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Determinants of supplement usage
Preventive Medicine 39 (2004) 932 – 939 www.elsevier.com/locate/ypmed
Determinants of supplement usage Dana Fennell, M.A. * University of Florida, Gainesville, FL 32611-7330, USA Available online 6 May 2004
Abstract Background. As the use of supplements is growing, this study examines the determinants of vitamin and herbal supplement usage. Instead of treating these as all-encompassing categories, they are broken into specific vitamins and herbs and compared to see if users are different. A measure of frequency of vitamin use is also created. Methods. Logistic and ordinal logistic regressions are run on a sample of 24,834 from the National Health Interview Survey (NHIS) 2000. Results. Women are generally more likely to use supplements than men. Non-Latino Whites are generally more likely to use supplements than non-Latino Blacks and Mexicans. However, despite these general trends, it proves fruitful to break up supplement use into smaller categories. The data provide some evidence that determinants vary by particular supplement. However, including a measure of frequency does not change the picture much. Conclusions. Considering this, more information is needed on why people use particular supplements and what their sources of information are. D 2004 The Institute For Cancer Prevention and Elsevier Inc. All rights reserved. Keywords: Alternative medicine; Supplements; Vitamins
Researchers have taken notice of the growing public interest in dietary and medicinal supplements. The World Health Organization has released a ‘‘global strategy’’ to critically address the safety and availability of traditional and alternative medicine [1]. Research in the past two decades (although largely conducted in industrial countries) has been accumulating on the prevalence of use and the characteristics of supplement users [2– 4]. This information is important for a number of reasons. First, studies have pointed out many patients do not report supplement use to their primary physicians [5 –9]. Because of concerns of dangerous drug/supplement or supplement/ supplement interactions [6,10,11], it is important to know the prevalence of real usage and what groups of people are taking these items. Second, there are concerns that consumers may be taking too many or not enough supplements and relying on unfounded claims that supplements will help them [12 – 14]. It again becomes relevant to know what people are taking and in what amounts. This information
* University of Florida, P.O. Box 117330, Gainesville, FL 32611-7330. E-mail address: [email protected].
may help educators target vulnerable populations [15] or influence public policies about public health dietary intake recommendations [16]. Third, many demographic and lifestyle variables have been shown to have a relationship with supplement use [2,14,17,18]. When researchers seek to investigate the relationship between supplement use and health outcomes, it is necessary to understand how these other variables (demographic and lifestyle) can confound the relationship [3,19]. Variables that have been found to have a relationship with supplement use are race or ethnicity, age, education, income, and lifestyle variables such as drinking, smoking, and exercising. Non-Hispanic Whites, females, those with more education, and those with more income seem to be comparatively more likely to use supplements (whether herbs, vitamins, or minerals) [2,4,15,20]. Age appears as a common variable, with many studies saying older people are more likely to use, although the age range specified sometimes refers to those who are middle aged [7] and in other studies to the elderly [3]. There is also some evidence that a healthier lifestyle (for example more exercise, higher levels of nutrients in dietary intake, less obesity) is associated with using supplements [15,21]. However, studies sometimes contradict each other on these variables. Ishihara’s study
0091-7435/$ - see front matter D 2004 The Institute For Cancer Prevention and Elsevier Inc. All rights reserved. doi:10.1016/j.ypmed.2004.03.031
D. Fennell / Preventive Medicine 39 (2004) 932–939
found that those who had better diets (intakes of energy and nutrients) were not as likely to take supplements compared to those with worse diets [3]. There are still gaps in the literature. There is more information about vitamin or mineral supplement use than herbal supplement use [22]. Explanatory variables may vary by the type of product being analyzed, whether a particular vitamin or herb [16,17,19,23]. This makes sense because supplements are sometimes used to prevent particular illnesses or used in response to them; illnesses themselves often have particular demographic and lifestyle variables associated with them. Some herbs are used primarily in Traditional Chinese Medicine (TCM), others more by American herbalists. The media have promoted some more than others. Finally, there is a need to understand the frequency with which people use supplements and what variables are associated with particular frequencies of use [19,24]. By employing data from the National Health Interview Survey (NHIS) of 2000,1 this study attempts to address these concerns. This study investigates the determinants of supplement use in the United States when treated as an allencompassing category (any vitamin or mineral use as a whole, any herbal use as a whole) and when broken down into individual categories (Multiple vitamin, vitamins A, C, and E, Astragulus, Dong Quai, Cat’s Claw, Echinacea, and St. John’s Wort). This is done to see if explanatory variables depend on the type of supplement. Further, this study creates a frequency variable to determine whether those who use are also the most frequent users.
Research design and methods Data and sample Data for this study come from the NHIS 2000. The NHIS is a yearly cross-sectional survey conducted of households within the United States (face-to-face interviews). The 2000 survey consists of the usual core sections plus a supplemental Cancer Control Module. The supplement is used in this study for information about respondents’ diet and nutrition, Hispanic acculturation, and physical activity. The response rate for sample adult respondents was 72.1%, with 32,374 adults of age 18+ being interviewed. Data from various core sections are combined to get sufficient data on demographic characteristics. The analytical sample contains 24,834 people. Table 1 provides descriptive statistics for the sample. As shown there, females make up 56.15% of the sample. Mexicans make
1 National Center for Health Statistics. Data File Documentation, National Health Interview Survey 2000. Maryland: National Center for Health Statistics; 2002. All interpretations or conclusions are the responsibility of the author, and not the NCHS.
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up 11.68%, non-Latinos 15.47%, and non-Latino Whites 72.86%. Data limitations include that supplement questions are not part of the yearly model, making it impossible to assess trends over time or have a large enough sample to break up racial or ethnic groups further. There is no information on the dosage of the supplements taken, so any frequency variables created neglect this information. Outweighing these are the advantages of a large sample size, questions that separate ethnicity and acculturation, and questions targeting both vitamin and herbal supplement users. Dependent variables: supplement use Dependent variables are based on survey questions asking whether particular supplements have been taken in the past 12 months. All missing and refused data are omitted, and dichotomous variables are formed as follows. Vitamin or mineral supplement use refers to whether a participant has taken either vitamin or mineral supplements in the past 12 months. It is not possible to differentiate vitamin and mineral use here because of survey wording. Then this category is broken down to create more specific variables. Separate variables are created for the use of multiple, A, C, and E vitamins. Multiple refers to whether a person has taken a multiple vitamin in the past 12 months; vitamin A if a person has taken vitamin A in the past 12 months, and so on. Herbal supplementation is distinguished from vitamin and mineral use. The variable herbs refers to whether a person has taken any mixed or single herbal or botanical supplements in the past 12 months. This larger category is broken down similar to the above. Separate variables are created for Astragalus, Dong Quai, Cat’s Claw, Echinacea, and St. John’s Wort supplementation. Astragalus refers to whether a person took Astragalus supplements in the past 12 months; Dong Quai if a person has taken Dong Quai supplements in the past 12 months, and so on. Frequency Separate dependent variables are formed to measure the frequency of vitamin or mineral usage. These variables distinguish frequent users, moderate users, and nonusers. Frequent users are daily users (survey questions asking the number of months used and the number of days used are combined to construct this), moderate users are anything less than daily, and nonusers do not use at all. Separate dependent variables are created for multiple, A, C, and E vitamin users. Regressions using the dichotomous variables are then compared to those that take into account frequency (these are the italicized columns in Table 3).
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D. Fennell / Preventive Medicine 39 (2004) 932–939
Table 1 Descriptive statistics for the sample n Race/ethnicity Non-Latino White Non-Latino Black Mexican Sex Women Age group 18 – 24 25 – 39 40 – 54 55 – 69 70 up Income Unknown income Education Less than high school High school Some college Degree Health insurance Smoking Current smoker Former smoker Nonsmoker Drinking Current drinker Former drinker Nondrinker Exercise Does not exercise Light exercise Moderate exercise Vigorous exercise Unable to exercise Weight Light weight Nonproblematic weight Overweight Obese Weight unknown Married Serious index Functional limitations Self-reported health Fair – poor Good – excellent Supplement use Uses vitamins or minerals Uses multiple vitamins Uses vitamin A Uses vitamin C Uses vitamin E Uses herbs Uses Cat’s Claw Uses Astragalus
Table1 (continued) 24,834 72.86 15.47 11.68 56.15 10.97 31.01 28.49 16.93 12.60 7.21 (5.25) 21.00 19.04 30.00 19.50 31.46 83.88 23.64 22.33 54.03 62.50 14.66 22.84 37.31 15.03 20.90 24.53 2.22 3.02 37.98 34.59 24.41 2.73 53.85 0.57 (1.00) 1.08 (1.89) 11.61 88.39 52.68 43.07 4.17 20.45 17.06 14.83 0.25 0.27
Supplement use Uses Dong Quai Uses Echinacea Uses St. John’s Wort
0.42 4.70 2.30
Numbers indicate either percentages or means (with standard deviations in parentheses). Source: National Center for Health Statistics (2002).
Independent variables Demographic variables used in this study are age, gender, and race or ethnicity, as they have been found to correlate with supplement usage in previous studies. Age is broken into the following age categories: 18 – 24, 25 – 39, 40– 54, 55– 69, and 70 and up. Race or ethnicity is broken into categories for non-Latino Whites, non-Latino Blacks, and Mexicans (includes Mexicans and Mexican Americans). The generic Latino category is not used because it does not adequately portray the racial differences in health that have been found to exist when this category is unbundled. The sample is not large enough to separate out other Latino groups into variables. Socioeconomic variables used in this study are income, health insurance, and education. Income refers to the ratio of family income to poverty threshold. Those persons with unknown income are retained for analysis due to the large size of the category. The health insurance variable compares those with and those without it. Education is constructed into categories based on degree completed: less than high school, high school degree (includes GED), some college, and a degree (includes AA degrees to technical degrees). Lifestyle (or health risk) factors included in this study are smoking, drinking, exercise, weight, and marital status (as an indication of social stress). Smoking and drinking variables distinguish among current users, former users, and nonusers. Exercise variables compare non-, light, moderate, and vigorous exercisers and those unable to exercise.2 Weight variables are based on body mass index, distinguishing those who are underweight (score of 0.03 – 18.99), overweight (score of 25– 29.99), obese (score of 30+), and those with a nonproblematic weight (score of 19 –24.99). Social stress is encompassed in a marital status variable, those married compared to those who are not (includes never married, divorced, widowed, and separated). Previous research questions whether health status is associated with supplement usage. This study measures health status with two indexes and a variable for selfreported health. The chronic index relates the number of diagnoses a person has received for one of nine major
2
Vigorous and moderate exercisers are those who do vigorous or moderate exercise for at least 10 minutes three or more times per week, respectively.
D. Fennell / Preventive Medicine 39 (2004) 932–939
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exercising variables become dichotomous, comparing those who do and do not perform these activities. Overweight and obese variables are combined.
medical conditions (hypertension, coronary heart disease, angina pectoris, heart attack, heart disease, stroke, emphysema, cancer, and diabetes). A functional limitation index is created based on how many of nine activities a person has difficulty doing. For each activity, survey respondents could choose from a scale of 0 to 4, 4 indicating they cannot do the activity at all. These are combined and made into a functional limitation scale ranging from 0 to 5; any scores greater than 5 are assigned a 5. Self-reported health is turned into a dichotomous variable comparing those who define their health as excellent to good, with those who rate it fair or poor. Because so few people in the sample take the individual herbs (see Table 2), when multivariate analyses are done on specific herbs, categories of independent variables are collapsed as follows (see Table 4). Some college is combined with high school degree. Smoking, drinking, and
Procedures Univariate and bivariate analyses are initially performed to see patterns of supplement use. Regression models are then constructed to examine the determinants of supplement use (see Tables 3 and 4). Logistic regression models are used for the dichotomous supplement use variables. By doing this, it is possible to see if explanatory variables depend on the type of supplement being discussed. Ordinal logistic regression models are constructed for the supplement variables that take into account frequency (see the italicized columns in Table 3). By doing this, it
Table 2 Use differentials of various supplements by demographics
na Race/ethnicity Non-Latino White Non-Latino Black Mexican
Sex Men Women
Age group 18 – 24 25 – 39 40 – 54 55 – 69 70 up
Education Less than high school High school Some college Degree
Vitamins or minerals
Herbs
Multiple
Vitamin A
Vitamin C
Vitamin E
Astragalus
Dong Quai
Cat’s Claw
Echinacea
St. John’s Wort
13,083
3682
10,696
1036
5078
4237
68
105
63
1168
571
57.81 (10,460) 41.60 (1598) 35.34 (1025)
16.90 (3057) 10.73 (412) 7.34 (213)
47.34 (8566) 33.35 (1281) 29.28 (849)
4.58 (829) 3.36 (129) 2.69 (78)
23.84 (4314) 13.69 (526) 8.21 (238)
20.22 (3659) 10.28 (395) 6.31 (183)
0.34 (62) 0.13 (5) 0.03 (1)
0.48 (86) 0.34 (13) 0.21 (6)
0.28 (51) 0.16 (6) 0.21 (6)
5.62 (1016) 3.02 (116) 1.24 (36)
2.80 (507) 1.20 (46) 0.62 (18)
46.04 (5014) 57.87 (8069)
13.37 (1456) 15.96 (2226)
37.67 (4102) 47.29 (6594)
3.70 (403) 4.54 (633)
19.25 (2096) 21.39 (2982)
14.72 (1603) 18.89 (2634)
0.27 (29) 0.28 (39)
0.20 (22) 0.60 (83)
0.28 (30) 0.24 (33)
3.63 (395) 5.54 (773)
1.75 (191) 2.73 (380)
40.22 (1096) 46.98 (3618) 55.21 (3907) 61.04 (2566) 60.61 (1896)
10.97 (299) 13.92 (1072) 17.95 (1270) 16.82 (707) 10.68 (334)
33.17 (904) 39.72 (3059) 45.42 (3214) 48.50 (2039) 47.31 (1480)
1.80 (49) 2.86 (220) 4.82 (341) 5.99 (252) 5.56 (174)
13.21 (360) 17.14 (1320) 22.51 (1593) 25.86 (1087) 22.95 (718)
5.10 (139) 7.87 (606) 19.45 (1376) 29.97 (1260) 27.37 (856)
0.15 (4) 0.23 (18) 0.35 (25) 0.38 (16) 0.16 (5)
0.26 (7) 0.32 (25) 0.72 (51) 0.40 (17) 0.16 (5)
0.11 (3) 0.18 (14) 0.37 (26) 0.33 (14) 0.19 (6)
4.44 (121) 5.32 (410) 5.86 (415) 3.83 (161) 1.95 (61)
1.91 (52) 2.49 (192) 3.21 (227) 1.81 (76) 0.77 (24)
36.69 (1735) 48.79 (3635) 56.61 (2741) 63.64 (4972)
6.72 (318) 12.07 (899) 17.82 (863) 20.50 (1602)
28.86 (1365) 39.07 (2911) 47.11 (2281) 52.98 (4139)
2.75 (130) 3.32 (247) 4.81 (233) 5.45 (426)
10.32 (488) 16.83 (1254) 22.78 (1103) 28.58 (2233)
9.73 (460) 15.37 (1145) 18.26 (884) 22.37 (1748)
0.02 (1) 0.13 (10) 0.37 (18) 0.50 (39)
0.19 (9) 0.23 (17) 0.62 (30) 0.63 (49)
0.06 (3) 0.27 (20) 0.33 (16) 0.31 (24)
0.89 (42) 2.89 (215) 5.89 (285) 8.01 (626)
0.95 (45) 1.72 (128) 2.66 (129) 3.44 (269)
Numbers indicate percentages, with n displayed below in parentheses. Source: National Center for Health Statistics (2002). a This n represents the total number of people who use each supplement, respectively.
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D. Fennell / Preventive Medicine 39 (2004) 932–939
Table 3 Parameter estimates for supplement use from regression models (comparison groups are in parentheses) Vitamins or minerals Race/ethnicity (non-Latino White) Non-Latino Black Mexican Sex Men (Women) Age group 18 – 24 (25 – 39) 40 – 54 55 – 69 70 up Income Unknown income Education Less than high school High school Some college (degree) Health insurance Smoking Current smoker Former smoker (nonsmoker) Drinking Current drinker Former drinker (nondrinker) Exercise Does not exercise Light exercise Moderate exercise (vigorous exercise) Unable to exercise Weight Light weight (nonproblematic weight) Overweight Obese Weight unknown Marriage status Unmarried (married) Serious index Functional limitations Self-reported health Fair – poor (good – excellent) F
Herbs
Multiple
Multiplea
Vitamin A
Vitamin Aa
Vitamin C
Vitamin Ca
Vitamin E
Vitamin Ea
0.33* 0.24*
0.29* 0.42*
0.30* 0.16*
0.30* 0.17*
0.04 0.02
0.04 0.01
0.34* 0.62*
0.34* 0.61*
0.39* 0.56*
0.42* 0.56*
0.63*
0.32*
0.50*
0.46*
0.20*
0.19*
0.25*
0.23*
0.41*
0.41*
0.20* 0.31* 0.66* 0.73* 0.03* 0.12*
0.17* 0.26* 0.26* 0.02 0.03* 0.10
0.24* 0.20* 0.43* 0.44* 0.02* 0.16*
0.27* 0.27* 0.61* 0.67* 0.02* 0.18*
0.50* 0.58* 0.98* 1.04* 0.03* 0.06
0.50* 0.58* 0.99* 1.06* 0.03* 0.07
0.26* 0.33* 0.62* 0.64* 0.03* 0.07
0.27* 0.37* 0.72* 0.75* 0.03* 0.08
0.32* 1.06* 1.73* 1.74* 0.03* 0.20*
0.32* 1.07* 1.78* 1.81* 0.03* 0.22*
0.69* 0.39* 0.09 0.19*
0.73* 0.37* 0.04 0.31*
0.63* 0.36* 0.07 0.22*
0.60* 0.30* 0.04 0.21*
0.53* 0.36* 0.003 0.26
0.53* 0.35* 0.001 0.26
0.68* 0.44* 0.12* 0.01
0.67* 0.41* 0.10* 0.02
0.63* 0.34* 0.05 0.05
0.64* 0.33* 0.06 0.06
0.22* 0.12*
0.004 0.13*
0.19* 0.10*
0.19* 0.09*
0.04 0.13
0.04 0.14
0.06 0.15*
0.07 0.15*
0.13* 0.16*
0.14* 0.16*
0.35* 0.24*
0.48* 0.36*
0.29* 0.17*
0.23* 0.15*
0.06 0.004
0.07 0.003
0.32* 0.23*
0.29* 0.21*
0.25* 0.17*
0.25* 0.17*
0.78* 0.34* 0.27* 0.43*
0.89* 0.32* 0.27* 0.78*
0.62* 0.30* 0.23* 0.24*
0.59* 0.35* 0.22* 0.20*
0.69* 0.50* 0.51* 0.56*
0.69* 0.51* 0.51* 0.57*
0.80* 0.24* 0.30* 0.47*
0.79* 0.28* 0.30* 0.44*
0.86* 0.33* 0.37* 0.66*
0.85* 0.36* 0.36* 0.66*
0.03 0.11* 0.24* 0.05
0.10 0.03 0.06 0.22
0.05 0.10* 0.22* 0.08
0.04 0.10* 0.21* 0.05
0.29 0.10 0.20 0.39
0.29 0.10 0.20 0.40
0.03 0.02 0.11* 0.04
< 0.001 0.02 0.11* 0.02
0.17 0.06 0.27* 0.14
0.16 0.06 0.26* 0.15
0.04 0.06* 0.05*
0.10* 0.02 0.12*
0.02 0.05* 0.02*
0.02 0.04* 0.009
0.05 0.03 0.05*
0.05 0.03 0.05*
0.07 0.01 0.05*
0.07 0.01 0.04*
0.08 0.06* 0.05*
0.08 0.07* 0.05*
0.15* 76.02*
0.07 33.69*
0.16* 49.09*
0.15* 48.37*
0.24 9.96*
0.25 9.95*
0.20* 45.15*
0.20* 46.30*
0.09 69.71*
0.10 74.50*
Source: National Center for Health Statistics (2002). a Italicized columns present the results from ordinal logistic regression where the dependent variables include a measure of frequency of supplement usage (versus a dichotomous variable). * P < 0.05.
is possible to see if the relationships between demographic, lifestyle, etc., factors and supplement use change when frequency is taken into account. Regressions are computed with the SAS (version 8.2) statistical program and then run through STATA (version 7) to adjust for standard errors.
Results As seen in Table 1, 52.7% of the sample are vitamin or mineral supplement users and 14.8% are herbal supplement users. Table 2 provides use differentials of various supplements by demographics. For example, in
the first column, 57.81% (10,469) indicates the percentage (and n) of non-Latino Whites that use vitamins or minerals out of all non-Latino Whites in the sample. The table shows that non-Latino Whites (compared to nonLatino Blacks and Mexicans), women (compared to men), and those with a college degree (compared to those with less education) generally have higher percentages of supplement use. What age group has a higher percentage of use changes depending on the supplement. How do these trends hold up in the regression models? The first two models in Table 3 provide results from regression models with dependent variables vitamin or mineral use and herbal supplement use. They largely bear out trends reported in previous studies.
D. Fennell / Preventive Medicine 39 (2004) 932–939 Table 4 Parameter estimates for herbal supplement use from regression models (comparison groups are in parentheses). Astragalus Dong Quai Race/ethnicity Non-Latino Black (non-Latino White) Mexican Sex Men (women) Age group 18 – 24 (25 – 39) 40 – 54 55 up Income Unknown income Education Less than high school High school (some college/degree) Health insurance Smokes Drinks Exercises Weight Light weight (nonproblematic weight) Overweight/obese Weight unknown Unmarried Serious index Functional limitations Self-reported health Fair – poor (good – excellent) F
Cat’s Claw
Echinacea St. John’s Wort
0.63
0.18
0.10
0.44*
0.90*
1.87
1.03*
0.27
1.10*
1.23*
0.18
1.36*
0.43
0.49*
0.63*
0.48 0.004 0.01 0.05 0.50
0.32 0.72* 0.14 0.05 0.64
0.14 0.66 0.57 0.02 0.42
0.08 0.11 0.33* 0.02 0.19
0.09 0.23* 0.62* 0.02 0.10
2.42* 0.55
0.79 0.30
2.26* 0.03
1.53* 0.40*
0.72* 0.43*
0.67 0.36 0.21 1.01*
0.84* 0.40 0.25 0.59*
1.07* 0.14 0.25 0.44
0.40* 0.23* 0.49* 0.95*
0.84* 0.31* 0.32* 0.65*
0.56
0.76
0.74
0.11
0.09
0.26 0.32 0.29 0.13 0.20*
0.03 1.55 0.003 0.29 0.19*
0.46 0.15 0.15 0.01 0.23*
0.23* 0.11 0.19* 0.02 0.08*
0.001 0.42 0.09 0.14* 0.13*
0.001
0.46
0.25
0.03
0.07
4.01*
5.15*
3.68* 24.64*
16.70*
Source: National Center for Health Statistics (2002). * P < 0.05.
With respect to demographic variables, women (compared to men) and non-Latino Whites (compared to Mexicans and non-Latino Blacks) are more likely to use both vitamins or minerals and herbal supplements. Those in middle age are more likely to use these supplements than those of younger ages. However, there appears to be an increasing use of vitamins or minerals with age, which does not occur for herbal use. There is some evidence that those with a higher income and more education are more likely to use these supplements. However, while those with health insurance are more likely to use vitamins or minerals, those without health insurance are more likely to use herbs. What about lifestyle? Former smokers are more likely to use supplements than nonsmokers. Compared to nonsmokers, current smokers are as likely to use herbs, yet less likely to use vitamins or minerals. Current and former drinkers are more likely to use supplements compared to nondrinkers. Those who do vigorous exercise are more
937
likely to use any of the supplements. While those who are overweight or obese are less likely to use vitamins or minerals (compared to those of nonproblematic weight), these differences are not seen in herbal use. Those who are unmarried are more likely to use herbs than those who are married, but this difference is not seen in vitamin or mineral use. With respect to health status, on the serious index, those with more conditions are more likely to use vitamins or minerals, but there is no significant relationship for herbal use. Those with more functional limitations are more likely to use any of the supplements. Those who rate their health poorly are less likely to use vitamins or minerals, but no difference of this sort is found for herbal use. Unbundling vitamin or mineral usage The above demonstrates that there are some differences in the determinants of different types of supplement usage. However, what happens when vitamin use is broken down into more specific vitamins? Perhaps using such a blanket category covers up the differences in vitamin or mineral use that should be attended to. Table 3 (see nonitalicized columns) shows the regressions where vitamin or mineral use is broken into multiple, A, C, and E use (note that these parameter estimates do not measure whether the differences across models are significant). While there are similar patterns seen in the bundled and unbundled regressions, it is clear there are differences. NonLatino Whites are more likely than non-Latino Blacks and Mexicans to use all the vitamins except vitamin A. Gender, age, and income for the specific vitamins follow the pattern for vitamin or mineral use bundled, but parameter estimates vary. Health insurance is not a significant determinant for vitamins A, C, and E, but is for multiple vitamins. Differences are seen in lifestyle and health conditions. Sometimes smoking and drinking are determinants of vitamin use, but sometimes not (as in the case of vitamin A). Having more serious conditions means one is more likely to use multiple vitamins or vitamin E, but not vitamin A or C. Reporting worse health means one is less likely to use multiple vitamins or vitamin C, but not vitamin A or E. Similarities include vigorous exercisers are more likely to be vitamin users than those who are not. Marriage status does not affect individual supplement use. Finally, a higher level on the functional limitations index means one is more likely to use any of the vitamins. Adding in frequency Frequency of use was calculated for these same vitamins: multiple, A, C, and E. Ordinal logistic regressions run with frequency of use of these vitamins as the dependent variables are shown in Table 3 in italicized columns. These
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regressions largely present a very similar picture to the regressions run without taking into account frequency. Unbundling herbal supplement usage In the survey, respondents who used herbal remedies were asked which ones they used. It seems probable that determinants of herbal supplement use vary depending on which herbs are being spoken of. Table 2 gives descriptive statistics for five specific herbs: Astragalus, Dong Quai, Cat’s Claw, Echinacea, and St. John’s Wort. These herbs were picked because theoretically it seems their determinants would vary. While Astragalus and Cat’s Claw can both be used to boost the immune system, Astragalus is often used in TCM (and more recently in Western medicine for things like cancer). Cat’s Claw is grown in South America. Dong Quai is another herb often used in TCM but has more recently become popular in Western medicine for menopause. Echinacea and St. John’s Wort are two herbs that have been heavily advertised as beneficial for cold symptoms and mild depression, respectively. The data bear some of these issues out. In sheer numbers, Echinacea and St. John’s Wort have the most users, followed by Dong Quai (1168, 571, and 105) (see Table 2). This may be an indication of the effect of advertising. Bivariate correlations show women and only the herbs Dong Quai, Echinacea, and St. John’s Wort as being correlated. The age group 40– 54 uses this herb heavily in relation to the other age groups (noting its use for menopause). Bivariate correlations for age and Dong Quai show that this age group is positively correlated with use, and the age group 70 up is negatively correlated. Logistic regressions were run with use of these herbs as dependent variables (see Table 4). However, note that because of the small number of users, the data are only suggestive. Table 4 does show that there are some differences in the determinants of herbal use. Race or ethnic variables are significant only in some of the models (bivariate analyses showed Whites to be positively correlated with all herbs except Cat’s Claw, Blacks to be negatively correlated with Echinacea and St. John’s Wort, and Mexicans to be negatively correlated with Astragalus, Echinacea, and St. John’s Wort). Regressions show Mexicans are less likely to be users of Dong Quai, Echinacea, and St. John’s Wort compared to Whites. Women are more likely to be users of Dong Quai, Echinacea, and St. John’s Wort, but no significant gender differences are seen for Astragalus and Cat’s Claw. Controlling for the other variables, persons of age 40 – 54 are more likely to be users of Dong Quai and St. John’s Wort compared to those aged 25 –39. Those of age 55 up are less likely to be users of Echinacea and St. John’s Wort compared to age 25– 39. Those with less than a high school education are less likely to use all of the herbs except Dong Quai. Those with a high school degree are less likely to use two of the herbs than those with a degree. Those with health
insurance are less likely to use all of the herbs except Astragalus. Smoking sometimes means one is more likely to use an herb and sometimes less likely. Those who drink are less likely to use Echinacea and St. John’s Wort. Those who exercise are more likely to use the herbs, excluding Cat’s Claw. Sometimes weight is a significant determinant and sometimes not. Marriage status is largely unsignificant. A higher score on the serious index means one is more likely to take St. John’s Wort, but otherwise remains unsignificant. Functional limitations are positively correlated with all of the herbs, and self-reported health is uncorrelated (The continuous variable for self-perceived health and the serious conditions index have a Pearson correlation of r = 0.43, P < 0.0001).
Conclusion The above demonstrates that women are generally the most common users of supplements. There are also many similarities in the determinants of usage of vitamins or minerals and herbs in general. Non-Latino Whites, for example, are more likely to use supplements than nonLatino Blacks and Mexicans. It is tempting to try to make generalizations such as these across more specific vitamins and herbs. However, this study adds to the literature that is beginning to stress the importance of unbundling supplement usage. This study provides some evidence that the determinants of supplement usage vary depending on what supplement is being modeled. While some of these differences may have to do with small sample size, future research should continue to investigate differences instead of treating supplement usage as all the same. However, the addition of a measurement of frequency turned out not to have a big impact on the determinants of vitamin usage. Although it may seem that those who use and those who use more frequently are the same, note that this frequency variable does not take into account supplement dosage. The above evidence demonstrates that unbundling supplements is important, but more work needs to be done in understanding why this is so. The data suggest that the media may be playing a large role in herbal supplement use—and not simply increasing attention to certain herbs but increasing attention to certain uses of these herbs. Dong Quai may be used for more than menopause but is a yin tonic in TCM and may be used for issues relating to menstruation and even pregnancy. However, in this survey, women around the age of menopause were more likely to be users of the herb, seemingly coinciding with media surrounding the herb in America. This raises the issue of where individuals are getting their information from on how to use supplements and the related issue of how these herbs are actually being used.
D. Fennell / Preventive Medicine 39 (2004) 932–939
One limitation of this survey is that it does not ask respondents these questions. To understand supplement use, especially with herbals, it is important to understand how the herbs are being used and why. Theoretically, it would seem to follow that there will be ethnic differences along these lines. It is also unclear if these survey questions fully address differences in language, as herbs have a variety of different names. Also, the original question asks whether the individual has taken any ‘‘mixed or single herbal or botanical supplements.’’ A number of respondents did not answer this question. Perhaps these people call their supplement provider by another name, do not refer to what they take as an herbal supplement, or take their herbs in another form (such as tea). Therefore, further research needs to spend more time on unbundling supplement use and attempting to capture the intricacies of supplement use.
Acknowledgments Thanks go to Dr. Barbara Zsembik for lending her knowledge and encouragement.
References [1] World Health Organization. WHO launches the first global strategy on traditional and alternative medicine; 2002. Available at: http:// www.who.int/inf/en/pr-2002-38.html. [2] Ernst E. Prevalence of use of complementary/alternative medicine: a systematic review. Bull WHO 2000;78(2):252 – 7. [3] Ishihara J, Sobue T, Yamamoto S, et al. Demographics, lifestyles, health characteristics, and dietary intake among dietary supplement users in Japan. Int J Epidemiol 2003;32:546 – 53. [4] Messerer M, Johansson S-E, Wolk A. Sociodemographic and health behaviour factors among dietary supplement and natural remedy users. Eur J Clin Nutr 2001;55:1104 – 10. [5] Blendon RJ, DesRoches CM, Benson JM, et al. Americans’ views on the use and regulation of dietary supplements. Arch Intern Med 2001;161(6):805 – 10. [6] Cohen RJ, Ek K, Pan CX. Complementary and alternative medicine (CAM) use by older adults: a comparison of self-report and physician chart documentation. J Gerontol, Ser A, Biol Sci Med Sci 2002; 57A(4):M223 – 7. [7] Eisenberg DM, Kessler RC, Foster C, et al. Unconventional medicine in the United States—Prevalence, costs, and patterns of use. N Engl J Med 1993;328(4):246 – 52.
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[8] Flaherty JH, Takahashi R, Teoh J, et al. Use of alternative therapies in older outpatients in the United States and Japan: prevalence, reporting patterns, and perceived effectiveness. J Gerontol, Ser A, Biol Sci Med Sci 2001;56A(10):M650 – 5. [9] Leung JM, Dzankic S, Manku K, et al. The prevalence and predictors of the use of alternative medicine in presurgical patients in five California hospitals. Anesth Analg 2001;93(4):1062 – 8. [10] Almeida JC, Grimsley EW. Coma from the health food store: interaction between kava and alprazolam. Ann Intern Med 1996; 125(11):940 – 1. [11] Balluz LS, Kieszak SM, Philen RM, et al. Vitamin and mineral supplement use in the United States: results from the third National Health and Nutrition Examination Survey. Arch Fam Med 2000; 9:258 – 62. [12] Jasti S, Siega-Riz AM, Bentley ME. Dietary supplement use in the context of health disparities: cultural, ethnic, and demographic determinants of use. J Nutr 2003;133:2010S – 3S. [13] Kim SH, Han JH, Keen CL. Vitamin and mineral supplement use by healthy teenagers in Korea: motivating factors and dietary consequences. Nutrition 2001;17:373 – 80. [14] Kirk SF, Cade JE, Barrett JH, et al. Diet and lifestyle characteristics associated with dietary supplement use in women. Public Health Nutr 1999;2(1):69 – 73. [15] Greger JL. Dietary supplement use: consumer characteristics and interests. J Nutr 2001;131:1339S – 43S. [16] Raiten DJ, Picciano MF, Coates PM. Dietary supplement use in women: current status and future directions—Introduction and conference summary. J Nutr 2003;133:1957S – 60S. [17] de Jong N, Ocke´ MC, Branderhorst AC, et al. Demographic and lifestyle characteristics of functional food consumers and dietary supplement users. Br J Nutr 2003;89:273 – 81. [18] McFarland B, Bigelow D, Zani B, et al. Complementary and alternative medicine use in Canada and the United States. Am J Public Health 2002;92(10):1616 – 8. [19] Lyle BJ, Mares-Perlman JA, Klein BEK, et al. Supplement users differ from nonusers in demographic, lifestyle, dietary and health characteristics. J Nutr 1998;128:2355 – 62. [20] Ervin RB, Kennedy-Stephenson J. Mineral intakes of elderly adult supplement and non-supplement users in the third National Health and Nutrition Examination Survey. J Nutr 2002; 132:3422 – 7. [21] Foote JA, Murphy SP, Wilkens LR, et al. Factors associated with dietary supplement use among healthy adults of five ethnicities. Am J Epidemiol 2003;157:888 – 97. [22] Gunther S, Patterson RE, Kristal AR, et al. Demographic and healthrelated correlates of herbal and specialty supplement use. J Am Diet Assoc 2004;104:27 – 34. [23] Radimer KL, Subar AF, Thompson FE. Nonvitamin, nonmineral dietary supplements: issues and findings from NHANES III. J Am Diet Assoc 2000;100(4):447 – 54. [24] Neuhouser ML. Dietary supplement use by American women: challenges in assessing patterns of use, motives and costs. J Nutr 2003;133:1992S – 6S.
Determinants of supplement usage Dana Fennell, M.A. * University of Florida, Gainesville, FL 32611-7330, USA Available online 6 May 2004
Abstract Background. As the use of supplements is growing, this study examines the determinants of vitamin and herbal supplement usage. Instead of treating these as all-encompassing categories, they are broken into specific vitamins and herbs and compared to see if users are different. A measure of frequency of vitamin use is also created. Methods. Logistic and ordinal logistic regressions are run on a sample of 24,834 from the National Health Interview Survey (NHIS) 2000. Results. Women are generally more likely to use supplements than men. Non-Latino Whites are generally more likely to use supplements than non-Latino Blacks and Mexicans. However, despite these general trends, it proves fruitful to break up supplement use into smaller categories. The data provide some evidence that determinants vary by particular supplement. However, including a measure of frequency does not change the picture much. Conclusions. Considering this, more information is needed on why people use particular supplements and what their sources of information are. D 2004 The Institute For Cancer Prevention and Elsevier Inc. All rights reserved. Keywords: Alternative medicine; Supplements; Vitamins
Researchers have taken notice of the growing public interest in dietary and medicinal supplements. The World Health Organization has released a ‘‘global strategy’’ to critically address the safety and availability of traditional and alternative medicine [1]. Research in the past two decades (although largely conducted in industrial countries) has been accumulating on the prevalence of use and the characteristics of supplement users [2– 4]. This information is important for a number of reasons. First, studies have pointed out many patients do not report supplement use to their primary physicians [5 –9]. Because of concerns of dangerous drug/supplement or supplement/ supplement interactions [6,10,11], it is important to know the prevalence of real usage and what groups of people are taking these items. Second, there are concerns that consumers may be taking too many or not enough supplements and relying on unfounded claims that supplements will help them [12 – 14]. It again becomes relevant to know what people are taking and in what amounts. This information
* University of Florida, P.O. Box 117330, Gainesville, FL 32611-7330. E-mail address: [email protected].
may help educators target vulnerable populations [15] or influence public policies about public health dietary intake recommendations [16]. Third, many demographic and lifestyle variables have been shown to have a relationship with supplement use [2,14,17,18]. When researchers seek to investigate the relationship between supplement use and health outcomes, it is necessary to understand how these other variables (demographic and lifestyle) can confound the relationship [3,19]. Variables that have been found to have a relationship with supplement use are race or ethnicity, age, education, income, and lifestyle variables such as drinking, smoking, and exercising. Non-Hispanic Whites, females, those with more education, and those with more income seem to be comparatively more likely to use supplements (whether herbs, vitamins, or minerals) [2,4,15,20]. Age appears as a common variable, with many studies saying older people are more likely to use, although the age range specified sometimes refers to those who are middle aged [7] and in other studies to the elderly [3]. There is also some evidence that a healthier lifestyle (for example more exercise, higher levels of nutrients in dietary intake, less obesity) is associated with using supplements [15,21]. However, studies sometimes contradict each other on these variables. Ishihara’s study
0091-7435/$ - see front matter D 2004 The Institute For Cancer Prevention and Elsevier Inc. All rights reserved. doi:10.1016/j.ypmed.2004.03.031
D. Fennell / Preventive Medicine 39 (2004) 932–939
found that those who had better diets (intakes of energy and nutrients) were not as likely to take supplements compared to those with worse diets [3]. There are still gaps in the literature. There is more information about vitamin or mineral supplement use than herbal supplement use [22]. Explanatory variables may vary by the type of product being analyzed, whether a particular vitamin or herb [16,17,19,23]. This makes sense because supplements are sometimes used to prevent particular illnesses or used in response to them; illnesses themselves often have particular demographic and lifestyle variables associated with them. Some herbs are used primarily in Traditional Chinese Medicine (TCM), others more by American herbalists. The media have promoted some more than others. Finally, there is a need to understand the frequency with which people use supplements and what variables are associated with particular frequencies of use [19,24]. By employing data from the National Health Interview Survey (NHIS) of 2000,1 this study attempts to address these concerns. This study investigates the determinants of supplement use in the United States when treated as an allencompassing category (any vitamin or mineral use as a whole, any herbal use as a whole) and when broken down into individual categories (Multiple vitamin, vitamins A, C, and E, Astragulus, Dong Quai, Cat’s Claw, Echinacea, and St. John’s Wort). This is done to see if explanatory variables depend on the type of supplement. Further, this study creates a frequency variable to determine whether those who use are also the most frequent users.
Research design and methods Data and sample Data for this study come from the NHIS 2000. The NHIS is a yearly cross-sectional survey conducted of households within the United States (face-to-face interviews). The 2000 survey consists of the usual core sections plus a supplemental Cancer Control Module. The supplement is used in this study for information about respondents’ diet and nutrition, Hispanic acculturation, and physical activity. The response rate for sample adult respondents was 72.1%, with 32,374 adults of age 18+ being interviewed. Data from various core sections are combined to get sufficient data on demographic characteristics. The analytical sample contains 24,834 people. Table 1 provides descriptive statistics for the sample. As shown there, females make up 56.15% of the sample. Mexicans make
1 National Center for Health Statistics. Data File Documentation, National Health Interview Survey 2000. Maryland: National Center for Health Statistics; 2002. All interpretations or conclusions are the responsibility of the author, and not the NCHS.
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up 11.68%, non-Latinos 15.47%, and non-Latino Whites 72.86%. Data limitations include that supplement questions are not part of the yearly model, making it impossible to assess trends over time or have a large enough sample to break up racial or ethnic groups further. There is no information on the dosage of the supplements taken, so any frequency variables created neglect this information. Outweighing these are the advantages of a large sample size, questions that separate ethnicity and acculturation, and questions targeting both vitamin and herbal supplement users. Dependent variables: supplement use Dependent variables are based on survey questions asking whether particular supplements have been taken in the past 12 months. All missing and refused data are omitted, and dichotomous variables are formed as follows. Vitamin or mineral supplement use refers to whether a participant has taken either vitamin or mineral supplements in the past 12 months. It is not possible to differentiate vitamin and mineral use here because of survey wording. Then this category is broken down to create more specific variables. Separate variables are created for the use of multiple, A, C, and E vitamins. Multiple refers to whether a person has taken a multiple vitamin in the past 12 months; vitamin A if a person has taken vitamin A in the past 12 months, and so on. Herbal supplementation is distinguished from vitamin and mineral use. The variable herbs refers to whether a person has taken any mixed or single herbal or botanical supplements in the past 12 months. This larger category is broken down similar to the above. Separate variables are created for Astragalus, Dong Quai, Cat’s Claw, Echinacea, and St. John’s Wort supplementation. Astragalus refers to whether a person took Astragalus supplements in the past 12 months; Dong Quai if a person has taken Dong Quai supplements in the past 12 months, and so on. Frequency Separate dependent variables are formed to measure the frequency of vitamin or mineral usage. These variables distinguish frequent users, moderate users, and nonusers. Frequent users are daily users (survey questions asking the number of months used and the number of days used are combined to construct this), moderate users are anything less than daily, and nonusers do not use at all. Separate dependent variables are created for multiple, A, C, and E vitamin users. Regressions using the dichotomous variables are then compared to those that take into account frequency (these are the italicized columns in Table 3).
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Table 1 Descriptive statistics for the sample n Race/ethnicity Non-Latino White Non-Latino Black Mexican Sex Women Age group 18 – 24 25 – 39 40 – 54 55 – 69 70 up Income Unknown income Education Less than high school High school Some college Degree Health insurance Smoking Current smoker Former smoker Nonsmoker Drinking Current drinker Former drinker Nondrinker Exercise Does not exercise Light exercise Moderate exercise Vigorous exercise Unable to exercise Weight Light weight Nonproblematic weight Overweight Obese Weight unknown Married Serious index Functional limitations Self-reported health Fair – poor Good – excellent Supplement use Uses vitamins or minerals Uses multiple vitamins Uses vitamin A Uses vitamin C Uses vitamin E Uses herbs Uses Cat’s Claw Uses Astragalus
Table1 (continued) 24,834 72.86 15.47 11.68 56.15 10.97 31.01 28.49 16.93 12.60 7.21 (5.25) 21.00 19.04 30.00 19.50 31.46 83.88 23.64 22.33 54.03 62.50 14.66 22.84 37.31 15.03 20.90 24.53 2.22 3.02 37.98 34.59 24.41 2.73 53.85 0.57 (1.00) 1.08 (1.89) 11.61 88.39 52.68 43.07 4.17 20.45 17.06 14.83 0.25 0.27
Supplement use Uses Dong Quai Uses Echinacea Uses St. John’s Wort
0.42 4.70 2.30
Numbers indicate either percentages or means (with standard deviations in parentheses). Source: National Center for Health Statistics (2002).
Independent variables Demographic variables used in this study are age, gender, and race or ethnicity, as they have been found to correlate with supplement usage in previous studies. Age is broken into the following age categories: 18 – 24, 25 – 39, 40– 54, 55– 69, and 70 and up. Race or ethnicity is broken into categories for non-Latino Whites, non-Latino Blacks, and Mexicans (includes Mexicans and Mexican Americans). The generic Latino category is not used because it does not adequately portray the racial differences in health that have been found to exist when this category is unbundled. The sample is not large enough to separate out other Latino groups into variables. Socioeconomic variables used in this study are income, health insurance, and education. Income refers to the ratio of family income to poverty threshold. Those persons with unknown income are retained for analysis due to the large size of the category. The health insurance variable compares those with and those without it. Education is constructed into categories based on degree completed: less than high school, high school degree (includes GED), some college, and a degree (includes AA degrees to technical degrees). Lifestyle (or health risk) factors included in this study are smoking, drinking, exercise, weight, and marital status (as an indication of social stress). Smoking and drinking variables distinguish among current users, former users, and nonusers. Exercise variables compare non-, light, moderate, and vigorous exercisers and those unable to exercise.2 Weight variables are based on body mass index, distinguishing those who are underweight (score of 0.03 – 18.99), overweight (score of 25– 29.99), obese (score of 30+), and those with a nonproblematic weight (score of 19 –24.99). Social stress is encompassed in a marital status variable, those married compared to those who are not (includes never married, divorced, widowed, and separated). Previous research questions whether health status is associated with supplement usage. This study measures health status with two indexes and a variable for selfreported health. The chronic index relates the number of diagnoses a person has received for one of nine major
2
Vigorous and moderate exercisers are those who do vigorous or moderate exercise for at least 10 minutes three or more times per week, respectively.
D. Fennell / Preventive Medicine 39 (2004) 932–939
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exercising variables become dichotomous, comparing those who do and do not perform these activities. Overweight and obese variables are combined.
medical conditions (hypertension, coronary heart disease, angina pectoris, heart attack, heart disease, stroke, emphysema, cancer, and diabetes). A functional limitation index is created based on how many of nine activities a person has difficulty doing. For each activity, survey respondents could choose from a scale of 0 to 4, 4 indicating they cannot do the activity at all. These are combined and made into a functional limitation scale ranging from 0 to 5; any scores greater than 5 are assigned a 5. Self-reported health is turned into a dichotomous variable comparing those who define their health as excellent to good, with those who rate it fair or poor. Because so few people in the sample take the individual herbs (see Table 2), when multivariate analyses are done on specific herbs, categories of independent variables are collapsed as follows (see Table 4). Some college is combined with high school degree. Smoking, drinking, and
Procedures Univariate and bivariate analyses are initially performed to see patterns of supplement use. Regression models are then constructed to examine the determinants of supplement use (see Tables 3 and 4). Logistic regression models are used for the dichotomous supplement use variables. By doing this, it is possible to see if explanatory variables depend on the type of supplement being discussed. Ordinal logistic regression models are constructed for the supplement variables that take into account frequency (see the italicized columns in Table 3). By doing this, it
Table 2 Use differentials of various supplements by demographics
na Race/ethnicity Non-Latino White Non-Latino Black Mexican
Sex Men Women
Age group 18 – 24 25 – 39 40 – 54 55 – 69 70 up
Education Less than high school High school Some college Degree
Vitamins or minerals
Herbs
Multiple
Vitamin A
Vitamin C
Vitamin E
Astragalus
Dong Quai
Cat’s Claw
Echinacea
St. John’s Wort
13,083
3682
10,696
1036
5078
4237
68
105
63
1168
571
57.81 (10,460) 41.60 (1598) 35.34 (1025)
16.90 (3057) 10.73 (412) 7.34 (213)
47.34 (8566) 33.35 (1281) 29.28 (849)
4.58 (829) 3.36 (129) 2.69 (78)
23.84 (4314) 13.69 (526) 8.21 (238)
20.22 (3659) 10.28 (395) 6.31 (183)
0.34 (62) 0.13 (5) 0.03 (1)
0.48 (86) 0.34 (13) 0.21 (6)
0.28 (51) 0.16 (6) 0.21 (6)
5.62 (1016) 3.02 (116) 1.24 (36)
2.80 (507) 1.20 (46) 0.62 (18)
46.04 (5014) 57.87 (8069)
13.37 (1456) 15.96 (2226)
37.67 (4102) 47.29 (6594)
3.70 (403) 4.54 (633)
19.25 (2096) 21.39 (2982)
14.72 (1603) 18.89 (2634)
0.27 (29) 0.28 (39)
0.20 (22) 0.60 (83)
0.28 (30) 0.24 (33)
3.63 (395) 5.54 (773)
1.75 (191) 2.73 (380)
40.22 (1096) 46.98 (3618) 55.21 (3907) 61.04 (2566) 60.61 (1896)
10.97 (299) 13.92 (1072) 17.95 (1270) 16.82 (707) 10.68 (334)
33.17 (904) 39.72 (3059) 45.42 (3214) 48.50 (2039) 47.31 (1480)
1.80 (49) 2.86 (220) 4.82 (341) 5.99 (252) 5.56 (174)
13.21 (360) 17.14 (1320) 22.51 (1593) 25.86 (1087) 22.95 (718)
5.10 (139) 7.87 (606) 19.45 (1376) 29.97 (1260) 27.37 (856)
0.15 (4) 0.23 (18) 0.35 (25) 0.38 (16) 0.16 (5)
0.26 (7) 0.32 (25) 0.72 (51) 0.40 (17) 0.16 (5)
0.11 (3) 0.18 (14) 0.37 (26) 0.33 (14) 0.19 (6)
4.44 (121) 5.32 (410) 5.86 (415) 3.83 (161) 1.95 (61)
1.91 (52) 2.49 (192) 3.21 (227) 1.81 (76) 0.77 (24)
36.69 (1735) 48.79 (3635) 56.61 (2741) 63.64 (4972)
6.72 (318) 12.07 (899) 17.82 (863) 20.50 (1602)
28.86 (1365) 39.07 (2911) 47.11 (2281) 52.98 (4139)
2.75 (130) 3.32 (247) 4.81 (233) 5.45 (426)
10.32 (488) 16.83 (1254) 22.78 (1103) 28.58 (2233)
9.73 (460) 15.37 (1145) 18.26 (884) 22.37 (1748)
0.02 (1) 0.13 (10) 0.37 (18) 0.50 (39)
0.19 (9) 0.23 (17) 0.62 (30) 0.63 (49)
0.06 (3) 0.27 (20) 0.33 (16) 0.31 (24)
0.89 (42) 2.89 (215) 5.89 (285) 8.01 (626)
0.95 (45) 1.72 (128) 2.66 (129) 3.44 (269)
Numbers indicate percentages, with n displayed below in parentheses. Source: National Center for Health Statistics (2002). a This n represents the total number of people who use each supplement, respectively.
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D. Fennell / Preventive Medicine 39 (2004) 932–939
Table 3 Parameter estimates for supplement use from regression models (comparison groups are in parentheses) Vitamins or minerals Race/ethnicity (non-Latino White) Non-Latino Black Mexican Sex Men (Women) Age group 18 – 24 (25 – 39) 40 – 54 55 – 69 70 up Income Unknown income Education Less than high school High school Some college (degree) Health insurance Smoking Current smoker Former smoker (nonsmoker) Drinking Current drinker Former drinker (nondrinker) Exercise Does not exercise Light exercise Moderate exercise (vigorous exercise) Unable to exercise Weight Light weight (nonproblematic weight) Overweight Obese Weight unknown Marriage status Unmarried (married) Serious index Functional limitations Self-reported health Fair – poor (good – excellent) F
Herbs
Multiple
Multiplea
Vitamin A
Vitamin Aa
Vitamin C
Vitamin Ca
Vitamin E
Vitamin Ea
0.33* 0.24*
0.29* 0.42*
0.30* 0.16*
0.30* 0.17*
0.04 0.02
0.04 0.01
0.34* 0.62*
0.34* 0.61*
0.39* 0.56*
0.42* 0.56*
0.63*
0.32*
0.50*
0.46*
0.20*
0.19*
0.25*
0.23*
0.41*
0.41*
0.20* 0.31* 0.66* 0.73* 0.03* 0.12*
0.17* 0.26* 0.26* 0.02 0.03* 0.10
0.24* 0.20* 0.43* 0.44* 0.02* 0.16*
0.27* 0.27* 0.61* 0.67* 0.02* 0.18*
0.50* 0.58* 0.98* 1.04* 0.03* 0.06
0.50* 0.58* 0.99* 1.06* 0.03* 0.07
0.26* 0.33* 0.62* 0.64* 0.03* 0.07
0.27* 0.37* 0.72* 0.75* 0.03* 0.08
0.32* 1.06* 1.73* 1.74* 0.03* 0.20*
0.32* 1.07* 1.78* 1.81* 0.03* 0.22*
0.69* 0.39* 0.09 0.19*
0.73* 0.37* 0.04 0.31*
0.63* 0.36* 0.07 0.22*
0.60* 0.30* 0.04 0.21*
0.53* 0.36* 0.003 0.26
0.53* 0.35* 0.001 0.26
0.68* 0.44* 0.12* 0.01
0.67* 0.41* 0.10* 0.02
0.63* 0.34* 0.05 0.05
0.64* 0.33* 0.06 0.06
0.22* 0.12*
0.004 0.13*
0.19* 0.10*
0.19* 0.09*
0.04 0.13
0.04 0.14
0.06 0.15*
0.07 0.15*
0.13* 0.16*
0.14* 0.16*
0.35* 0.24*
0.48* 0.36*
0.29* 0.17*
0.23* 0.15*
0.06 0.004
0.07 0.003
0.32* 0.23*
0.29* 0.21*
0.25* 0.17*
0.25* 0.17*
0.78* 0.34* 0.27* 0.43*
0.89* 0.32* 0.27* 0.78*
0.62* 0.30* 0.23* 0.24*
0.59* 0.35* 0.22* 0.20*
0.69* 0.50* 0.51* 0.56*
0.69* 0.51* 0.51* 0.57*
0.80* 0.24* 0.30* 0.47*
0.79* 0.28* 0.30* 0.44*
0.86* 0.33* 0.37* 0.66*
0.85* 0.36* 0.36* 0.66*
0.03 0.11* 0.24* 0.05
0.10 0.03 0.06 0.22
0.05 0.10* 0.22* 0.08
0.04 0.10* 0.21* 0.05
0.29 0.10 0.20 0.39
0.29 0.10 0.20 0.40
0.03 0.02 0.11* 0.04
< 0.001 0.02 0.11* 0.02
0.17 0.06 0.27* 0.14
0.16 0.06 0.26* 0.15
0.04 0.06* 0.05*
0.10* 0.02 0.12*
0.02 0.05* 0.02*
0.02 0.04* 0.009
0.05 0.03 0.05*
0.05 0.03 0.05*
0.07 0.01 0.05*
0.07 0.01 0.04*
0.08 0.06* 0.05*
0.08 0.07* 0.05*
0.15* 76.02*
0.07 33.69*
0.16* 49.09*
0.15* 48.37*
0.24 9.96*
0.25 9.95*
0.20* 45.15*
0.20* 46.30*
0.09 69.71*
0.10 74.50*
Source: National Center for Health Statistics (2002). a Italicized columns present the results from ordinal logistic regression where the dependent variables include a measure of frequency of supplement usage (versus a dichotomous variable). * P < 0.05.
is possible to see if the relationships between demographic, lifestyle, etc., factors and supplement use change when frequency is taken into account. Regressions are computed with the SAS (version 8.2) statistical program and then run through STATA (version 7) to adjust for standard errors.
Results As seen in Table 1, 52.7% of the sample are vitamin or mineral supplement users and 14.8% are herbal supplement users. Table 2 provides use differentials of various supplements by demographics. For example, in
the first column, 57.81% (10,469) indicates the percentage (and n) of non-Latino Whites that use vitamins or minerals out of all non-Latino Whites in the sample. The table shows that non-Latino Whites (compared to nonLatino Blacks and Mexicans), women (compared to men), and those with a college degree (compared to those with less education) generally have higher percentages of supplement use. What age group has a higher percentage of use changes depending on the supplement. How do these trends hold up in the regression models? The first two models in Table 3 provide results from regression models with dependent variables vitamin or mineral use and herbal supplement use. They largely bear out trends reported in previous studies.
D. Fennell / Preventive Medicine 39 (2004) 932–939 Table 4 Parameter estimates for herbal supplement use from regression models (comparison groups are in parentheses). Astragalus Dong Quai Race/ethnicity Non-Latino Black (non-Latino White) Mexican Sex Men (women) Age group 18 – 24 (25 – 39) 40 – 54 55 up Income Unknown income Education Less than high school High school (some college/degree) Health insurance Smokes Drinks Exercises Weight Light weight (nonproblematic weight) Overweight/obese Weight unknown Unmarried Serious index Functional limitations Self-reported health Fair – poor (good – excellent) F
Cat’s Claw
Echinacea St. John’s Wort
0.63
0.18
0.10
0.44*
0.90*
1.87
1.03*
0.27
1.10*
1.23*
0.18
1.36*
0.43
0.49*
0.63*
0.48 0.004 0.01 0.05 0.50
0.32 0.72* 0.14 0.05 0.64
0.14 0.66 0.57 0.02 0.42
0.08 0.11 0.33* 0.02 0.19
0.09 0.23* 0.62* 0.02 0.10
2.42* 0.55
0.79 0.30
2.26* 0.03
1.53* 0.40*
0.72* 0.43*
0.67 0.36 0.21 1.01*
0.84* 0.40 0.25 0.59*
1.07* 0.14 0.25 0.44
0.40* 0.23* 0.49* 0.95*
0.84* 0.31* 0.32* 0.65*
0.56
0.76
0.74
0.11
0.09
0.26 0.32 0.29 0.13 0.20*
0.03 1.55 0.003 0.29 0.19*
0.46 0.15 0.15 0.01 0.23*
0.23* 0.11 0.19* 0.02 0.08*
0.001 0.42 0.09 0.14* 0.13*
0.001
0.46
0.25
0.03
0.07
4.01*
5.15*
3.68* 24.64*
16.70*
Source: National Center for Health Statistics (2002). * P < 0.05.
With respect to demographic variables, women (compared to men) and non-Latino Whites (compared to Mexicans and non-Latino Blacks) are more likely to use both vitamins or minerals and herbal supplements. Those in middle age are more likely to use these supplements than those of younger ages. However, there appears to be an increasing use of vitamins or minerals with age, which does not occur for herbal use. There is some evidence that those with a higher income and more education are more likely to use these supplements. However, while those with health insurance are more likely to use vitamins or minerals, those without health insurance are more likely to use herbs. What about lifestyle? Former smokers are more likely to use supplements than nonsmokers. Compared to nonsmokers, current smokers are as likely to use herbs, yet less likely to use vitamins or minerals. Current and former drinkers are more likely to use supplements compared to nondrinkers. Those who do vigorous exercise are more
937
likely to use any of the supplements. While those who are overweight or obese are less likely to use vitamins or minerals (compared to those of nonproblematic weight), these differences are not seen in herbal use. Those who are unmarried are more likely to use herbs than those who are married, but this difference is not seen in vitamin or mineral use. With respect to health status, on the serious index, those with more conditions are more likely to use vitamins or minerals, but there is no significant relationship for herbal use. Those with more functional limitations are more likely to use any of the supplements. Those who rate their health poorly are less likely to use vitamins or minerals, but no difference of this sort is found for herbal use. Unbundling vitamin or mineral usage The above demonstrates that there are some differences in the determinants of different types of supplement usage. However, what happens when vitamin use is broken down into more specific vitamins? Perhaps using such a blanket category covers up the differences in vitamin or mineral use that should be attended to. Table 3 (see nonitalicized columns) shows the regressions where vitamin or mineral use is broken into multiple, A, C, and E use (note that these parameter estimates do not measure whether the differences across models are significant). While there are similar patterns seen in the bundled and unbundled regressions, it is clear there are differences. NonLatino Whites are more likely than non-Latino Blacks and Mexicans to use all the vitamins except vitamin A. Gender, age, and income for the specific vitamins follow the pattern for vitamin or mineral use bundled, but parameter estimates vary. Health insurance is not a significant determinant for vitamins A, C, and E, but is for multiple vitamins. Differences are seen in lifestyle and health conditions. Sometimes smoking and drinking are determinants of vitamin use, but sometimes not (as in the case of vitamin A). Having more serious conditions means one is more likely to use multiple vitamins or vitamin E, but not vitamin A or C. Reporting worse health means one is less likely to use multiple vitamins or vitamin C, but not vitamin A or E. Similarities include vigorous exercisers are more likely to be vitamin users than those who are not. Marriage status does not affect individual supplement use. Finally, a higher level on the functional limitations index means one is more likely to use any of the vitamins. Adding in frequency Frequency of use was calculated for these same vitamins: multiple, A, C, and E. Ordinal logistic regressions run with frequency of use of these vitamins as the dependent variables are shown in Table 3 in italicized columns. These
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D. Fennell / Preventive Medicine 39 (2004) 932–939
regressions largely present a very similar picture to the regressions run without taking into account frequency. Unbundling herbal supplement usage In the survey, respondents who used herbal remedies were asked which ones they used. It seems probable that determinants of herbal supplement use vary depending on which herbs are being spoken of. Table 2 gives descriptive statistics for five specific herbs: Astragalus, Dong Quai, Cat’s Claw, Echinacea, and St. John’s Wort. These herbs were picked because theoretically it seems their determinants would vary. While Astragalus and Cat’s Claw can both be used to boost the immune system, Astragalus is often used in TCM (and more recently in Western medicine for things like cancer). Cat’s Claw is grown in South America. Dong Quai is another herb often used in TCM but has more recently become popular in Western medicine for menopause. Echinacea and St. John’s Wort are two herbs that have been heavily advertised as beneficial for cold symptoms and mild depression, respectively. The data bear some of these issues out. In sheer numbers, Echinacea and St. John’s Wort have the most users, followed by Dong Quai (1168, 571, and 105) (see Table 2). This may be an indication of the effect of advertising. Bivariate correlations show women and only the herbs Dong Quai, Echinacea, and St. John’s Wort as being correlated. The age group 40– 54 uses this herb heavily in relation to the other age groups (noting its use for menopause). Bivariate correlations for age and Dong Quai show that this age group is positively correlated with use, and the age group 70 up is negatively correlated. Logistic regressions were run with use of these herbs as dependent variables (see Table 4). However, note that because of the small number of users, the data are only suggestive. Table 4 does show that there are some differences in the determinants of herbal use. Race or ethnic variables are significant only in some of the models (bivariate analyses showed Whites to be positively correlated with all herbs except Cat’s Claw, Blacks to be negatively correlated with Echinacea and St. John’s Wort, and Mexicans to be negatively correlated with Astragalus, Echinacea, and St. John’s Wort). Regressions show Mexicans are less likely to be users of Dong Quai, Echinacea, and St. John’s Wort compared to Whites. Women are more likely to be users of Dong Quai, Echinacea, and St. John’s Wort, but no significant gender differences are seen for Astragalus and Cat’s Claw. Controlling for the other variables, persons of age 40 – 54 are more likely to be users of Dong Quai and St. John’s Wort compared to those aged 25 –39. Those of age 55 up are less likely to be users of Echinacea and St. John’s Wort compared to age 25– 39. Those with less than a high school education are less likely to use all of the herbs except Dong Quai. Those with a high school degree are less likely to use two of the herbs than those with a degree. Those with health
insurance are less likely to use all of the herbs except Astragalus. Smoking sometimes means one is more likely to use an herb and sometimes less likely. Those who drink are less likely to use Echinacea and St. John’s Wort. Those who exercise are more likely to use the herbs, excluding Cat’s Claw. Sometimes weight is a significant determinant and sometimes not. Marriage status is largely unsignificant. A higher score on the serious index means one is more likely to take St. John’s Wort, but otherwise remains unsignificant. Functional limitations are positively correlated with all of the herbs, and self-reported health is uncorrelated (The continuous variable for self-perceived health and the serious conditions index have a Pearson correlation of r = 0.43, P < 0.0001).
Conclusion The above demonstrates that women are generally the most common users of supplements. There are also many similarities in the determinants of usage of vitamins or minerals and herbs in general. Non-Latino Whites, for example, are more likely to use supplements than nonLatino Blacks and Mexicans. It is tempting to try to make generalizations such as these across more specific vitamins and herbs. However, this study adds to the literature that is beginning to stress the importance of unbundling supplement usage. This study provides some evidence that the determinants of supplement usage vary depending on what supplement is being modeled. While some of these differences may have to do with small sample size, future research should continue to investigate differences instead of treating supplement usage as all the same. However, the addition of a measurement of frequency turned out not to have a big impact on the determinants of vitamin usage. Although it may seem that those who use and those who use more frequently are the same, note that this frequency variable does not take into account supplement dosage. The above evidence demonstrates that unbundling supplements is important, but more work needs to be done in understanding why this is so. The data suggest that the media may be playing a large role in herbal supplement use—and not simply increasing attention to certain herbs but increasing attention to certain uses of these herbs. Dong Quai may be used for more than menopause but is a yin tonic in TCM and may be used for issues relating to menstruation and even pregnancy. However, in this survey, women around the age of menopause were more likely to be users of the herb, seemingly coinciding with media surrounding the herb in America. This raises the issue of where individuals are getting their information from on how to use supplements and the related issue of how these herbs are actually being used.
D. Fennell / Preventive Medicine 39 (2004) 932–939
One limitation of this survey is that it does not ask respondents these questions. To understand supplement use, especially with herbals, it is important to understand how the herbs are being used and why. Theoretically, it would seem to follow that there will be ethnic differences along these lines. It is also unclear if these survey questions fully address differences in language, as herbs have a variety of different names. Also, the original question asks whether the individual has taken any ‘‘mixed or single herbal or botanical supplements.’’ A number of respondents did not answer this question. Perhaps these people call their supplement provider by another name, do not refer to what they take as an herbal supplement, or take their herbs in another form (such as tea). Therefore, further research needs to spend more time on unbundling supplement use and attempting to capture the intricacies of supplement use.
Acknowledgments Thanks go to Dr. Barbara Zsembik for lending her knowledge and encouragement.
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