Evidence map›Paper›PMID 42343378›Full record

ArticleBMC health services research2026

Health care utilization before and after COVID-19 diagnosis: a multidisease matched-cohort study by sociodemographic factors.

Alina Peluso, Omar Aljawfi, Kelley M Anderson, Margret V Bjarnadottir, Nawar Shara

Abstract read
In one paragraph

Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Alina PelusoOak Ridge National Laboratory, Oak Ridge, TN, 37830, USA. pelusoa@ornl.gov.
Omar AljawfiMedStar Health Research Institute, Hyattsville, MD, 21044, USA.
Kelley M AndersonUniversity of Virginia, Charlottesville, VA, 22903, USA.
Margret V BjarnadottirUniversity of Maryland, College Park, MD, 20742, USA.
Nawar SharaMedStar Health Research Institute, Hyattsville, MD, 21044, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic substantially disrupted healthcare utilization across clinical conditions, populations, and care settings. Consistent and equitable access to care is essential to mitigate the long-term consequences of COVID-19, particularly among individuals with pre-existing conditions. While prior studies have documented broad patterns in healthcare use during the pandemic, limited evidence exists on how utilization shifts before and after a COVID-19 diagnosis, particularly across specific diseases, encounter types, and sociodemographic subgroups.

methodsWe conducted a matched retrospective cohort study using electronic health record data from MedStar Health between March 25, 2019, and June 30, 2021. A difference-in-differences design was used to compare changes in health care utilization between adult patients who tested positive for COVID-19 (exposure group) and those who tested negative (comparison group), across the 6-month periods before and after testing. Outcomes included encounters across outpatient, inpatient, emergency department, and virtual settings, overall and stratified by pre-existing conditions (heart failure, diabetes mellitus, kidney disease, malignant neoplasm, hypothyroidism, hyperthyroidism, anxiety disorder, depressive disorder, and drug poisoning/overdose), while accounting for demographic characteristics (sex, age, race/ethnicity, insurance type, state of residence) and census tract-level socioeconomic indicators (median household income, educational attainment, and poverty/food access). To estimate the absolute burden of health care utilization associated with COVID-19, we converted DiD rate ratios into excess encounters per 1,000 individuals in the exposed group.

resultsCOVID-19 exposure was associated with a 4% overall reduction in health care utilization during the post-COVID period (RR = 0.96, p < 0.001, 262 fewer visits per 1,000 patients), primarily driven by decreased outpatient visits (RR = 0.96, p < 0.001). Significant declines were observed among adults aged ≥ 65 years (RR = 0.92, p < 0.001), males (RR = 0.90, p < 0.001), Black individuals (RR = 0.96, p < 0.001), and Medicare beneficiaries (RR = 0.91, p < 0.001). Geographically, encounter rates declined significantly in DC (RR = 0.93, p < 0.001) and Maryland (RR = 0.98, p = 0.023), but not in Virginia. Socioeconomic indicators also correlated with healthcare utilization: patients residing in areas with lower income (RR = 0.94, p = 0.003), lower educational attainment (RR = 0.95, p = 0.011), or higher rates of food stamp/SNAP participation (RR = 0.93, p < 0.001) experienced greater declines in utilization. Among clinical conditions, the largest reductions in healthcare visits were seen for patients with kidney disease (RR = 0.88, p < 0.001), drug poisoning/overdose (RR = 0.88, p < 0.001), hypothyroidism (RR = 0.91, p < 0.001), diabetes mellitus (RR = 0.94, p < 0.001), and heart failure (RR = 0.94, p = 0.002). In contrast, encounters for patient cohorts with hyperthyroidism (RR = 1.14, p = 0.004) increased notably, while those for depressive disorders (RR = 1.03, p = 0.072) showed a modest increase. No significant changes were observed in patient cohorts with anxiety (RR = 1.00, p = 0.83) and cancer (RR = 1.00, p = 0.81).

conclusionsCOVID-19 exposure was associated with broad but varied changes in healthcare utilization during the post-COVID period. Several chronic conditions showed notable declines in encounter rates, although these patterns were not consistent across all conditions. The heterogeneity in utilization patterns suggests potential gaps in ongoing care and highlights the need for targeted efforts to support continued management of health conditions after COVID-19.

Indexed as

COVID-19Patient Acceptance of Health CareAdultAgedElectronic Health RecordsFemaleHumansMaleMiddle AgedRetrospective StudiesSARS-CoV-2Sociodemographic FactorsSocioeconomic Disparities in HealthSocioeconomic FactorsUnited StatesChronic conditionsCOVID-19DemographicsDifference-in-differences (DiD)Electronic Health Records (EHR)Excess visitsHealthcare utilizationMental healthPandemicSocioeconomic factorsTelehealth

Identifiers

PMID42343378
PMCPMC13401288

What OpenQuestion holds

Textmetadata
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.