Top

BMC Public Health

Published in:

Open Access 01-12-2021 | Heart Failure | Research article

Non-employment and low educational level as risk factors for inequitable treatment and mortality in heart failure: a population-based cohort study of register data

Authors: Anna Ohlsson, Nils Eckerdal, Bertil Lindahl, Marianne Hanning, Ragnar Westerling

Published in: BMC Public Health | Issue 1/2021

Abstract

Background

The risk of heart failure is disproportionately high among the socioeconomically disadvantaged. Furthermore, socioeconomically deprived patients are at risk of inequitable access to heart failure treatment and poor outcomes. Non-employment as a risk factor in this respect has not previously been studied at the level of the individual. The aim of this register-based cohort study was to analyse equity in access to renin-angiotensin system blockers and mortality, by employment status and educational level.

Methods

The study population consisted of Swedish patients aged 20–64 years hospitalised for heart failure in July 2006–December 2010, without a heart failure hospitalisation within one year or more before index hospitalisation and without renin-angiotensin system blocker dispensation in the 6 months preceding index hospitalisation. Non-access to renin-angiotensin system blockers, measured as drug dispensations, was investigated by employment status and educational level through logistic regression. Cox regression models were used to obtain hazard ratios for all-cause death by educational level and employment status. Interaction analysis was used to test whether associations between access to treatment and mortality differed by employment status.

Results

Among the 3874 patients, 1239 (32%) were women. The median age was 57 years. Fifty-three percent were employed. The non-employed patients had more comorbidity and lower access (68%) to renin-angiotensin system blockers compared with the employed (82%). The adjusted odds ratio for non-access to renin-angiotensin system blockers among the non-employed was 1.76. Non-employment was associated with an adjusted hazard ratio of 1.76 for death. Low educational level was associated with a higher death risk. Mortality was highest among the non-employed without access to renin-angiotensin system blockers and the association between access to renin-angiotensin system blockers and survival was slightly weaker in this group.

Conclusions

Non-employment and low educational level were associated with elevated mortality in heart failure. Non-employment was a risk factor for lower access to evidence-based treatment, and among the non-employed access to treatment was associated with a slightly smaller risk reduction than among the employed. The results underscore that clinicians need to be aware of the importance of socioeconomic factors in heart failure care.

Available only for authorised users

International Classification of Diseases (ICD-10) codes: I11.0, I13.0, I13.2, I42.0, I42.3–I42.9, I50.0, I50.1, I50.9.

Anatomical Therapeutic Chemical class codes: C09A and C09B for ACEI; C09C and C09D for ARB.

Converted from Swedish krona; rate from the Swedish central bank (Riksbanken); https://www.riksbank.se/sv/statistik/ accessed 14 September 2020.

Ziaeian B, Fonarow GC. Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13(6):368–78. https://doi.org/10.1038/nrcardio.2016.25.CrossRefPubMedPubMedCentral

Witte KK, Patel PA, Walker AMN, Schechter CB, Drozd M, Sengupta A, et al. Socioeconomic deprivation and mode-specific outcomes in patients with chronic heart failure. Heart. 2018;104(12):993–8. https://doi.org/10.1136/heartjnl-2017-312539.CrossRefPubMed

Lawson CA, Zaccardi F, Squire I, Ling S, Davies MJ, Lam CSP, et al. 20-year trends in cause-specific heart failure outcomes by sex, socioeconomic status, and place of diagnosis: a population-based study. Lancet Public Health. 2019;4(8):e406–e20. https://doi.org/10.1016/S2468-2667(19)30108-2.CrossRefPubMedPubMedCentral

Schaufelberger M, Rosengren A. Heart failure in different occupational classes in Sweden. Eur Heart J. 2007;28(2):212–8. https://doi.org/10.1093/eurheartj/ehl435.CrossRefPubMed

McAlister FA, Murphy NF, Simpson CR, Stewart S, MacIntyre K, Kirkpatrick M, et al. Influence of socioeconomic deprivation on the primary care burden and treatment of patients with a diagnosis of heart failure in general practice in Scotland: population based study. Bmj. 2004;328(7448):1110. https://doi.org/10.1136/bmj.38043.414074.EE.CrossRefPubMedPubMedCentral

Rathore SS, Masoudi FA, Wang Y, Curtis JP, Foody JM, Havranek EP, et al. Socioeconomic status, treatment, and outcomes among elderly patients hospitalized with heart failure: findings from the National Heart Failure Project. Am Heart J. 2006;152(2):371–8. https://doi.org/10.1016/j.ahj.2005.12.002.CrossRefPubMedPubMedCentral

Ohlsson A, Lindahl B, Hanning M, Westerling R. Inequity of access to ACE inhibitors in Swedish heart failure patients: a register-based study. J Epidemiol Community Health. 2016;70(1):97–103. https://doi.org/10.1136/jech-2015-205738.CrossRefPubMed

Lund LH, Carrero JJ, Farahmand B, Henriksson KM, Jonsson A, Jernberg T, et al. Association between enrolment in a heart failure quality registry and subsequent mortality-a nationwide cohort study. Eur J Heart Fail. 2017;19(9):1107–16. https://doi.org/10.1002/ejhf.762.CrossRefPubMed

Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey SG. Indicators of socioeconomic position (part 2). J Epidemiol Community Health. 2006;60(2):95–101. https://doi.org/10.1136/jech.2004.028092.CrossRefPubMedPubMedCentral

10.

Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey SG. Indicators of socioeconomic position (part 1). J Epidemiol Community Health. 2006;60(1):7–12. https://doi.org/10.1136/jech.2004.023531.CrossRefPubMedPubMedCentral

11.

Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, Leinsalu M, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008;358(23):2468–81. https://doi.org/10.1056/NEJMsa0707519.CrossRefPubMed

12.

Westerling R, Gullberg A, Rosén M. Socioeconomic differences in 'avoidable' mortality in Sweden 1986-1990. Int J Epidemiol. 1996;25(3):560–7. https://doi.org/10.1093/ije/25.3.560.CrossRefPubMed

13.

Ahs AM, Westerling R. Health care utilization among persons who are unemployed or outside the labour force. Health Policy. 2006;78(2–3):178–93. https://doi.org/10.1016/j.healthpol.2005.10.010.CrossRefPubMed

14.

Bongers FJ, Schellevis FG, Bakx C, van den Bosch WJ, van der Zee J. Treatment of heart failure in Dutch general practice. BMC Fam Pract. 2006;7(1):40. https://doi.org/10.1186/1471-2296-7-40.CrossRefPubMedPubMedCentral

15.

Conrad N, Judge A, Canoy D, Tran J, O'Donnell J, Nazarzadeh M, et al. Diagnostic tests, drug prescriptions, and follow-up patterns after incident heart failure: a cohort study of 93,000 UK patients. PLoS Med. 2019;16(5):e1002805. https://doi.org/10.1371/journal.pmed.1002805.CrossRefPubMedPubMedCentral

16.

Swedish National Board of Health and Welfare. Swedish National Patient Register [Web page] https://www.socialstyrelsen.se/en/statistics-and-data/registers/register-information/the-national-patient-register/. Accessed 12 May 2021.

17.

Swedish National Board of Health and Welfare. Swedish Prescribed Drug Register [Web page] https://www.socialstyrelsen.se/en/statistics-and-data/registers/register-information/the-swedish-prescribed-drug-register/. Accessed 12 May 2021.

18.

Swedish National Board of Health and Welfare. Swedish Cause of Death Register [Web page] https://www.socialstyrelsen.se/statistik-och-data/register/alla-register/dodsorsaksregistret/ [Swedish]. Accessed 12 May 2021.

19.

Statistics Sweden (SCB). Longitudinal integrated database for health insurance and labour market studies (LISA) [Web page] https://www.scb.se/en/services/ordering-data-and-statistics/guidance-for-researchers-and-universities/vilka-mikrodata-finns/longitudinella-register/longitudinal-integrated-database-for-health-insurance-and-labour-market-studies-lisa/. Accessed 12 May 2021.

20.

Li R, Chambless L. Test for additive interaction in proportional hazards models. Ann Epidemiol. 2007;17(3):227–36. https://doi.org/10.1016/j.annepidem.2006.10.009.CrossRefPubMed

21.

Haider AH, Schneider EB, Sriram N, Dossick DS, Scott VK, Swoboda SM, et al. Unconscious race and social class bias among acute care surgical clinicians and clinical treatment decisions. JAMA Surg. 2015;150(5):457–64. https://doi.org/10.1001/jamasurg.2014.4038.CrossRefPubMed

22.

Sorensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, et al. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health. 2012;12(1):80. https://doi.org/10.1186/1471-2458-12-80.CrossRefPubMedPubMedCentral

23.

Mixon AS, Myers AP, Leak CL, Lou Jacobsen JM, Cawthon C, Goggins KM, et al. Characteristics associated with postdischarge medication errors. Mayo Clin Proc. 2014;89(8):1042–51. https://doi.org/10.1016/j.mayocp.2014.04.023.CrossRefPubMed

24.

Noureldin M, Plake KS, Morrow DG, Tu W, Wu J, Murray MD. Effect of health literacy on drug adherence in patients with heart failure. Pharmacotherapy. 2012;32(9):819–26. https://doi.org/10.1002/j.1875-9114.2012.01109.x.CrossRefPubMed

25.

Pandit AU, Tang JW, Bailey SC, Davis TC, Bocchini MV, Persell SD, et al. Education, literacy, and health: mediating effects on hypertension knowledge and control. Patient Educ Couns. 2009;75(3):381–5. https://doi.org/10.1016/j.pec.2009.04.006.CrossRefPubMed

26.

Dunlavy AC, Garcy AM, Rostila M. Educational mismatch and health status among foreign-born workers in Sweden. Soc Sci Med. 2016;154:36–44. https://doi.org/10.1016/j.socscimed.2016.02.018.CrossRefPubMed

27.

Garcy AM, Vagero D. The length of unemployment predicts mortality, differently in men and women, and by cause of death: a six year mortality follow-up of the Swedish 1992-1996 recession. Soc Sci Med. 2012;74(12):1911–20. https://doi.org/10.1016/j.socscimed.2012.01.034.CrossRefPubMed

28.

Nie J, Wang J, Aune D, Huang W, Xiao D, Wang Y, et al. Association between employment status and risk of all-cause and cause-specific mortality: a population-based prospective cohort study. J Epidemiol Community Health. 2020;74(5):428–36. https://doi.org/10.1136/jech-2019-213179.CrossRefPubMed

29.

Kriegbaum M, Lund R, Schmidt L, Rod NH, Christensen U. The joint effect of unemployment and cynical hostility on all-cause mortality: results from a prospective cohort study. BMC Public Health. 2019;19(1):293. https://doi.org/10.1186/s12889-019-6622-7.CrossRefPubMedPubMedCentral

30.

Lundin A, Lundberg I, Hallsten L, Ottosson J, Hemmingsson T. Unemployment and mortality--a longitudinal prospective study on selection and causation in 49321 Swedish middle-aged men. J Epidemiol Community Health. 2010;64(1):22–8. https://doi.org/10.1136/jech.2008.079269.CrossRefPubMed

31.

Vagero D, Garcy AM. Does unemployment cause long-term mortality? Selection and causation after the 1992-96 deep Swedish recession. Eur J Pub Health. 2016;26(5):778–83. https://doi.org/10.1093/eurpub/ckw053.CrossRef

32.

Helgesson M, Johansson B, Nordqvist T, Lundberg I, Vingard E. Unemployment at a young age and later sickness absence, disability pension and death in native swedes and immigrants. Eur J Pub Health. 2013;23(4):606–10. https://doi.org/10.1093/eurpub/cks099.CrossRef

33.

Bartley M. Unemployment and ill health: understanding the relationship. J Epidemiol Community Health. 1994;48(4):333–7. https://doi.org/10.1136/jech.48.4.333.CrossRefPubMedPubMedCentral

34.

Backhans MC, Hemmingsson T. Unemployment and mental health--who is (not) affected? Eur J Pub Health. 2012;22(3):429–33. https://doi.org/10.1093/eurpub/ckr059.CrossRef

35.

De Moortel D, Hagedoorn P, Vanroelen C, Gadeyne S. Employment status and mortality in the context of high and low regional unemployment levels in Belgium (2001-2011): a test of the social norm hypothesis across educational levels. PLoS One. 2018;13(2):e0192526. https://doi.org/10.1371/journal.pone.0192526.CrossRefPubMedPubMedCentral

36.

Mackenbach JP, Kulhanova I, Menvielle G, Bopp M, Borrell C, Costa G, et al. Trends in inequalities in premature mortality: a study of 3.2 million deaths in 13 European countries. J Epidemiol Community Health. 2015;69(3):207–17. https://doi.org/10.1136/jech-2014-204319.CrossRefPubMed

37.

Statistics Sweden (SCB). Labour market statistics, statistical database. [Web page] https://www.statistikdatabasen.scb.se/pxweb/en/ssd/START__AM/. Accessed 12 May 2021.

38.

Cainzos-Achirica M, Capdevila C, Vela E, Cleries M, Bilal U, Garcia-Altes A, et al. Individual income, mortality and healthcare resource use in patients with chronic heart failure living in a universal healthcare system: a population-based study in Catalonia, Spain. Int J Cardiol. 2019;277:250–7. https://doi.org/10.1016/j.ijcard.2018.10.099.CrossRefPubMed

39.

Schjodt I, Johnsen SP, Stromberg A, Kristensen NR, Logstrup BB. Socioeconomic factors and clinical outcomes among patients with heart failure in a universal health care system. JACC Heart Fail. 2019;7(9):746–55. https://doi.org/10.1016/j.jchf.2019.06.003.CrossRefPubMed

40.

Ahmad K, Chen EW, Nazir U, Cotts W, Andrade A, Trivedi AN, et al. Regional variation in the Association of Poverty and Heart Failure Mortality in the 3135 counties of the United States. J Am Heart Assoc. 2019;8(18):e012422. https://doi.org/10.1161/JAHA.119.012422.CrossRefPubMedPubMedCentral

41.

Zou H, Chen Y, Fang W, Zhang Y, Fan X. The mediation effect of health literacy between subjective social status and depressive symptoms in patients with heart failure. J Psychosom Res. 2016;91:33–9. https://doi.org/10.1016/j.jpsychores.2016.10.006.CrossRefPubMed

42.

Gast A, Mathes T. Medication adherence influencing factors-an (updated) overview of systematic reviews. Syst Rev. 2019;8(1):112. https://doi.org/10.1186/s13643-019-1014-8.CrossRefPubMedPubMedCentral

43.

Ahs AM, Westerling R. Mortality in relation to employment status during different levels of unemployment. Scand J Public Health. 2006;34(2):159–67. https://doi.org/10.1080/14034940510032374.CrossRefPubMed

44.

Lofman I, Szummer K, Dahlstrom U, Jernberg T, Lund LH. Associations with and prognostic impact of chronic kidney disease in heart failure with preserved, mid-range, and reduced ejection fraction. Eur J Heart Fail. 2017;19(12):1606–14. https://doi.org/10.1002/ejhf.821.CrossRefPubMed

Title: Non-employment and low educational level as risk factors for inequitable treatment and mortality in heart failure: a population-based cohort study of register data
Authors: Anna Ohlsson
Nils Eckerdal
Bertil Lindahl
Marianne Hanning
Ragnar Westerling
Publication date: 01-12-2021
Publisher: BioMed Central
Keyword: Heart Failure
Published in: BMC Public Health / Issue 1/2021
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-021-10919-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Non-employment and low educational level as risk factors for inequitable treatment and mortality in heart failure: a population-based cohort study of register data

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Self-reported sexually transmitted infections among sexually active men in Ghana

Early public adherence with and support for stay-at-home COVID-19 mitigation strategies despite adverse life impact: a transnational cross-sectional survey study in the United States and Australia

Understanding measures of racial discrimination and microaggressions among American Indian and Alaska Native college students in the Southwest United States

A systematic review of the effectiveness of non- health facility based care delivery of antiretroviral therapy for people living with HIV in sub-Saharan Africa measured by viral suppression, mortality and retention on ART

Effectiveness of the German disease management programs: quasi-experimental analyses assessing the population-level health impact

Dietary and lifestyle inflammatory scores and risk of incident diabetes: a prospective cohort among participants of Tehran lipid and glucose study