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. 2024 Aug 8;3(9):101177. doi: 10.1016/j.jacadv.2024.101177

Effect of the COVID-19 Pandemic on Monitoring and Control of Cardiovascular Risk Factors and Disparities

Yuan Lu a,b,, John E Brush Jr c,d,∗,, Yuntian Liu a, Shu-Xia Li a, Jordan R Asher c, Harlan M Krumholz a,b,e
PMCID: PMC11364117  PMID: 39220712

Corresponding author

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The COVID-19 pandemic, a global health crisis, has significantly impacted health care operations and cardiovascular risk factor management.1 As the end of the public health emergency, it is an opportune time to evaluate what occurred with risk factor management. Using data from a large regional health system from 2017 to 2023, we assessed the pandemic's impact on cardiovascular risk factor monitoring and control. Additionally, we examined whether the pandemic’s impact on cardiovascular risk factors varied by racial and ethnic subgroups.

We utilized electronic health record (EHR) data from Sentara Health, a large nonprofit health care system in Virginia and Northeastern North Carolina. EHR data were standardized to the Observational Medical Outcomes Partnership common data model, version 5.3.2 The study population included adults ≥18 years who visited the system between January 1, 2017, and December 31, 2023, and measurements included blood pressure (BP) and body mass index (BMI) from outpatient visits and hemoglobin A1c (HbA1c) and total cholesterol measurements from all types of visits.

We performed a serial cross-sectional analysis, dividing data into calendar quarters to evaluate outpatient, inpatient/emergency, and telehealth visits. Patients were categorized by elevated levels of BMI (≥30 kg/m2), BP (systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg), HbA1c (≥6.5%), or total cholesterol (≥240 mg/dL) to calculate age-adjusted rates. Seasonal, autoregressive integrated moving average models were applied to fit the prepandemic data (2017-2020) and estimate expected outcomes after the pandemic.3 Data were analyzed using the Microsoft SQL Server (Microsoft Corporation, One Microsoft Way, Redmond, WA) and R software (version 4.2.3; R Foundation for Statistical Computing, c/o Institute for Statistics and Mathematics, Vienna, Austria). The Institutional Review Board at Eastern Virginia Medical School approved the study.

During the study period, 2,999,242 unique adults accessed health services at Sentara Health. In Q2 2020, there was an early decline in outpatient visits (from 380,304 to 258,335) and inpatient/emergency visits (from 156,024 to 109,314), followed by a rapid recovery and a corresponding sharp rise in telehealth visits. This period also saw a decrease in the frequency of BP and BMI measurements, which necessitated in-person visits. These measurements later rebounded, indicating a gradual resumption of regular health care and monitoring practices.

The Figure 1 presents the age-adjusted rates of elevated BP, BMI, HbA1c, and total cholesterol levels. During the early pandemic stage in Q2 2020, there was a significant increase in the age-adjusted rates of elevated BP by 2.0 percentage points, with the observed-to-expected ratio being 1.18 (95% CI: 1.11-1.27). This elevation persisted in subsequent periods, and by Q1 2022, the rate of elevated BP reached 22.6%, marking a noticeable rise of 1.1 percentage points from the prepandemic level recorded in Q1 2020. High total cholesterol levels did not change significantly initially but showed a delayed rise starting in Q2 2021, increasing by 3.1 percentage points by Q1 2022 (observed-to-expected ratio: 1.28 [95% CI: 1.13-1.48]), before declining. The age-adjusted rates of obesity fluctuated minimally throughout the study period, maintaining a consistent trend with an overall standard deviation of only 0.9 percentage points. In contrast, elevated HbA1c levels showed a marked decrease of 5.0 percentage points in Q3 2020 (observed-to-expected ratio: 0.91 [95% CI: 0.78-1.09]), and remained lower than prepandemic levels.

Figure 1.

Figure 1

Trends in Elevated Cardiovascular Risk Factor Levels, 2017-2023

Trends in (A) elevated cardiovascular risk factor levels and (B) racial and ethnic disparity in blood pressure levels, 2017 to 2023.

BMI = body mass index; HbA1c = hemoglobin A1c.

Significant racial and ethnic disparities were observed in all cardiovascular risk factors. Before the pandemic, non-Hispanic Black adults had the highest age-adjusted rates of elevated BP and BMI (25.6% and 58.1%, respectively), while Hispanic adults had the highest age-adjusted rates of elevated total cholesterol (9.1%). These disparities persisted postpandemic. For elevated HbA1c, the racial and ethnic disparities became more pronounced, with non-Hispanic Black adults not only showing the highest levels but also experiencing the most significant increase in elevated HbA1c rates after the pandemic, reaching 38.2%.

This study represents one of the most up-to-date and comprehensive real-world analyses in examining the impact of the COVID-19 pandemic on the monitoring and control of cardiovascular risk factors in the United States. This study demonstrated the COVID-19 pandemic's significant impact on health care delivery, including an initial decline in outpatient and inpatient visits, a surge in telehealth, and varied trends in cardiovascular risk factors. Measurements of BP and BMI were most affected, likely because those measurements require an in-person appointment. Persistent racial and ethnic disparities were observed before and after the pandemic, with non-Hispanic Black adults showing the highest rates of elevated BP, HbA1c, and obesity, and Hispanic adults showing higher rates of elevated total cholesterol.

The findings highlight a critical need for adaptable health care systems and targeted public health interventions to monitor health care delivery and manage risk factors during a pandemic.4 Additionally, the persistent racial and ethnic disparities in all cardiovascular risk factors both before and during the pandemic renew the call for more focused and equitable public health interventions.5

This study is limited by its observational design, which cannot definitively establish causality between the pandemic and changes in cardiovascular risk factors. The focus on a single regional health system may restrict the generalizability of the findings. However, the diversity of the population served by this health system, encompassing a wide range of racial and ethnic subgroups and socioeconomic status, lends a degree of robustness and relevance to the findings.

Footnotes

This research was partly funded by the Batten Foundation, Norfolk, Virginia, USA, and the Hampton Roads Biomedical Research Consortium, Portsmouth, Virginia, USA. In the past 3 years, Dr Krumholz has received expenses and/or personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried and Jensen Law Firm, Arnold and Porter Law Firm, and Martin/Baughman Law Firm; and is a cofounder of Refactor Health and HugoHealth and is associated with contracts through Yale New Haven Hospital, from the Centers for Medicare & Medicaid Services and through Yale University from Johnson & Johnson. Dr Brush has received royalties from Dementi Milestone Publishing for the book “The Science of the Art of Medicine: A Guide to Medical Reasoning.” Dr Lu has received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under awards R01HL69954 and R01HL169171), and the Patient-Centered Outcomes Research Institute (under award HM-2022C2-28354) outside of the submitted work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.

References

  • 1.Driggin E., Madhavan M.V., Bikdeli B., et al. Cardiovascular considerations for patients, health care workers, and health systems during the COVID-19 pandemic. J Am Coll Cardiol. 2020;75:2352–2371. doi: 10.1016/j.jacc.2020.03.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Hripcsak G., Duke J.D., Shah N.H., et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inf. 2015;216:574–578. [PMC free article] [PubMed] [Google Scholar]
  • 3.Abraham B., Ledolter J. Forecast functions implied by autoregressive integrated moving average models and other related forecast procedures. Inter Stat Review/Revue Internationale de Statistique. 1986;54:51–66. [Google Scholar]
  • 4.Austin J.M., Kachalia A. The state of health care quality measurement in the era of COVID-19: the importance of doing better. JAMA. 2020;324:333–334. doi: 10.1001/jama.2020.11461. [DOI] [PubMed] [Google Scholar]
  • 5.Russo R.G., Li Y., Ðoàn L.N., et al. COVID-19, social determinants of health, and opportunities for preventing cardiovascular disease: a conceptual framework. J Am Heart Assoc. 2021;10 doi: 10.1161/JAHA.121.022721. [DOI] [PMC free article] [PubMed] [Google Scholar]

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