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Observational Study
. 2025 Jul 23;20(7):e0327218.
doi: 10.1371/journal.pone.0327218. eCollection 2025.

Effects of long COVID on healthcare utilization

Michael Gottlieb  1 , Erica S Spatz  2   3   4 , Huihui Yu  2   4 , Imtiaz Ebna Mannan  4 , Michelle Santangelo  1 , Caitlin Malicki  5 , Nicole L Gentile  6   7 , Rachel E Geyer  6 , Alex Charlton  8 , Jonathan W Dyal  9 , Joann G Elmore  10 , Kristyn Gatling  11 , Mandy J Hill  12 , Juan Carlos C Montoy  13 , Kelli N O'Laughlin  14 , Kristin L Rising  8   15 , Sharon Saydah  16 , Kari A Stephens  6   17 , Ralph C Wang  13 , Lauren E Wisk  10 , Arjun K Venkatesh  4   5 , Robert A Weinstein  11   18 ; Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) Group
Affiliations
Observational Study

Effects of long COVID on healthcare utilization

Michael Gottlieb et al. PLoS One. .

Abstract

Background: While most research on Long COVID (LC) has focused on symptoms and quality of life, there remains a critical need to better understand the effect of LC on resource utilization. This study sought to determine the type and amount of healthcare utilization among participants with versus without LC.

Methods: This was a secondary analysis of a prospective, longitudinal, multicenter U.S. study of adult participants with symptomatic COVID-19, confirmed with testing, who completed 3-month post-infection surveys and had electronic health record data for at least 180 days pre- and post-index testing. We excluded participants with any COVID-19 infections within the 6 months following enrollment. Consistent with prior work, LC was defined as ≥3 post-infectious symptoms at 3 months, while those with <3 symptoms were categorized as not having LC. Our primary outcome was to compare the change in visit types between pre- and post-index testing (hospitalization, emergency department visit, office visit, procedure, telehealth, and other). As secondary outcomes, we assessed differences in visit complexity using the summative length of each encounter for each category as a measure of total healthcare usage.

Results: A total of 847 participants met inclusion criteria (179 LC, 668 non-LC). When compared with the pre-index period, there was an overall increase in visit numbers of all six visit categories during the post-index period for all groups, most pronounced in office and telehealth visits. When compared with the non-LC group, the LC group was less likely to have ED visits (OR: 0.1; 95% CI 0.0-0.5). However, among those with LC who had at least one hospitalization, they were more likely to have additional hospitalizations (OR: 2.6; 95% CI 1.5-4.6). Visit length for office visits and hospitalization in the LC group was increased when compared with the non-LC group, though this diminished after adjustment for patient baseline characteristics.

Conclusions: All participants who were infected with SARS-CoV-2 had a marked increase in healthcare utilization during the subsequent 180 days. The LC group had significantly higher rates of additional hospitalization compared with those without LC, which may help to inform healthcare resource planning.

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Conflict of interest statement

We have read the journal’s policy and the authors of this manuscript have the following competing interests: This study was funded by the Centers for Disease Control and Prevention, as stated in the Funding statement. In addition, we report the following additional funding: MG has grant support through the Bill and Melinda Gates Foundation, Biomedical Advanced Research and Development Authority, and the Society for Academic Emergency Medicine Foundation. NG has grant support through the National Center for Complementary and Integrative Health, National Institute on Aging, Agency for Healthcare Research and Quality, and University of Washington Royalty Research Fund. ESS receives grant funding from the Centers for Disease Control and Prevention (20042801-Sub01), the U.S. Food and Drug Administration to support projects within the Yale-Mayo Clinic Center of Excellence in Regulatory Science and Innovation (CERSI, U01FD005938), the National Heart, Lung, and Blood Institute (R01HL151240), and the Patient Centered Outcomes Research Institute (HM-2022C2-28354). JGE serves as Editor-in-Chief of adult primary care topics for UpToDate. KNO has grant support through the National Institutes of Health (R01MH130216, R01AI166967). KLR has grant support through the Agency for Healthcare Research and Quality, Centers for Disease Control and Prevention, MANNA Institute, National Institutes of Health, Patient Centered Outcomes Research Institute, Society for Academic Emergency Medicine, Abbott Diagnostics, and CovardaDx. AKV receives support from the Centers for Medicare and Medicaid Services to develop hospital and health system quality measures including analyses related to COVID-19 as well as grant funding from the Society for Academic Emergency Medicine Foundation to study the impact of COVID-19 on health system outcomes. Study data is owned and managed directly by the grant recipient (Rush University) and the funder (Centers for Disease Control and Prevention).

Figures

Fig 1
Fig 1. Flow diagram of study cohort. a Incomplete surveys due to withdrawal, deceased, and loss to follow-up. b Incomplete EHR data includes participants who shared EHR data but did not have EHR data
+ /-180 days from index test.
Fig 2
Fig 2. Observed healthcare utilization at pre- and post-index periods across the four cohorts.
A, Percent of participants with each encounter type and the median number of encounters among those participants. B, Percent of participants with encounter type and the usage hours among those participants.
Fig 3
Fig 3. Estimated difference in change of healthcare utilization from pre- to post-index period across the four cohorts.
A, Unadjusted zero-inflated and Poisson model estimates of changes in counts of encounters. LC, Long COVID; OR, odds ratio; IRR, incidence-rate ratio. B, Adjusted zero-inflated and Poisson model estimates of changes in counts of encounters. LC, Long COVID; aOR, adjusted odds ratio; aIRR adjusted incidence-rate ratio. C, Unadjusted estimate of changes in length of each encounter. LC, Long COVID; CI, confidence interval. Note: To calculate the length of each encounter, we applied the Winsorization approach to handle encounters with an atypical length (85th percentile for office and telehealth visits; 95th percentile for hospitalization and emergency visit). D, Adjusted estimate of changes in length of each encounter. LC, Long COVID; CI, confidence interval. Note: To calculate the length of each encounter, we applied the Winsorization approach to handle encounters with an atypical length (85th percentile for office and telehealth visits; 95th percentile for hospitalization and emergency visit).
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References

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