Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Prospective investigation of complementary and alternative medicine use and subsequent hospitalizations

BMC Complementary and Alternative MedicineThe official journal of the International Society for Complementary Medicine Research (ISCMR)20088:19

DOI: 10.1186/1472-6882-8-19

Received: 23 July 2007

Accepted: 08 May 2008

Published: 08 May 2008

Abstract

Background

The prevalence of complementary and alternative medicine (CAM) use has been estimated to be as high as 65% in some populations. However, there has been little objective research into the possible risks or benefits of unmanaged CAM therapies.

Methods

In this prospective study of active duty US Navy and Marine Corps personnel, the association between self-reported practitioner-assisted or self-administered CAM use and future hospitalization was investigated. Cox regression models were used to examine risk of hospitalization due to any cause over the follow-up period from date of questionnaire submission, until hospitalization, separation from the military, or end of observation period (June 30, 2004), whichever occurred first.

Results

After adjusting for baseline health, baseline trust and satisfaction with conventional medicine, and demographic characteristics, those who reported self-administering two or more CAM therapies were significantly less likely to be hospitalized for any cause when compared with those who did not self-administer CAM (HR = 0.38; 95% CI = 0.17, 0.86). Use of multiple practitioner-assisted CAM was not associated with a significant decrease or increase of risk for future hospitalization (HR = 1.86; 95 percent confidence interval = 0.96-3.63).

Conclusion

While there were limitations to these analyses, this investigation utilized an objective measure of health to investigate the potential health effects of CAM therapies and found a modest reduction in the overall risk of hospitalization associated with self-administration of two or more CAM therapies. In contrast, use of practitioner-assisted CAM was not associated with a protective effect.

Background

Complementary and alternative medicine (CAM) therapies have been used for centuries in one form or another, with deep roots in many ancient cultures [1, 2]. Numerous studies have reported prevalence of CAM use to be from 9% to 65% in the United States, Canada, Australia, Denmark, and the United Kingdom [312]. Variations in the reported prevalence of CAM use likely come from descriptions and classification of CAM [4]. CAM has been described as a diagnosis, treatment, and/or prevention that complements mainstream medicine by contributing to a common whole, satisfying demand not met by orthodoxy or diversifying the conceptual frameworks of medicine [3]. With annual out-of-pocket expenditures related to CAM use in the United States estimated at more than $27 billion in 1997 [5], the prevalence of CAM use highlights its significance in the current healthcare environment.

Investigations of CAM use in US adult populations have suggested that women, those of white race/ethnicity, and those with higher education levels are more likely to use CAM therapies [5, 7, 13]. Investigations of US military personnel have also suggested that those who use CAM are more likely to be officers, in technical support occupations, report more sick days, and report more bodily pain [7].

CAM is often used as an alternative or complement to conventional medicine practices for prevention and/or treatment of disease. Among the most commonly reported CAM therapies are herbal medicine, massage, high-dose megavitamins, and relaxation therapies [5, 7, 1317]. Uses range from daily vitamin or herbal supplementation to ward off common colds, to therapies for symptom reduction of chronic illnesses such as fibromyalgia [18] or rheumatic diseases [19], for slowing or treating cancers [2023], and for relief of symptoms from other serious diseases such as AIDS [24, 25] or diabetes [26].

There has been little research, however, into the possible risks of unmanaged therapies [2729] as well as the benefits CAM use may offer in protection against disease or illness [30]. The objective of this study was to prospectively investigate the association of CAM use with subsequent hospitalization in a healthy, active-duty US military population with equal access to conventional medical care.

Methods

Study population

In December 2000, a random sample of 5,000 US active duty and Reserve Navy and Marine Corps personnel was drawn from military rosters of approximately 550,000. Prospective participants were sent a questionnaire to assess baseline health and use of CAM therapies [7]. There were 1,446 who responded out of 3,683 who received a study questionnaire and were eligible to participate. Among the ineligible subjects, 16 did not meet the initial enrollment criteria and 1,301 could not be located. Of the 3,683 eligible subjects, 49 refused to participate, 2,188 did not respond after repeated mailings, and 1,446 responded. This resulted in a response rate of 39.3%. Some proportional differences existed between responders and nonresponders when considering gender, age, marital status, rank, service branch, and occupational category. Respondents were slightly different than the target population of US Navy and Marine Corps, and tended to be older, married, officers, in the Navy, and in the field of healthcare [7]. Reserve personnel (n = 74) were excluded from these analyses due to differential access to Department of Defense (DoD) healthcare, leaving 1,372 respondents included in this analysis.

This research has been conducted in compliance with all applicable federal regulations governing the protection of human subjects in research (Protocol NHRC.2001.0001).

Questionnaire data

The 10-page survey instrument was developed by the study team to ascertain the use of CAM therapies, feelings of general health, and some demographic characteristics, while taking no more than 30 minutes to complete. Each invited participant was mailed up to 3 surveys with prepaid return envelopes, based on a modified version of the Dilman method [31]. Self-reported data included race/ethnicity (white, Hispanic, black, Asian or Pacific Islander, other), and salary (<$19,999; $20,000–34,999; $35,000–49,999; $50,000–74,999; ≥ $75,000). Also included in the questionnaire were questions to assess general health, bodily pain, medical care, and 24 questions regarding health conditions in the previous 12 months (lung problems, pneumonia, respiratory infections; high blood pressure; heart problems; diabetes; tumors or cancers; digestive problems; urinary tract problems; gynecologic or menstrual problems; neurological problems; sprains or muscle strains; skin or dermatological problems; allergies; dizziness; anxiety; depression; insomnia or trouble falling asleep; addictive problems with alcohol or drugs; obesity; chronic dental problems; arthritis or rheumatism; back problems; severe headaches; any other kind of chronic pain; and chronic fatigue syndrome). Health conditions reported in the previous 12 months were categorized into 0–1, 2–3, 4–5, or 6 or more.

CAM data

Included in the questionnaire data were 13 practitioner-assisted CAM therapies in the previous 12 months (acupuncture, chiropractic services, homeopathy, spiritual/religious healing, energy healing, folk remedies, massage therapies, self-help group, biofeedback, hypnosis, exercise/movement therapy, psychotherapy, and art/music therapy; categorized into 0, 1, or 2 or more), 8 self-administered CAM uses in the previous 12 months (high-dose mega-vitamin, herbal therapy, homeopathy, prayer/spiritual practice, energy healing, folk remedies, relaxation techniques, and aromatherapy; categorized into 0, 1, or 2 or more),

Demographic data

Demographic data were provided by the Defense Manpower Data Center, Monterey Bay, California. These data included sex, age (categorized by approximate quartile age groups: 18 to 24 years, 25 to 31 years, 32 to 38 years, and 39 to 57 years), rank (categorized into enlisted and officer), marital status (married or not), service branch (Navy and Marine Corps), DoD primary occupational specialties (10 major categories, defined by the DoD Occupational Conversion Manual) [32], highest level of education (some high school or diploma, some college, and college degree), and date of separation from military service.

Hospitalization data

Hospitalization data for each service member included, if applicable, date of admission and up to 8 discharge diagnoses coded using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) [33]. Data were captured from all DoD military treatment facilities as well as any private hospitals billing DoD for care during the period of December 1, 2000, through June 30, 2004. Hospitalizations were scanned in chronological order, and diagnostic fields were scanned in numeric order for the diagnostic codes of interest. Probability of hospitalization for "any cause" was examined using time until first hospitalization during the time period of survey submission until June 30, 2004. Hospitalization data indicating a diagnosis in the broad diagnostic category of complications of pregnancy, childbirth, and puerperium were not included.

Statistical analyses

Univariate analyses were performed to assess the significance of associations between hospitalization, demographic, and baseline self-reported variables. An exploratory analysis was developed to further assess variables of interest for significant associations and possible confounding, while simultaneously adjusting for all other variables in the model. Using regression diagnostics, collinearity among variables was assessed.

Cox's proportional hazards time-to-event modeling was used to independently compare (1) the hospitalization experience of practitioner-assisted CAM users with non practitioner-assisted CAM users and (2) the hospitalization experience of self-administered CAM users with the hospitalization experience of non self-administered CAM users, while accounting for attrition from active-duty service over the follow-up period [34]. Follow-up time was calculated from date of enrollment until hospitalization, separation from active-duty service, or June 30, 2004, whichever occurred first. The saturated Cox regression model was reduced by a manual, backward, stepwise elimination approach to investigate confounding, removing those variables that were not independently associated at an alpha cutoff level of 0.05 and not determined to be confounders. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for the hazard of hospitalization. Additionally, the cumulative probability of hospitalization as a function of time was graphed after stratification by different levels of CAM use. All data management and analyses were completed using SAS, version 9.1.2 (SAS Institute, Inc., Cary, North Carolina).

Results

Data for this analysis were complete and available for 1,319 of the 1,372 active-duty personnel who had elected to participate and submitted their questionnaire (96.1%). There were 434 (32.9%) who reported practitioner-assisted CAM therapies and 427 (32.4%) who reported self-administered CAM therapies in the 12 months prior to taking the survey (Table 1). Although the prevalence of those reporting practitioner-assisted and self-administered CAM use was similar, those using practitioner-assisted CAM included only half of those self-administering CAM (Table 1). As shown in Table 1, 55% of the study population reported fewer than 4 conditions in the previous year, 67% reported very good or excellent health, 56% reported none or very mild bodily pain in the past 4 weeks, 45% reported being very satisfied with their conventional medicine doctor, and 50% reported trusting their conventional medicine doctor completely or a lot. Demographic and military characteristics indicated that 78% of the study population were male, 50% were aged 18–31 years, 60% were married, 66% were white, 50% had some college education, 52% earned $35,000 or more each year, 82% were enlisted personnel, 77% were Navy, and 22% were combat specialists.
Table 1

Population characteristics by practitioner-assisted or self-administered CAM therapies

Characteristic

Population

N = 1,319

Practitioner-assisted CAM

n = 434 (32.9%)

Self-administered CAM

n = 427 (32.4%)

 

Number

%

Number

%

Number

%

Practitioner-assisted CAM

      

   0 therapies

885

67.1

--

--

192

45.0

   1 therapy

254

19.2

--

--

102

23.9

   ≥ 2 therapies

180

13.7

--

--

133

31.2

Self-administered CAM

      

   0 therapies

892

67.6

199

45.8

--

--

   1 therapy

237

18.0

114

26.3

--

--

   ≥ 2 therapies

190

14.4

121

27.9

--

--

Health conditions in past 12 months

      

   0–1 conditions

216

16.4

29

6.7

45

10.5

   2–3 conditions

507

38.4

145

33.4

145

34.0

   4–5 conditions

327

24.8

127

29.3

112

26.2

   ≥ 6 conditions

269

20.4

133

30.6

125

29.3

Bodily pain in past 4 weeks

      

   None/very mild

742

56.2

174

40.1

228

53.4

   Mild

308

23.4

122

28.1

89

20.8

   Moderate/severe

269

20.4

138

31.8

110

25.8

Satisfaction with conventional medical doctor

      

   Not at all or not very much

196

14.9

77

17.7

73

17.1

   Somewhat

528

40.0

187

43.1

185

43.3

   Very

595

45.1

170

39.2

169

39.6

Level of trust in conventional medical doctor

      

   Little or not at all

238

18.0

77

17.7

88

20.6

   Some

423

32.0

140

32.3

130

30.4

   Completely or a lot

658

49.9

217

50.0

209

49.0

General health

      

   Poor or fair

65

4.9

26

6.0

27

6.3

   Good

367

27.8

146

33.6

108

25.3

   Very good or excellent

887

67.3

262

60.4

292

68.4

Sex

      

   Male

1,029

78.0

323

74.4

301

70.5

   Female

290

22.0

111

25.6

126

29.5

Age (years)

      

   18–24

305

23.1

102

23.5

108

25.3

   25–31

348

26.4

114

26.3

102

23.9

   32–38

342

25.9

113

26.0

111

26.0

   39–57

324

24.6

105

24.2

106

24.8

Marital status

      

   Single or divorced

528

40.0

199

45.9

183

42.9

   Married

791

60.0

235

54.1

244

57.1

Race/ethnicity

      

   White

866

65.7

303

69.8

293

68.6

   Hispanic

123

9.3

28

6.5

37

8.7

   Black

190

14.4

59

13.6

54

12.7

   Asian and Pacific Islander

92

7.0

31

7.1

27

6.3

   Other

48

3.6

13

3.0

16

3.7

Education level

      

   Some high school or diploma

367

27.8

115

26.5

102

23.9

   Some college

654

49.6

226

52.1

221

51.8

   College degree

298

22.6

93

21.4

104

24.3

Salary

      

   ≤ $19,999

260

19.7

83

19.1

94

22.0

   $20,000-$34,999

377

28.6

123

28.3

111

26.0

   $35,000-$49,999

285

21.6

100

23.1

92

21.6

   $50,000-$74,999

239

18.1

79

18.2

73

17.1

   ≥ $75,000

158

12.0

49

11.3

57

13.3

Rank

      

   Enlisted

1,082

82.0

362

83.4

339

79.4

   Officer

237

18.0

72

16.6

88

20.6

Service

      

   Regular Navy

1,017

77.1

333

76.7

341

79.9

   Regular Marine Corps

302

22.9

101

23.3

86

20.1

Occupational category

      

   Combat specialists

287

21.8

89

20.5

103

24.1

   Electronic equipment repair

192

14.6

63

14.5

56

13.1

   Communications/intelligence

97

7.4

37

8.5

32

7.5

   Healthcare

115

8.7

38

8.8

44

10.3

   Functional support

220

16.7

70

16.1

60

14.1

   Electrical/mechanical repair

236

17.9

71

16.4

72

16.9

   Craft workers

33

2.5

17

3.9

11

2.6

   Service and supply handlers

82

6.2

24

5.5

27

6.3

   Students, trainees, other

57

4.3

25

5.8

22

5.2

Those reporting practitioner-assisted CAM therapy in the previous 12 months were proportionally more likely to be female, enlisted, single or divorced, white, with some college education, report 4 or more health problems, good general health, and moderate to severe bodily pain. With some variations, those reporting self-administering at least 1 CAM therapy in the previous 12 months were proportionally more likely to be female, officers, single or divorced, combat specialists, white, with a college degree, report 6 or more health conditions, and report moderate to severe bodily pain (Table 1).

Regression diagnostics for investigation of the pairwise correlations and the variance inflation factor suggested no discernable collinearity among the variables, although salary and rank were noted as moderately correlated. The proportional hazards assumption was assured by visual inspection of cumulative distribution function plots and tested by the addition to the model of interaction terms between the main variables and time. There were no statistically significant time-by-variable interactions observed using a p-value cutoff of 0.10.

After adjustment for potential confounders, reporting 1 or reporting 2 or more practitioner-assisted CAM therapy was not associated with any-cause hospitalization (Table 2). After adjustment for potential confounders in the Cox proportional hazards regression, those self-administering 1 CAM therapy appeared potentially protected (HR = 0.62; 95% CI, 0.33–1.17) and those self-administering 2 or more CAM therapies were significantly protected (HR = 0.38; 95% CI, 0.17–0.86) from any-cause hospitalization, in comparison with those who did not report self-administering CAM therapies (Table 2).
Table 2

Adjusted hazard ratios for any-cause hospitalization in personnel reporting practitioner-assisted or self-administered CAM therapies

Characteristic

Population

Hospitalized

  
 

Number

%

Number

%

HR*

95% CI

Practitioner-assisted CAM

      

   0 therapies

885

67.1

57

66.3

1.00

--

   1 therapy

254

19.3

14

16.3

0.99

0.53, 1.84

   ≥ 2 therapies

180

13.6

15

17.4

1.86

0.96, 3.63

Self-administered CAM

      

   0 therapies

892

67.6

65

75.6

1.00

--

   1 therapy

237

18.0

13

15.1

0.62

0.33, 1.17

   ≥ 2 therapies

190

14.4

8

9.3

0.38

0.17, 0.86

Health conditions in past 12 months

      

   0–1 conditions

216

16.4

11

12.8

1.00

--

   2–3 conditions

507

38.4

26

30.2

1.10

0.53, 2.28

   4–5 conditions

327

24.8

22

26.7

1.46

0.68, 3.18

   ≥ 6 conditions

269

20.4

27

31.4

2.22

0.99, 4.98

Bodily pain in past 4 weeks

      

   None/very mild

742

56.2

45

52.3

1.00

--

   Mild

308

23.4

22

25.6

1.02

0.58, 1.79

   Moderate/severe

269

20.4

19

22.1

0.94

0.50, 1.77

Level of satisfaction with conventional medical doctor

      

   Not at all or not very

196

14.9

10

11.6

1.00

--

   Somewhat

528

40.0

32

37.2

1.78

0.79, 4.02

   Very

595

45.1

44

51.2

2.30

0.91, 5.82

Level of trust in conventional medical doctor

      

   Little or not at all

238

18.0

15

17.4

1.00

--

   Some

423

32.0

27

31.4

0.80

0.39, 1.66

   Completely or a lot

658

49.9

44

51.2

0.69

0.31, 1.53

General health

      

   Poor or fair

65

4.9

8

9.3

1.00

--

   Good

367

27.8

27

31.4

0.45

0.20, 1.03

   Very good or excellent

887

67.3

51

59.3

0.39

0.17, 0.90

Sex

      

   Male

1,029

78.0

63

73.3

1.00

--

   Female

290

22.0

23

26.7

1.25

0.72, 2.18

Age (years)

      

   18–24

305

23.1

18

20.9

1.00

--

   25–31

348

26.4

17

19.8

0.64

0.30, 1.36

   32–38

342

25.9

24

27.9

1.15

0.53, 2.48

   39–57

324

24.6

27

31.4

1.17

0.51, 2.70

Marital status

      

   Single or divorced

528

40.0

27

31.4

1.00

--

   Married

791

60.0

59

68.6

1.68

0.99, 2.86

Race/ethnicity

      

   White

866

65.7

54

62.8

1.00

--

   Hispanic

123

9.3

11

12.8

1.61

0.81, 3.20

   Black

190

14.4

11

12.8

0.90

0.46, 1.79

   Asian and Pacific Islander

92

7.0

6

7.0

0.77

0.32, 1.87

   Other

48

3.6

4

4.7

1.20

0.42, 3.48

Education level

      

   Some high school or diploma

367

27.8

25

29.1

1.00

--

   Some college

654

49.6

37

43.0

0.85

0.49, 1.46

   College degree

298

22.6

24

27.9

1.44

0.63, 3.32

Salary

      

   ≤ $19,999

260

19.7

11

12.8

1.00

--

   $20,000-$34,999

377

28.6

35

40.7

1.92

0.87, 4.22

   $35,000-$49,999

285

21.6

15

17.4

1.01

0.38, 2.64

   $50,000-$74,999

239

18.1

12

14.0

0.68

0.24, 1.95

   ≥ $75,000

158

12.0

13

15.1

1.01

0.30, 3.47

Rank

      

   Enlisted

1,082

82.0

70

81.4

1.00

--

   Officer

237

18.0

16

18.6

1.02

0.40, 2.60

Service

      

   Regular Navy

1,017

77.1

69

80.2

1.00

--

   Regular Marine Corps

302

22.9

17

19.8

1.17

0.64, 2.13

Occupational category

      

   Combat specialists

287

21.8

15

17.4

1.00

--

   Electronic equipment repair

192

14.6

7

8.1

0.73

0.29, 1.85

   Communications/intelligence

97

7.4

5

5.8

1.17

0.41, 3.37

   Healthcare

115

8.7

16

18.6

2.63

1.21, 5.72

   Functional support

220

16.7

18

20.9

1.60

0.76, 3.35

   Electrical/mechanical repair

236

17.9

19

22.1

1.60

0.76, 3.36

   Craft workers

33

2.5

1

1.2

0.44

0.06, 3.55

   Service and supply handlers

82

6.2

2

2.3

0.52

0.12, 2.30

   Students, trainees, other

57

4.3

3

3.5

1.11

0.31, 3.99

*HR = adjusted hazards ratio; CI = 95% confidence interval.

Reference category.

Table 2 also shows other independent risk factors for hospitalization. Those reporting very good or excellent health were at 0.39 times the risk of hospitalization when compared with those reporting poor or fair health (95% CI, 0.17–0.90). Healthcare workers were at 2.63 times the risk of hospitalization in comparison with combat specialists (95% CI, 1.21–5.72).

Figure 1 shows the cumulative probability of hospitalization based on practitioner-assisted and self-administered CAM use, while simultaneously adjusting for all other variables in the model. The statistically significant difference associated with 2 or more self-administered CAM therapies (lowest probability of hospitalization) is apparent when compared with the curve of the highest probability of hospitalization among those not self-administering any CAM therapy. Although the step functions showed some sign of instability over the follow-up period, there was no statistically significant sign (p value < 0.10) of temporal bias within the two groups of CAM users.
https://static-content.springer.com/image/art%3A10.1186%2F1472-6882-8-19/MediaObjects/12906_2007_Article_178_Fig1_HTML.jpg
Figure 1

Cumulative probability of any-cause hospitalization in Department of Defense hospitals from date of questionnaire submission until June 30, 2004, by practitioner-assisted and self-administered CAM therapy.

Investigation of the 13 specific practitioner-assisted and the 8 self-administered CAM therapies identified only two statistically significant predictors of hospitalization (Table 3). After adjustment for potential confounders in the Cox proportional hazards regression, those who were assisted by a practitioner with acupuncture (HR, 3.92; 95% CI, 1.53–10.06) or chiropractic services (HR = 1.96; 95% CI, 1.01–3.80) were at increased risk for future hospitalization in comparison with those not self-reporting such CAM use. Investigation of self-administered CAM uses found no statistically significant associations, but did find many of the therapies to tend toward a protective effect. Those self-administering high-dose mega-vitamins, herbal therapies, prayer or spiritual practices, and aromatherapies, while statistically insignificant individually, likely added to the overall significant protective effect seen in Table 1.
Table 3

Adjusted hazard for any-cause hospitalization in personnel reporting specific practitioner-assisted and self-administered CAM uses in the last 12 months

CAM characteristic

CAM use

Hospitalized

  
 

Number

%

Number

%

HR*

95% CI

Practitioner-assisted CAM

      

   Exercise/movement therapy

245

18.6

17

19.8

1.13

0.64, 1.97

   Massage therapies

181

13.7

12

14.0

0.98

0.51, 1.86

   Chiropractic services

113

8.6

11

12.8

1.96

1.01, 3.80

   Art or music therapy

41

3.1

0

0.0

--

--

   Acupuncture

32

2.4

5

5.8

3.92

1.53, 10.06

   Spiritual/religious healing

30

2.3

2

2.3

1.14

0.27, 4.81

   Folk remedies

30

2.3

3

3.5

1.52

0.46, 5.09

   Energy healing

24

1.8

3

3.5

2.12

0.63, 7.17

   Psychotherapy

18

1.4

3

3.5

1.57

0.46, 5.41

   Homeopathy

17

1.3

1

1.2

--

--

   Self-help group

17

1.3

0

0.0

--

--

   Hypnosis

11

0.8

0

0.0

--

--

   Biofeedback

9

0.7

0

0.0

--

--

Self-administered CAM

      

   Herbal therapy

208

15.8

9

10.5

0.62

0.30, 1.26

   High-dose or mega-vitamin therapy

148

11.2

5

5.8

0.42

0.17, 1.06

   Relaxation techniques

139

10.5

9

10.5

1.97

0.47, 1.99

   Prayer or spiritual practice

132

10.0

4

4.7

0.39

0.14, 1.07

   Aromatherapy

68

5.2

2

2.3

0.37

0.09, 1.53

   Energy healing

34

2.6

3

3.5

1.28

0.39, 4.19

   Folk remedies

32

2.4

0

0.0

--

--

   Homeopathy

18

1.4

0

0.0

--

--

*HR = adjusted hazards ratio; CI = 95% confidence interval.

Discussion

The increasing trend of complementary and alternative medicine (CAM) use is likely to continue as physicians and patients search for new therapies to improve quality of life or identify alternative and less-toxic forms of therapy while remaining congruent with personal values and beliefs [5, 16, 35, 36]. As the prevalence of CAM use grows however, so does concern over possible side effects from misuse, abuse, and interactions of unmanaged therapies [2730]. Since there is little known about the risks and benefits of CAM use, and more than 1 in 3 US Navy and Marine Corps personnel use some form of CAM [7], this investigation sought to prospectively document the association of CAM use and subsequent hospitalization in a healthy active-duty population. These data suggest that practitioner-assisted CAM use was marginally associated with an increased risk of future hospitalization, while use of self-administered CAM therapies was significantly associated with a decrease in risk of future hospitalization.

Although not statistically significant, elevated hospitalization risk associated with practitioner-assisted CAM merits consideration. Although these analyses controlled for differences in self-reported general health, bodily pain, and health conditions, this elevated risk may be reflective of seeking healthcare from a professional for a longstanding condition for which conventional medicine has not provided a solution. This explanation is supported by the investigation of specific CAM treatments (Table 3). Only those who received practitioner-assisted acupuncture and chiropractic therapy were at increased risk of hospitalization, suggesting that these CAM users may have had conditions characterized by chronic pain, necessitating increased uses of both CAM and conventional medicine. However, investigation of diagnostic codes did not reveal specific trends of illness. More study into the temporal sequence of CAM and conventional medicine use among those with chronic pain conditions is likely worthwhile.

The finding that those self-administering a single CAM therapy were somewhat protected from future hospitalization, and those self-administering 2 or more CAM therapies were statistically significantly protected, is interesting. This may reflect an elevated individual role and interest in personal health, leading to more involvement in one's own self-care. This explanation is supported by investigation of the specific CAM uses. Table 3 shows that no single self-administered CAM use was statistically significant, but high-dose mega-vitamins, prayer or spiritual practice, and aromatherapy all had more than a suggested twofold protective effect against hospitalization. Another possible explanation of the observed health of CAM users may be the placebo affect [3739]. The application of hospitalization, as a relatively objective measure of severe health problems, makes this explanation much less likely. Whether the apparent decrease in risk for adverse health outcomes is due to a healthy mindset and hypervigilance towards one's own health or a direct effect of a certain therapy should be considered for future study using a more controlled analytic design.

This study had important limitations that should be noted. First, we selected a broad definition of CAM based on our available survey data. CAM may be defined differently in other research, especially since CAM is, by its nature, somewhat dynamic and evolving. The CAM treatments "psychotherapy" and "self-help groups" are among the more contested inclusions in any definition of CAM. Please note, however, that fewer than 2% of our sample reported use of these treatments. Exclusion of psychotherapy and self-help groups from the CAM definition did not change results of this analysis. Still, these analyses should be interpreted with the various CAM definitions in mind. Approximately 40% of the Navy and Marine Corps population contacted elected to participate, diminishing the ability to generalize these findings to all Navy and Marine Corps personnel or the US military in general. These data were self-reported, and recall bias may be problematic when trying to identify and quantify CAM use. Further, uncertainty caused by labels given to CAM therapies that may not explicitly describe the varying forms of the therapy within the label may cause over or under-reporting of a therapy. Additionally, these data may lack the power to identify significant results with modest levels of association. Conversely, because these analyses were not mathematically adjusted for multiple comparisons, some findings may simply be due to chance. Furthermore, it was not possible to determine if differences in CAM use occurred from the time between survey completion and end of follow-up for hospitalization surveillance. The average time from survey submission to hospitalization was over one year and only CAM use at the time of the survey completion was able to be assessed. Lastly, this investigation focused on morbidity severe enough to require hospitalization, not a broader spectrum of health outcomes.

Despite these limitations, these analyses offer the first exploratory, population-based epidemiologic investigation of a diverse set of CAM therapies that may be of benefit or risk of morbidity among CAM users and nonusers. Hospitalizations are an objective outcome measure, in contrast to self-reported symptoms or illnesses. These hospitalization data are very complete because active-duty military personnel have ready access to essentially free medical care in DoD facilities and have medical care coverage in private hospitals for emergencies. Since active-duty personnel seldom seek care outside the DoD healthcare system, it is likely that these data captured virtually 100% of the most serious health outcomes. Lastly, the use of sophisticated statistical modeling techniques with many variables to adjust for possible confounding, and the integration of data from diverse sources, allowed for the quantification of hospitalization risk over time associated with CAM use.

Conclusion

This analysis prospectively quantified reported CAM use in relation to adverse health outcomes requiring hospitalization. Using an objective outcome measure in a well-defined population of young adults, these data suggest a statistically significant protective effect associated with self-administrated CAM therapies. In contrast, use of practitioner-assisted CAM therapies was not associated with decreased rates of subsequent hospitalization. More rigorous testing of CAM therapies and interactions with conventional therapies is possible and should be conducted.

Declarations

Acknowledgements

We thank Scott L. Seggerman from the Management Information Division, Defense Manpower Data Center, Seaside, California, for providing the sample of US Navy and Marine Corps personnel and associated demographic data. Additionally, we thank Michelle Stoia for technical review, and Gia Gumbs, MPH; Robb Reed, MS; and Jim Whitmer for help with data accession and management, from the US Department of Defense Center for Deployment Health Research at the Naval Health Research Center, San Diego, CA, USA. We also thank Dr. Gregory Gray from the University of Iowa and Dr. James Riddle from Wright-Patterson Air Force Base. We appreciate the support of the Henry M. Jackson Foundation for the Advancement of Military Medicine, Rockville, Maryland, USA.

This represents report 07–25, supported by the US Department of Defense, under work unit no. 60002. The views expressed in this article are those of the authors and do not reflect the official policy or position of the US Department of the Navy, US Department of Defense, or the US Government.

This research has been conducted in compliance with all applicable federal regulations governing the protection of human subjects in research (NHRC Protocol #31284).

Authors’ Affiliations

(1)
Department of Defense Center for Deployment Health Research, Naval Health Research Center

References

  1. Vickers A, Zollman C: ABC of complementary medicine: herbal medicine. BMJ. 1999, 319 (7216): 1050-1053.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Davis J: Native Americans knew the power of herbs but to modern-day scientists, many botanicals remain a mystery. Edited by: News WMDM. [http://www.my.webmd.com/content/article/1728.60591]
  3. Ernst E: Prevalence of use of complementary/alternative medicine: a systematic review. Bull World Health Organ. 2000, 78 (2): 252-257.PubMedPubMed CentralGoogle Scholar
  4. Harris P, Rees R: The prevalence of complementary and alternative medicine use among the general population: a systematic review of the literature. Complement Ther Med. 2000, 8 (2): 88-96. 10.1054/ctim.2000.0353.View ArticlePubMedGoogle Scholar
  5. Eisenberg DM, Davis RB, Ettner SL, Appel S, Wilkey S, Van Rompay M, Kessler RC: Trends in alternative medicine use in the United States, 1990-1997: results of a follow-up national survey. JAMA. 1998, 280 (18): 1569-1575. 10.1001/jama.280.18.1569.View ArticlePubMedGoogle Scholar
  6. Oldendick R, Coker AL, Wieland D, Raymond JI, Probst JC, Schell BJ, Stoskopf CH: Population-based survey of complementary and alternative medicine usage, patient satisfaction, and physician involvement. South Carolina Complementary Medicine Program Baseline Research Team. South Med J. 2000, 93 (4): 375-381.View ArticlePubMedGoogle Scholar
  7. Smith TC, Ryan MA, Smith B, Reed RJ, Riddle JR, Gumbs GR, Gray GC: Complementary and alternative medicine use among US Navy and Marine Corps personnel. BMC Complement Altern Med. 2007, 7 (16):
  8. Millar WJ: Use of alternative health care practitioners by Canadians. Can J Public Health. 1997, 88 (3): 154-158.PubMedGoogle Scholar
  9. MacLennan AH, Wilson DH, Taylor AW: Prevalence and cost of alternative medicine in Australia. Lancet. 1996, 347 (9001): 569-573. 10.1016/S0140-6736(96)91271-4.View ArticlePubMedGoogle Scholar
  10. Rasmussen NK, Morgall JM: The use of alternative treatments in the Danish adult population. Complementary Medical Research. 1990, 4: 16-22.Google Scholar
  11. Thomas KJ, Nicholl JP, Coleman P: Use and expenditure on complementary medicine in England: a population based survey. Complement Ther Med. 2001, 9 (1): 2-11. 10.1054/ctim.2000.0407.View ArticlePubMedGoogle Scholar
  12. Barnes PM, Powell-Griner E, McFann K, Nahin RL: Complementary and alternative medicine use among adults: United States, 2002. Adv Data. 2004, 1-19.Google Scholar
  13. Rafferty AP, McGee HB, Miller CE, Reyes M: Prevalence of complementary and alternative medicine use: state-specific estimates from the 2001 Behavioral Risk Factor Surveillance System. Am J Public Health. 2002, 92 (10): 1598-1600.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Eisenberg DM, Kessler RC, Foster C, Norlock FE, Calkins DR, Delbanco TL: Unconventional medicine in the United States. N Engl J Med. 1993, 328: 246-252. 10.1056/NEJM199301283280406.View ArticlePubMedGoogle Scholar
  15. Ni H, Simile C, Hardy AM: Utilization of complementary and alternative medicine by United States adults: results from the 1999 National Health Interview Survey. Med Care. 2002, 40 (4): 353-358. 10.1097/00005650-200204000-00011.View ArticlePubMedGoogle Scholar
  16. Astin JA: Why patients use alternative medicine: results of a national study. JAMA. 1998, 279 (19): 1548-1553. 10.1001/jama.279.19.1548.View ArticlePubMedGoogle Scholar
  17. Elder NC, Gillcrist A, Minz R: Use of alternative health care by family practice patients. Arch Fam Med. 1997, 6: 181-184. 10.1001/archfami.6.2.181.View ArticlePubMedGoogle Scholar
  18. Berman BM, Swyers JP: Complementary medicine treatments for fibromyalgia syndrome. Best Pract Res Clin Rheumatol. 1999, 13 (3): 487-492. 10.1053/berh.1999.0039.View ArticleGoogle Scholar
  19. Ernst E: Usage of complementary therapies in rheumatology: a systematic review. Clin Rheumatol. 1998, 17 (4): 301-305. 10.1007/BF01451009.View ArticlePubMedGoogle Scholar
  20. Ponholzer A, Struhal G, Madersbacher S: Frequent use of complementary medicine by prostate cancer patients. Eur Urol. 2003, 43 (6): 604-608.View ArticlePubMedGoogle Scholar
  21. Ernst E, Cassileth BR: The prevalence of complementary/alternative medicine in cancer: a systematic review. Cancer. 1998, 83 (4): 777-782. 10.1002/(SICI)1097-0142(19980815)83:4<777::AID-CNCR22>3.0.CO;2-O.View ArticlePubMedGoogle Scholar
  22. Ernst E, Filshie J, Hardy J: Evidence-based complementary medicine for palliative cancer care: does it make sense?. Palliat Med. 2003, 17 (8): 704-707.PubMedGoogle Scholar
  23. Bernstein BJ, Grasso T: Prevalence of complementary and alternative medicine use in cancer patients. Oncology (Huntingt). 2001, 15 (10): 1267-72; discussion 1272-8, 1283.Google Scholar
  24. Sparber A, Wootton JC, Bauer L, Curt G, Eisenberg D, Levin T, Steinberg SM: Use of complementary medicine by adult patients participating in HIV/AIDS clinical trials. J Altern Complement Med. 2000, 6 (5): 415-422. 10.1089/acm.2000.6.415.View ArticlePubMedGoogle Scholar
  25. Ernst E: Complementary AIDS therapies: the good, the bad and the ugly. Int J STD AIDS. 1997, 8 (5): 281-285. 10.1258/0956462971920028.View ArticlePubMedGoogle Scholar
  26. Egede LE, Ye X, Zheng D, Silverstein MD: The prevalence and pattern of complementary and alternative medicine use in individuals with diabetes. Diabetes Care. 2002, 25 (2): 324-329. 10.2337/diacare.25.2.324.View ArticlePubMedGoogle Scholar
  27. Silverstein DD, Spiegel AD: Are physicians aware of the risks of alternative medicine?. J Community Health. 2001, 26 (3): 159-174. 10.1023/A:1010303528081.View ArticlePubMedGoogle Scholar
  28. Ernst E: Complementary medicine: where is the evidence?. J Fam Pract. 2003, 52 (8): 630-634.PubMedGoogle Scholar
  29. Ernst E: Complementary medicine: its hidden risks. Diabetes Care. 2001, 24 (8): 1486-1488. 10.2337/diacare.24.8.1486.View ArticlePubMedGoogle Scholar
  30. Barrett B, Kiefer D, Rabago D: Assessing the risks and benefits of herbal medicine: an overview of scientific evidence.[see comment]. Altern Ther Health Med. 1999, 5 (4): 40-49.PubMedGoogle Scholar
  31. Dillman DA: Mail and telephone surveys: the total design method. 1978, New York , Wiley, xvi, 325-Google Scholar
  32. Office of the Under Secretary of Defense Personnel and Readiness: DoD Instruction 1312.1-I, DoD Occupational Conversion Index, 03/2001. 2001, Washington, DC , Department of Defense, Office of the Assistant Secretary of Defense, Force Management and PersonnelGoogle Scholar
  33. The International Classification of Diseases, 9th Revision, Clinical Modification. Edited by: Speirs L. 1998, Salt Lake City , Medicode Publication, 272-5th
  34. Cox DR: Regression models and life tables (with discussion). J R Stat Soc [Ser B]. 1972, B34: 187-220.Google Scholar
  35. Kurtz ME, Nolan RB, Rittinger WJ: Primary care physicians' attitudes and practices regarding complementary and alternative medicine. J Am Osteopath Assoc. 2003, 103 (12): 597-602.PubMedGoogle Scholar
  36. Berman BM, Singh BB, Hartnoll SM, Singh BK, Reilly D: Primary care physicians and complementary-alternative medicine: training, attitudes, and practice patterns.[see comment]. J Am Board Fam Pract. 1998, 11 (4): 272-281.View ArticlePubMedGoogle Scholar
  37. Kienle GS, Kiene H: The powerful placebo effect: fact or fiction?. J Clin Epidemiol. 1997, 50 (12): 1311-1318. 10.1016/S0895-4356(97)00203-5.View ArticlePubMedGoogle Scholar
  38. Kaptchuk TJ: The placebo effect in alternative medicine: can the performance of a healing ritual have clinical significance?. Ann Intern Med. 2002, 136 (11): 817-825.View ArticlePubMedGoogle Scholar
  39. So DW: Acupuncture outcomes, expectations, patient-provider relationship, and the placebo effect: implications for health promotion. Am J Public Health. 2002, 92 (10): 1662-1667.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6882/8/19/prepub

Copyright

© Smith et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement