Skip to main content
Download PDF

COVID-19 pandemic and initiation of treatment for atrial fibrillation: a nationwide analysis of claims data

BMC Cardiovascular Disorders volume 23, Article number: 604 (2023) Cite this article

Abstract

Background

The COVID-19 pandemic profoundly disrupted the delivery of medical care. It remains unclear whether individuals diagnosed with new onset disease during the pandemic were less likely to initiate treatments after diagnosis. We sought to evaluate changes in the treatment initiation of patients newly diagnosed with atrial fibrillation (AF) after the onset of the COVID-19 pandemic.

Methods

In this retrospective cohort study, we identified individuals with incident AF from 01/01/2016–09/30/2021 using Optum’s de-identified Clinformatics® Data Mart Database. The primary outcome was initiation of oral anticoagulation (OAC) within 30 days of AF diagnosis. Secondary outcomes included initiation of OAC within 180 days of diagnosis, initiation of warfarin, direct oral anticoagulants (DOACs), rhythm control medications and electrical cardioversion within 30 days of diagnosis. We constructed interrupted time series analyses to examine changes in the outcomes following the onset of the pandemic.

Results

A total of 573,524 patients (age 73.0 ± 10.9 years) were included in the study. There were no significant changes in the initiation of OAC, DOAC, and rhythm control medications associated with the onset of the pandemic. There was a significant decrease in initiation of electrical cardioversion associated with the onset of the pandemic. The rate of electronic cardioversion within 30 days of diagnosis decreased by 4.9% per 1,000 patients after the onset of the pandemic and decreased by about 35% in April 2020, compared to April 2019, from 5.53% to 3.58%.

Conclusion

The COVID-19 pandemic did not affect the OAC initiation within 30 days of AF diagnosis but was associated with a decline in the provision of procedures for patients newly diagnosed with AF.

Peer Review reports

Introduction

The COVID-19 pandemic had a profound impact on health care access. A robust body of literature has documented declines in patient encounters with the health care system in the early days of the COVID-19 pandemic, which resulted in apparent decreases in rates of clinical events, even for life-threatening conditions [1,2,3,4,5]. Decreased use of imaging tests, laboratory services or provider-administered drugs have also been described in the literature [6,7,8]. Although this emerging literature is crucial to understand the negative impacts of the COVID-19 pandemic beyond COVID cases and deaths, prior studies mostly focused on the evaluation of clinical services that required access to health care facilities, rather than evaluating disruptions in outpatient pharmacotherapy. Additionally, few studies have evaluated patterns of health services delivered to patients with new onset disease during the COVID-19 pandemic. As a result, it remains unclear to what extent pandemic disruptions of health care access have resulted in forgone or delayed initiation of treatment for new onset disease.

Our study used US nationwide data from Optum’s de-identified Clinformatics® Data Mart Database to evaluate changes in treatment initiation in patients newly diagnosed with atrial fibrillation (AF). AF is a crucial disease to evaluate disruptions in care associated with the COVID-19 pandemic because early initiation of oral anticoagulation and consistent adherence is essential to prevent ischemic stroke events [9, 10]. Additionally, the coexistence of COVID-19 and AF creates a potentially deadly combination, substantially increasing the risk of pulmonary embolism, stroke, and venous thromboembolism [11,12,13]. The adverse outcomes observed when these two conditions converge underscore the significance of promptly initiating treatments for patients with AF.

MethodS

Data sources and study population

We obtained claims data between 01/01/2016–09/30/2021 from Optum’s de-identified Clinformatics® Data Mart Database (most recent data available at the time of analysis). Optum’s de-identified Clinformatics® Data Mart Database data are derived from administrative health claims for large commercial and Medicare Advantage health plans. The data included verified, adjudicated, and de-identified medical and pharmacy claims for a geographically diverse population spanning all 50 states. We selected the study population in six steps (Fig. 1). First, we selected patients aged over 18 years and who were continuously enrolled for at least 12 months in 1/1/2016-8/31/2021 (n = 28,025,929). Second, we excluded patients who had a diagnosis of AF in the first 12 months of continuous enrollment or who had incomplete covariate data (n = 1,245,066). This ensured we had 12 months of complete data prior to AF diagnosis for the definition of baseline characteristics and the exclusion of prevalent AF patients. AF was defined as having an inpatient or outpatient claim with International Classification of Diseases Ninth Revision (ICD-9) code 427.31 or International Classification of Diseases Tenth Revision (ICD-10) codes I48.0, I48.1, I48.2, or I48.91 in the first or second diagnosis fields [14]. Third, we selected patients who were newly diagnosed with AF after the 12-month washout period (n = 630,207). The index date was defined as the AF diagnosis date. Fourth, we excluded patients who had a diagnosis of valvular disease in the 12 months prior to the index date (n = 24,566). Valvular disease was defined as having ICD-9 codes 394.0, V43.3 or ICD-10 codes I05.0, Z95.2 in any diagnosis field [15]. Fifth, we excluded patients who died (n = 24,857) or did not have continuous enrollment (n = 7,220) for at least 30 days after the index date. This ensured we did not have missing data for the primary outcome. Finally, we excluded patients who initiated multiple anticoagulants on the same day (n = 40). The final sample included 573,524 eligible patients with incident AF between 01/01/2016–09/30/2021. Patients were followed from the index date for 180 days or until death, disenrollment, initiation of outcomes, or end of the study (9/30/2021). The Institutional Review Board at the University of California, San Diego approved this study as exempt as de-identified data were used in analyses.

Fig. 1
figure 1

Overview of the Sample Selection. Atrial fibrillation was defined as having an inpatient or outpatient claim with International Classification of Diseases Ninth Revision (ICD-9) code 427.31 or International Classification of Diseases Tenth Revision (ICD-10) codes I48.0, I48.1, I48.2, or I48.91 in the first or second diagnosis field. Valvular disease was defined as having ICD-9 codes 394.0, V43.3 or ICD-10 codes I05.0, Z95.2 at any diagnosis field

Full size image

Outcomes

The primary outcome of interest was oral anticoagulation initiation within 30 days of AF diagnosis. This was defined as filling a prescription for warfarin or a direct oral anticoagulant (DOAC) within 30 days of the AF diagnosis. Secondary outcomes were initiation of oral anticoagulation within 180 days of AF diagnosis, initiation of warfarin, initiation of DOACs, and other AF treatment measures, including initiation of rhythm control medications and electrical cardioversion, all within 30 days of AF diagnosis. The definition of oral anticoagulant initiation within 180 days of AF diagnosis enabled for the capture of treatment initiation among individuals who may have been hospitalized at the time of diagnosis or who may have received free samples from providers, whose early initiation during the inpatient admission or through free samples would not be captured in Medicare pharmacy claims. This expanded time frame also enabled capture of treatment initiation among individuals who may have had to overcome administrative barriers for insurance coverage, such as prior authorization. Electrical cardioversion was defined as having a claim with CPT-4 code 92,960 or 92,961. Initiation of rhythm control medications was defined as filling a prescription for amiodarone, disopyramide, dofetilide, dronedarone, flecainide, mexiletine, propafenone, quinidine, and sotalol. All outcomes were reported in 30-day-intervals.

Independent variables

The main independent variable of interest was time after the World Health Organization declaration of the pandemic (3/11/2020) [16]. Covariates included patient demographics, setting of AF diagnosis, clinical characteristics and health insurance factors identified as of index date. Demographic characteristics included age, gender, race, and ethnicity. Age was categorized into ≥ 75 and ˂75 years subgroups. Race and ethnicity were categorized into non-Hispanic White, non-Hispanic Black, Hispanic, and other. Race and ethnicity data were collected using public records and imputation with commercial algorithms developed using census data with first and last names [17]. Setting of AF diagnosis included inpatient and outpatient and was defined using the place of service code in the claim of AF diagnosis. Diagnoses claims with place of service code 21 were categorized as inpatient and claims that did not originate from the inpatient setting were categorized as outpatient.

Clinical characteristics included CHAD2DS2-VACs score and HAS-BLED score. CHA2DS2-VASc predicts stroke risk in AF patients and gives one point for each of the following factors: age 65 to 74 years, congestive heart failure, hypertension, diabetes mellitus, vascular disease, and female sex. Two points are given for age ≥ 75 years and a history of stroke or transient ischemic attack [18]. HAS-BLED scores estimate the risk of major bleeding with anticoagulation and are calculated on the basis of following risk factors: age > 65 years, hypertension, renal disease, liver disease, stroke and bleeding history, drugs or alcohol use [19]. Since claims data does not include international normalized ratio (INR) information, we calculated HAS-BLED scores as the sum of all above factors except for labile INR, as previously done in the literature [20, 21]. Health insurance factors included Medicare enrollment, dual eligibility, receipt of low-income subsidy, enrollment in a health savings account, and type of health plan including health maintenance organization, preferred provider organization, point of service, exclusive provider organization, and other.

Statistical analysis

We described patient characteristics in the overall cohort. For each 30-day-interval, we reported the incidence rate of each outcome calculated as the proportion of patients at risk in each 30-day interval who experienced the outcome in the given interval. For the primary outcome of oral anticoagulation initiation, we performed subgroup analyses by age, gender, race, ethnicity, and clinical setting of AF diagnosis.

We performed interrupted time series analyses to formally test changes in outcomes following the onset of the COVID-19 pandemic. Interrupted time series analysis is the preferred methodology to evaluate the effects of public health interventions or disruptions introduced at a population level within a well-defined timeframe, as it is the case of the COVID-19 pandemic [22,23,24]. In our interrupted time series model, the outcome variable was regressed against a continuous variable for time (measured in 30-day-intervals), an indicator variable for time after the onset of the COVID-19 pandemic, and the interaction variable between the two of them. The indicator variable for time after the onset of the COVID-19 pandemic represents whether there is a change in the level of the outcome, that is, a change immediately after the breakpoint (represented by a change in the intercept of the regression model). The interaction variable between continuous time and time after the onset of the COVID-19 pandemic represents changes in the trend of the outcome. In essence, it reflects alterations in the slope of the regression model, enabling us to identify prolonged changes in the outcome over time. The model is not adjusted for other covariates. Two-tailed P-values less than 0.05 were defined as statistically significant. Analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC).

ResultS

Study sample

The final cohort included 573,524 patients newly diagnosed with AF. The mean (SD) age for the cohort was 73 ± 10.9 years, 48.0% of study participants were female and 74.2% were Non-Hispanic White (Table 1). Medicare beneficiaries accounted for 84.5% of the study participants.

Table 1 Baseline Patient Characteristics
Full size table

Changes in initiation of oral anticoagulation

The proportion of patients initiating oral anticoagulation within 30 days of diagnosis increased from an average of 22.1% in 2016 to 34.9% in 2021 (Fig. 2, upper panel). The proportion of patients initiating oral anticoagulation within 180 days of diagnosis increased from 35.5% to 2016 to 48.5% in 2021. There were no significant changes in the initiation of oral anticoagulation within 30 days of AF diagnosis following with the onset of the pandemic (p-value for level change > 0.05 and p-value for trend change > 0.05, Table 2). The trends in the initiation of oral anticoagulation were consistent between patients diagnosed with AF in the inpatient and outpatient setting (Supplemental Fig. 1) and across subgroups defined by age, gender, race and ethnicity (Supplemental Fig. 2). The trends of changes in the proportion of patients initiating oral anticoagulation within 30 days and 180 days of AF diagnosis are similar in both the inpatient and outpatient settings (Supplemental Fig. 1). Specifically, the proportion of patients initiating oral anticoagulation within 30 days (and 180 days) of diagnosis increased from 22.7% (36.1%) in 2016 to 37.2% (49.9%) in 2021 for inpatient setting and from 21.9% (35.2%) to 33.8% (47.8%) in the outpatient setting.

Fig. 2
figure 2

Initiation of Any Oral Anticoagulants within 30 Days of Atrial Fibrillation Diagnosis. Abbreviations: DOACs, direct oral anticoagulants. The upper panel shows trends in the initiation any oral anticoagulant agent within 30 and 180 days of atrial fibrillation diagnosis. The lower panel shows trends in the initiation of warfarin and direct oral anticoagulants separately. Data are shown in 30-day intervals, from 01/01/2016 to 09/30/2021.

Full size image
Table 2 Results of Interrupted Time Series Analysis
Full size table

Changes in initiation of DOAC, warfarin, rhythm control medications and electrical cardioversion

The proportion of patients initiating DOAC within 30 days of diagnosis increased from an average of 13.7% in 2016 to 32.7% in 2021 (Fig. 2, lower panel). The proportion of patients initiating warfarin within 30 days of diagnosis decreased from an average of 8.4% in 2016 to 2.2% in 2021. There were no significant changes in DOAC initiation after the onset of the pandemic (p-value for level change > 0.05 and p-value for trend change > 0.05, Table 2). There was, however, an increase in the trend of warfarin initiation following the pandemic onset (p value < 0.01).

The rates for initiation of rhythm control medications were similar across the study period (Fig. 3, upper panel). The proportion of patients newly diagnosed with AF who underwent electrical cardioversion increased from an average of 4.3% in 2016 to 5.3% in 2021 (Fig. 3, lower panel). There was a statistically significant decrease in the level of electrical cardioversion immediately after the onset of the pandemic (p value = 0.02, Table 2). Specifically, the rate of electronic cardioversion within 30 days of AF diagnosis decreased by 4.9% (95% CI 0.6%-9.3%) per 1000 patients after the onset of the pandemic. The rate of electronic cardioversion within 30 days of AF diagnosis decreased by about 35% in April, 2020, compared to April 2019, from 5.53% to 3.58%.

Fig. 3
figure 3

Initiation of Rhythm Control Medications, Electrical Cardioversion within 30 Days of Atrial Fibrillation Diagnosis. The upper panel shows trends in the initiation of rhythm control therapy within 30 days and 180 days of atrial fibrillation diagnosis. The lower panel shows trends in electrical cardioversion procedures within 30 days of atrial fibrillation diagnosis. All outcomes are expressed in 30-day intervals, from 01/01/2016 to 09/30/2021

Full size image

Discussion

In this retrospective analysis, we observed no changes in the initiation of medications for patients newly diagnosed with AF following the onset of the pandemic, including anticoagulation and antiarrhythmic therapies. The findings were consistent across age, gender, race, and ethnicity subgroups. However, we observed an immediate decrease in the level of electronic cardioversion procedures after the onset of the COVID-19 pandemic.

Several studies have reported the COVID-19 pandemic led to delayed or forgone care in patients with chronic diseases [25,26,27,28,29,30,31,32,33]. For example, one recent study based on French data observed a significant decrease in the initiation of antihypertensive therapy [27]. Prior US-based studies have also reported significant decreases in initiation of treatments in patients with end-stage renal diseases and liver diseases during the pandemic [25, 28]. The therapies evaluated in previous studies, however, are provider-administered, as opposed to outpatient pharmacotherapies obtained in community pharmacies assessed in our analysis. The decreased provision of provider-administered therapies in the early days of the COVID-19 pandemic may be explained by to fear of contagion or overburdened health systems, which would affect the dispensing of outpatient drugs to a lesser extent.

Our study found an amelioration in the rates of warfarin initiation following the onset of the pandemic. However, this finding may not be clinically significant and may not be associated with the COVID-19 pandemic [34]. It is possible the downward trend of warfarin initiation over time may have slowed down as the proportion of patients initiating warfarin approached zero. In fact, a visual inspection of the data suggests the rates of warfarin initiation may soon plateau. Initiation rates may not decrease below a minimum proportion of patients who represent individuals who may begin warfarin due to potential contraindications for DOAC treatment.

Our analysis found a decrease in the rates of electrical cardioversion immediately after the onset of the COVID-19 pandemic. This observed decrease is consistent with previous studies that found decreases in surgical procedures after March 2020 [32]. Several factors may explain why the COVID-19 pandemic disrupted the delivery of procedures but not of outpatient pharmacological treatments. These reasons include provider-initiated cancellations of appointments as health systems were overwhelmed with COVID-19 cases, patient-initiated cancellations for fear of exposure to the virus in healthcare settings, or increased emphasis on the delivery of medical care through telemedicine [35]. Our findings suggest the rate of electrical cardioversion returned to pre-pandemic levels in 2021. Future studies should evaluate if the patients who would have undergone electrical cardioversion under normal circumstances, eventually received the procedure.

Our study has important implications beyond the findings of the disruptions of care associated with the COVID-19 pandemic. Our results shed light on temporal trends in the initiation of therapy for AF and demonstrate that while the proportion of individuals newly diagnosed with AF who receive oral anticoagulation has increased, AF remains largely undertreated. This finding is consistent with previous literature that also used Optum’s de-identified Clinformatics® Data Mart Database data to demonstrate less than 40% of individuals with AF initiated oral anticoagulation within 6 months of diagnosis [36].

Our study is subject to some limitations. First, claims data did not capture prescriptions not covered by insurance, such as warfarin obtained through $4 generic programs [37, 38]. This could have resulted in an underestimation of the treatment initiation rate but should not impact temporal trends in treatment initiation or changes after the onset of the pandemic. Second, the claims data had limited information about patient sociodemographic status and these factors have been shown to influence treatment initiation [39]. Therefore, it is possible the absence of this information may have led to residual confounding in our results. Third, there are limitations and the potential for misclassification by using ICD codes to identify patients with incident AF, which could possibly impact the accuracy of our results. Fourth, our data are limited to individuals with commercial insurance and Medicare Advantage and thus, our findings may not generalize to patients without commercial insurance or the approximately 55% of Medicare beneficiaries enrolled in Medicare fee-for-service [40].

Conclusion

The initiation of oral anticoagulation within 30 days of AF diagnosis was not disrupted by the onset of the pandemic. However, there was a significant decline in the proportion of patients who underwent electrical cardioversion in the early months of the COVID-19 pandemic. Our study adds important evidence on the impact of the COVID-19 pandemic on the delivery of health care and demonstrates the initiation of pharmacotherapy was not as disrupted as the delivery of provider services.

Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

AF:

Atrial fibrillation

OAC:

Oral anticoagulation

DOAC:

Direct oral anticoagulant

ICD-9:

International Classification of Diseases Ninth Revision

ICD-10:

International Classification of Diseases Tenth Revision

SD:

Standard deviation

References

  1. Hernandez I, Gabriel N, He M, Guo J, Tadrous M, Suda KJ et al. Effect of the COVID-19 pandemic on adversity in individuals receiving anticoagulation for atrial fibrillation: A nationally representative administrative health claims analysis. American Heart Journal Plus: Cardiology Research and Practice. 2022 2022/01/01/;13:100096.

  2. Baum A, Schwartz MD. Admissions to Veterans affairs hospitals for Emergency conditions during the COVID-19 pandemic. JAMA. 2020;324(1):96–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Blecker S, Jones SA, Petrilli CM, Admon AJ, Weerahandi H, Francois F, et al. Hospitalizations for Chronic Disease and Acute conditions in the time of COVID-19. JAMA Intern Med. 2021;181(2):269–71.

    Article  CAS  PubMed  Google Scholar 

  4. Reschen ME, Bowen J, Novak A, Giles M, Singh S, Lasserson D, et al. Impact of the COVID-19 pandemic on emergency department attendances and acute medical admissions. BMC Emerg Med. 2021;2021/11(20):143.

    Article  Google Scholar 

  5. Wilson SJ, Connolly MJ, Elghamry Z, Cosgrove C, Firoozi S, Lim P, et al. Effect of the COVID-19 pandemic on ST-Segment-Elevation Myocardial Infarction presentations and In-Hospital outcomes. Circ Cardiovasc Interv. 2020;13(7):e009438.

    Article  CAS  PubMed  Google Scholar 

  6. Patt D, Gordan L, Diaz M, Okon T, Grady L, Harmison M, et al. Impact of COVID-19 on Cancer Care: how the pandemic is delaying Cancer diagnosis and treatment for American seniors. JCO Clin Cancer Inform. 2020;4:1059–71.

    Article  PubMed  Google Scholar 

  7. Durant TJS, Peaper DR, Ferguson D, Schulz WL. Impact of COVID-19 pandemic on Laboratory utilization. J Appl Lab Med. 2020;5(6):1194–205.

    Article  PubMed  Google Scholar 

  8. Messina MD, Stein MW, Armstrong IJ, Wolf EL. Impact of the COVID-19 pandemic on radiology department emergency ultrasound utilization. Emerg Radiol. 2021;28(5):869–75.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hernandez I, He M, Chen N, Brooks MM, Saba S, Gellad WF. Trajectories of oral anticoagulation adherence among Medicare beneficiaries newly diagnosed with Atrial Fibrillation. J Am Heart Association. 2019;8(12):e011427.

    Article  Google Scholar 

  10. Hernandez I, He M, Brooks MM, Saba S, Gellad WF. Adherence to Anticoagulation and Risk of Stroke among Medicare beneficiaries newly diagnosed with Atrial Fibrillation. Am J Cardiovasc Drugs. 2020;20(2):199–207.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kanuri SH, Jayesh Sirrkay P, Ulucay AS. COVID-19 HEART unveiling as atrial fibrillation: pathophysiology, management and future directions for research. Egypt Heart J. 2023;75(1):36.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Raatikainen P, Lassila R. COVID-19: another reason for anticoagulation in patients with atrial fibrillation. Heart. 2022;108(12):902–4.

    Article  CAS  PubMed  Google Scholar 

  13. Rattanawong P, Shen W, Masry HE, Sorajja D, Srivathsan K, Valverde A, et al. Guidance on short-term management of Atrial Fibrillation in Coronavirus Disease 2019. J Am Heart Association. 2020;9(14):e017529.

    Article  CAS  Google Scholar 

  14. Service CfMaM. Chronic Conditions Data Warehouse Data Dictionaries 2020 [cited 2020 12.16]; Available from: https://www2.ccwdata.org/web/guest/data-dictionaries.

  15. Newman TV, Chen N, He M, Saba S, Hernandez I. Effectiveness and safety of restarting oral anticoagulation in patients with Atrial Fibrillation after an intracranial Hemorrhage: analysis of Medicare Part D Claims Data from 2010–2016. Am J Cardiovasc Drugs. 2020;20(5):471–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. CDC, COVID DATA TRACKER. 2023 [cited 2023 01/20]; Available from: https://www.cdc.gov/museum/timeline/covid19.html#:~:text=March%2011%2C%202020,declares%20COVID%2D19%20a%20pandemic.

  17. DeFrank JT, Bowling JM, Rimer BK, Gierisch JM, Skinner CS. Triangulating differential nonresponse by race in a telephone survey. Prev Chronic Dis. 2007;4(3):A60.

    PubMed  PubMed Central  Google Scholar 

  18. Joundi Raed A, Cipriano Lauren E, Sposato Luciano A, Saposnik G. Null n. ischemic Stroke risk in patients with Atrial Fibrillation and CHA2DS2-VASc score of 1. Stroke. 2016 2016/05/01;47(5):1364–7.

  19. Zhu W, He W, Guo L, Wang X, Hong K. The HAS-BLED score for Predicting Major bleeding risk in anticoagulated patients with Atrial Fibrillation: a systematic review and Meta-analysis. Clin Cardiol. 2015;38(9):555–61.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hernandez I, Zhang Y, Saba S. Comparison of the effectiveness and safety of Apixaban, Dabigatran, Rivaroxaban, and Warfarin in newly diagnosed Atrial Fibrillation. Am J Cardiol. 2017;120(10):1813–9.

    Article  CAS  PubMed  Google Scholar 

  21. Yang L, Brooks M, Glynn N, Zhang Y, Saba S, Hernandez I. Real-world direct comparison of the effectiveness and safety of Apixaban, Dabigatran, Rivaroxaban, and Warfarin in Medicare beneficiaries with Atrial Fibrillation. Am J Cardiol. 2020 04/01;126.

  22. Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017;46(1):348–55.

    PubMed  Google Scholar 

  23. Soumerai SB, Starr D, Majumdar SR. How do you know which Health Care Effectiveness Research you can trust? A guide to Study Design for the Perplexed. Prev Chronic Dis. 2015;12:E101.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Penfold RB, Zhang F. Use of interrupted Time Series Analysis in evaluating Health Care Quality improvements. Acad Pediatr. 2013 2013/11/01/;13(6, Supplement):S38–S44.

  25. Wetmore JB, Johansen KL, Liu J, Peng Y, Gilbertson DT, Weinhandl ED. Changes in treatment of patients with Incident ESKD during the Novel Coronavirus Disease 2019 Pandemic. J Am Soc Nephrol. 2021;32(11).

  26. Kutschera M, Ritschl V, Reichardt B, Stamm T, Kiener H, Maier H, et al. Impact of COVID-19 pandemic on initiation of Immunosuppressive Treatment in Immune-mediated inflammatory Diseases in Austria: a Nationwide Retrospective Study. J Clin Med. 2022;11(18):5308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mathieu C, Pambrun E, Bénard-Laribière A, Noize P, Faillie JL, Bezin J, et al. Impact of the COVID-19 pandemic and its control measures on cardiovascular and antidiabetic Drugs use in France in 2020: a nationwide repeated cohort study. Eur J Epidemiol. 2022;37(10):1049–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hoenigl M, Abramovitz D, Flores Ortega RE, Martin NK, Reau N. Sustained impact of the Coronavirus Disease 2019 pandemic on Hepatitis C Virus Treatment initiations in the United States. Clin Infect Diseases: Official Publication Infect Dis Soc Am. 2022 2022/08//;75(1):e955–e61.

  29. Kocijan R, Stockinger T, Haschka J, Reichardt B, Resch H, Zwerina J, et al. Dispensing anti-osteoporotic Drugs changed during the COVID-19 pandemic. Bone. 2022;162:116477.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Spencer K, Jones CM, Girdler R, Roe C, Sharpe M, Lawton S, et al. The impact of the COVID-19 pandemic on radiotherapy services in England, UK: a population-based study. Lancet Oncol. 2021;22(3):309–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lee S, Heo J. COVID-19 pandemic: a new cause of unplanned interruption of radiotherapy in Breast cancer patients. Med Oncol. 2021;39(1):5.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Mattingly AS, Rose L, Eddington HS, Trickey AW, Cullen MR, Morris AM, et al. Trends in US Surgical procedures and Health Care System response to policies curtailing Elective Surgical Operations during the COVID-19 pandemic. JAMA Netw open. 2021;4(12):e2138038–e.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Okado I, Liu-Duerr M, Elhajj C, Holcombe RF. Time to diagnosis and treatment initiation during the COVID-19 pandemic among rural patients with cancer. J Clin Oncol. 2022;40(28suppl):132.

    Article  Google Scholar 

  34. Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, McKenzie JE. Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series. BMC Med Res Methodol. 2021 2021/06/26;21(1):134.

  35. Vitolo M, Ziveri V, Gozzi G, Busi C, Imberti JF, Bonini N et al. DIGItal health literacy after COVID-19 outbreak among Frail and Non-frail Cardiology patients: the DIGI-COVID Study. J Pers Med. 2022;13(1).

  36. Guo JS, He M, Gabriel N, Magnani JW, Kimmel SE, Gellad WF, et al. Underprescribing vs underfilling to oral anticoagulation: an analysis of linked medical record and claims data for a nationwide sample of patients with atrial fibrillation. J Managed care Specialty Pharm. 2022;28(12):1400–9.

    Article  Google Scholar 

  37. Riley E. Here’s How the Walmart $4 Prescriptions Program Works and Which Medications Are Included. 2019 [cited 2023 02/10]; Available from: https://www.goodrx.com/healthcare-access/pharmacies/the-walmart-4-generic-prescriptions-list.

  38. Zhang Y, Zhou L, Gellad WF. Potential savings from Greater Use of $4 generic Drugs. Arch Intern Med. 2011;171(5):468–9.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lunde ED, Joensen AM, Fonager K, Lundbye-Christensen S, Johnsen SP, Larsen ML, et al. Socioeconomic inequality in oral anticoagulation therapy initiation in patients with atrial fibrillation with high risk of Stroke: a register-based observational study. BMJ open. 2021;11(5):e048839.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Jacobson GA, Blumenthal D. Medicare advantage enrollment growth: implications for the US Health Care System. JAMA. 2022;327(24):2393–4.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank all individuals and organizations who have contributed to this study.

Funding

This work was funded by the National Heart, Lung and Blood Institute (grants K01HL142847 and R01HL15705). Dr. Benjamin is funded by R01HL092577; American Heart Association AF AHA_18SFRN34110082. Dr. Magnani is funded by R33HL144669, R01HL143010, R01HL160749, and K24HL160527. Dr. Guo is funded by NIDDK R01DK133465 and PhRMA Foundation Research Starter Award. The funders had no role in design and conduct of the study, collection, management, analysis, and interpretation of the data, preparation, review, or approval of the manuscript, and decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Department of Pharmacy and Therapeutics, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA

    Lanting Yang

  2. Division of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA

    Shangbin Tang, Meiqi He, Nico Gabriel & Inmaculada Hernandez

  3. Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL, USA

    Jingchuan Guo

  4. Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Gretchen Swabe, Samir Saba & Jared W Magnani

  5. Division of General Internal Medicine, Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Walid F Gellad

  6. Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, Center for the Study of Healthcare Innovation, Implementation & Policy, University of California, Greater Los Angeles VA Healthcare System, Los Angeles, Los Angeles, CA, USA

    Utibe R Essien

  7. Department of Epidemiology, Boston University School of Public Health, Department of Medicine, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, USA

    Emelia J Benjamin

Authors
  1. Lanting Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shangbin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meiqi He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingchuan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nico Gabriel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gretchen Swabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Walid F Gellad
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Utibe R Essien
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Samir Saba
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Emelia J Benjamin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jared W Magnani
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Inmaculada Hernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

IH led the study conception. LY, ST, MH, JG, NG, WFG, URE, SS, EJB and JWM contributed to the study design. LY, ST and MH performed the data analysis. LY and IH drafted the manuscript. JG, NG,GS,WFG,URE,SS EJ and JWM revised and critically reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Inmaculada Hernandez.

Ethics declarations

Ethics approval and consent to participate

The Institutional Review Board at the University of California, San Diego approved this study as exempt from review and waived the informed consent requirement due to the utilization of deidentified data in all analyses. We confirm that all methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not Applicable.

Competing interests

Hernandez has received consulting fees from Pfizer and Bristol Myers Squibb, outside of the submitted work. The remaining authors have no competing interests to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, L., Tang, S., He, M. et al. COVID-19 pandemic and initiation of treatment for atrial fibrillation: a nationwide analysis of claims data. BMC Cardiovasc Disord 23, 604 (2023). https://doi.org/10.1186/s12872-023-03614-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12872-023-03614-z

Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

Contact us