Regional Disparities in Atrial Fibrillation Catheter Ablation Based on the Japanese National Survey

Abstract

Background: The prevalence of atrial fibrillation (AF) has increased with aging populations, making catheter ablation essential. However, access to this treatment across regions, even with universal healthcare, is not well understood. This study aims to explore potential regional disparities in AF catheter ablation rates and identify associated factors.

Methods and Results: This cross-sectional study analyzed data from the Vital Statistics survey of Japanese events in 2022 (124,947,000 people), the Japanese government Survey of Household Economy, the Survey on the Impact of the Diagnosis Procedure Combination System, and publicly available data from the Japan Heart Rhythm Society. Principal component regression analysis revealed that the number of AF hospitalizations exhibited a positive correlation with the prevalence of hypertension and the percentage of unemployed men. The number of ablations positively correlated with overtime hours, university graduate salaries, part-time employment rates, and the number of arrhythmia specialists. AF hospitalization rates exhibited a negative correlation with AF ablation rates. The number of arrhythmia specialists correlated positively with ablation rates but negatively with AF hospitalization rates.

Conclusions: Socioeconomic factors appear to influence AF hospitalizations and treatment decisions. An increase in arrhythmia specialists may optimize ablation rates and reduce AF hospitalization rates.

The prevalence of atrial fibrillation (AF) has increased among the elderly population in Japan, which has the world’s highest aging rate.^1,2 AF was once considered incurable, but the discovery that pulmonary vein triggers can initiate AF³ has led to the widespread use of catheter ablation. In the past decade, remarkable advances in the technology and techniques for AF ablation have been made.^4–6

AF ablations have become widely available in Japan.⁷ Although AF ablations are costly,⁸ the universal health insurance system in Japan allows patients to undergo the procedure at a reasonable cost. However, disparities in AF treatment exist within the country. For example, access to AF ablations may be limited in rural areas because large comprehensive hospitals tend to be concentrated in urban centers, as seen in many other countries.⁹ This study aimed to determine if access to AF ablations is equitable across different regions in Japan and identify underlying factors associated with regional disparities (Figure 1).

Figure 1.

(A) Research question. The objective of this study was to determine if patients in Japan, a country with a universal healthcare system, have equitable access to catheter ablation procedures. This study also determined if healthcare disparities can occur in countries with universal health insurance systems like Japan. Japan has a population of approximately 12,947,000 and extends from north to south, resulting in regional differences in climate. Additionally, Japan is known as one of the most rapidly aging societies in the world. Data were analyzed at the prefectural level. The image shown is downloaded from Frame illust (https://frame-illust.com/). The materials distributed on the site may be downloaded and used free of charge by individuals and businesses alike. No prior notice or credit is required; anyone can use the images in commercial print materials, websites, and visual media. (B) Temperature and humidity in Japan. The average temperature, minimum temperature, and annual average humidity for each prefecture in Japan are shown. The background colors of the bars in this graph correspond to the colors in (A).

Obtaining objectively accurate data on the total number of patients with AF nationwide is challenging due to difficulties in diagnosing AF. Sustained AF can be easily detected through electrocardiograms during health checkups. However, paroxysmal AF is more difficult to diagnose. Patients with mild symptoms may not seek medical attention, and some patients may require prolonged monitoring, such as Holter monitoring, for a definitive diagnosis. Thus, we first analyzed the number of hospitalized patients with AF as 1 objective measure, and then analyzed the number of AF ablation procedures and considered factors influencing AF incidence, such as population, climate, and pre-existing conditions in each prefecture. Japan collects a wide range of data, including population, climate, health, and living environment data at the prefectural level. We utilized these data to identify factors associated with the number of AF ablation procedures.

Methods

Ethical Approval

This study did not involve personal information or animal samples. All data used in this study were obtained from publicly available sources. The ethics committee of the National Center for Geriatrics and Gerontology determined that an ethical review was not necessary for research using such public data.

Integration of Public Data

Statistical data, including population, natural environment, economic base, administrative base, education, labor, residence, health, medical care, welfare, and social security, were obtained from e-Stat (https://www.e-stat.go.jp/). The e-Stat database of the Japanese government provides statistical tables, data, and information. The vital statistics includes events in Japan in 2022 (124,947,000 people). Data on diseases were obtained from the Survey of the Household Economy, which provides data on all households and their members nationwide. Household data and health questionnaires were collected from approximately 300,000 households and 674,000 individuals in 5,530 stratified randomly selected districts in the 2020 national census.

The number of AF ablation procedures, and AF admission, were estimated using the Survey on the Impact of the Diagnosis Procedure Combination (DPC) System (https://www.mhlw.go.jp/stf/shingi2/newpage_39119.html). The DPC system is a per diem payment system based on diagnostic procedures for acute-phase inpatient care. Data are collected annually to verify the impact of the DPC system in Japan. Data on ablation procedures were collected from the hospitals participating in the DPC data collection. According to the 2022 Japan Ablation Registry Annual Report (J-AB), a survey on the status of catheter ablation in Japan, 75.9% of ablations performed in Japan in 2022 were for AF, and 80.9% of the ablations were first-time ablations.¹⁰ The numbers of first and total AF ablations were calculated using the DPC and J-AB registries by multiplying the number of ablation cases obtained from the DPC data by the AF rate obtained from the J-AB. Unlike previous studies, no genetic differences are involved. Thus, this simple formula could be used.¹¹ The J-AB registry reported that 55.4% of ablations performed in Japan in 2022 were for paroxysmal AF, and 44.6% of ablations were for persistent AF.¹⁰ The number of procedures was determined without distinguishing between paroxysmal and persistent AF, as our primary objective was to verify whether ablation was being performed equally in each prefecture. Similarly, for tachyarrhythmia hospitalizations recorded in the DPC, we defined the number as the approximate number of AF hospitalizations because, as noted above, most tachyarrhythmia hospitalizations are presumed to be AF hospitalizations.

The number of Japan Heart Rhythm Society (JHRS)-certified arrhythmia specialists in each prefecture was obtained from publicly available data on the specialist list of JHRS (https://new.jhrs.or.jp/specialist-index/specialist-list/, accessed October 1, 2024).

Data Analysis and Graph Creation

All graphs and figures were created and all statistical analyses were performed using GraphPad Prism 9.5.0. The ratio of each item in each prefecture was calculated. To improve clarity, a map of Japan was color-coded by region based on grouping geographically close prefectures into the Hokkaido, Tohoku, Kanto, Chubu, Kinki, Chugoku, Shikoku, Kyushu, and Okinawa regions. The background color of the bar graphs matches the colors used for regional divisions.

For the principal component regression analysis of the relationship between AF ablation and various parameters, a P value of 0.05 was considered statistically significant.

Results

Japan is a long, narrow archipelago with significant variations in the average temperature and humidity among prefectures (Figure 1B). Regions shown in purple and blue on the left side of the figure are relatively cold, and regions closer to red on the right side have higher average temperatures. Figure 2A presents the number of hospitalized patients with AF per population, but no observed disparity by region. Supplementary Figure 1A shows the first and total AF ablations per capita for each prefecture. Significant differences in the ablation rates were observed, even among geographically close regions. The total population of each prefecture is shown in Supplementary Figure 1B, and the number of first and total AF ablations in each prefecture is shown in Supplementary Figure 2A. Figure 2B shows the number of arrhythmia specialists in each prefecture and the number of total AF ablation per arrhythmia specialist. Prefectures with fewer arrhythmia specialists tended to have more AF ablations per specialist. Supplementary Figure 2B illustrates a positive correlation between the number of AF ablations and the number of arrhythmia specialists, and a negative correlation between the number of AF ablations and AF hospitalizations.

Figure 2.

(A) Atrial fibrillation (AF) hospitalizations are defined as the number of tachycardia hospitalizations using Diagnosis Procedure Combination (DPC) data, corrected for total population. However, the number of AF hospitalizations remains unchanged in both cold and warm weather conditions. (B) The relationship between specialists and ablation procedures. The number of Japanese Heart Rhythm Society-certified arrhythmia specialists and the total AF ablations per specialist in each prefecture are shown.

Using heat maps, Figure 3 summarizes the population dynamics and social environment related to AF. Figure 3A shows the population aged ≥65 years, ≥75 years, and ≥85 years in each prefecture. Figure 3B shows the income of male and female high school or college graduates with the same starting conditions. Figure 3C,D shows the total working hours and the overtime working hours, respectively. Pre-existing conditions significantly contribute to the incidence of AF; therefore, the number of people with diseases per 1,000 people in each prefecture is summarized in Figure 4A. Figure 4B summarizes the smoking and drinking rates; hypertension may be less prevalent in regions geographically located in the center of Japan and Okinawa compared with other regions. The rate of alcohol consumption may be higher in colder regions. Figure 4C shows sleep duration; the percentage of people sleeping <5 h per day was highly variable among the prefectures.

Figure 3.

(A) Elderly population. The heat map shows the elderly population in each prefecture, particularly those aged ≥65 (over 65 y/o), ≥75 (over 75 y/o), and ≥85 (over 85 y/o) years. (B) Income comparison. The incomes of new graduates in each prefecture are shown. ‘Highschool’ indicates high school graduates and ‘University’ indicates university graduates. The data is divided by gender. (C) Standard working hours. The average standard working hours for males and females in each prefecture. (D) Overtime working hours. The average overtime working hours for males and females in each prefecture.

Figure 4.

(A) Prevalence of preexisting diseases. The prevalences of various diseases in each prefecture are shown. Hypertension has a significantly higher prevalence compared with other diseases. (B) Smoking and drinking. The percentage of current smokers (Current smoker %), past smokers (Past smoker %), and individuals who drink alcohol >3 times per week (alcohol % [3/week]) in each prefecture. (C) Comparison of sleep duration. The distribution of sleep duration in each prefecture. The percentage of individuals who sleep <5 h per day varies significantly among the prefectures.

Given the potential multifactorial nature of the number of hospitalizations for AF and the number of catheter ablations, principal component regression analysis was performed to ascertain the significant factors involved. The number of AF hospitalizations exhibited a positive correlation with the presence of hypertension and the percentage of unemployed men. In contrast, the percentage of part-time workers in both men and women and the number of arrhythmia specialists demonstrated a negative correlation with the number of AF hospitalizations (Table 1).

Table 1.

Principal Component Regression Analysis of AF Hospitalization

	Estimate	Standard error	|t|	P value
Hospital visits	0.000000057	0.000000031	1.83	0.074
Hypertension	0.00000020	0.000000097	2.09	0.042
Diabetes	0.00000047	0.00000024	1.99	0.053
Dyslipidemia	0.00000025	0.00000074	0.35	0.73
Kidney disease	0.0000010	0.0000021	0.50	0.62
Thyroid disease	0.00000073	0.0000010	0.71	0.48
Psychiatric disease	−0.00000045	0.0000013	0.35	0.73
Dementia	0.0000011	0.00000069	1.6	0.11
Stroke	0.00000061	0.0000011	0.56	0.58
Coronary artery disease	0.00000073	0.00000073	1.00	0.32
Chronic obstructive pulmonary disease	0.0000014	0.0000013	1.08	0.29
Asthma	−0.0000000038	0.00000091	0.0042	1.00
Current smoker (%)	0.00000099	0.00000050	1.99	0.053
Past smoker (%)	−0.00000028	0.0000048	0.059	0.95
Alcohol (%; >3 times per week)	0.00000073	0.00000046	1.61	0.12
Sleep duration <5 h	−0.00000038	0.00000071	0.54	0.59
Working hours (male)	0.00000093	0.00000051	1.82	0.075
Working hours (female)	0.0000011	0.0000012	0.90	0.37
Overtime working hours (male)	−0.00000049	0.00000031	1.52	0.13
Overtime working hours (female)	−0.0000017	0.0000013	1.32	0.19
New graduate salary (high school, male)	−0.00000022	0.00000011	1.90	0.064
New graduate salary (high school, female)	−0.00000016	0.00000014	1.17	0.25
New graduate salary (university, male)	−0.00000013	0.000000070	1.84	0.073
New graduate salary (university, female)	−0.000000010	0.000000076	0.13	0.89
House work hours (male)	−0.0091	0.0052	1.74	0.088
House work hours (female)	−0.000000032	0.000000079	0.41	0.69
Part-time worker (male; %)	−0.00000053	0.00000025	2.09	0.043
Part-time worker (female; %)	−0.00000029	0.00000013	2.20	0.033
Unemployed (male; %)	0.00000086	0.00000040	2.17	0.035
Unemployed (female; %)	0.00000062	0.00000032	1.92	0.061
Older worker (%)	−0.00000026	0.0000016	0.17	0.87
AF ablation	−0.000000025	0.000000021	1.18	0.24
Arrhythmia specialist	−0.000000042	0.000000020	2.11	0.041

AF, atrial fibrillation.

Positively correlated with the number of first AF ablations (Table 2) and total AF ablations (Table 3) were overtime hours worked for men and women, New Graduate Salary for university and high school graduates for men and women, percentage of part-time workers for men and women, and number of arrhythmia specialists. However, negative correlations were observed with respect to hospital visits, hypertension, diabetes, chronic obstructive pulmonary disease, percentage of current smokers, percentage of current alcohol drinkers, hours worked for both men and women, percentage of those still working for both men and women, and number of AF hospitalizations. The number of ablations exhibited a positive correlation with the hours spent by women on domestic work, while no such correlation was observed for men.

Table 2.

Principal Component Regression Analysis of First Ablations for AF

	Estimate	Standard error	|t|	P value
Hospital visits	−0.00000015	0.000000045	3.43	0.0013
Hypertension	−0.00000061	0.00000014	4.27	0.0001
Diabetes	−0.0000014	0.00000035	4.12	0.0002
Dyslipidemia	−0.00000029	0.0000011	0.28	0.781
Kidney disease	−0.0000042	0.0000030	1.42	0.16
Thyroid disease	−0.0000028	0.0000015	1.84	0.073
Psychiatric disease	0.0000020	0.0000018	1.13	0.27
Dementia	−0.0000029	0.0000010	2.87	0.0063
Stroke	−0.0000012	0.0000015	0.77	0.45
Coronary artery disease	−0.0000017	0.0000010	1.68	0.10
Chronic obstructive pulmonary disease	−0.0000049	0.0000020	2.52	0.016
Asthma	0.00000061	0.0000013	0.46	0.65
Current smoker (%)	−0.0000027	0.00000072	3.79	0.00050
Past smoker (%)	0.0000039	0.0000070	0.56	0.58
Alcohol (%; >3 times per week)	−0.0000020	0.00000065	3.00	0.0045
Sleep duration <5 h	0.00000062	0.0000011	0.59	0.56
Working hours (male)	−0.0000029	0.00000074	3.88	0.00040
Working hours (female)	−0.0000038	0.0000017	2.19	0.034
Overtime working hours (male)	0.0000013	0.00000045	2.91	0.0057
Overtime working hours (female)	0.0000054	0.0000018	2.97	0.0048
New graduate salary (high school, male)	0.00000060	0.00000016	3.64	0.00070
New graduate salary (high school, female)	0.00000054	0.00000020	2.67	0.011
New graduate salary (university, male)	0.00000041	0.00000011	3.80	0.00040
New graduate salary (university, female)	−0.000000023	0.00000011	0.20	0.84
House work hours (male)	0.00000014	0.00000011	1.28	0.22
House work hours (female)	0.000000084	0.000000031	2.72	0.0094
Part-time worker (male; %)	0.0000016	0.00000037	4.27	0.00010
Part-time worker (female; %)	0.00000085	0.00000019	4.40	<0.0001
Unemployed (male; %)	−0.0000025	0.00000058	4.26	0.0001
Unemployed (female; %)	−0.0000017	0.00000047	3.57	0.0009
Older worker (%)	−0.00000021	0.0000022	0.093	0.93
AF hospitalization	−0.032	0.0075	4.19	0.00010
Arrhythmia specialist	0.00000013	0.000000030	4.28	0.00010

AF, atrial fibrillation.

Table 3.

Principal Component Regression Analysis of Total Ablations for AF

	Estimate	Standard error	|t|	P value
Hospital visits	−0.00000019	0.000000055	3.43	0.0013
Hypertension	−0.00000076	0.00000018	4.28	0.00010
Diabetes	−0.0000018	0.00000043	4.11	0.00020
Dyslipidemia	−0.00000037	0.0000013	0.29	0.78
Kidney disease	−0.0000052	0.0000036	1.41	0.16
Thyroid disease	−0.0000035	0.0000019	1.83	0.074
Psychiatric disease	0.0000025	0.0000022	1.12	0.27
Dementia	−0.0000036	0.0000012	2.88	0.0062
Stroke	−0.0000015	0.0000019	0.77	0.44
Coronary artery disease	−0.0000022	0.0000013	1.69	0.099
Chronic obstructive pulmonary disease	−0.0000061	0.0000024	2.51	0.015
Asthma	0.00000074	0.0000016	0.45	0.65
Current smoker (%)	−0.0000034	0.00000089	3.79	0.00050
Past smoker (%)	0.0000048	0.0000086	0.55	0.58
Alcohol (%; >3 times per week)	−0.0000024	0.00000081	3.00	0.0044
Sleep duration <5 h	0.00000078	0.0000013	0.60	0.55
Working hours (male)	−0.0000035	0.00000091	3.88	0.00040
Working hours (female)	−0.0000047	0.0000022	2.19	0.034
Overtime working hours (male)	0.0000016	0.00000055	2.91	0.0056
Overtime working hours (female)	0.0000067	0.0000022	2.97	0.0048
New graduate salary (high school, male)	0.00000074	0.00000020	3.65	0.00070
New graduate salary (high school, female)	0.00000067	0.00000025	2.67	0.011
New graduate salary (university, male)	0.00000051	0.00000013	3.80	0.00040
New graduate salary (university, female)	−0.000000027	0.00000014	0.20	0.85
House work hours (male)	0.00000017	0.00000014	1.24	0.22
House work hours (female)	0.00000010	0.000000038	2.72	0.0094
Part-time worker (male; %)	0.0000020	0.00000046	4.27	0.00010
Part-time worker (female; %)	0.0000010	0.00000024	4.41	<0.0001
Unemployed (male; %)	−0.0000031	0.00000072	4.27	0.00010
Unemployed (female; %)	−0.0000021	0.00000058	3.57	0.00090
Older worker (%)	−0.00000024	0.0000028	0.087	0.93
AF hospitalization	−0.039	0.0093	4.19	0.00010
Arrhythmia specialist	0.00000016	0.000000037	4.28	<0.0001

AF, atrial fibrillation.

Discussion

This study meticulously examined the correlations between climatological and socioeconomic determinants and AF hospitalization and AF catheter ablation rates across Japanese prefectures.

Impact of Climatic Factors on AF

Due to Japan’s diverse geographical landscape, substantial variations in temperature and humidity are observed across prefectures (Figure 1B). Prior investigations have posited that the incidence of paroxysmal AF escalates during winter and diminishes in summer, suggesting a potential inverse relationship between AF incidence and temperature.¹² However, the present study did not reveal a disproportionately elevated AF hospitalization frequency in colder regions (Figure 2A). These findings indicate that temperature is not the sole determinant of AF patient volume. Furthermore, significant regional disparities in population-adjusted ablation rates were evident in frigid areas (Hokkaido and Tohoku; Figures 1B,2B). These observations imply that while climate may modulate AF prevalence, a more intricate interplay of factors governs regional variations in AF hospitalization and ablation rates.

Socioeconomic Factors and AF Hospitalization

AF hospitalization rates positively correlated with hypertension prevalence and the proportion of unemployed males. This suggests that in addition to hypertension being a principal AF risk factor, stress and economic hardship may influence AF onset and exacerbation. Conversely, the proportion of part-time workers (both genders) and the number of arrhythmia specialists negatively correlated with AF hospitalization rates. With its reduced work hours compared with full-time positions, part-time employment may mitigate stress, although economic constraints could also deter hospitalization. Intriguingly, arrhythmia specialists may enhance access to appropriate diagnosis and treatment, reducing hospitalizations.

Prior research has established that advanced age contributes to AF incidence,^13–15 and prolonged work hours are recognized as an AF risk factor.¹⁶ Additionally, workplace stress has been associated with AF development.^17–19 The AF pathogenesis remains incompletely elucidated,^20–25 so sex-specific risk factors may exist. In this study, a trend towards a positive correlation between men working long hours and AF hospitalization rates was observed, albeit not statistically significant. This trend was not apparent in women.

Socioeconomic Factors and AF Catheter Ablation

Ablation rates positively correlated with overtime hours, university graduate salaries, part-time employment rates, and the number of arrhythmia specialists. These findings suggest that economic activity and healthcare resource availability influence ablation rates. Conversely, a negative correlation was observed with outpatient visit frequency, hypertension, diabetes, chronic obstructive pulmonary disease, smoking and alcohol consumption, working hours, unemployment rates, and AF hospitalization rates. This suggests that patients with higher comorbidity prevalence may hesitate to undergo ablation due to concerns about complication risks, or healthcare providers may adopt a more cautious approach.

Employment status (including part-time) correlated positively with ablation rates but negatively with unemployment rates, potentially indicating that employed individuals are more likely to be diagnosed with AF through health screenings and are more financially capable of undergoing ablation. Furthermore, the positive correlation between female domestic labor hours and ablation rates suggests that sex-specific stressors and lifestyle factors may influence AF treatment, necessitating further investigation.

Relationship Between AF Hospitalization, Ablation Rates, and Arrhythmia Specialists

Interestingly, AF hospitalization rates exhibited a negative correlation with ablation rates but a positive correlation with the number of arrhythmia specialists. The number of arrhythmia specialists correlated positively with ablation rates but negatively with AF hospitalization rates (Supplementary Figure 2). These results suggest that increased arrhythmia specialists may optimize ablation rates and reduce AF hospitalization rates.

Study Limitations

This study has several limitations. As an ecological study, it cannot account for individual patient-level factors. Furthermore, prefecture-level aggregate data may not fully reflect detailed regional variations. Additionally, the complete elimination of residual confounding factors is challenging.

Conclusions

This study elucidated the association between climatological and socioeconomic determinants and AF hospitalization and ablation rates across Japanese prefectures. While climate may modulate AF prevalence, a broader spectrum of factors governs regional disparities. Notably, socioeconomic factors appear to influence AF morbidity and treatment decisions. An increase in arrhythmia specialists may optimize ablation rates and reduce AF hospitalization rates.

Acknowledgments

The authors extend their appreciation to the staff who supported this study and, in particular, to Ms. Shihoko Matsuda (National Center for Geriatrics and Gerontology) for her laboratory assistance.

Sources of Funding

This study was supported by JSPS KAKENHI Grant no. 23K19602 (to T.K.).

Disclosures

None of the authors have any competing interests regarding this study.

IRB Information

This study did not involve personal information or animal samples. All data used in this study were obtained from publicly available sources. The ethics committee of the National Center for Geriatrics and Gerontology determined that an ethical review was not necessary for research using such public data.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circrep.CR-25-0025

References

• 1.   Joglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM, et al. 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2024; 149: e1–e156.
• 2.   Okoye C, Qiu C, Xia X, Lip GYH, Bellelli G, Welmer AK, et al. Atrial fibrillation accelerates functional decline in older adults: A 15-year follow-up population-based study. Europace 2024; 26: euae173.
• 3.   Haïssaguerre M, Jaïs P, Shah DC, Takahashi A, Hocini M, Quiniou G, et al. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med 1998; 339: 659–666.
• 4.   Al-Kaisey AM, Parameswaran R, Bryant C, Anderson RD, Hawson J, Chieng D, et al. Atrial fibrillation catheter ablation vs medical therapy and psychological distress: A randomized clinical trial. JAMA 2023; 330: 925–933.
• 5.   Hong KL, Borges J, Glover B. Catheter ablation for the management of atrial fibrillation: Current technical perspectives. Open Heart 2020; 7: e001207.
• 6.   Parameswaran R, Al-Kaisey AM, Kalman JM. Catheter ablation for atrial fibrillation: Current indications and evolving technologies. Nat Rev Cardiol 2021; 18: 210–225.
• 7.   Noro M, Kujime S, Ito N, Enomoto Y, Nakamura K, Sakai T, et al. Cost effectiveness of radiofrequency catheter ablation vs. medical treatment for atrial fibrillation in Japan: Cost performance for atrial fibrillation. Circ J 2011; 75: 1860–1866.
• 8.   Kimura T, Igarashi A, Ikeda S, Nakajima K, Kashimura S, Kunitomi A, et al. A cost-utility analysis for catheter ablation of atrial fibrillation in combination with warfarin and dabigatran based on the CHADS(2) score in Japan. J Cardiol 2017; 69: 89–97.
• 9.   Singh SM, Webster L, Ko DT, Tu JV, Wijeysundera HC. Factors associated with cardiac electrophysiologist assessment and catheter ablation procedures in patients with atrial fibrillation. JACC Clin Electrophysiol 2017; 3: 302–309.
• 10.   Kusano K, Inoue K, Kanaoka K, Miyamoto K, Okumura Y, Iwasaki YK, et al. The Japanese Catheter Ablation Registry (J-AB): Annual report in 2022. J Arrhythm 2024; 40: 1053–1058.
• 11.   Whiteman DC, Neale RE, Baade P, Olsen CM, Pandeya N. Changes in the incidence of melanoma in Australia, 2006–2021, by age group and ancestry: A modelling study. Med J Aust 2024; 221: 251–257.
• 12.   Loomba RS. Seasonal variation in paroxysmal atrial fibrillation: A systematic review. J Atr Fibrillation 2015; 7: 1201.
• 13.   Mannina C, Ito K, Jin Z, Yoshida Y, Russo C, Nakanishi K, et al. Left atrial strain and incident atrial fibrillation in older adults. Am J Cardiol 2023; 206: 161–167.
• 14.   Parks AL, Frankel DS, Kim DH, Ko D, Kramer DB, Lydston M, et al. Management of atrial fibrillation in older adults. BMJ 2024; 386: e076246.
• 15.   Polidori MC, Alves M, Bahat G, Boureau AS, Ozkok S, Pfister R, et al. Atrial fibrillation: A geriatric perspective on the 2020 ESC guidelines. Eur Geriatr Med 2022; 13: 5–18.
• 16.   Kivimäki M, Nyberg ST, Batty GD, Kawachi I, Jokela M, Alfredsson L, et al. Long working hours as a risk factor for atrial fibrillation: A multi-cohort study. Eur Heart J 2017; 38: 2621–2628.
• 17.   Fransson EI, Nordin M, Magnusson Hanson LL, Westerlund H. Job strain and atrial fibrillation - Results from the Swedish Longitudinal Occupational Survey of Health and meta-analysis of three studies. Eur J Prev Cardiol 2018; 25: 1142–1149.
• 18.   Torén K, Schiöler L, Söderberg M, Giang KW, Rosengren A. The association between job strain and atrial fibrillation in Swedish men. Occup Environ Med 2015; 72: 177–180.
• 19.   Fransson EI, Stadin M, Nordin M, Malm D, Knutsson A, Alfredsson L, et al. The association between job strain and atrial fibrillation: Results from the Swedish WOLF Study. Biomed Res Int 2015; 2015: 371905.
• 20.   Brundel B, Ai X, Hills MT, Kuipers MF, Lip GYH, de Groot NMS. Atrial fibrillation. Nat Rev Dis Primers 2022; 8: 21.
• 21.   Lau DH, Linz D, Sanders P. New findings in atrial fibrillation mechanisms. Card Electrophysiol Clin 2019; 11: 563–571.
• 22.   Sagris M, Vardas EP, Theofilis P, Antonopoulos AS, Oikonomou E, Tousoulis D. Atrial fibrillation: Pathogenesis, predisposing factors, and genetics. Int J Mol Sci 2021; 23: 6.
• 23.   Wijesurendra RS, Casadei B. Mechanisms of atrial fibrillation. Heart 2019; 105: 1860–1867.
• 24.   Kamihara T, Hirashiki A, Kokubo M, Shimizu A. Transcriptome discovery of genes in the three phases of autophagy that are upregulated during atrial fibrillation. Circ Rep 2023; 5: 114–122.
• 25.   Kamihara T, Kawano R, Kinoshita T, Omura T, Kaneko S, Hirashiki A, et al. Differences in iron kinetics during cardiac load between patients with atrial fibrillation and those with sinus rhythm. Cardiology 2024; 149: 580–589.