Advertisement

Use of Complementary and Alternative Medicine in Women With Heart Disease, Hypertension and Diabetes (from the Australian Longitudinal Study on Women's Health)

      The uptake of complementary and alternative medicine (CAM) is common, especially among patients with chronic illness. However, the use of CAM by women with cardiovascular disease and how this influences the interface with conventional medicine is poorly understood. To examine the relation between heart disease, hypertension, and diabetes and the use of CAM and conventional medicine in a cohort of women, data were taken from the 2010 survey (n = 9,748) of the 1946 to 1951 cohort of the Australian Longitudinal Study on Women's Health (ALSWH). Analyses focused on women who had been diagnosed or treated for heart disease, diabetes, and/or hypertension. The outcome measures were the use of conventional or CAM treatments in the previous year. Most women had hypertension only (n = 2,335), and few (n = 78) reported having heart disease, hypertension, and diabetes. Women with hypertension were less likely (odds ratio [OR] 0.82, 95% confidence interval [CI] 0.74 to 0.91) to consult with a CAM practitioner and less likely (OR 0.86, 95% CI 0.77 to 0.97) to use self-prescribed CAM, while women with diabetes were also less likely (OR 0.66, 95% CI 0.54 to 0.81) to consult with a CAM practitioner and less likely (OR 0.68, 95% CI 0.55 to 0.83) to use self-prescribed CAM. In conclusion, compared with studies conducted on CAM use and other chronic illness groups, the use of CAM by women with heart disease, hypertension, and/or diabetes in this study was lower, and future research is needed to explore patients' perceptions of cardiovascular risk and the role of CAM in their self-management in the community, among other issues.
      We still know relatively little about the use of complementary and alternative medicine (CAM) among cardiovascular patients. In response, we provide a detailed examination of conventional and CAM practitioner consultations as well as the use of self-prescribed CAM among women who have been diagnosed with heart disease, diabetes, and hypertension.

      Methods

      This research was conducted as part of the Australian Longitudinal Study on Women's Health (ALSWH), which was designed to investigate multiple factors affecting the health and well-being of women over a 20-year period. Women in 3 age groups (young, 18 to 23 years of age; middle age, 45 to 50 years of age; and older, 70 to 75 years of age) were randomly selected from the national Medicare database.
      • Brown W.
      • Bryson L.
      • Byles J.
      • Dobson A.
      • Lee C.
      • Mishra G.
      Women's Health Australia: recruitment for a national longitudinal cohort study.
      Australia has a nationalized medical care system (Medicare), funded directly through the taxation system, under which all citizens and permanent residents, including refugees and immigrants, receive medical and public hospital care at minimal cost. This system is centrally administered by the Health Insurance Commission, which maintains records of Australians' health care use as well as of all registered providers of medical services. Under Medicare, all Australians are eligible to claim benefits for family doctor and specialist medical services, and almost all women residing in Australia are registered with Medicare. The focus of the present analysis was on women from the middle-age cohort. The baseline survey, comprising 14,099 women, was conducted in 1996, and the respondents have been shown to be broadly representative of the national population of women in the target age groups.
      • Brown W.J.
      • Dobson A.J.
      • Bryson L.
      • Byles J.E.
      Women's Health Australia: on the progress of the main study cohorts.
      Analyses for this research were focused on the most recent postal survey, which was conducted in 2010, when the women were aged 59 to 64 years.
      Postal code of residence at the time of the baseline survey was used to classify area of residence as urban or nonurban. Women were asked about their current marital status and the highest educational qualification they had completed. The women were also asked about income and whether they had private health insurance with ancillary coverage.
      Women were asked how often they experienced a list of symptoms in the previous 12 months. The list included allergies or hay fever or sinusitis, indigestion, chest pain, headaches or migraines, severe tiredness, stiff or painful joints, back pain, hemorrhoids, other bowel problems, hot flashes, night sweats, and leaking urine. Women were also asked whether a doctor had ever told them that they had any of the following chronic medical conditions: arthritis, diabetes, heart disease, hypertension, low iron level, asthma, anxiety disorder, depression, and cancer (except skin cancer). Responses to questions about history of smoking and alcohol use were also included.
      The women were asked about their frequency of use in the previous 12 months of general practitioners (GPs) and specialist doctors. In addition, they were asked if they had consulted with a range of conventional providers (i.e., physiotherapists, counselors, nurses, optometrists, dieticians, and podiatrists) and CAM practitioners (i.e., massage therapists, naturopaths or herbalists, chiropractors, osteopaths, acupuncturists, and other alternative health practitioners), as well as their consumption of self-prescribed CAM (i.e., vitamins and minerals, yoga and meditation, herbal medicines, aromatherapy oils, Chinese medicine, and other alternative therapies) in the previous 12 months.
      Chi-square tests were used to examine the associations between categorical variables. Logistic regression models were used to examine the association between health care use (i.e., CAM practitioners, allied health care practitioners, and self-prescribed CAM use) and heart disease status, hypertension status, and diabetes status. For each logistic regression model, the odds ratios were adjusted for all the demographic, symptom, and diagnosis variables listed earlier. Poisson regression models were used to examine the association between consultations with a doctor (i.e., GP or specialist) and heart disease, hypertension, and diabetes status. For each Poisson regression model, the risk ratios were adjusted for all the demographic, symptom, and diagnosis variables listed earlier. In response to the large sample size, a p value <0.005 was adopted for statistical significance. All analyses were conducted using SAS (SAS Institute Inc., Cary, North Carolina).

      Results

      In 2010 (i.e., survey 6), 9,748 women returned completed questionnaires (an 80.4% response rate). There were 428 women (4.4%) who had been diagnosed or treated for heart disease (i.e., myocardial infarction or angina), 750 women (7.7%) who had been diagnosed or treated for diabetes, and 2,945 (30.2%) women who had been diagnosed or treated for hypertension. Few women (n = 78 [0.8%]) reported having heart disease, hypertension, and diabetes. In addition to these conditions, the women had been diagnosed or treated for, on average, 0.9 ± 1.1 other conditions and had experienced an average of 3.6 ± 2.3 different symptoms. Most of the women were nonsmokers (91.1%), with 8.9% being current smokers. In terms of alcohol consumption, 8.4% of women were nondrinkers, 84.5% low-risk drinkers, and 7.2% risky or high-risk drinkers.
      Table 1 lists the distribution of consultations with CAM practitioners among women with heart disease, hypertension, and diabetes. Women with diabetes were 0.82 (95% confidence interval [CI] 0.65 to 1.04) times less likely to consult with massage therapists, 0.68 (95% CI 0.50 to 0.92) times less likely to consult with chiropractors, 0.36 (95% CI 0.21 to 0.70) times less likely to consult with osteopaths, and 0.66 (95% CI 0.54 to 0.81) times less likely to consult with CAM practitioners in general. In addition, women with hypertension were 0.79 (95% CI 0.70 to 0.89) times less likely to consult with massage therapists, 0.79 (95% CI 0.65 to 0.95) times less likely to consult with naturopaths or herbalists, and 0.82 (95% CI 0.74 to 0.91) times less likely to consult with CAM practitioners in general. There were no statistically significant associations between heart disease and any of the CAM practitioner groups.
      Table 1The distribution of consultations with complementary and alternative medicine (CAM) practitioners amongst women with heart disease, hypertension and/or diabetes
      ConditionConsultations With CAM Practitioners in the previous 12 months
      Massage

      Therapist
      Naturopath /

      Herbalist
      ChiropractorOsteopathAcupuncturistOther AH

      Practitioner
      Total

      CAM
      Yes

      n=2454
      No

      n=7014
      Yes

      n=858
      No

      n=8516
      Yes

      n=1489
      No

      n=7941
      Yes

      n=408
      No

      n=8954
      Yes

      n=546
      No

      n=8833
      Yes

      n=583
      No

      n=8650
      Yes

      n=3934
      No

      n=5634
      Heart Disease4%4%4%5%4%4%4%5%4%5%4%4%4%5%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      1.000.791.050.650.650.860.93
       (95% CI)(0.76, 1.32)(0.51, 1.24)(0.76, 1.47)(0.32, 1.31)(0.37, 1.15)(0.51, 1.44)(0.73, 1.19)
      Hypertension
      statistically significant association with massage therapist (p<0.005).
      ,
      statistically significant association with naturopaths/herbalists (p<0.005).
      ,
      statistically significant association with Total CAM (p<0.005).
      28%32%26%32%30%31%28%31%29%31%28%31%29%32%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      0.790.790.950.800.880.810.82
       (95% CI)(0.70, 0.89)(0.65, 0.95)(0.82, 1.10)(0.601, 1.06)(0.70, 1.11)(0.65, 1.02)(0.74, 0.91)
      Diabetes Mellitus
      statistically significant association with massage therapist (p<0.005).
      ,
      statistically significant association with chiropractors (p<0.005).
      ,
      statistically significant association with osteopaths (p<0.005).
      ,
      statistically significant association with other AH practitioners (p<0.005).
      ,
      statistically significant association with Total CAM (p<0.005).
      6%9%6%8%6%8%4%8%8%8%5%8%6%9%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      0.820.610.680.360.920.600.66
       (95% CI)(0.65, 1.04)(0.41, 0.92)(0.50, 0.92)(0.21, 0.70)(0.60, 1.42)(0.37, 0.96)(0.54, 0.81)
      statistically significant association with massage therapist (p<0.005).
      statistically significant association with naturopaths/herbalists (p<0.005).
      statistically significant association with chiropractors (p<0.005).
      § statistically significant association with osteopaths (p<0.005).
      statistically significant association with Total CAM (p<0.005).
      statistically significant association with other AH practitioners (p<0.005).
      ∗∗ adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      The distribution of self-prescribed CAM among women with heart disease, hypertension, and diabetes are presented in Table 2. Women with diabetes were 0.68 (95% CI 0.55 to 0.82) times less likely to consume vitamins and minerals, 0.59 (95% CI 0.43 to 0.79) times less likely to use yoga and meditation, and 0.68 (95% CI 0.55 to 0.83) times less likely to use CAM in general. Women with hypertension were 0.78 (95% CI 0.68 to 0.90) times less likely to use yoga and meditation, 0.87 (95% CI 0.78 to 0.98) times less likely to use herbal medicines, and 0.67 (95% CI 0.55 to 0.82) times less likely to use other alternative therapies. There were no statistically significant (adjusted) associations between heart disease and any of the self-prescribed CAM.
      Table 2The distribution of use of self-prescribed complementary and alternative medicines (CAM) amongst women with heart disease, hypertension and/or diabetes
      ConditionUse of self-prescribed CAM treatments in the previous 12 months
      Vitamins /

      Minerals
      Yoga /

      Meditation
      Herbal

      Medicines
      Aroma-

      therapy Oils
      Chinese

      Medicine
      Other Alter.

      Therapies
      Total

      CAM
      Yes

      n=6681
      No

      n=2981
      Yes

      n=1702
      No

      n=7775
      Yes

      n=2606
      No

      n=6898
      Yes

      n=1434
      No

      n=8045
      Yes

      n=365
      No

      n=9088
      Yes

      n=813
      No

      n=8585
      Yes

      n=7332
      No

      n=2385
      Heart Disease
      statistically significant association with aromatherapy oils (p<0.005).
      5%4%5%4%4%5%6%4%4%5%5%4%5%4%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      1.051.080.841.250.720.931.08
       (95% CI)(0.80, 1.37)(0.79, 1.47)(0.64, 1.10)(0.92, 1.68)(0.36, 1.45)(0.61, 1.43)(0.81, 1.45)
      Hypertension
      statistically significant association with yoga/meditation (p<0.005).
      ,
      statistically significant association with herbal medicines (p<0.005).
      ,
      statistically significant association with other alternative therapies (p<0.005).
      30%32%26%32%29%31%32%31%27%31%25%32%30%32%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      0.890.780.870.990.750.670.86
       (95% CI)(0.79, 0.99)(0.68, 0.90)(0.78, 0.98)(0.87, 1.15)(0.56, 0.99)(0.55, 0.82)(0.77, 0.97)
      Diabetes Mellitus
      statistically significant association with vitamins/minerals (p<0.005).
      ,
      statistically significant association with yoga/meditation (p<0.005).
      ,
      statistically significant association with Total CAM (p<0.005).
      7%10%5%9%7%8%8%8%6%8%7%8%7%10%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      0.680.590.860.880.650.810.68
       (95% CI)(0.55, 0.82)(0.43, 0.79)(0.69, 1.08)(0.68, 1.15)(0.36, 1.19)(0.56, 1.19)(0.55, 0.83)
      statistically significant association with vitamins/minerals (p<0.005).
      statistically significant association with yoga/meditation (p<0.005).
      statistically significant association with herbal medicines (p<0.005).
      § statistically significant association with aromatherapy oils (p<0.005).
      statistically significant association with Total CAM (p<0.005).
      statistically significant association with other alternative therapies (p<0.005).
      ∗∗ adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      Table 3 lists the distribution of consultations with doctors among women with heart disease, hypertension, and diabetes. Women with diabetes were 1.13 (95% CI 1.06 to 1.20) times more likely to consult with a GP and 1.14 (95% CI 1.03 to 1.25) times more likely to consult with a specialist. Women with hypertension were 1.22 (95% CI 1.18 to 1.27) times more likely to consult with a GP and 1.10 (95% CI 1.04 to 1.17) times more likely to consult with a specialist. Women with heart disease were 1.13 (95% CI 1.05 to 1.21) times more likely to consult with a GP and 1.35 (95% CI 1.22 to 1.51) times more likely to consult with a specialist.
      Table 3The distribution of consultations with general practitioners (GPs) and specialists amongst women with heart disease, hypertension and/or diabetes
      ConditionNumber of consultations with doctors in the previous 12 months
      General Practitioner (GP)Specialist
      0

      n=454
      1 - 2

      n=3047
      3 - 4

      n=3030
      5 - 6

      n=1683
      7 - 12

      n=1021
      13 - 24

      n=361
      25+

      n=101
      0

      n=4528
      1 - 2

      n=3171
      3 - 4

      n=1011
      5 - 6

      n=372
      7 - 12

      n=200
      13 - 24

      n=52
      25+

      n=34
      Heart Disease
      statistically significant association with GP (p<0.005).
      ,
      statistically significant association with specialist (p<0.005).
      1%2%3%6%11%15%15%2%5%9%13%10%12%10%
       Risk Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      1.131.35
       (95% CI)(1.05, 1.21)(1.22, 1.51)
      Hypertension
      statistically significant association with GP (p<0.005).
      ,
      statistically significant association with specialist (p<0.005).
      6%18%34%41%45%49%58%27%32%38%42%37%30%47%
       Risk Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      1.221.10
       (95% CI)(1.18, 1.27)(1.04, 1.17)
      Diabetes Mellitus
      statistically significant association with GP (p<0.005).
      ,
      statistically significant association with specialist (p<0.005).
      2%2%8%11%15%21%23%5%8%13%13%17%20%24%
       Risk Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      1.131.14
       (95% CI)(1.06, 1.20)(1.03, 1.25)
      statistically significant association with GP (p<0.005).
      statistically significant association with specialist (p<0.005).
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      The distribution of consultations with allied health care practitioners among women with heart disease, hypertension, and diabetes is listed in Table 4. Women with diabetes were 2.16 (95% CI 1.71 to 2.72) times more likely to consult with a nurse, 1.81 (95% CI 1.46 to 2.24) times more likely to consult with an optometrist, 5.08 (95% CI 3.92 to 6.58) times more likely to consult with a dietician, and 3.31 (95% CI 2.69 to 4.07) times more likely to consult with a podiatrist. Women with hypertension were 1.24 (95% CI 1.01 to 1.53) times more likely to consult with a counselor, 1.53 (95% CI 1.32 to 1.78) times more likely to consult with a nurse, 1.42 (95% CI 1.15 to 1.77) times more likely to consult with a dietician, and 1.21 (95% CI 1.06 to 1.39) times more likely to consult with a podiatrist. Women with heart disease were 1.50 (95% CI 1.15 to 1.96) times more likely to consult with a physiotherapist and 1.73 (95% CI 1.08 to 2.72) times more likely to consult with a counselor.
      Table 4The distribution of consultations with allied health care practitioners amongst women with heart disease, hypertension and/or diabetes
      ConditionConsultations with allied health care practitioners in the previous 12 months
      PhysiotherapistCounsellorNurseOptometristDietitianPodiatrist
      Yes

      n=6681
      No

      n=2981
      Yes

      n=1702
      No

      n=7775
      Yes

      n=2606
      No

      n=6898
      Yes

      n=1434
      No

      n=8045
      Yes

      n=365
      No

      n=9088
      Yes

      n=813
      No

      n=8585
      Heart Disease
      statistically significant association with physiotherapist (p<0.005).
      ,
      statistically significant association with counsellor (p<0.005).
      ,
      statistically significant association with nurse (p<0.005).
      ,
      statistically significant association with dietitian (p<0.005).
      ,
      statistically significant association with podiatrist (p<0.005).
      7%4%7%4%8%4%5%4%10%4%7%4%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      1.501.731.350.951.391.31
       (95% CI)(1.15, 1.96)(1.08, 2.72)(0.99, 1.83)(0.74, 1.22)(0.95, 2.06)(0.98, 1.73)
      Hypertension
      statistically significant association with physiotherapist (p<0.005).
      ,
      statistically significant association with nurse (p<0.005).
      ,
      statistically significant association with dietitian (p<0.005).
      ,
      statistically significant association with podiatrist (p<0.005).
      34%30%34%31%43%29%32%30%47%30%36%30%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      1.111.241.531.051.421.21
       (95% CI)(0.98, 1.26)(1.01, 1.53)(1.32, 1.78)(0.95, 1.17)(1.15, 1.77)(1.06, 1.39)
      Diabetes Mellitus
      statistically significant association with counsellor (p<0.005).
      ,
      statistically significant association with nurse (p<0.005).
      ,
      statistically significant association with optician (p<0.005).
      ,
      statistically significant association with dietitian (p<0.005).
      ,
      statistically significant association with podiatrist (p<0.005).
      8%8%11%7%17%6%9%5%33%6%17%6%
       Odds Ratio
      adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.
      0.861.202.161.815.083.31
       (95% CI)(0.68, 1.09)(0.83, 1.75)(1.71, 2.72)(1.46, 2.24)(3.92, 6.58)(2.69, 4.07)
      statistically significant association with physiotherapist (p<0.005).
      statistically significant association with counsellor (p<0.005).
      statistically significant association with nurse (p<0.005).
      § statistically significant association with optician (p<0.005).
      statistically significant association with dietitian (p<0.005).
      statistically significant association with podiatrist (p<0.005).
      ∗∗ adjusted for level of education, area of residence, marital status, income, health insurance, comorbidities, symptoms, smoking status, and alcohol consumption.

      Discussion

      This is the first study to date to provide a detailed examination of CAM use, distinguishing practitioner consultations and self-prescribed CAM, by women with heart disease, diabetes, and/or hypertension. The findings highlight a number of key issues that affect the care provided by conventional health providers.
      Our data show that women with diabetes were less likely to consult with CAM practitioners in general (specifically massage therapists, chiropractors, and osteopaths) and less likely to use self-prescribed CAM in general (specifically yoga and meditation). These findings are similar to those of a recent study in Australia,
      • Lui C.W.
      • Dower J.
      • Donald M.
      • Coll J.R.
      Patterns and determinants of complementary and alternative medicine practitioner use among adults with diabetes in Queensland, Australia.
      which showed that 7.7% of patients with diabetes used CAM practitioners alongside or as a complement to conventional health care services during a 12-month period. This observation may be related to lower self-efficacy for physical activity as shown in cardiac rehabilitation.
      • Davidson P.M.
      • Mitchell J.A.
      • DiGiacomo M.
      • Inglis S.C.
      • Newton P.J.
      • Harman J.
      Cardiovascular disease in women: implications for improving health outcomes.
      Issues such as incontinence are commonly neglected in health care interventions for women and may act as barriers to such engagement.
      • Tak E.C.
      • van Hespen A.
      • van Dommelen P.
      • Hopman-Rock M.
      Does improved functional performance help to reduce urinary incontinence in institutionalized older women? A multicenter randomized clinical trial.
      Moreover, regular exposure to health care professionals in relation to medical management of diabetes may increase patients' skepticism toward CAM providers. Furthermore, the increased use of mainstream services may decrease patients' threshold to explore other treatment and care options. Such higher conventional health care utilization likely reflects increased incentives to access conventional cardiovascular care through government-funded payments (e.g., for providing services for individuals with diabetes).
      Medicare
      Practice Incentives Program (PIP).
      Women with hypertension were less likely to consult with CAM practitioners in general (specifically massage therapists and naturopaths or herbalists) and less likely to use self-prescribed CAM in general (specifically yoga and meditation and herbal medicines). This finding contrasts those of Gohar et al,
      • Gohar F.
      • Greenfield S.M.
      • Beevers D.G.
      • Lip G.Y.H.
      • Jolly K.
      Self-care and adherence to medication: a survey in the hypertension outpatient clinic.
      who identified the prevalence of CAM use in hypertensive patients as higher than in the general British population. Bell et al
      • Bell R.
      • Suerken C.
      • Grzywacz J.
      • Lang W.
      • Quandt S.
      • Arcury T.
      CAM use among older adults age 65 or older with hypertension in the United States: general use and disease treatment.
      found higher CAM use in adults >65 years of age with hypertension compared with those without diagnosed hypertension (69.5% vs 65.6%). Yet just 7.8% of these CAM users reported using CAM to manage their hypertension.
      The lower use of CAM in our study sample may be related to fears of adverse events or sociocultural factors influencing CAM use.
      • Van der Schee E.
      • Groenewegen P.
      Determinants of public trust in complementary and alternative medicine.
      • White A.
      • Boon H.
      • Alræk T.
      • Lewith G.
      • Liu J.
      Reducing the risk of complementary and alternative medicine (CAM): challenges and priorities.
      Alternatively, there was a large number of women in our study with lone hypertension and, as a consequence, low symptom burden. A lower symptom burden may explain the low level of CAM use identified in our study. Treatment adherence in hypertension is noted to be low, mainly because of the “asymptomatic” nature of the condition,
      • Marshall I.J.
      • Wolf D.
      • McKevitt C.
      Lay perspectives on hypertension and drug adherence: systematic review of qualitative research.
      so exploring the relation between CAM use and adherence to conventional medications would be of interest in future studies, as well as understanding cardiovascular patients' decision-making regarding using or not using CAM.
      In the present study, self-reported heart disease was not associated with any CAM use (self-prescribed or consultations). This is an interesting observation that may be associated with a higher perception of risk for CAM use, a lower perception of efficacy of CAM, or potentially less engagement in self-management. The lower CAM use shown in our analysis parallels the findings of Armstrong et al,
      • Armstrong A.R.
      • Thiebaut S.P.
      • Brown L.J.
      • Nepal B.
      Australian adults use complementary and alternative medicine in the treatment of chronic illness: a national study.
      who identified <10% of patients with heart or circulatory conditions as using CAM, with most of their sample preferring conventionally medically prescribed treatments. Steinsbekk et al
      • Steinsbekk A.
      • Adams J.
      • Sibbritt D.
      • Johnsen R.
      • Jacobsen G.
      • Holmen J.
      Socio-demographic characteristics and health perceptions among male and female CAM users in a Norwegian total population study (HUNT 2).
      also reported that adults with cardiovascular disease were less likely to consult with a CAM practitioner, instead preferring to consult with a GP.
      Our study and disparate rates and patterns across illness groups and countries suggest that as well as investigating CAM use within a sociocultural context, considering funding models and drivers in the health care system is likely important. Studies from the United States have shown that the use of CAM is influenced by high costs in accessing mainstream services and also perceptions of intervention efficacy.
      • Shah S.H.
      • Engelhardt R.
      • Ovbiagele B.
      Patterns of complementary and alternative medicine use among United States stroke survivors.
      In particular, 1 study showed that the increases in relative cost of conventional health care was a factor in adults being more likely to use CAM when not getting or delaying needed medical care.
      • Pagán J.A.
      • Pauly M.V.
      Access to conventional medical care and the use of complementary and alternative medicine.
      In patients with heart disease, higher proportions of patients who used CAM were non-Caucasian (31% vs 12%), were uninsured (12% vs 7%), experienced socioeconomic deprivation (58% vs 29%), and had depression (13% vs 6%).
      • Decker C.
      • Huddleston J.
      • Kosiborod M.
      • Buchanan D.
      • Stoner C.
      • Jones A.
      Self-reported use of complementary and alternative medicine in patients with previous acute coronary syndrome.
      Yet a recent study from the United States suggests that rates of CAM use are plateauing.
      • Davis M.A.
      • Martin B.I.
      • Coulter I.D.
      • Weeks W.B.
      US Spending on complementary and alternative medicine during 2002-08 Plateaued, suggesting role in Reformed health system.
      Alternatively, access to universal health care coverage in Australia may moderate CAM use, which is predominantly practiced outside the public health care system and can attract significant out-of-pocket expenses.
      • Pagán J.A.
      • Pauly M.V.
      Access to conventional medical care and the use of complementary and alternative medicine.
      • Xue C.L.
      • Zhang A.
      • Lin V.
      • Da Costa C.
      • Story D.
      Complementary and alternative medicine use in Australia: a national population-based survey.
      The low prevalence of CAM use identified in our study may reflect the potential saturation of consultations with “conventional” practitioners (e.g., general, medical, and allied health practitioners) and subsequent financial restrictions for women in also consulting CAM practitioners. Therefore, in contrast to American CAM users who may be seeking health care options that are less expensive than conventional medicine, Australian CAM users may be those who have more financial resources, as the universal health care model makes conventional medicine the less expensive option. This context also raises interesting issues regarding patient expectations and perceptions around the efficacy of CAM, and any international comparison of CAM use for cardiovascular diseases must consider the cost of treatment as a mediating factor.
      The interpretation of our findings is limited by some survey design issues. Issues in reporting and characterizing CAM (including the details around any cross over between self-prescribed CAM use and advice provided by CAM therapists) may be subject to interpretation and also to issues of responder bias and self-report of heart disease, hypertension, and diabetes. In addition, the women in our sample were aged 59 to 64 years, and as such our findings may not be generalizable to the wider population. Despite these limitations, these data provide an important snapshot into the health-seeking behaviors in terms of CAM of women living with chronic illnesses.

      Acknowledgment

      The research on which this report is based was conducted as part of the ALSWH, University of Newcastle and University of Queensland. We are grateful to the Australian Department of Health and Ageing for funding and to the women who provided the survey data.

      Disclosures

      The authors have no conflicts of interest to disclose.

      References

        • Brown W.
        • Bryson L.
        • Byles J.
        • Dobson A.
        • Lee C.
        • Mishra G.
        Women's Health Australia: recruitment for a national longitudinal cohort study.
        Women Health. 1998; 28: 23-40
        • Brown W.J.
        • Dobson A.J.
        • Bryson L.
        • Byles J.E.
        Women's Health Australia: on the progress of the main study cohorts.
        J Womens Health Gend Based Med. 1999; 8: 681-688
        • Lui C.W.
        • Dower J.
        • Donald M.
        • Coll J.R.
        Patterns and determinants of complementary and alternative medicine practitioner use among adults with diabetes in Queensland, Australia.
        Evid Based Complement Altern Med. 2012; : 1-7
        • Davidson P.M.
        • Mitchell J.A.
        • DiGiacomo M.
        • Inglis S.C.
        • Newton P.J.
        • Harman J.
        Cardiovascular disease in women: implications for improving health outcomes.
        Collegian. 2012; 19: 5-13
        • Tak E.C.
        • van Hespen A.
        • van Dommelen P.
        • Hopman-Rock M.
        Does improved functional performance help to reduce urinary incontinence in institutionalized older women? A multicenter randomized clinical trial.
        BMC Geriatr. 2012; 12: 51
        • Medicare
        Practice Incentives Program (PIP).
        Department of Health, Australia2012 (Available at:) (Accessed on December 5, 2014)
        • Gohar F.
        • Greenfield S.M.
        • Beevers D.G.
        • Lip G.Y.H.
        • Jolly K.
        Self-care and adherence to medication: a survey in the hypertension outpatient clinic.
        BMC Complement Altern Med. 2008; 8: 4-14
        • Bell R.
        • Suerken C.
        • Grzywacz J.
        • Lang W.
        • Quandt S.
        • Arcury T.
        CAM use among older adults age 65 or older with hypertension in the United States: general use and disease treatment.
        J Altern Complement Med. 2006; 2: 903-909
        • Van der Schee E.
        • Groenewegen P.
        Determinants of public trust in complementary and alternative medicine.
        BMC Public Health. 2010; 10: 128-138
        • White A.
        • Boon H.
        • Alræk T.
        • Lewith G.
        • Liu J.
        Reducing the risk of complementary and alternative medicine (CAM): challenges and priorities.
        Eur J Integr Med. 2014; 6: 404-408
        • Marshall I.J.
        • Wolf D.
        • McKevitt C.
        Lay perspectives on hypertension and drug adherence: systematic review of qualitative research.
        BMJ. 2012; 345: e3953
        • Armstrong A.R.
        • Thiebaut S.P.
        • Brown L.J.
        • Nepal B.
        Australian adults use complementary and alternative medicine in the treatment of chronic illness: a national study.
        Aust N Z J Public Health. 2011; 35: 384-390
        • Steinsbekk A.
        • Adams J.
        • Sibbritt D.
        • Johnsen R.
        • Jacobsen G.
        • Holmen J.
        Socio-demographic characteristics and health perceptions among male and female CAM users in a Norwegian total population study (HUNT 2).
        Forsch Komplementmed. 2008; 15: 146-151
        • Shah S.H.
        • Engelhardt R.
        • Ovbiagele B.
        Patterns of complementary and alternative medicine use among United States stroke survivors.
        J Neurol Sci. 2008; 271: 180-185
        • Pagán J.A.
        • Pauly M.V.
        Access to conventional medical care and the use of complementary and alternative medicine.
        Health Aff. 2005; 24: 255-262
        • Decker C.
        • Huddleston J.
        • Kosiborod M.
        • Buchanan D.
        • Stoner C.
        • Jones A.
        Self-reported use of complementary and alternative medicine in patients with previous acute coronary syndrome.
        Am J Cardiol. 2007; 99: 930-933
        • Davis M.A.
        • Martin B.I.
        • Coulter I.D.
        • Weeks W.B.
        US Spending on complementary and alternative medicine during 2002-08 Plateaued, suggesting role in Reformed health system.
        Health Aff. 2013; 32: 45-52
        • Xue C.L.
        • Zhang A.
        • Lin V.
        • Da Costa C.
        • Story D.
        Complementary and alternative medicine use in Australia: a national population-based survey.
        J Altern Complement Med. 2007; 13: 643-650