTelemedicineAdultAgedAnxietyChronic DiseaseCohort StudiesDepressionDiabetes MellitusFemaleHeart FailureHumansMaleMiddle AgedNeoplasmscohort studyCOVID-19demographic differenceshealth care utilizationtelehealthtelehealth adaptiontelehealth utilization

Identifiers

PMID39361360
PMCPMC11487209

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.