The impact of health literacy and life style risk factors on health-related quality of life of Australian patients.

Upali W Jayasinghe, Mark Fort Harris, Sharon M Parker, John Litt, Mieke van Driel, Danielle Mazza, Chris Del Mar, Jane Lloyd, Jane Smith, Nicholas Zwar, Richard Taylor, Preventive Evidence into Practice (PEP) Partnership Group
Author Information
  1. Upali W Jayasinghe: Centre for Primary Health Care and Equity, Level 3 AGSM, University of New South Wales Australia, Sydney, NSW, 2052, Australia. upali.jay@unsw.edu.au.
  2. Mark Fort Harris: Centre for Primary Health Care and Equity, Level 3 AGSM, University of New South Wales Australia, Sydney, NSW, 2052, Australia.
  3. Sharon M Parker: Centre for Primary Health Care and Equity, Level 3 AGSM, University of New South Wales Australia, Sydney, NSW, 2052, Australia.
  4. John Litt: Discipline of General Pratice, School of Medicine, Flinders University, Adelaide, South Australia, Australia.
  5. Mieke van Driel: Discipline of General Practice, University of Queensland, Brisbane, QLD, Australia.
  6. Danielle Mazza: Department of General Practice, Monash University, Notting Hill, VIC, Australia.
  7. Chris Del Mar: Faculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD, Australia.
  8. Jane Lloyd: Centre for Primary Health Care and Equity, Level 3 AGSM, University of New South Wales Australia, Sydney, NSW, 2052, Australia.
  9. Jane Smith: Faculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD, Australia.
  10. Nicholas Zwar: School of Public Health and Community Medicine, University of New South Wales Australia, Sydney, NSW, Australia.
  11. Richard Taylor: School of Public Health and Community Medicine, University of New South Wales Australia, Sydney, NSW, Australia.

Abstract

BACKGROUND: Limited evidence exists regarding the relationship between health literacy and health-related quality of life (HRQoL) in Australian patients from primary care. The objective of this study was to investigate the impact of health literacy on HRQoL in a large sample of patients without known vascular disease or diabetes and to examine whether the difference in HRQoL between low and high health literacy groups was clinically significant.
METHODS: This was a cross-sectional study of baseline data from a cluster randomised trial. The study included 739 patients from 30 general practices across four Australian states conducted in 2012 and 2013 using the standard Short Form Health Survey (SF-12) version 2. SF-12 physical component score (PCS-12) and mental component score (MCS-12) are derived using the standard US algorithm. Health literacy was measured using the Health Literacy Management Scale (HeLMS). Multilevel regression analysis (patients at level 1 and general practices at level 2) was applied to relate PCS-12 and MCS-12 to patient reported life style risk behaviours including health literacy and demographic factors.
RESULTS: Low health literacy patients were more likely to be smokers (12 % vs 6 %, P = 0.005), do insufficient physical activity (63 % vs 47 %, P < 0.001), be overweight (68 % vs 52 %, P < 0.001), and have lower physical health and lower mental health with large clinically significant effect sizes of 0.56 (B (regression coefficient) = -5.4, P < 0.001) and 0.78(B = -6.4, P < 0.001) respectively after adjustment for confounding factors. Patients with insufficient physical activity were likely to have a lower physical health score (effect size = 0.42, B = -3.1, P < 0.001) and lower mental health (effect size = 0.37, B = -2.6, P < 0.001). Being overweight tended to be related to a lower PCS-12 (effect size = 0.41, B = -1.8, P < 0.05). Less well-educated, unemployed and smoking patients with low health literacy reported worse physical health. Health literacy accounted for 45 and 70 % of the total between patient variance explained in PCS-12 and MCS-12 respectively.
CONCLUSIONS: Addressing health literacy related barriers to preventive care may help reduce some of the disparities in HRQoL. Recognising and tailoring health related communication to those with low health literacy may improve health outcomes including HRQoL in general practice.

Keywords

References

  1. Health Qual Life Outcomes. 2009 Jun 03;7:50 [PMID: 19493336]
  2. Qual Life Res. 1999;8(1-2):1-8 [PMID: 10457733]
  3. J Gen Intern Med. 2006 Aug;21(8):857-61 [PMID: 16881947]
  4. Int J Qual Health Care. 2008 Apr;20(2):105-14 [PMID: 18158292]
  5. J Public Health Med. 2001 Sep;23(3):187-94 [PMID: 11585190]
  6. Aust N Z J Public Health. 2002 Aug;26(4):343-5 [PMID: 12233955]
  7. Patient Educ Couns. 2014 Jul;96(1):3-12 [PMID: 24795073]
  8. Patient Educ Couns. 2013 May;91(2):228-35 [PMID: 23419326]
  9. Med Care. 2001 Aug;39(8):867-78 [PMID: 11468505]
  10. PLoS One. 2015 Jun 08;10(6):e0129289 [PMID: 26053024]
  11. J Gen Intern Med. 2004 Dec;19(12):1228-39 [PMID: 15610334]
  12. Med Sci Sports Exerc. 2010 Apr;42(4):665-71 [PMID: 19952838]
  13. BMC Fam Pract. 2014 Oct 25;15:171 [PMID: 25928342]
  14. Intern Med J. 2007 Jan;37(1):6-11 [PMID: 17199838]
  15. Br J Sports Med. 2005 May;39(5):294-7; discussion 294-7 [PMID: 15849294]
  16. J Gerontol A Biol Sci Med Sci. 2001 Oct;56 Spec No 2:23-35 [PMID: 11730235]
  17. J Epidemiol Community Health. 1998 Oct;52(10):657-64 [PMID: 10023466]
  18. Int J Obes (Lond). 2007 Feb;31(2):321-7 [PMID: 16703001]
  19. N S W Public Health Bull. 2003;14 Suppl 4:1-148 [PMID: 14872217]
  20. Health Promot Pract. 2006 Jul;7(3):331-5 [PMID: 16760237]
  21. J Nutr Health Aging. 2013 Apr;17(4):315-21 [PMID: 23538652]
  22. Med Sci Sports Exerc. 2004 May;36(5):890-6 [PMID: 15126726]
  23. J Am Board Fam Pract. 2004 Jan-Feb;17(1):44-7 [PMID: 15014052]
  24. Implement Sci. 2013 Jan 18;8:8 [PMID: 23327664]
  25. BMC Womens Health. 2015 Apr 15;15:34 [PMID: 25887361]
  26. Aust Fam Physician. 2009 Mar;38(3):144-7 [PMID: 19283256]
  27. Ann Intern Med. 2011 Jul 19;155(2):97-107 [PMID: 21768583]
  28. Int J Epidemiol. 2007 Apr;36(2):338-45 [PMID: 17329315]
  29. Med J Aust. 2012 Oct 1;197(7):387-93 [PMID: 23025735]
  30. Health Qual Life Outcomes. 2013 Sep 11;11:153 [PMID: 24020618]
  31. Am J Public Health. 1997 Jun;87(6):1027-30 [PMID: 9224190]
  32. BMC Health Serv Res. 2013 Aug 16;13:319 [PMID: 23958036]
  33. Soz Praventivmed. 2002;47(3):172-7 [PMID: 12238299]
  34. CA Cancer J Clin. 2002 May-Jun;52(3):134-49 [PMID: 12018928]
  35. Soc Sci Med. 2008 Dec;67(12):2072-8 [PMID: 18952344]
  36. Patient Educ Couns. 2015 Nov;98(11):1295-307 [PMID: 26162954]
  37. J Public Health (Oxf). 2009 Dec;31(4):490-5 [PMID: 19454605]
  38. J Clin Epidemiol. 1998 Nov;51(11):1171-8 [PMID: 9817135]
  39. Health Serv Res. 2000 Oct;35(4):885-904 [PMID: 11055454]
  40. Diabetes Metab Syndr Obes. 2012;5:303-11 [PMID: 22952412]
  41. Obesity (Silver Spring). 2006 May;14(5):870-83 [PMID: 16855197]
  42. J Epidemiol Community Health. 2004 Apr;58(4):333-9 [PMID: 15026450]
  43. Prev Med. 2003 Nov;37(5):520-8 [PMID: 14572437]
  44. Health Qual Life Outcomes. 2013 Jun 21;11:102 [PMID: 23800331]
  45. BMC Health Serv Res. 2012 Aug 03;12:234 [PMID: 22856459]
  46. Health Qual Life Outcomes. 2007 Sep 28;5:55 [PMID: 17900374]
  47. BMC Public Health. 2008 Jul 21;8:246 [PMID: 18638419]
  48. Cancer. 2012 Aug 1;118(15):3842-51 [PMID: 22180041]
  49. J Nutr Health Aging. 2014 Apr;18(4):359-64 [PMID: 24676315]

MeSH Term

Adult
Aged
Attitude to Health
Australia
Cross-Sectional Studies
Female
Health Literacy
Health Surveys
Humans
Life Style
Male
Middle Aged
Patients
Quality of Life
Risk Factors
Socioeconomic Factors

Word Cloud

Created with Highcharts 10.0.0healthliteracypatients%P < 0physical001lifeHRQoLHealthscorelowercomponentPCS-12factorseffectAustralianstudylowgeneralusingSF-122mentalMCS-12regressionstyleriskvssize = 0relatedhealth-relatedqualitycareimpactlargeclinicallysignificantpracticesstandardversionMultilevelanalysislevel1patientreportedincludinglikely6insufficientactivityoverweight04respectivelyB =mayBACKGROUND:LimitedevidenceexistsregardingrelationshipprimaryobjectiveinvestigatesamplewithoutknownvasculardiseasediabetesexaminewhetherdifferencehighgroupsMETHODS:cross-sectionalbaselinedataclusterrandomisedtrialincluded73930acrossfourstatesconducted20122013ShortFormSurveyderivedUSalgorithmmeasuredLiteracyManagementScaleHeLMSappliedrelatebehavioursdemographicRESULTS:Lowsmokers12P = 000563476852sizes56Bcoefficient =-578B = -6adjustmentconfoundingPatients42-337-2tended41B = -1805Lesswell-educatedunemployedsmokingworseaccounted4570totalvarianceexplainedCONCLUSIONS:AddressingbarrierspreventivehelpreducedisparitiesRecognisingtailoringcommunicationimproveoutcomespracticeLifeMentalPhysicalQuality

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