You are here

Article Text

Article menu

Download PDFPDF

Original research
Income and outcomes of patients with incident atrial fibrillation
  1. Fausto Biancari1,2,
  2. Konsta Teppo3,
  3. Jussi Jaakkola3,4,
  4. Olli Halminen5,
  5. Miika Linna6,7,
  6. Jari Haukka8,
  7. Jukka Putaala9,
  8. Pirjo Mustonen3,
  9. Janne Kinnunen9,
  10. Juha Hartikainen10,
  11. Aapo Aro1,
  12. Juhani Airaksinen3,
  13. Mika Lehto1,11
  1. 1Heart and Lung Center, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
  2. 2Clinica Montevergine, GVM Care & Research, Mercogliano, Italy
  3. 3Heart Center, University of Turku, Turku University Hospital, Turku, Finland
  4. 4Heart Unit, Satakunta Central Hospital, Pori, Finland
  5. 5Department of Industrial Engineering and Management, Aalto University, Espoo, Finland
  6. 6University of Eastern Finland, Kuopio, Finland
  7. 7Aalto University, Espoo, Finland
  8. 8Public Health, University of Helsinki, Helsinki, Finland
  9. 9Department of Neurology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
  10. 10Heart Center, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
  11. 11Department of Internal Medicine, Lohja Hospital, Lohja, Finland
  1. Correspondence to Dr Fausto Biancari, Gruppo Villa Maria SpA, Mercogliano, Campania, 83013, Italy; faustobiancari{at}yahoo.it

Abstract

Background Socioeconomic disparities can be associated with adverse outcomes in patients with cardiovascular diseases. The impact of personal income on the outcomes of patients with atrial fibrillation (AF) is unclear.

Methods Nationwide observational registry-based study on patients with incident AF in Finland during 2007–2018.

Results 203 154 patients (mean age 73.0±13.5; females 49.0%) were diagnosed with incident AF during the study period. Overall, 16 272 (8.0%) patients experienced first-ever ischaemic stroke and 63 420 (31.2%) died (mean follow-up 4.3±3.3 years). After adjusting for confounding factors, low personal income was associated with increased risk of overall mortality in all age strata and the incidence of first-ever stroke in patients aged <65 years and 65–74 years, but not in those ≥75 years. The magnitude of this effect was greatest in patients aged <65 years. After propensity score matching of patients <65 years in the lowest and highest quintiles of maximum personal annual income, at 10 years, those in the highest income quintile (≥€54 000) had significantly lower risk of first-ever stroke (subdistribution HR 0.495, 95% CI 0.391 to 0.628) and overall mortality (HR 0.307, 95% CI 0.269 to 0.351) compared with patients in the lowest income quintile (≤€12 000).

Conclusions Personal annual income has a significant impact on the incidence of first-ever ischaemic stroke and overall mortality among patients with incident AF, particularly among patients of working age. Low-income indicate the need for intervention strategies to improve outcomes of AF.

Trial registration number NCT04645537.

  • CARDIOVASCULAR DISEASES
  • COHORT STUDIES
  • Health inequalities

Data availability statement

No data are available. Data not available due to ethical restrictions.

https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/jech-2022-219190

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • Socioeconomic status may affect the outcome of cardiovascular diseases.

    WHAT THIS STUDY ADDS

    • First-ever stroke and mortality are more frequent in low-income atrial fibrillation (AF) patients, particularly among those of working age.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE AND/OR POLICY

    • Low-income indicates the need for intervention strategies to improve outcomes of AF.

    Introduction

    Atrial fibrillation (AF) significantly increases the risk of stroke and mortality,1 2 particularly among patients with advanced age and cardiovascular comorbidities.3–5 Optimal oral anticoagulation therapy is the cornerstone treatment to reduce the risk of adverse events in patients at moderate to high risk of thromboembolism.3 6 Socioeconomic disparities are considered among the contributors to death from cardiovascular causes.7–9 Low socioeconomic status seems also to be associated with higher prevalence of AF10–12 as well as increased risk of stroke,13 bleeding13 14 and mortality14–17 among patients with AF. These findings may be driven by lack of measures or adherence to drug treatment for maintaining sinus rhythm and preventing thromboembolism after AF diagnosis.18 19 However, previous analyses did not consider socioeconomic status as age-dependent and its definition has been heterogeneous. The present analysis evaluates the effect of maximum personal annual income as a measure of socioeconomic status on 10-year outcomes in different age strata of patients with AF from a nationwide registry.

    Methods

    Study population

    The Study (Finnish AntiCoagulation in Atrial Fibrillation) (ClinicalTrials Identifier: NCT04645537; ENCePP Identifier: EUPAS29845) is a retrospective nationwide registry-based cohort study, which includes all patients diagnosed with AF in Finland during 2004–2018.20 Patients with a diagnosis of AF were identified from the following national healthcare registers: the hospitalisations and outpatient specialist visits (HILMO) registry, the primary healthcare (AvoHILMO) registry and the National Reimbursement Register upheld by the Social Insurance Institute (KELA) registry. The inclusion criterion for the cohort was the International Classification of Diseases, 10th Revision (ICD-10) diagnosis code I48 (including AF and atrial flutter, together referred as AF) recorded between 2004 and 2018. Cohort entry occurred on the date of the first recorded AF diagnosis. The exclusion criteria were the following: (1) age <18 years at AF diagnosis; (2) permanent migration abroad before 31 December 2018; (3) any stroke or transient ischaemic attack (TIA) before AF diagnosis. Follow-up continued until death or 31 December 2018. This study focused on 203 154 patients with incident AF without history of stroke or TIA and the study was conducted within a cohort of patients diagnosed with incident AF during 2007–2018. The study flow chart summarises patients’ selection (online supplemental figure 1).

    Supplemental material

    Income

    Patients’ personal highest personal annual taxable income during 2004–2018 was gathered from the national Tax Register. The personal annual income was capped to a maximum of €100 000 to avoid identification of patients with highest incomes. Since income level was expected to inversely correlate with age, analyses were performed separately for the age strata <65 years, 65–74 years and ≥75 years.

    Educational level

    Data on patients’ highest achieved educational level were obtained from Statistics Finland and was categorised according to the International Standard Classification of Education (ISCED) classification. Educational level was divided into three categories: category 1: ISCED 0–2 (no registered, preprimary, primary or lower secondary education); category 2: ISCED 3 (Upper secondary or vocational education); category 3: ISCED 5–8 (tertiary, bachelor-level, master-level or doctoral level education). ISCED category 4 does not exist in Finland.

    Study outcomes

    Study outcomes were all-cause mortality and first-ever ischaemic stroke. Ischaemic stroke was considered to occur on the first date of a recorded ICD-10 diagnosis code I63 in the hospital register or in the National Death Register upheld by Statistics Finland. Dates of death were retrieved from the National Death Register.

    Statistical analysis

    Analyses were performed separately for patients aged <65 years, 65–74 years and ≥75 years at cohort entry. Maximum personal annual income was entered as quintiles of income for each age stratum. The χ2 test was used to analyse differences between categorical variables, and the Kruskall-Wallis’ test to compare continuous variables. Continuous variables are reported means and standard errors, and categorical variables as counts and percentages. Unadjusted rates of adverse events were estimated using competing risk and the Kaplan-Meier’s methods. Cox regression was used to estimate the HRs of all-cause mortality for income quintiles. Proportional hazard assumption was evaluated by assessing the survival curves and using a test based on Schoenfeld residuals. Since stroke may be hindered by mortality occurring during the study period, competing risk analyses using the Fine-Gray regression model with all-cause death as a competing event were performed to estimate the unadjusted and adjusted subdistribution HRs (SHRs) for incidence of ischaemic stroke in each maximum personal annual income quintile. In the Fine-Gray and Cox regression models, adjustments were made for age, gender, calendar year of AF diagnosis, hypertension, prior myocardial infarction, any coronary or peripheral vascular disease, heart failure, dyslipidaemia, liver disorder, kidney failure, bleeding events, diabetes, alcohol abuse, dementia, psychiatric disorders, education level and maximum personal annual income. The definitions of comorbidities are displayed in online supplemental table 1.

    Since baseline characteristics differed significantly among groups of subgroups of patients identified by income quintiles, with higher prevalence of comorbidities among patients with lower income, propensity score matching analysis was performed to adjust for such imbalances between patients in the lowest and highest personal annual income quintiles. A propensity score was estimated for each age stratum using logistic regression with the lowest and highest quintiles of maximum personal annual income as the dependent variable considering the following covariates: age, gender, calendar year of AF diagnosis, hypertension, prior myocardial infarction, any coronary or peripheral vascular disease, heart failure, dyslipidaemia, liver disorder, kidney failure, bleeding events, diabetes, alcohol abuse, dementia, psychiatric disorders and education level. Propensity score matching was performed using a caliper width of 0.2 the SE of the logit, that is, 0.4. Standardised difference <0.1 was considered a non-significant imbalance between the covariates. P<0.05 was set for statistical significance. Statistical analyses were performed with the IBM SPSS Statistics software (V.27.0, SPSS) and Stata (V.15.1, StataCorp).

    Results

    Overall series

    The mean age of the patients was 73.0±13.5 years and 49.0% were female; 16 272 (8.0%) experienced first-ever ischaemic stroke and 63 420 (31.2%) died during a mean follow-up of 4.3±3.3 years. The mean personal annual income was €36 061±€31 000 among patients <65 years, €24 660±€24 570 among 65–74 years patients and €13 400±€18 544 among patients ≥75 years. The mean personal annual income compared rather well with those of the general population as estimated in 2010 only for young patients, while the personal annual income of patients with AF was markedly lower in the elderly strata (general population: 50–64 years, €32 712; 65–74 years, €26 627; ≥75 years, €21 250).

    Patients in the lower income quintiles had lower educational status and higher prevalence of comorbidities in all age groups (tables 1–3). This translated into a higher CHA2DS2-VASc score in lower income quintiles. The income-related disparities in the prevalence of comorbidities were largest among patients <65 years (table 1). In particular, among these younger patients, those with lower income had increased prevalence of alcohol abuse, diabetes, heart failure, vascular disease and psychiatric disorders. These comorbidities were more prevalent in lower income patients also among 65–74 years patients (table 2). Among patients ≥75 years, such disparities were numerically less evident, but still statistically significant (table 3). The use of any oral anticoagulant before a stroke event was markedly lower among 65–74 years and ≥75 years with low income (tables 2–3), but not among younger patients.

    View this table:
    Table 1

    Characteristics of patients aged <65 years according to their maximum personal annual income quintiles

    View this table:
    Table 2

    Characteristics of patients aged 65–74 years according to their maximum personal annual income quintiles

    View this table:
    Table 3

    Characteristics of patients aged ≥75 years according to their maximum personal annual income quintiles

    Overall series

    In unadjusted analyses, overall mortality and incidence of first-ever ischaemic stroke were higher in the lowest income quintile compared with higher income quintiles in all age groups table 4, online supplemental figure 1). Patients in the lowest personal annual income quintile had a remarkably high risk of adverse events as estimated with competing risk and the Kaplan-Meier’s methods, particularly among patients <65 years (online supplemental figure 1).

    View this table:
    Table 4

    Impact of maximum personal annual income on 10-year first-ever ischaemic stroke and all-cause mortality among patients with atrial fibrillation in different age strata

    Multivariate analyses demonstrated that lower income quintiles were associated with higher risk of adverse events even after adjusting for other baseline covariates (table 4). Lower income was associated with increased risk of overall mortality in all age strata, while the risk of ischaemic stroke was increased among patients <65 years and 65–74 years patients, but not in those aged ≥75 years (table 4).

    Among covariates of interest, adjusted analyses showed that lower educational level was an independent risk factor of overall mortality, but not of ischaemic stroke, in all age strata (online supplemental figure 3, online supplemental table 2). The results of multivariate analyses for identification of independent risk factors for adverse events are summarised in online supplemental tables 3–8.

    Propensity score matched analysis

    Propensity score matching of the lowest and highest income quintiles in the age group <65 years resulted in 4237 pairs of patients (online supplemental table 9). These matched cohorts had similar baseline characteristics but higher prevalence of alcohol abuse among higher income patients (online supplemental table 9). Patients in the highest personal annual income quintile (≥€54 000) had significantly lower risk of first-ever ischaemic stroke (SHR 0.495, 95% CI 0.391 to 0.628) and all-cause mortality (HR 0.307, 95% CI 0.269 to 0.351) compared with patients in the lowest income quintile (≤€12 000) (figure 1).

    Figure 1
    Figure 1

    Competing risk and Kaplan-Meier’s estimates of first-ever ischaemic stroke and all-cause mortality in the lowest and highest quintiles of maximum personal annual income among propensity score matched patients with incident atrial fibrillation aged <65 years. HR, hazard ratio; SHR, subdistribution HR; CI, confidence interval.

    Propensity score matching of the lowest and highest income quintiles in the age group 65–74 years resulted in 4470 pairs of patients with similar baseline characteristics (online supplemental table 10). Patients in the highest personal annual income quintile (≥€40 000) had significantly lower risk of first-ever ischaemic stroke (SHR 0.772, 95% CI 0.651 to 0.918) and all-cause mortality (HR 0.529, 95% CI 0.482 to 0.582) compared with patients in the lowest income quintile (≤€4000).

    Propensity score matching of the lowest and highest income quintiles in the age group ≥75 years resulted in 9473 pairs of patients with similar baseline characteristics (online supplemental table 11). Patients in the highest personal annual income quintile (≥€22 000) had significantly lower risk of all-cause mortality (HR 0.746, 95% CI 0.715 to 0.779) compared with patients in the lowest income quintile (<€1000 €). However, the risk of first-ever ischaemic stroke (SHR 1.065, 95% CI 0.969 to 1.170) was similar between matched pairs in the lowest and highest income quintiles.

    Discussion

    The results of the present analysis showed that, after adjusting for multiple covariates, low personal income was associated with increased risk of overall mortality and first-ever ischaemic stroke, particularly in working age patients.

    Low socioeconomic status is dependent on several factors, which may have significant impact on living conditions and access to health services. In turn, severe diseases may underlie a poor socioeconomic status and both may contribute to poor outcome of these patients. In this study, we observed that this may be true for a large proportion of patients with AF with low personal income. Still, it is difficult to discern the contribution of each of these factors either on socioeconomic status or adverse events. The prognostic impact of personal income on the outcome of AF patients may be explained by non-healthy lifestyle which per se may be the underlying cause of AF. This in turn may be associated with a reduced trigger to access to health services also in countries like Finland with free access to health services. Low income patient population may be less prone to ablation therapy and the adherence to drug treatment for maintaining sinus rhythm, and preventing thromboembolism may be suboptimal. Therefore, low personal income should be considered when planning treatment strategies in AF patients, particularly among those at working age.

    In this analysis, age was inversely related with maximum personal income and the magnitude of the impact of income disparities was most evident among patients with AF <65 years (table 4, online supplemental figure 2). Low socioeconomic status in working age patients might have a different impact on the outcome as poor personal income is frequently related to unemployment, which itself carries a risk for poverty, individual and family distress, increased social isolation and non-healthy lifestyle as well as lack of adherence to therapies for diseases.21 In Finland, the general retirement age for the national pension is 65 years, which means that patients aged >65 years are expected to have lower income, and therefore, its impact on adverse events is less evident. Here, we observed a difference of about 30% in 10-year mortality between the lowest and the highest income quintiles of patients with AF aged less than 65 years. Such a marked difference in overall mortality provides a clear figure of the magnitude of the effect of poor socioeconomic status on outcome of younger patients. Importantly, such differences were evident when adjusted for confounders in propensity score analysis (figure 1).

    Educational level is among the main potential contributors of low income, but we observed that its negative prognostic impact may be limited in patients with AF (online supplemental figure 3, online supplemental table 2). After adjusting for other confounders, higher educational level might have contributed to lower mortality in patients aged <75 years, but it did not affect the risk of first-time ischaemic stroke. Again, AF patients with the highest educational level had significantly higher maximum personal annual income in each age stratum and its contribution to adverse events cannot be conclusively excluded. However, higher education is not always a guarantee for adequate income and healthy lifestyle. The present findings suggest income may be the ultimate barrier to optimal antiarrhythmic and anticoagulation treatment in AF.

    Few studies have evaluated the effect of socioeconomic status on the incidence, treatment or outcome of AF considering several measures of social, educational and economic status at individual, family or regional levels.10–19 Data on tax registry or self-reported personal or family income10 16 18 or neighbourhood/regional income11 14 17 19 have been investigated in some studies. Socioeconomic status was estimated according to a composite of individual or regional education level, housing quality, household income, employment and occupation in other studies.12 13 15

    Despite its heterogeneous definition, socioeconomic status has been shown to univocally affect the outcome of patients with AF. This study confirmed previous findings in a large nationwide unselected cohort of patients with a long follow-up. Using patients’ individual income instead of household income has significant advantages in the analysis of socioeconomic status. In fact, income includes only wages, salaries, and income from self-employment for each patient. Household income includes earnings for each household member as well as income from social security and other sources. Household income may be representative of the socioeconomic status of family members, but not necessarily of each of its members. Neighbourhood or regional socioeconomic status may introduce even a larger bias by considering the exposure within a large area, while socioeconomic conditions may be different at individual level. Furthermore, the strength of our analysis is that analyses were performed in different age strata considering individual educational level and comorbidities as potential confounders.

    Limitations

    The challenges inherent to the retrospective study design and use of administrative data are the main limitations of our study. Second, although the year of cohort entry was considered in all adjusted analyses, some differences in salaries during the study period might still affect the results. Third, analyses did not consider individual changes in annual income. However, we do believe that the maximum annual income may be representative of the overall socioeconomic status of these patients. Finally, even though we were able to adjust our analyses for several comorbidities obtained from nationwide registries, other unmeasured confounders might have had an impact on the socioeconomic status of the patients and their outcomes. In particular, the lack of information on lifestyle and clinical biomarkers in these patients does not allow a more in depth analysis of these relevant risk factors.

    Conclusions

    Personal annual income has a significant impact on 10-year rates of first-ever ischaemic stroke and overall mortality among patients with AF, particularly among patients <65 years. Low personal income should be considered when planning strategies to improve the outcome of patients with AF, particularly those at working age.

    Data availability statement

    No data are available. Data not available due to ethical restrictions.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The study protocol was approved by the Ethics Committee of the Medical Faculty of Helsinki University, Helsinki, Finland (no. 15/2017) and granted research permission from the Helsinki University Hospital (HUS/46/2018). Respective permissions were obtained from the Finnish register holders (KELA 138/522/2018; THL 2101/5.05.00/2018; Population Register Centre VRK/1291/2019-3 and Tax Register VH/874/07.01.03/2019)). The patients’ identification numbers were pseudonymised, and the research group received individualised, but unidentifiable data. Informed consent was waived due to the retrospective registry nature of the study. The study conforms to the Declaration of Helsinki as revised in 2002.

    References

    1. ↵
      1. Wolf PA,
      2. Abbott RD,
      3. Kannel WB
      . Atrial fibrillation as an independent risk factor for stroke: the Framingham study. Stroke 1991;22:983–8.doi:10.1161/01.STR.22.8.983pmid:http://www.ncbi.nlm.nih.gov/pubmed/1866765
      OpenUrlAbstract/FREE Full Text
    2. ↵
      1. Lip GYH,
      2. Nieuwlaat R,
      3. Pisters R, et al
      . Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the Euro heart survey on atrial fibrillation. Chest 2010;137:263–72.doi:10.1378/chest.09-1584pmid:http://www.ncbi.nlm.nih.gov/pubmed/19762550
      OpenUrlCrossRefPubMedWeb of Science
    3. ↵
      1. Nuotio I,
      2. Hartikainen JEK,
      3. Grönberg T, et al
      . Time to cardioversion for acute atrial fibrillation and thromboembolic complications. JAMA 2014;312:647–9.doi:10.1001/jama.2014.3824pmid:http://www.ncbi.nlm.nih.gov/pubmed/25117135
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      1. Benjamin EJ,
      2. Wolf PA,
      3. D'Agostino RB, et al
      . Impact of atrial fibrillation on the risk of death: the Framingham heart study. Circulation 1998;98:946–52.doi:10.1161/01.cir.98.10.946pmid:http://www.ncbi.nlm.nih.gov/pubmed/9737513
      OpenUrlAbstract/FREE Full Text
    5. ↵
      1. Dai H,
      2. Zhang Q,
      3. Much AA, et al
      . Global, regional, and national prevalence, incidence, mortality, and risk factors for atrial fibrillation, 1990-2017: results from the global burden of disease study 2017. Eur Heart J Qual Care Clin Outcomes 2021;7:574–82.doi:10.1093/ehjqcco/qcaa061pmid:http://www.ncbi.nlm.nih.gov/pubmed/32735316
      OpenUrlCrossRefPubMed
    6. ↵
      1. Hylek EM,
      2. Go AS,
      3. Chang Y, et al
      . Effect of intensity of oral anticoagulation on stroke severity and mortality in atrial fibrillation. N Engl J Med 2003;349:1019–26.doi:10.1056/NEJMoa022913pmid:http://www.ncbi.nlm.nih.gov/pubmed/12968085
      OpenUrlCrossRefPubMedWeb of Science
    7. ↵
      1. Mackenbach JP,
      2. Bos V,
      3. Andersen O, et al
      . Widening socioeconomic inequalities in mortality in six Western European countries. Int J Epidemiol 2003;32:830–7.doi:10.1093/ije/dyg209pmid:http://www.ncbi.nlm.nih.gov/pubmed/14559760
      OpenUrlCrossRefPubMedWeb of Science
    8. ↵
      1. Davey Smith G,
      2. Hart C,
      3. Hole D, et al
      . Education and occupational social class: which is the more important indicator of mortality risk? J Epidemiol Community Health 1998;52:153–60.doi:10.1136/jech.52.3.153pmid:http://www.ncbi.nlm.nih.gov/pubmed/9616419
      OpenUrlAbstract
    9. ↵
      1. Smith GD,
      2. Hart C,
      3. Blane D, et al
      . Lifetime socioeconomic position and mortality: prospective observational study. BMJ 1997;314:547–52.doi:10.1136/bmj.314.7080.547pmid:http://www.ncbi.nlm.nih.gov/pubmed/9055712
      OpenUrlAbstract/FREE Full Text
    10. ↵
      1. Mou L,
      2. Norby FL,
      3. Chen LY, et al
      . Lifetime risk of atrial fibrillation by race and socioeconomic status: ARIC study (atherosclerosis risk in communities). Circ Arrhythm Electrophysiol 2018;11:e006350.doi:10.1161/CIRCEP.118.006350pmid:http://www.ncbi.nlm.nih.gov/pubmed/30002066
      OpenUrlAbstract/FREE Full Text
    11. ↵
      1. Wang X,
      2. Fu Q,
      3. Song F, et al
      . Prevalence of atrial fibrillation in different socioeconomic regions of China and its association with stroke: results from a national stroke screening survey. Int J Cardiol 2018;271:92–7.doi:10.1016/j.ijcard.2018.05.131pmid:http://www.ncbi.nlm.nih.gov/pubmed/29885822
      OpenUrlCrossRefPubMed
    12. ↵
      1. Ramkumar S,
      2. Ochi A,
      3. Yang H, et al
      . Association between socioeconomic status and incident atrial fibrillation. Intern Med J 2019;49:1244–51.doi:10.1111/imj.14214pmid:http://www.ncbi.nlm.nih.gov/pubmed/30582260
      OpenUrlCrossRefPubMed
    13. ↵
      1. Ravvaz K,
      2. Weissert JA,
      3. Jahangir A, et al
      . Evaluating the effects of socioeconomic status on stroke and bleeding risk scores and clinical events in patients on oral anticoagulant for new onset atrial fibrillation. PLoS One 2021;16:e0248134.doi:10.1371/journal.pone.0248134pmid:http://www.ncbi.nlm.nih.gov/pubmed/33735259
      OpenUrlCrossRefPubMed
    14. ↵
      1. Cressman AM,
      2. Macdonald EM,
      3. Yao Z, et al
      . Socioeconomic status and risk of hemorrhage during warfarin therapy for atrial fibrillation: a population-based study. Am Heart J 2015;170:133–40.doi:10.1016/j.ahj.2015.03.014pmid:http://www.ncbi.nlm.nih.gov/pubmed/26093874
      OpenUrlCrossRefPubMed
    15. ↵
      1. Kargoli F,
      2. Shulman E,
      3. Aagaard P, et al
      . Socioeconomic status as a predictor of mortality in patients admitted with atrial fibrillation. Am J Cardiol 2017;119:1378–81.doi:10.1016/j.amjcard.2017.01.041pmid:http://www.ncbi.nlm.nih.gov/pubmed/28400027
      OpenUrlCrossRefPubMed
    16. ↵
      1. Hagengaard L,
      2. Andersen MP,
      3. Polcwiartek C, et al
      . Socioeconomic differences in outcomes after hospital admission for atrial fibrillation or flutter. Eur Heart J Qual Care Clin Outcomes 2021;7:295–303.doi:10.1093/ehjqcco/qcz053pmid:http://www.ncbi.nlm.nih.gov/pubmed/31560375
      OpenUrlCrossRefPubMed
    17. ↵
      1. Wändell P,
      2. Carlsson AC,
      3. Gasevic D, et al
      . Socioeconomic factors and mortality in patients with atrial fibrillation-a cohort study in Swedish primary care. Eur J Public Health 2018;28:1103–9.doi:10.1093/eurpub/cky075pmid:http://www.ncbi.nlm.nih.gov/pubmed/29746622
      OpenUrlCrossRefPubMed
    18. ↵
      1. Lunde ED,
      2. Joensen AM,
      3. Fonager K, et al
      . Socioeconomic inequality in oral anticoagulation therapy initiation in patients with atrial fibrillation with high risk of stroke: a register-based observational study. BMJ Open 2021;11:e048839.doi:10.1136/bmjopen-2021-048839pmid:http://www.ncbi.nlm.nih.gov/pubmed/34059516
      OpenUrlAbstract/FREE Full Text
    19. ↵
      1. Eberly LA,
      2. Garg L,
      3. Yang L, et al
      . Racial/Ethnic and socioeconomic disparities in management of incident paroxysmal atrial fibrillation. JAMA Netw Open 2021;4:e210247.doi:10.1001/jamanetworkopen.2021.0247pmid:http://www.ncbi.nlm.nih.gov/pubmed/33635328
      OpenUrlCrossRefPubMed
    20. ↵
      1. Lehto M,
      2. Halminen O,
      3. Mustonen P, et al
      . The nationwide Finnish anticoagulation in atrial fibrillation (FinACAF): study rationale, design, and patient characteristics. Eur J Epidemiol 2022;37:95–102.doi:10.1007/s10654-021-00812-xpmid:http://www.ncbi.nlm.nih.gov/pubmed/34985732
      OpenUrlCrossRefPubMed
    21. ↵
      1. Bartley M
      . Unemployment and ill health: understanding the relationship. J Epidemiol Community Health 1994;48:333–7.doi:10.1136/jech.48.4.333pmid:http://www.ncbi.nlm.nih.gov/pubmed/7964329
      OpenUrlAbstract/FREE Full Text

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Footnotes

    • Twitter @halminolli, @jari.haukka

    • Correction notice This article has been corrected since it was first published. The open access licence has been updated to CC BY.

    • Contributors FB had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.Concept and design: FB, KT, JJ, JP, PM, JH, JA and ML. Acquisition, analysis or interpretation of data: all authors. Drafting of the manuscript: FB. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: FB. Obtained funding: ML. Administrative, technical or material support: JJ, OH and JH. Supervision: JJ, JP, PM, JH, JA and ML.

    • Funding This work was supported by the Aarne Koskelo Foundation, The Finnish Foundation for Cardiovascular Research, and Helsinki and Uusimaa Hospital District research fund (TYH2019309).

    • Competing interests FB: none. KT: none. JJ: none. OH: none. JP reports personal fees from Boehringer-Ingelheim, personal fees and other from Bayer, grants and personal fees from BMS-Pfizer, personal fees from Portola, other from Amgen, personal fees from Herantis Pharma, personal fees from Terve Media, other from Vital Signum, personal fees from Abbott, outside the submitted work. PM: Consultant: Roche, BMS-Pfizer-alliance, Novartis Finland, Boehringer Ingelheim, MSD Finland. JH: Consultant: Research Janssen R&D; Speaker: Bayer Finland. ML: Speaker: BMSPfizer-alliance, Bayer, Boehringer-Ingelheim. JH: Research grants: The Finnish Foundation for Cardiovascular Research, EU Horizon 2020, EU FP7. Advisory Board Member: BMS-Pfizer-alliance, Novo Nordisk, Amgen. Speaker: Cardiome, Bayer. AA: Research grants: The Finnish Foundation for Cardiovascular Research, Sigrid Juselius Foundation; Speaker: Abbott, Bayer, Boehringer-Ingelheim. JA: Research grants: The Finnish Foundation for Cardiovascular Research; Speaker: Bayer, Pfizer and Boehringer-Ingelheim. Member in the advisory boards: Bayer, Pfizer and AstraZeneca. ML: Consultant: BMS-Pfizer-alliance, Bayer, Boehringer-Ingelheim, and MSD; Speaker: BMS-Pfizer-alliance, Bayer, Boehringer Ingelheim, MSD, Terve Media and Orion Pharma. Research grants: Aarne Koskelo Foundation, The Finnish Foundation for Cardiovascular Research, and Helsinki and Uusimaa Hospital District research fund, Boehringer-Ingelheim.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.