COVID-19 pandemic and trends in new diagnosis of atrial fibrillation: A nationwide analysis of claims data

Inmaculada Hernandez; Meiqi He; Jingchuan Guo; Mina Tadrous; Nico Gabriel; Gretchen Swabe; Walid F Gellad; Utibe R Essien; Samir Saba; Emelia J Benjamin; Jared W Magnani

doi:10.1371/journal.pone.0281068

. 2023 Feb 2;18(2):e0281068. doi: 10.1371/journal.pone.0281068

COVID-19 pandemic and trends in new diagnosis of atrial fibrillation: A nationwide analysis of claims data

Inmaculada Hernandez ^1,^*, Meiqi He ¹, Jingchuan Guo ², Mina Tadrous ³, Nico Gabriel ¹, Gretchen Swabe ⁴, Walid F Gellad ⁵, Utibe R Essien ⁵, Samir Saba ⁴, Emelia J Benjamin ⁶, Jared W Magnani ⁴

Editor: Han Eol Jeong⁷

¹Division of Clinical Pharmacy, University of California, San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences, La Jolla, California, United States of America

²Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, Florida, United States of America

³Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada

⁴Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America

⁵Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA and Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System Pittsburgh, Pennsylvania, United States of America

⁶Department of Medicine, Boston Medical Center and Boston University Department of Medicine, Chobanian and Avedisian School of Medicine, and Department of Epidemiology, School of Public Health, Boston, Massachusetts, United States of America

⁷Sungkyunkwan University, REPUBLIC OF KOREA

Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Hernandez has received consulting fees from Pfizer and Bristol Myers Squibb, outside of the submitted work. Our conflicts do not alter our adherence to PLOS ONE policies on sharing data and materials.

^✉

* E-mail: inhernandez@health.ucsd.edu

Roles

Inmaculada Hernandez: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft

Meiqi He: Formal analysis, Methodology

Jingchuan Guo: Methodology, Writing – review & editing

Mina Tadrous: Methodology, Writing – review & editing

Nico Gabriel: Methodology, Writing – review & editing

Gretchen Swabe: Data curation, Writing – review & editing

Walid F Gellad: Conceptualization, Funding acquisition, Investigation, Writing – review & editing

Utibe R Essien: Funding acquisition, Writing – review & editing

Samir Saba: Conceptualization, Funding acquisition, Investigation, Writing – review & editing

Emelia J Benjamin: Conceptualization, Investigation, Writing – review & editing

Jared W Magnani: Conceptualization, Funding acquisition, Investigation, Writing – review & editing

Han Eol Jeong: Editor

PMCID: PMC9894497 PMID: 36730318

Abstract

Background

Atrial fibrillation (AF) is associated with a five-fold increased risk of stroke and a two-fold increased risk of death. We aimed to quantify changes in new diagnoses of AF following the onset of the COVID-19 pandemic. Investigating changes in new diagnoses of AF is of relevance because delayed diagnosis interferes with timely treatment to prevent stroke, heart failure, and death.

Methods

Using De-identified Optum’s Clinformatics® Data Mart, we identified 19,500,401 beneficiaries continuously enrolled for 12 months in 2016-Q3 2020 with no history of AF. The primary outcome was new AF diagnoses per 30-day interval. Secondary outcomes included AF diagnosis in the inpatient setting, AF diagnosis in the outpatient setting, and ischemic stroke as initial manifestation of AF. We constructed seasonal autoregressive integrated moving average models to quantify changes in new AF diagnoses after the onset of the COVID-19 pandemic (3/11/2020, date of pandemic declaration). We tested whether changes in the new AF diagnoses differed by race and ethnicity.

Results

The average age of study participants was 51.0±18.5 years, and 52% of the sample was female. During the study period, 2.7% of the study sample had newly-diagnosed AF. New AF diagnoses decreased by 35% (95% CI, 21%-48%) after the onset of the COVID-19 pandemic, from 1.14 per 1000 individuals (95% CI, 1.05–1.24) to 0.74 per 1000 (95% CI, 0.64 to 0.83, p-value<0.001). New AF diagnoses decreased by 37% (95% CI, 13%- 55%) in the outpatient setting and by 29% (95% CI, 14%-43%) in the inpatient setting. The decrease in new AF diagnoses was similar across racial and ethnic subgroups.

Conclusion

In a nationwide cohort of 19.5 million individuals, new diagnoses of AF decreased substantially following the onset of the COVID-19 pandemic. Our findings evidence pandemic disruptions in access to care for AF, which are concerning because delayed diagnosis interferes with timely treatment to prevent complications.

Introduction

Disruptions in access to care associated with the COVID-19 pandemic represent potentially important health impacts of the pandemic beyond COVID cases and deaths [1]. An emerging body of literature has reported decreases in the number of patients seeking emergency care in the earlier months of the COVID-19 pandemic [2–5]. Significant reductions also have been observed in outpatient care for patients with chronic disease, even after accounting for the increased uptake of telemedicine [6]. Other reports have documented decreased provision of imaging, laboratory services, screening, surgical interventions, or provider-administered drugs for non-COVID disease [7–9]. These prior studies constitute important contributions to the understanding of disruptions in health care for non-COVID disease during the COVID-19 pandemic. However, because they focused on measuring rates of events as opposed to following a population cohort over time, prior investigations do not allow for a differentiation of disruptions in care for new-onset versus existing disease.

Evaluating pandemic distortions of diagnosis of new-onset chronic disease is important because delayed diagnosis may interfere with treatment, resulting in worsened prognosis. This is particularly relevant for diseases with life-threatening complications that can be prevented through medical or pharmacological interventions. One such example is atrial fibrillation (AF), the most common cardiac arrhythmia globally [10, 11]. AF is associated with a five-fold increased risk of stroke and two-fold increased risk of death [12]. Oral anticoagulation is available for patients diagnosed with AF and reduces the risk of stroke by over 60% and death by over 20% [13]. AF is a critical disease state to measure the effects of the COVID-19 pandemic on non-COVID related chronic disease because every aspect of stroke prevention is vulnerable to disruption, including diagnosis, initiation of anticoagulation therapy, and treatment monitoring.

We aimed to quantify changes in new diagnoses of AF following the onset of the COVID-19 pandemic. Because the COVID-19 pandemic disproportionately affected underrepresented racial/ethnic groups, it was relevant to evaluate whether changes in diagnoses following pandemic declaration differed across racial/ethnic groups.

Methods

Data source and study population

We obtained 1/1/2016-9/30/2020 de-identified data from Optum’s Clinformatics® Data Mart, which is derived from a database of administrative health claims for members of large commercial and Medicare Advantage health plans. These data include verified, adjudicated and de-identified medical and pharmacy claims for a geographically diverse population spanning all 50 states in the U.S. We selected the study population in four steps (S1 Fig): First, we selected patients who were continuously enrolled for at least 12 months in 1/1/2016-3/30/2020. Second, we constrained sampling to those older than 18 years of age. Third, we excluded patients who had a diagnosis of AF in the first 12 months of continuous enrollment (definition of AF is listed under the outcomes section). We used this 12-month washout to exclude individuals who represent patients with prevalent AF. Finally, we excluded patients with incomplete covariate information (2.4% of the sample). The final sample included 19,500,401 individuals free of AF on index date, which was defined as the first day after completing the 12-month washout period. Patients were followed until the first of the following events: death, disenrollment, diagnosis of AF, or end of the study (9/30/2020). The Institutional Review Board at the University of California, San Diego approved this study as exempt.

Outcomes

The primary outcome was new AF diagnosis, which 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 [14]. Previous studies have estimated the positive predictive value of our definition to be 93–97% [15, 16]. We defined the primary outcome as one inpatient or one outpatient diagnosis as opposed to one inpatient or two outpatient diagnoses [14, 17–19] for two reasons: first, the objective was to measure changes in new AF diagnoses after the COVID-19 pandemic, and the requirement for two outpatient diagnosis would have affected the ability to detect immediate changes after the pandemic declaration due to the time lapse between first and second diagnosis, second, the requirement for second outpatient diagnosis would have affected the identification of ischemic stroke as initial manifestation of AF.

Secondary outcomes included AF diagnosis in the inpatient setting, AF diagnosis in the outpatient setting, and ischemic stroke as initial manifestation of AF. The setting of AF diagnosis was ascertained using the place of service in the claim with the first recorded AF diagnosis; places of service with code 21 (inpatient hospital) were categorized as inpatient. Diagnoses that did not originate from the inpatient setting were categorized as outpatient. Ischemic stroke as initial manifestation of AF was defined as having a stroke event on the day or in the 30 days prior to the first AF diagnosis, as previously reported in the literature [20]. The stroke event was identified using inpatient claims and ICD-10 diagnosis code I63.

Independent variables

The main independent variable of interest was time after the World Health Organization declaration of pandemic (3/11/2020). Covariates included age, sex, race, ethnicity, and state and were identified as of index date. Race and ethnicity were categorized into non-Hispanic White, non-Hispanic Black, Hispanic, and other, which included Asian and unknown. In Optum, race and ethnicity data are collected using public records and imputation with commercial software that uses algorithms developed with census data and first and last names. This method has 71% positive predictive value for estimating Black race [21].

Statistical analysis

We determined the rate of AF diagnosis for each 30-day interval during the study period as the quotient between the number of patients who had a first AF diagnosis in the given time interval (numerator) and the population who remained at risk in each 30-day interval (denominator). An individual was included in the denominator after the 12-month washout period and was censored and excluded from the denominator at death, disenrollment, or first AF diagnosis.

We constructed seasonal autoregressive integrated moving average (ARIMA) models that accounted for seasonality to test changes in the level of diagnosis after pandemic declaration (intercept) and the trend of diagnosis after pandemic declaration (slope). We added an indicator variable for the period after 03/11/2020 and a ramp function to specify the increase in intervals after 03/11/2020. ARIMA models were selected because this technique accommodates the seasonality and autocorrelation commonly found in time-series of diagnosis and healthcare data [22, 23]. ARIMA models included autoregressive terms, moving average terms, the differences of raw values, and seasonal terms with a period set to 12 months. We reported observed results and values predicted with ARIMA models for the overall sample and for subgroups defined by gender and race, and ethnicity. We also used ARIMA models to predict trends in AF diagnosis in the absence of the COVID-19 pandemic.

We conducted interrupted time series analyses with linear regression to formally test whether changes in outcomes after pandemic declaration differed by subgroup [24], represented with a three-way interaction between subgroup, continuous time, and the indicator for the post-pandemic period. Interrupted time series analyses with linear regression models were necessary to formally test whether changes in outcomes after pandemic declaration differed by subgroup because ARIMA models are not able to accommodate this interaction analysis.

Finally, we investigated state variation in the changes in new AF diagnoses by comparing the rates of diagnosis in the 30-day interval before versus after pandemic declaration. We reported results for states with at least 200,000 study participants to ensure stability of estimates. We examined whether changes in new AF diagnosis were related to the incidence of COVID-19 cases per 100,000 state residents as of 4/9/2020. COVID case data were obtained from the Centers for Disease Control and Prevention COVID Data Tracker [25]. Two-tailed p value <0.05 was defined as significant. Analyses were conducted using SAS 9.4 (Cary, NC).

Results

Study sample

The cohort included 19,500,401 individuals free of AF on the index date. The cohort was 52% women with nearly half <50 years of age, 18.3% age 65–74, and 11.1% age≥75 years (Table 1). Non-Hispanic White individuals comprised the majority of the cohort (59.8%) followed by Hispanic individuals (11.7%), and non-Hispanic Black individuals (9.2%). Medicare beneficiaries accounted for 30.9% of study participants.

Table 1. Baseline patient characteristics.

Variable		Total Population (n = 19,500,401)	Not Diagnosed with AF (n = 18,980,451, 97.3%)	Diagnosed with AF (n = 519,950, 2.7%)
Female, No. (%)		10,147,297 (52.0)	9,898,009 (52.1)	249,288 (47.9)
Age, Mean years (Std.)		51.02 (18.5)	50.40 (18.3)	73.50 (11.0)
Age, years
	<50, No. (%)	9,284,245 (47.6)	9,266,428 (48.8)	17,817 (3.4)
	50–64, No. (%)	4,481,400 (23.0)	4,413,175 (23.3)	68,225 (13.1)
	65–74, No. (%)	3,571,769 (18.3)	3,401,055 (17.9)	170,714 (32.8)
	≥75, No. (%)	2,162,987 (11.1)	1,899,793 (10.0)	263,194 (50.6)
Race and Ethnicity
	Non-Hispanic White, No. (%)	11,657,821 (59.8)	11,305,424 (59.6)	352,397 (67.8)
	Non-Hispanic Black, No. (%)	1,788,365 (9.2)	1,740,517 (9.2)	47,848 (9.2)
	Hispanic, No. (%)	2,282,787 (11.7)	2,239,340 (11.8)	43,447 (8.4)
	Other, No. (%)	3,771,428 (19.3)	3,695,170 (19.5)	76,258 (14.7)
Medicare, No. (%)		6,035,047 (30.9)	5,596,390 (29.5)	438,657 (84.4)

Open in a new tab

Abbreviations: AF, atrial fibrillation.

Across the study period, 519,950 individuals or 2.7% of the cohort were newly diagnosed with AF. As expected, study participants who developed AF during follow-up had a higher average age than those who did not and were more likely to be Medicare beneficiaries.

Changes in new atrial fibrillation diagnoses

The 30-day incidence rate of new AF diagnoses presented a seasonal pattern, with higher incidence in winter months (Fig 1). Following the COVID-19 pandemic declaration, AF diagnoses were estimated to decrease by 35% (95% CI, 21%-48%), from 1.14 per 1000 individuals (95% CI, 1.05 to 1.24) before the onset of the COVID-19 pandemic to 0.74 per 1000 (95% CI, 0.64 to 0.83) after pandemic onset (p-value for level of change<0.001), Fig 1 and S1 Table. The observed incidence of new AF diagnosis was substantially lower than predicted by regression models in the first 90 days after the declaration of the COVID-19 pandemic, however these differences narrowed by summer 2020. As of June 2020, the observed incidence of new AF diagnoses was only 6% lower than predicted.

On average, in the pre-pandemic period, ischemic stroke as initial manifestation of AF accounted for 4.4% of all new AF diagnoses. Following the COVID-19 pandemic declaration, the rate of ischemic stroke as initial manifestation of AF was estimated to decrease by 31% (95% CI, 4%-51%), from 0.055 per 1000 (95% CI, 0.048 to 0.063) before the onset of the COVID-19 pandemic to 0.038 per 1000 after pandemic onset (95% CI, 0.031 to 0.046), p-value for level of change<0.001.

Changes in new atrial fibrillation diagnoses by setting

Fig 2 shows the trend in the 30-day incidence rate of new AF diagnoses by setting of diagnosis. Following the onset of the COVID-19 pandemic, AF diagnoses were estimated to decrease by 37% (95% CI,13%- 55%) in the outpatient setting, from 0.74 per 1000 before pandemic declaration to 0.47 per 1000 after (p-value for level of change<0.001) and by 29% (95% CI, 14%-43%) in the inpatient setting, from 0.39 per 1000 before pandemic declaration to 0.27 per 1000 after (p-value for level of change<0.001), Fig 2 and S1 Table.

The upper panel shows trends in new atrial fibrillation diagnoses per 1000 individuals in the inpatient setting for 30-day intervals, from 01/02/2016 to 09/06/2020. The lower panel shows trends in new atrial fibrillation diagnoses per 1000 individuals in the outpatient setting for 30-day intervals, from 01/02/2016 to 09/06/2020. Solid red lines represent observed values. Solid blue lines represent predictions from ARIMA models the absence of the COVID-19 pandemic. In other words, they represent trends in AF diagnosis that would have been observed if there had not been a change in AF diagnoses following the COVID-19 pandemic. Dashed blue lines represent confidence intervals.

Changes in new atrial fibrillation diagnoses by subgroup

In the 30-day period preceding the onset of the COVID-19 pandemic (2/10/2020-3/10/2020), the estimated 30-day incidence rate of new AF diagnoses per 1000 individuals averaged 1.22 for White individuals, 1.15 for Black individuals, 0.82 for Hispanic individuals, and 1.05 for individuals of other races or ethnicities (Fig 3). Following the onset of the COVID-19 pandemic, the estimated decrease in new AF diagnoses was significant across all racial and ethnic subgroups, but the magnitude of the decrease did not differ across racial or ethnic subgroups (p = 0.34), as shown in Fig 3 and S2 and S3 Tables.

Fig 3 — Abbreviations: UCL, upper confidence limit; LCL, lower confidence limit.

The upper left panel shows trends in new atrial fibrillation diagnoses per 1000 individuals in White individuals for 30-day intervals, from 01/02/2016 to 09/06/2020. The upper right panel shows trends in new atrial fibrillation diagnoses per 1000 individuals in Black individuals for 30-day intervals, from 01/02/2016 to 09/06/2020. The lower left panel shows trends in new atrial fibrillation diagnoses per 1000 individuals in Hispanic individuals for 30-day intervals, from 01/02/2016 to 09/06/2020. The lower right panel shows trends in new atrial fibrillation diagnoses per 1000 individuals in individuals of other races for 30-day intervals, from 01/02/2016 to 09/06/2020. Solid red lines represent observed values. Solid blue lines represent predictions from ARIMA models the absence of the COVID-19 pandemic. In other words, they represent trends in AF diagnosis that would have been observed if there had not been a change in AF diagnoses following the COVID-19 pandemic. Dashed blue lines represent confidence intervals.

The estimated decrease in new AF diagnoses was significant across both sex subgroups, but the magnitude of the decrease did not differ across sex groups, as shown in S2 Fig and S4 Table.

New AF diagnoses decreased across all states examined (Fig 4). There was no apparent relationship between the magnitude of the decrease of AF diagnoses and the cumulative number of COVID-19 cases in the first 30-day interval after pandemic declaration.

The left panel shows the cumulative number of COVID-19 cases per 100,000 state residents as of 04/09/2020 (with New York excluding New York City). The right panel shows the change in the rate of new atrial fibrillation diagnoses in the first 30-day interval after pandemic declaration (03/11/2020 to 04/09/2020), compared to the 30-day interval immediately before pandemic declaration (02/10/2020 to 03/10/2020). Only 28 states with a study sample size larger than 200,000 were examined, excluding 22 states and the District of Columbia.

Discussion

In a large nationwide study, we observed that new AF diagnoses decreased immediately after the declaration of the COVID-19 pandemic; however, they returned to predicted levels by summer 2020. This decrease was consistent across racial and ethnic subgroups and states. The decrease in the rate of new AF diagnoses was larger for diagnoses originating from the outpatient setting than AF diagnoses originating from the inpatient setting.

A recent paper using Danish nationwide data observed a decrease in the number of new diagnoses of heart failure in the first four weeks after the COVID-19 pandemic declaration, when Denmark instituted a lockdown [26]. Even though the US did not impose a nationwide lockdown, our findings are consistent with those by Andersson et al. [26]. Previous US studies have reported significant decreases in the number of hospitalizations and emergency room visits for common chronic conditions related to the pandemic, but have not evaluated new onset of chronic disease specifically, which was the objective of our study [2–5]. We observed that the magnitude of decrease in new AF diagnoses was lower in the inpatient setting and for ischemic stroke as initial manifestation of AF, which are more severe presentations of new-onset AF than those typically diagnosed in the outpatient setting. The less pronounced decrease in incidence of events observed for more severe outcomes is consistent with prior studies that reported that medical care avoidance in the early months of the COVID-19 pandemic was less pronounced for more severe events, including strokes [27, 28].

Notably, we observed no apparent relationship between the state-level magnitude of the decrease of AF diagnoses and the number of COVID-19 cases in the first 30-day interval after pandemic declaration. The lack of state variation may indicate that the decrease in AF diagnosis was more representative of patient’s avoidance to seek medical care rather than the unavailability of providers and health care resources in hardest-hit areas. The behavioral changes in healthcare-seeking behavior may not have presented regional variation because of the nationwide reach of news reports on COVID cases in the early weeks of the COVID pandemic.

After the drop in new AF diagnosis observed immediately after the start of the COVID-19 pandemic, the incidence of AF nearly returned to predicted levels by summer 2020. We speculate that decreased diagnosis of AF may have represented fear of COVID-19 contagion in health care facilities, particularly in the first weeks after the declaration of the COVID-19 pandemic. It is possible that patients experiencing more frequent or severe symptoms decided to seek medical care after realizing that the risk of contagion would not cease in the short term, which would explain the return to expected levels within a few months of the pandemic onset. Nevertheless, these are speculative hypotheses as our data do not enable us to observe the reasons behind the changes in health care encounters noted in the study.

Interestingly, we observed a similar incidence of AF among White individuals and Black individuals. Racial differences in the incidence of AF have been observed in multiple studies, which have associated Black race with a lower incidence of AF [29–32]. This lower incidence, however, could represent ascertainment bias due to differential provider assessment of AF in Black individuals or lower access to care, which would limit the detection of AF [31, 33]. Under this hypothesis, it is likely that we observed similar rates of AF incidence across Black and White individuals because we used data from commercial insurance and Medicare Advantage beneficiaries, which represent a sample with higher income and access to medical care than the general population.

Our study has important implications for public health planning in future emergencies. We demonstrate that the effects of the COVID-19 pandemic extended beyond COVID-19 cases and deaths and evidences the nationwide impact of patients’ avoidance of medical care in the earlier months of the COVID-19 pandemic. Delayed diagnosis of AF is concerning because pharmacotherapy with oral anticoagulation interestingand anti-arrhythmic therapy is available to prevent the increased risk of stroke associated with AF [34]; however, delayed diagnosis precludes timely treatment. As more data from the post-pandemic period become available, it will be relevant to assess the consequences of delayed diagnosis on clinical outcomes. For example, it will be important to evaluate whether the rate of ischemic stroke as initial manifestation of AF increases over time, as individuals that went undiagnosed with AF present with complications.

Our analysis is subject to some limitations. First, our data are limited to the first months after the onset of the COVID-19 pandemic. Although the lack of availability of more recent data is a limitation of our study, the time period captured is of major relevance for the study of pandemic disruptions of non-COVID disease due to lower access to care. This is because prior reports have demonstrated that patients avoidance of medical care was particularly profound in the early months of the COVID-19 pandemic [2–5]. Second, our analyses are based on claims data; therefore we were only able to capture medical events that triggered interactions with the health care system. Third, we did not conduct subgroup analyses for patients presenting with COVID-19, due to the inconsistent coding of COVID-19 cases in the first months of the pandemic. Finally, our sample is representative of commercial insurance and Medicare Advantage beneficiaries and therefore our findings are not generalizable to the overall population.

Conclusions

Supporting information

S1 Table. Results of seasonal autoregressive integrated moving average models, primary analyses.

The estimated level change shows the immediate change in the outcome following the World Health Organization declaration of pandemic on 3/11/2020. The estimated trend change depicts the further change from the predicted every 30 days (slope).

(PDF)

Click here for additional data file.^{(142.3KB, pdf)}

S2 Table. Results of seasonal autoregressive integrated moving average models, racial/ethnic subgroup analyses.

The estimated level change shows the immediate change in the outcome following the World Health Organization declaration of pandemic on 3/11/2020. The estimated trend change shows the further change from the predicted every 30 days (slope).

(PDF)

Click here for additional data file.^{(140.3KB, pdf)}

S3 Table. Results of interrupted time series analyses, racial/ethnic subgroup analyses.

The linear regression adjusted for autocorrelation using Newey-West standard error correction. Analysis was conducted using PROC MODEL with the SAS/ETS software. A joint Wald test shows no statistically significant difference in the change in the level of AF diagnoses after pandemic declaration across racial/ethnic subgroups (p = 0.34).

(PDF)

Click here for additional data file.^{(106.7KB, pdf)}

S4 Table. Results of seasonal autoregressive integrated moving average models, sex subgroup analyses.

(PDF)

Click here for additional data file.^{(111.2KB, pdf)}

S1 Fig. Selection of study sample.

The study cohort was selected using 2016—Q3 2020 data. Atrial fibrillation was defined as having a diagnosis claim with the ICD-9 code of 427.31 or ICD-10 codes of I48.0, I48.1, I48.2, or I48.91 in the first or second diagnosis field.

(PDF)

Click here for additional data file.^{(76.2KB, pdf)}

S2 Fig. Observed and predicted new atrial fibrillation diagnoses by sex.

The upper panel shows trends in new atrial fibrillation diagnoses per 1000 female individuals for 30-day intervals, from 01/02/2016 to 09/06/2020. The lower panel shows trends in new atrial fibrillation diagnoses per 1000 male individuals for 30-day intervals, from 01/02/2016 to 09/06/2020. Solid red lines represent observed values. Solid blue lines represent predictions from ARIMA models the absence of the COVID-19 pandemic. In other words, they represent trends in AF diagnosis that would have been observed if there had not been a change in AF diagnoses following the COVID-19 pandemic. Dashed blue lines represent confidence intervals.

(PDF)

Click here for additional data file.^{(317.5KB, pdf)}

Data Availability

Optum’s Clinformatics® Data Mart (CDM) is derived from a database of administrative health claims for members of large commercial and Medicare Advantage health plans. Clinformatics® Data Mart is statistically de-identified under the Expert Determination method consistent with HIPAA and managed according to Optum® customer data use agreements. CDM administrative claims submitted for payment by providers and pharmacies are verified, adjudicated and de-identified prior to inclusion. This data, including patient-level enrollment information, is derived from claims submitted for all medical and pharmacy health care services with information related to health care costs and resource utilization. The population is geographically diverse, spanning all 50 states. Optum’s Clinformatics® Data Mart are licensed and were accessed for the study under a data user agreement that does not allow data sharing. The authors obtained access to the data under a data user agreement. Other investigators engaging in a similar license and data user agreement would be able to access the same data if they purchased access for the same years of data and files. Investigators not engaging in a license and data user agreement would not be able to access the data as the data are not publicly available. Investigators interested in obtaining access can contact optum at 1-866-306-1321 or connected@optum.com.

Funding Statement

This work was funded by the National Heart, Lung and Blood Institute (grants K01HL142847 and R01HL15705). Dr. Benjamin is funded by the National Heart, Lung and Blood Institute (R01HL092577); and the American Heart Association (AHA_18SFRN34110082). Dr. Magnani is funded by the National Heart, Lung and Blood Institute (R33HL144669 and R01HL143010). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Rosenbaum L. The Untold Toll—The Pandemic’s Effects on Patients without Covid-19. N Engl J Med. 2020. Jun 11;382(24):2368–71. doi: 10.1056/NEJMms2009984 [DOI] [PubMed] [Google Scholar]
2.Baum A, Schwartz MD. Admissions to Veterans Affairs Hospitals for Emergency Conditions During the COVID-19 Pandemic. JAMA. 2020. Jul 7;324(1):96–9. [DOI] [PMC free article] [PubMed] [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. Feb 1;181(2):269–71. doi: 10.1001/jamainternmed.2020.3978 [DOI] [PMC free article] [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. Nov 20;21(1):143. doi: 10.1186/s12873-021-00529-w [DOI] [PMC free article] [PubMed] [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. Jul;13(7):e009438. doi: 10.1161/CIRCINTERVENTIONS.120.009438 [DOI] [PubMed] [Google Scholar]
6.Wosik J, Clowse MEB, Overton R, Adagarla B, Economou-Zavlanos N, Cavalier J, et al. Impact of the COVID-19 pandemic on patterns of outpatient cardiovascular care. Am Heart J. 2021. Jan 1;231:1–5. doi: 10.1016/j.ahj.2020.10.074 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.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. Nov;4:1059–71. doi: 10.1200/CCI.20.00134 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Durant TJS, Peaper DR, Ferguson D, Schulz WL. Impact of COVID-19 Pandemic on Laboratory Utilization. J Appl Lab Med. 2020. Nov 1;5(6):1194–205. doi: 10.1093/jalm/jfaa121 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Messina MD, Stein MW, Armstrong IJ, Wolf EL. Impact of the COVID-19 pandemic on radiology department emergency ultrasound utilization. Emerg Radiol. 2021. Oct;28(5):869–75. doi: 10.1007/s10140-021-01936-z [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Lippi G, Sanchis-Gomar F, Cervellin G. Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. Int J Stroke Off J Int Stroke Soc. 2021. Feb;16(2):217–21. doi: 10.1177/1747493019897870 [DOI] [PubMed] [Google Scholar]
11.Kornej J, Börschel CS, Benjamin EJ, Schnabel RB. Epidemiology of Atrial Fibrillation in the 21st Century. Circ Res. 2020. Jun 19;127(1):4–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001. May 9;285(18):2370–5. doi: 10.1001/jama.285.18.2370 [DOI] [PubMed] [Google Scholar]
13.Hart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med. 2007. Jun 19;146(12):857–67. doi: 10.7326/0003-4819-146-12-200706190-00007 [DOI] [PubMed] [Google Scholar]
14.Condition Categories—Chronic Conditions Data Warehouse [Internet]. [cited 2022 Feb 8]. Available from: https://www2.ccwdata.org/web/guest/condition-categories.
15.Jensen PN, Johnson K, Floyd J, Heckbert SR, Carnahan R, Dublin S. A systematic review of validated methods for identifying atrial fibrillation using administrative data. Pharmacoepidemiol Drug Saf. 2012. Jan;21 Suppl 1(0 1):141–7. doi: 10.1002/pds.2317 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Sundbøll J, Adelborg K, Munch T, Frøslev T, Sørensen HT, Bøtker HE, et al. Positive predictive value of cardiovascular diagnoses in the Danish National Patient Registry: a validation study. BMJ Open. 2016. Nov 18;6(11):e012832. doi: 10.1136/bmjopen-2016-012832 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.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 Assoc. 2019. Jun 18;8(12):e011427. doi: 10.1161/JAHA.118.011427 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Chen N, Gabriel N, Brooks MM, Hernandez I. Joint Latent Class Analysis of Oral Anticoagulation Use and Risk of Stroke or Systemic Thromboembolism in Patients with Atrial Fibrillation. Am J Cardiovasc Drugs Drugs Devices Interv. 2021. Sep;21(5):573–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Hernandez I, Baik SH, Piñera A, Zhang Y. Risk of bleeding with dabigatran in atrial fibrillation. JAMA Intern Med. 2015. Jan;175(1):18–24. doi: 10.1001/jamainternmed.2014.5398 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Lubitz SA, Yin X, McManus DD, Weng LC, Aparicio HJ, Walkey AJ, et al. Stroke as the Initial Manifestation of Atrial Fibrillation: The Framingham Heart Study. Stroke. 2017. Feb;48(2):490–2. doi: 10.1161/STROKEAHA.116.015071 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.DeFrank JT, Bowling JM, Rimer BK, Gierisch JM, Skinner CS. Triangulating differential nonresponse by race in a telephone survey. Prev Chronic Dis. 2007. Jul;4(3):A60. [PMC free article] [PubMed] [Google Scholar]
22.Forecasting: Principles and Practice (2nd ed) [Internet]. [cited 2022 Apr 1]. Available from: https://Otexts.com/fpp2/.
23.Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions | BMC Medical Research Methodology | Full Text [Internet]. [cited 2022 Apr 1]. Available from: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01235-8 [DOI] [PMC free article] [PubMed]
24.Linden A. Conducting Interrupted Time-series Analysis for Single- and Multiple-group Comparisons. Stata J. 2015. Jun 1;15(2):480–500. [Google Scholar]
25.CDC. COVID Data Tracker [Internet]. Centers for Disease Control and Prevention. 2020 [cited 2022 Mar 25]. Available from: https://covid.cdc.gov/covid-data-tracker.
26.Andersson C, Gerds T, Fosbøl E, Phelps M, Andersen J, Lamberts M, et al. Incidence of New-Onset and Worsening Heart Failure Before and After the COVID-19 Epidemic Lockdown in Denmark. Circ Heart Fail. 2020. Jun;13(6):e007274. [DOI] [PubMed] [Google Scholar]
27.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. Am Heart J Plus Cardiol Res Pract. 2022. Jan;13:100096. doi: 10.1016/j.ahjo.2022.100096 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Khot UN, Reimer AP, Brown A, Hustey FM, Hussain MS, Kapadia SR, et al. Impact of COVID-19 Pandemic on Critical Care Transfers for ST-Segment–Elevation Myocardial Infarction, Stroke, and Aortic Emergencies. Circ Cardiovasc Qual Outcomes. 2020. Aug;13(8):e006938. doi: 10.1161/CIRCOUTCOMES.120.006938 [DOI] [PubMed] [Google Scholar]
29.Jensen PN, Thacker EL, Dublin S, Psaty BM, Heckbert SR. Racial Differences in the Incidence of and Risk Factors for Atrial Fibrillation in Older Adults: The Cardiovascular Health Study. J Am Geriatr Soc. 2013;61(2):276–80. doi: 10.1111/jgs.12085 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Lau CP, Gbadebo TD, Connolly SJ, Van Gelder IC, Capucci A, Gold MR, et al. Ethnic Differences in Atrial Fibrillation Identified Using Implanted Cardiac Devices. J Cardiovasc Electrophysiol. 2013;24(4):381–7. doi: 10.1111/jce.12066 [DOI] [PubMed] [Google Scholar]
31.O’Neal WT, Judd SE, Limdi NA, McIntyre WF, Kleindorfer DO, Cushman M, et al. Differential Impact of Risk Factors in Blacks and Whites in the Development of Atrial Fibrillation: the Reasons for Geographic And Racial Differences in Stroke (REGARDS) Study. J Racial Ethn Health Disparities. 2017. Aug 1;4(4):718–24. doi: 10.1007/s40615-016-0275-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Volgman AS, Bairey MCN, Benjamin EJ, Curtis AB, Fang MC, Lindley KJ, et al. Sex and Race/Ethnicity Differences in Atrial Fibrillation. J Am Coll Cardiol. 2019. Dec 3;74(22):2812–5. doi: 10.1016/j.jacc.2019.09.045 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Heckbert SR, Austin TR, Jensen PN, Chen LY, Post WS, Floyd JS, et al. Differences by Race/Ethnicity in the Prevalence of Clinically Detected and Monitor-Detected Atrial Fibrillation. Circ Arrhythm Electrophysiol. 2020. Jan;13(1):e007698. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Rillig A, Borof K, Breithardt G, Camm AJ, Crijns HJGM, Goette A, et al. Early Rhythm Control in Patients With Atrial Fibrillation and High Comorbidity Burden. Circulation. 2022. Sep 13;146(11):836–47. doi: 10.1161/CIRCULATIONAHA.122.060274 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0281068.r001

Decision Letter 0

Han Eol Jeong

9 Nov 2022

PONE-D-22-29038COVID-19 Pandemic and Trends in New Diagnosis of Atrial Fibrillation: A Nationwide Analysis of Claims DataPLOS ONE

Dear Dr. Hernandez,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Dec 24 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Han Eol Jeong, M.P.H., Ph.D.

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating the following in the Competing Interests section:

“I have read the journal's policy and the authors of this manuscript have the following competing interests: Hernandez has received consulting fees from Pfizer and Bristol Myers Squibb, outside of the submitted work.”

Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials, by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have conducted a population-based cohort study to quantify changes in new diagnoses of AF following the onset of the COVID-19 pandemic using a nationwide claims database. Although the research is interesting and well done, it needs further clarification in discussing and complementing the methodology.

Major comments

1. Introduction: I can understand that there is a problem in the diagnosis of chronic diseases due to the pandemic. However, the authors need additional logic as to why they studied AF.

2. Methods: Authors should present positive predictive value (PPV) together to enhance the reliability of AF diagnosis. In addition, to increase the validity of the definition of AF diagnosis, it is recommended to perform sensitivity analysis with AF event defined as Warfarin or NOAC prescription on the same date with AF diagnosis code.

3. Methods: Isn't it more appropriate to perform a stratified analysis considering the hospital setting, the number of hospitals by state, or the number of cardiologists, rather than analyzing by the state? Did the risk factors not affect AF diagnosis, except for ethnic differences?

4. Discussions: AF diagnosis decreases immediately after the pandemic declaration. However, it is soon observed to be similar to the predicted value. The author will need to offer a plausible explanation related to this part.

5. Discussions: Explain why the pattern of inpatient is different from the outpatient.

Minor comments

1. Methods; Outcomes: What is code 21 stand for? Authors should provide additional information on that code.

2. For the Hispanic population, a pick is observed in 2017, why is this observed?

3. Please add commas per 1000 units to Table 1

4. Please harmonize the date format between the manuscript and figure (e.g. 1/3/2019 or 1/Mar/2019)

5. Incorrect spacing appears throughout the manuscript. Again, please review in general.

Reviewer #2: For this study, the authors use data from a Optum’s Clinformatics® Data Mart database covering members of commercial insurance and Medicare beneficiaries to investigate the changes in incidence of new atrial fibrillation (AF) diagnosis following the COVID-19 pandemic. Outcome data were obtained from inpatient and outpatient claims record with primary and secondary diagnosis position. The authors compared the incidence rates of newly diagnosed AF per 30-day interval before and after the COVID-19 pandemic declaration (2020-03-11), and they tested changes and predicted trends in incidence rates of AF diagnosis using seasonal ARIMA model taking into account seasonality. Subgroup analyses by gender and race/ethnicity were performed and state variation in the changes in incidence rates of AF diagnoses were investigated. The authors noted substantial decrease in incidence rate of new AF diagnosis following the COVID-19 pandemic declaration, as supported by the additional analyses.

This study has addressed an important research question on the impact of pandemic on diagnosis of chronic disease. However, some of the data reported on changes in incidence rates of new AF diagnoses make question how adequately the magnitude of changes has been measured in this study. Providing relevant data would help strengthen this study.

My comments and suggestions for the authors are described in detail below.

Major Comment:

Comment 1. Assessment of changes in incidence rate of new AF diagnosis

a. Results for changes in 30-day incidence rate of new AF diagnosis (page 7, line 16-18 & 22-23; page 8, line 1 & 6-9; Figure 4; page 2, line 17-20). Considering the seasonality in AF diagnosis with higher incidence in winter, Jan to Feb (Figure 1-3), comparing the 30-day incidence rates immediately before and after COVID-19 pandemic declaration (2020-03-11) would like to make concerns the possibility of overestimation in measuring magnitude of change. The reviewer recommends comparing incidence rate of 30-day immediately after COVID-19 pandemic declaration with that of corresponding time-period in the previous year, or averaged in previous years, or predicted value, instead of immediately before 30-day incidence rate.

b. Based on the reported data (Figure 1 to 3), the incidence rate of AF diagnosis after COVID-19 pandemic declaration in summer (2020-08-08) does not seems to be largely different when compared with that of corresponding dates in previous years. Additional data on comparison of incidence rates in other time period after COVID-19 pandemic declaration with averaged incidence rates in corresponding time-period in previous years or predicted values (e.g., 31-60, 61-90, 91-120, 121-150, 151-180 days) would helpful to understand how long does the COVID-19 pandemic declaration or patients’ avoidance of medical care critically affect to the AF diagnosis, and strengthen the implication of this study.

Minor comment:

Comment 1. Subgroup analysis.

The authors note in the discussion that they provide evidence on the impact of patients’ avoidance of medical care in the earlier months of the COVID-19 pandemic (page 10). They further reported that Medicare Advantage beneficiaries were 84.4% of individuals newly diagnosed with AF, while 30.9% of study population were Medicare Advantage beneficiaries (Table 1). Higher proportion of Medicare Advantage beneficiaries in individuals newly diagnosed with AF may be because of that the incidence of AF is more common in elderly population, but it would be difficult to disregard differential patterns in the use of medical care by type of health insurance. Thus, it would be helpful to present data on the subgroup analysis by Medicare Advantage beneficiaries for readers to understand the effects of COVID-19 pandemic on the access to medical care for AF.

Comment 2. Discussion on state variation.

Please clarify what is meant by “There was no apparent relationship between the magnitude of the decrease of AF diagnoses and the cumulative number of COVID-19 cases in the first 30-day interval after pandemic declaration” (page 8). Does it mean that the changes in AF diagnoses immediately after pandemic declaration may have associated with the patients’ avoidance of medical care, rather than the accessibility? It would be helpful for reader to add to the discussion some consideration of why there was no apparent relationship between the magnitude of the changes in AF diagnoses and the number of COVID-19 cases.

Comment 3. Measurement of average 30-day incidence rate.

The authors note that “Before the onset of the COVID-19 pandemic, the estimated 30-day incidence rate of new AF diagnoses per 1000 individuals averaged 1.22 for White individuals, 1.15 for Black individuals, 0.82 for Hispanic individuals, and 1.05 for individuals of other races or ethnicities” (page 8). How long does the assessment period? From 2016-04-01 to 2020-03-11 OR 2016-01-01 to 2020-03-11? Considering the seasonality, it would be good to provide some detailed description in the Method section.

Comment 4. Structure of the Figure 4.

Suggest modifying the structure of the Figure 4 to improve readability as following: one y-axis of state in central, percentages of changes in new AF diagnoses in left-side, cumulative numbers of COVID-19 cases in right-side. Also, how about modify the order of states in y-axis in reverse order, from Oregon to Iowa, focusing on the magnitude of the changes in AF diagnoses?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 Feb 2;18(2):e0281068. doi: 10.1371/journal.pone.0281068.r002

Author response to Decision Letter 0

4 Jan 2023

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

We have ensured that our manuscript meets PLOS ONE's style requirements, including those for file naming.

2. Thank you for stating the following in the Competing Interests section:

Our conflicts do not alter our adherence to PLOS ONE policies on sharing data and materials.

Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

We have included our updated statement of competing interests in the cover letter.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

We obtained access to the data under a data user agreement that specifies that the sole proprietor of the raw data is Optum. This data user agreement explicitly prohibits the sharing of data to any individual who is not covered under the data user agreement. We are not able to make publicly available the minimal data set because of these legal restrictions.

We have included captions for Supporting Information files at the end of the manuscript (pages 18 and 19).

Reviewer #1:

The authors have conducted a population-based cohort study to quantify changes in new diagnoses of AF following the onset of the COVID-19 pandemic using a nationwide claims database. Although the research is interesting and well done, it needs further clarification in discussing and complementing the methodology.

Major comments

1. Introduction: I can understand that there is a problem in the diagnosis of chronic diseases due to the pandemic. However, the authors need additional logic as to why they studied AF.

We have added a sentence explaining why AF is a crucial disease state to measure COVID-19 disruptions of chronic disease care (page 3, lines 19-22): “AF is a critical disease state to measure the effects of the COVID-19 pandemic on non-COVID related chronic disease because every aspect of stroke prevention is vulnerable to disruption, including diagnosis, initiation of anticoagulation therapy, and treatment monitoring.”

Previous studies have estimated the positive predictive value of our definition to be 93-97%.1,2 We have incorporated these references in the methods section outcomes subsection (page 5, lines 1-2).

We did not perform sensitivity analyses requiring initiation of anticoagulation to define AF because claims data do not contain information on oral anticoagulation initiated during inpatient stays. This is because oral anticoagulants are not separately billed during inpatient stays. This would prevent us from identifying diagnosis in the inpatient setting according to the definition proposed by the reviewer. In addition, this definition does not consider the strong underutilization of oral anticoagulation in AF in the US, which has been well-described in the literature.3–5

Optum data do not enable us to identify the hospital where diagnosis took place, so we are not able to perform analyses stratifying by number of cardiologists in a hospital. We performed analysis stratified for inpatient and outpatient setting (page 9, lines 5-10, Figure 2). We do not follow the rationale for performing analyses based on the number of hospitals in each state.

We did not incorporate other risk factors for AF in the analysis because the objective of our study was not to predict AF diagnosis but rather to understand changes in trends in AF diagnosis associated with the onset of the COVID-19 pandemic. Because the COVID-19 pandemic disproportionately affected underrepresented racial/ethnic groups, it was relevant to evaluate whether changes in diagnoses following pandemic declaration differed across racial/ethnic groups. We have incorporated this sentence in the introduction section (page 3 line 24, page 4 lines 1-2).

We believe that the immediate drop in diagnosis represents the fear of COVID-19 contagion in health care facilities, particularly in the first weeks after the declaration of the COVID-19 pandemic. It is possible that patients experiencing more frequent or severe symptoms decided to seek medical care after realizing that the risk of contagion would not cease in the short term. Nevertheless, this is a speculation as our data do not enable us to observe the reasons why AF diagnosis decreased. We have added a sentence in the first paragraph of the discussion section acknowledging that the incidence of new AF diagnoses returned to predicted levels in summer 2020 (page 11, lines 10-11): “In a large nationwide study, we observed that new AF diagnoses decreased immediately after the declaration of the COVID-19 pandemic; however, they returned to predicted levels by summer 2020”. We have incorporated the explanation provided above in the fourth paragraph of the discussion section, and we have acknowledged it is speculative and not based on findings from our study (page 12, lines 10-17): “After the drop in new AF diagnosis observed immediately after the start of the COVID-19 pandemic, the incidence of AF nearly returned to predicted levels by summer 2020. We speculate that decreased diagnosis of AF may have represented fear of COVID-19 contagion in health care facilities, particularly in the first weeks after the declaration of the COVID-19 pandemic. It is possible that patients experiencing more frequent or severe symptoms decided to seek medical care after realizing that the risk of contagion would not cease in the short term, which would explain the return to expected levels within a few months of the pandemic onset. Nevertheless, these are speculative hypotheses as our data do not enable us to observe the reasons behind the changes in health care encounters noted in the study”.

5. Discussions: Explain why the pattern of inpatient is different from the outpatient.

We discuss in the second paragraph of the discussion section why the decrease in new AF diagnoses was less pronounced in the inpatient setting (page 11 lines 21-24, page 12 lines 1-2): “We observed that the magnitude of decrease in new AF diagnoses was lower in the inpatient setting and for ischemic stroke as initial manifestation of AF, which are more severe presentations of new-onset AF than those typically diagnosed in the outpatient setting. The less pronounced decrease in incidence of events observed for more severe outcomes is consistent with prior studies that reported that medical care avoidance in the early months of the COVID-19 pandemic was less pronounced for more severe events, including strokes”.

Minor comments

1. Methods; Outcomes: What is code 21 stand for? Authors should provide additional information on that code.

Thank you for seeking clarification. Place of service 21 represents inpatient hospital, which we now state in the methods (page 5, lines 11-12).

2. For the Hispanic population, a pick is observed in 2017, why is this observed?

The peak coincides with the beginning of the year, which is when the majority of beneficiaries are added to the sample, as the enrollment in health plans often starts on January 1. Similar peaks are observed every year in January for the overall population. We hypothesize that the 2017 peak is particularly pronounced for the Hispanic subgroup because the lower sample size of this ethnic group makes the trend particularly sensitive to enrollment changes.

3. Please add commas per 1000 units to Table 1

Commas have been added.

4. Please harmonize the date format between the manuscript and figure (e.g. 1/3/2019 or 1/Mar/2019)

We have not made this change as we are unsure of the date format preferred by PLOS ONE. We believe this change can be handled at the proofing / editing stage.

5. Incorrect spacing appears throughout the manuscript. Again, please review in general.

We have revised the manuscript to ensure correct spacing.

Reviewer #2

For this study, the authors use data from a Optum’s Clinformatics® Data Mart database covering members of commercial insurance and Medicare beneficiaries to investigate the changes in incidence of new atrial fibrillation (AF) diagnosis following the COVID-19 pandemic. Outcome data were obtained from inpatient and outpatient claims record with primary and secondary diagnosis position. The authors compared the incidence rates of newly diagnosed AF per 30-day interval before and after the COVID-19 pandemic declaration (2020-03-11), and they tested changes and predicted trends in incidence rates of AF diagnosis using seasonal ARIMA model taking into account seasonality. Subgroup analyses by gender and race/ethnicity were performed and state variation in the changes in incidence rates of AF diagnoses were investigated. The authors noted substantial decrease in incidence rate of new AF diagnosis following the COVID-19 pandemic declaration, as supported by the additional analyses.

My comments and suggestions for the authors are described in detail below.

Major Comment:

Comment 1. Assessment of changes in incidence rate of new AF diagnosis

Thank you for the suggestion. The incidence in new AF diagnosis in the first 30-day period after pandemic declaration (3/11/2020-4/9/2020) was 0.74, this is 35% lower than in the period immediately before pandemic declaration (incidence rate=1.14). The average incidence rate in the March periods from previous years was 1.134. Using this estimate as reference, the estimated decrease in AF diagnosis is 34.7%, which rounds to 35%. We have not made the change in the manuscript as our method does not lead to an overestimation of the magnitude of change.

We have modified the results section following this suggestion (page 8, lines 9-12): “The observed incidence of new AF diagnosis was substantially lower than predicted by regression models in the first 90 days after the declaration of the COVID-19 pandemic, however these differences closed by summer 2020. As of June 2020, the observed incidence of new AF diagnoses was only 6% lower than predicted.”

We have added a sentence in the first paragraph of the discussion section acknowledging that the incidence of new AF diagnoses returned to predicted levels in summer 2020 (page 11, lines 10-11): “In a large nationwide study, we observed that new AF diagnoses decreased immediately after the declaration of the COVID-19 pandemic; however, they returned to predicted levels by summer 2020”. We have also added a paragraph in the discussion section commenting on this important finding, as suggested by both reviewers (page 12, lines 10-17): “After the drop in new AF diagnosis observed immediately after the start of the COVID-19 pandemic, the incidence of AF nearly returned to predicted levels by summer 2020. We speculate that decreased diagnosis of AF may have represented fear of COVID-19 contagion in health care facilities, particularly in the first weeks after the declaration of the COVID-19 pandemic. It is possible that patients experiencing more frequent or severe symptoms decided to seek medical care after realizing that the risk of contagion would not cease in the short term, which would explain the return to expected levels within a few months of the pandemic onset. Nevertheless, these are speculative hypotheses as our data do not enable us to observe the reasons behind the changes in health care encounters noted in the study”.

Minor comments:

Comment 1. Subgroup analysis.

Thank you for the comment. Optum data includes information on beneficiaries of commercial plans and of Medicare Advantage plans offered by a single insurer (UnitedHealthcare). All Medicare beneficiaries included in the analyses are enrolled in Medicare Advantage (no Medicare fee-for-service beneficiaries). For this reason, it is not possible to differentiate between the effect of age and type of health plan. The primary findings of the study are driven by Medicare beneficiaries (as opposed to commercial beneficiaries) because atrial fibrillation is an age-related disease. We have not added a subgroup analysis for Medicare beneficiaries because: 1) the subgroup analyses do not allow a differentiation between effects of age eligibility for Medicare enrollment and true differences in insurance design; 2) the differences in insurance factors as they relate to AF diagnosis are minimal as both insurance products are offered by the same carrier.

Comment 2. Discussion on state variation.

In performing these analyses, we hypothesized that decreases in new AF diagnosis would be larger in states with a higher burden of COVID cases because of patients’ avoidance of medical care and because of lower availability of providers and health care resources for non-COVID-disease. However, this hypothesis was not confirmed by our analyses. We have added a new paragraph in the discussion section commenting this finding, as requested by the reviewer (page 12, lines 3-9): “Notably, we observed no apparent relationship between the state-level magnitude of the decrease of AF diagnoses and the number of COVID-19 cases in the first 30-day interval after pandemic declaration. The lack of state variation may indicate that the decrease in AF diagnosis was more representative of patient’s avoidance to seek medical care rather than the unavailability of providers and health care resources in hardest-hit areas. The behavioral changes in healthcare-seeking behavior may not have presented regional variation because of the nationwide reach of news reports on COVID cases in the early weeks of the COVID pandemic.”

Comment 3. Measurement of average 30-day incidence rate.

We apologize for the confusion; these statistics refer to the period immediately before the declaration of the COVID-19 pandemic (2/10/2020-3/10/2020). We have now added this information to the text (page 10, line 1).

Comment 4. Structure of the Figure 4.

We appreciate the suggestion and have reformatted the figure as requested.

References

1. Rix TA, Riahi S, Overvad K, Lundbye-Christensen S, Schmidt EB, Joensen AM. Validity of the diagnoses atrial fibrillation and atrial flutter in a Danish patient registry. Scand Cardiovasc J. 2012;46(3):149-153.

2. Sundbøll J, Adelborg K, Munch T, et al. Positive predictive value of cardiovascular diagnoses in the Danish National Patient Registry: a validation study. BMJ Open. 2016;6(11):e012832.

3. 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 Assoc. 2019;8(12):e011427.

4. Dhamane AD, Hernandez I, Di Fusco M, et al. Non-persistence to oral anticoagulation treatment in patients with non-valvular atrial fibrillation in the USA. Am J Cardiovasc Drugs. 2022;22(3):333-343.

5. Hernandez I, Saba S, Zhang Y. Geographic variation in the use of oral anticoagulation therapy in stroke prevention in atrial fibrillation. Stroke. 2017;48(8):2289-2291.

Attachment

Submitted filename: response to reviewers 11-28-2022.doc

Click here for additional data file.^{(83KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0281068.r003

Decision Letter 1

Han Eol Jeong

16 Jan 2023

COVID-19 Pandemic and Trends in New Diagnosis of Atrial Fibrillation: A Nationwide Analysis of Claims Data

PONE-D-22-29038R1

Dear Dr. Hernandez,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Han Eol Jeong, M.P.H., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0281068.r004

Acceptance letter

Han Eol Jeong

20 Jan 2023

PONE-D-22-29038R1

COVID-19 pandemic and trends in new diagnosis of atrial fibrillation: A nationwide analysis of claims data

Dear Dr. Hernandez:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Han Eol Jeong

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Results of seasonal autoregressive integrated moving average models, primary analyses.

(PDF)

Click here for additional data file.^{(142.3KB, pdf)}

S2 Table. Results of seasonal autoregressive integrated moving average models, racial/ethnic subgroup analyses.

(PDF)

Click here for additional data file.^{(140.3KB, pdf)}

S3 Table. Results of interrupted time series analyses, racial/ethnic subgroup analyses.

(PDF)

Click here for additional data file.^{(106.7KB, pdf)}

S4 Table. Results of seasonal autoregressive integrated moving average models, sex subgroup analyses.

(PDF)

Click here for additional data file.^{(111.2KB, pdf)}

S1 Fig. Selection of study sample.

(PDF)

Click here for additional data file.^{(76.2KB, pdf)}

S2 Fig. Observed and predicted new atrial fibrillation diagnoses by sex.

(PDF)

Click here for additional data file.^{(317.5KB, pdf)}

Attachment

Submitted filename: response to reviewers 11-28-2022.doc

Click here for additional data file.^{(83KB, doc)}

Data Availability Statement

[pone.0281068.ref001] 1.Rosenbaum L. The Untold Toll—The Pandemic’s Effects on Patients without Covid-19. N Engl J Med. 2020. Jun 11;382(24):2368–71. doi: 10.1056/NEJMms2009984 [DOI] [PubMed] [Google Scholar]

[pone.0281068.ref002] 2.Baum A, Schwartz MD. Admissions to Veterans Affairs Hospitals for Emergency Conditions During the COVID-19 Pandemic. JAMA. 2020. Jul 7;324(1):96–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref003] 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. Feb 1;181(2):269–71. doi: 10.1001/jamainternmed.2020.3978 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref004] 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. Nov 20;21(1):143. doi: 10.1186/s12873-021-00529-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref005] 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. Jul;13(7):e009438. doi: 10.1161/CIRCINTERVENTIONS.120.009438 [DOI] [PubMed] [Google Scholar]

[pone.0281068.ref006] 6.Wosik J, Clowse MEB, Overton R, Adagarla B, Economou-Zavlanos N, Cavalier J, et al. Impact of the COVID-19 pandemic on patterns of outpatient cardiovascular care. Am Heart J. 2021. Jan 1;231:1–5. doi: 10.1016/j.ahj.2020.10.074 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref007] 7.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. Nov;4:1059–71. doi: 10.1200/CCI.20.00134 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref008] 8.Durant TJS, Peaper DR, Ferguson D, Schulz WL. Impact of COVID-19 Pandemic on Laboratory Utilization. J Appl Lab Med. 2020. Nov 1;5(6):1194–205. doi: 10.1093/jalm/jfaa121 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref009] 9.Messina MD, Stein MW, Armstrong IJ, Wolf EL. Impact of the COVID-19 pandemic on radiology department emergency ultrasound utilization. Emerg Radiol. 2021. Oct;28(5):869–75. doi: 10.1007/s10140-021-01936-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref010] 10.Lippi G, Sanchis-Gomar F, Cervellin G. Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. Int J Stroke Off J Int Stroke Soc. 2021. Feb;16(2):217–21. doi: 10.1177/1747493019897870 [DOI] [PubMed] [Google Scholar]

[pone.0281068.ref011] 11.Kornej J, Börschel CS, Benjamin EJ, Schnabel RB. Epidemiology of Atrial Fibrillation in the 21st Century. Circ Res. 2020. Jun 19;127(1):4–20. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref012] 12.Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001. May 9;285(18):2370–5. doi: 10.1001/jama.285.18.2370 [DOI] [PubMed] [Google Scholar]

[pone.0281068.ref013] 13.Hart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med. 2007. Jun 19;146(12):857–67. doi: 10.7326/0003-4819-146-12-200706190-00007 [DOI] [PubMed] [Google Scholar]

[pone.0281068.ref014] 14.Condition Categories—Chronic Conditions Data Warehouse [Internet]. [cited 2022 Feb 8]. Available from: https://www2.ccwdata.org/web/guest/condition-categories.

[pone.0281068.ref015] 15.Jensen PN, Johnson K, Floyd J, Heckbert SR, Carnahan R, Dublin S. A systematic review of validated methods for identifying atrial fibrillation using administrative data. Pharmacoepidemiol Drug Saf. 2012. Jan;21 Suppl 1(0 1):141–7. doi: 10.1002/pds.2317 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref016] 16.Sundbøll J, Adelborg K, Munch T, Frøslev T, Sørensen HT, Bøtker HE, et al. Positive predictive value of cardiovascular diagnoses in the Danish National Patient Registry: a validation study. BMJ Open. 2016. Nov 18;6(11):e012832. doi: 10.1136/bmjopen-2016-012832 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref017] 17.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 Assoc. 2019. Jun 18;8(12):e011427. doi: 10.1161/JAHA.118.011427 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref018] 18.Chen N, Gabriel N, Brooks MM, Hernandez I. Joint Latent Class Analysis of Oral Anticoagulation Use and Risk of Stroke or Systemic Thromboembolism in Patients with Atrial Fibrillation. Am J Cardiovasc Drugs Drugs Devices Interv. 2021. Sep;21(5):573–80. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref019] 19.Hernandez I, Baik SH, Piñera A, Zhang Y. Risk of bleeding with dabigatran in atrial fibrillation. JAMA Intern Med. 2015. Jan;175(1):18–24. doi: 10.1001/jamainternmed.2014.5398 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref020] 20.Lubitz SA, Yin X, McManus DD, Weng LC, Aparicio HJ, Walkey AJ, et al. Stroke as the Initial Manifestation of Atrial Fibrillation: The Framingham Heart Study. Stroke. 2017. Feb;48(2):490–2. doi: 10.1161/STROKEAHA.116.015071 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref021] 21.DeFrank JT, Bowling JM, Rimer BK, Gierisch JM, Skinner CS. Triangulating differential nonresponse by race in a telephone survey. Prev Chronic Dis. 2007. Jul;4(3):A60. [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref022] 22.Forecasting: Principles and Practice (2nd ed) [Internet]. [cited 2022 Apr 1]. Available from: https://Otexts.com/fpp2/.

[pone.0281068.ref023] 23.Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions | BMC Medical Research Methodology | Full Text [Internet]. [cited 2022 Apr 1]. Available from: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01235-8 [DOI] [PMC free article] [PubMed]

[pone.0281068.ref024] 24.Linden A. Conducting Interrupted Time-series Analysis for Single- and Multiple-group Comparisons. Stata J. 2015. Jun 1;15(2):480–500. [Google Scholar]

[pone.0281068.ref025] 25.CDC. COVID Data Tracker [Internet]. Centers for Disease Control and Prevention. 2020 [cited 2022 Mar 25]. Available from: https://covid.cdc.gov/covid-data-tracker.

[pone.0281068.ref026] 26.Andersson C, Gerds T, Fosbøl E, Phelps M, Andersen J, Lamberts M, et al. Incidence of New-Onset and Worsening Heart Failure Before and After the COVID-19 Epidemic Lockdown in Denmark. Circ Heart Fail. 2020. Jun;13(6):e007274. [DOI] [PubMed] [Google Scholar]

[pone.0281068.ref027] 27.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. Am Heart J Plus Cardiol Res Pract. 2022. Jan;13:100096. doi: 10.1016/j.ahjo.2022.100096 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref028] 28.Khot UN, Reimer AP, Brown A, Hustey FM, Hussain MS, Kapadia SR, et al. Impact of COVID-19 Pandemic on Critical Care Transfers for ST-Segment–Elevation Myocardial Infarction, Stroke, and Aortic Emergencies. Circ Cardiovasc Qual Outcomes. 2020. Aug;13(8):e006938. doi: 10.1161/CIRCOUTCOMES.120.006938 [DOI] [PubMed] [Google Scholar]

[pone.0281068.ref029] 29.Jensen PN, Thacker EL, Dublin S, Psaty BM, Heckbert SR. Racial Differences in the Incidence of and Risk Factors for Atrial Fibrillation in Older Adults: The Cardiovascular Health Study. J Am Geriatr Soc. 2013;61(2):276–80. doi: 10.1111/jgs.12085 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref030] 30.Lau CP, Gbadebo TD, Connolly SJ, Van Gelder IC, Capucci A, Gold MR, et al. Ethnic Differences in Atrial Fibrillation Identified Using Implanted Cardiac Devices. J Cardiovasc Electrophysiol. 2013;24(4):381–7. doi: 10.1111/jce.12066 [DOI] [PubMed] [Google Scholar]

[pone.0281068.ref031] 31.O’Neal WT, Judd SE, Limdi NA, McIntyre WF, Kleindorfer DO, Cushman M, et al. Differential Impact of Risk Factors in Blacks and Whites in the Development of Atrial Fibrillation: the Reasons for Geographic And Racial Differences in Stroke (REGARDS) Study. J Racial Ethn Health Disparities. 2017. Aug 1;4(4):718–24. doi: 10.1007/s40615-016-0275-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref032] 32.Volgman AS, Bairey MCN, Benjamin EJ, Curtis AB, Fang MC, Lindley KJ, et al. Sex and Race/Ethnicity Differences in Atrial Fibrillation. J Am Coll Cardiol. 2019. Dec 3;74(22):2812–5. doi: 10.1016/j.jacc.2019.09.045 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref033] 33.Heckbert SR, Austin TR, Jensen PN, Chen LY, Post WS, Floyd JS, et al. Differences by Race/Ethnicity in the Prevalence of Clinically Detected and Monitor-Detected Atrial Fibrillation. Circ Arrhythm Electrophysiol. 2020. Jan;13(1):e007698. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281068.ref034] 34.Rillig A, Borof K, Breithardt G, Camm AJ, Crijns HJGM, Goette A, et al. Early Rhythm Control in Patients With Atrial Fibrillation and High Comorbidity Burden. Circulation. 2022. Sep 13;146(11):836–47. doi: 10.1161/CIRCULATIONAHA.122.060274 [DOI] [PubMed] [Google Scholar]

PERMALINK

COVID-19 pandemic and trends in new diagnosis of atrial fibrillation: A nationwide analysis of claims data

Inmaculada Hernandez

Meiqi He

Jingchuan Guo

Mina Tadrous

Nico Gabriel

Gretchen Swabe

Walid F Gellad

Utibe R Essien

Samir Saba

Emelia J Benjamin

Jared W Magnani

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Data source and study population

Outcomes

Independent variables

Statistical analysis

Results

Study sample

Table 1. Baseline patient characteristics.

Changes in new atrial fibrillation diagnoses

Fig 1. Observed and predicted new atrial fibrillation diagnoses, overall sample.

Changes in new atrial fibrillation diagnoses by setting

Fig 2. Observed and predicted new atrial fibrillation diagnoses by setting.

Changes in new atrial fibrillation diagnoses by subgroup

Fig 3. Observed and predicted new atrial fibrillation diagnoses by race and ethnicity.

Fig 4. Observed change in new atrial fibrillation diagnoses by state.

Discussion

Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Han Eol Jeong

Roles

Author response to Decision Letter 0

Decision Letter 1

Han Eol Jeong

Roles

Acceptance letter

Han Eol Jeong

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases