Lower educational level and unemployment increase the impact of cardiometabolic conditions on the quality of life: results of a population-based study in South Australia
- 702 Downloads
To investigate if sociodemographic characteristics increase the adverse effects of cardiovascular diseases (CVD) and cardiometabolic risk factors (CMRF) on health-related quality of life (HRQoL).
Cross-sectional, face-to-face survey investigating 2379 adults living in South Australia in 2015 (57.1 ± 14 years; 51.7% females). Questions included diagnosis of CMRF (obesity, diabetes, hypertension, dyslipidaemia) and CVD. Physical and mental HRQoL were assessed using the SF-12v1 questionnaire. Multiple linear regression models including confounders (sociodemographic, lifestyle, use of preventive medication) and interaction terms between sociodemographic variables and cardiometabolic conditions were used in adjusted analysis.
The prevalence of CMRF (one or more) was 54.6% and CVD was 13.0%. The physical HRQoL reduced from 50.8 (95%CI 50.2–51.4) in healthy individuals to 45.1 (95%CI 44.4–45.9) and 39.1 (95%CI 37.7–40.5) among those with CMRF and CVD, respectively. Adjustment for sociodemographic variables reduced these differences in 33%, remaining stable after controlling for lifestyle and use of preventive medications (p < 0.001). Differences in physical HRQoL according to cardiometabolic conditions were twice as high among those with lower educational level, or if they were not working. Among unemployed, having a CMRF or a CVD had the same impact on the physical HRQoL (9.7 lower score than healthy individuals). The inverse association between cardiometabolic conditions and mental HRQoL was subtle (p = 0.030), with no evidence of disparities due to sociodemographic variables.
A lower educational level and unemployment increase the adverse effects of cardiometabolic conditions on the physical HRQoL. Targeted interventions for reducing CMRF and/or CVD in these groups are necessary to improve HRQoL.
KeywordsQuality of life Cardiovascular disease Metabolic disease Socioeconomic factors Health status disparities
The author acknowledges the participants of the 2015 Spring Health Omnibus Survey for their participation in this study.
Compliance with ethical standards
D.A. González-Chica received a part Fellowship from the NHMRC Centre of Research Excellence to Reduce Inequality in Heart Disease to conduct this study.
Conflict of interest
All the authors declare they have no conflict of interest.
This study was approved by the University of Adelaide Human Research Ethics Committee (project H-097-2010). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Participants provided verbal rather than written informed consent, due to the practicalities of carrying out a large-scale survey and the low-risk nature of the survey content.
- 2.WHO. (2011). WHO maps noncommunicable disease trends in all countries: Country profiles on noncommunicable disease trends in 193 countries. Central European Journal of Public Health, 19(3), 130–138.Google Scholar
- 3.AIHW. (2014). Australian Institute of Health and Welfare. Cardiovascular disease, diabetes and chronic kidney disease—Australian facts: Prevalence and incidence. Cardiovascular, diabetes and chronic kidney disease. Series No. 2. Cat. No. CDK 2. Canberra: AIHW. (Available at http://www.aihw.gov.au/WorkArea/DownloadAsset.aspx?id=60129549614) Accessed on 15 Mar 2016.
- 6.Chowdhury, R., Khan, H., Heydon, E., Shroufi, A., Fahimi, S., Moore, C., Stricker, B., Mendis, S., Hofman, A., Mant, J., & Franco, O. H. (2013). Adherence to cardiovascular therapy: A meta-analysis of prevalence and clinical consequences. European Heart Journal, 34(38), 2940–2948.CrossRefPubMedGoogle Scholar
- 7.Conn, V. S., Ruppar, T. M., Maithe Enriquez, R., & Cooper, P. S. (2016). Patient-centered outcomes of medication adherence interventions: Systematic review and meta-analysis. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 19(2), 277–285.CrossRefGoogle Scholar
- 8.Mark, D. B. (2016). Assessing quality-of-life outcomes in cardiovascular clinical research. Nat Rev Cardiol.Google Scholar
- 11.Hutchinson, A. F., Graco, M., Rasekaba, T. M., Parikh, S., Berlowitz, D. J., & Lim, W. K. (2015). Relationship between health-related quality of life, comorbidities and acute health care utilisation, in adults with chronic conditions. Health and Quality of Life Outcomes, 13, 69.CrossRefPubMedPubMedCentralGoogle Scholar
- 12.Gonzalez-Chica, D. A., Mnisi, Z., Avery, J., Duszynski, K., Doust, J., Tideman, P., Murphy, A., Burgess, J., Beilby, J., & Stocks, N. (2016). Effect of health literacy on quality of life amongst patients with Ischaemic heart disease in Australian General Practice. PLoS One, 11(3), e0151079.CrossRefPubMedPubMedCentralGoogle Scholar
- 13.Mello Ade, C., Engstrom, E. M., & Alves, L. C. (2014). Health-related and socio-demographic factors associated with frailty in the elderly: A systematic literature review. Cadernos de saude publica/Ministerio da Saude, Fundacao Oswaldo Cruz, Escola Nacional de Saude Publica, 30(6), 1143–1168.Google Scholar
- 17.Taylor, A., Dal Grande, E., & Wilson, D. (2006). The South Australian Health Omnibus Survey 15 years on: has public health benefited? Public Health Bull (S Aust), 3(1), 30–32. Available at http://pandora.nla.gov.au/pan/133553/20120522-0000/www.sahealth.sa.gov.au/wps/wcm/connect/9d76de80440e1c688bc8af63794072bf/phb-chronicdisease065ef3.pdf. Accessed on 16 Jun 2016.
- 18.ABS. (2016). Australian Bureau of Statistics. Table Builder. Available at http://www.abs.gov.au/websitedbs/censushome.nsf/home/tablebuilder. Accessed on 10 May 2016.
- 19.ABS. (2011). Australian Bureau of Statistics. Census of Population and Housing: Socio-economic indexes for areas (SEIFA), Australia. Available at http://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001. Accessed on 01 Mar 2014 (Vol. cat. no. 2033.0.55.001).
- 20.Gandek, B., Ware, J. E., Aaronson, N. K., Apolone, G., Bjorner, J. B., Brazier, J. E., Bullinger, M., Kaasa, S., Leplege, A., Prieto, L., & Sullivan, M. (1998). Cross-validation of item selection and scoring for the SF-12 Health Survey in nine countries: Results from the IQOLA Project. International Quality of Life Assessment. Journal of Clinical Epidemiology, 51(11), 1171–1178CrossRefPubMedGoogle Scholar
- 22.Mitchel, M. (2012). Interpreting and visualizing regression models using STATA, first ed (First ed.). Texas:Stata Press.Google Scholar
- 24.Avery, J., Dal Grande, E., & Taylor, A. (2004). Quality of life in South Australia as measured by the SF-12 Health Status Questionnaire: Population norms for 2003: Trends from 1997 to 2003. ISBN 0730893294. Available at http://www.health.adelaide.edu.au/pros/docs/reports/general/qol_quality_of_life_sf_12.pdf. Accessed on 05 Jun 2015 (No. .). South Australia: South Australia. Dept. of Human Services. Population Research and Outcome Studies Unit.
- 26.Hawkins, N. M., Jhund, P. S., McMurray, J. J., & Capewell, S. (2012). Heart failure and socioeconomic status: Accumulating evidence of inequality. European Journal of Heart Failure: Journal of the Working Group on Heart Failure of the European Society of Cardiology, 14(2), 138–146.CrossRefGoogle Scholar
- 31.AIHW. (2014). Australian Institute of Health and Welfare. Mortality inequalities in Australia 2009–2011. Bulletin no. 124. Cat. no. AUS 184. Canberra: AIHW. Available at http://www.aihw.gov.au/publication-detail/?id=60129548021. Accessed on 03 Mar 2016.