Patterns and Perceived Benefits of Utilizing Seven Major Complementary Health Approaches in U.S. Older Adults

Patterns and Perceived Benefits of Utilizing Seven Major Complementary Health Approaches in U.S.... Abstract Objectives To examine patterns and perceived benefits of seven major complementary health approaches (CHA) among older adults in the United States. Methods Data from the 2012 National Health Interview Survey (NHIS), which represents non-institutionalized adults aged 65 or older (n = 7,116 unweighted), were used. We elicited seven most common CHA used in older adults, which are acupuncture, herbal therapies, chiropractic, massage, meditation, Tai Chi, and yoga. Survey participants were asked to self-report perceived benefits (eg, maintaining health and stress reduction) in their CHA used. We estimated prevalence and perceived benefits of CHA use. We also investigated socio-demographic and clinical factors associated with the use of any of these seven CHA. Results Overall, 29.2% of older adults used any of seven CHA in the past year. Most commonly used CHA included herbal therapies (18.1%), chiropractic (8.4%), and massage (5.7%). More than 60% of older CHA users reported that CHA were important for maintaining health and well-being. Other perceived benefits included improving overall health and feeling better (52.3%), giving a better sense of control over health (27.4%), and making it easier to cope with health problems (24.7%). Older adults with higher education and income levels, ≥2 chronic conditions, and functional limitations had greater odds of using CHA (p < .01, respectively). Conclusion A substantial number of older CHA users reported CHA-related benefits. CHA may play a crucial role in improving health status among older adults. At the population level, further research on the effects of CHA use on bio-psycho-social outcomes is needed to promote healthy aging in older adults. Complementary and alternative medicine, Complementary health approaches, Integrative medicine, Older adults, Perceived benefits Complementary health approaches (CHA) are ‘a group of diverse medical and health care systems, practices, and products that are not generally considered part of conventional medicine,’ (1) such as acupuncture, chiropractic, herbal therapies, and yoga. In 2012, the prevalence of utilizing CHA was 31% among adults ages 50 or older in the United States (2). Recent studies suggest that Americans are using CHA to improve and/or manage their health (2,3). For example, individuals have diverse motivations for using CHA from treating specific conditions, such as back and neck pain (eg, chiropractic and massage) (4–7), to improving general health and wellness (eg, herbal therapies and meditation) (2,8–12). These studies, however, lacked specificity of CHA or focused on the general U.S. population (13,14). Furthermore, these studies did not address self-reported perceived benefits of using CHA in older adults. Compared to their younger counterparts, older adults are more likely to have chronic conditions (eg, arthritis, heart and pulmonary diseases, and diabetes) (15), and the cost of living longer increases steadily from age 66 ($5,562) to age 96 ($16,145) on average per capita Medicare spending, as health services utilization increases with age in older adults (16). Yet, relatively little is known about to what extent older adults use CHA and their self-reported benefits of using CHA. CHA were previously known as complementary and alternative medicine (CAM), but the term became blurry as most people use these non-mainstream health practices in addition to their conventional medicine (17). In particular, it is important to understand why older adults use CHA in addition to their potentially burdensome and fragmented care, so that healthcare providers and public health professionals can better evaluate patient-value based care (18), by consideration of integrating CHA into conventional healthcare practices. As a result, we investigated the patterns and perceived benefits of utilizing CHA in adults aged 65 or older. We sought to answer the following questions: First, what are the prevalence of seven major CHA types, and which socio-demographic and clinical factors are associated with the use of CHA? Second, what are self-perceived benefits of using CHA in older adults? Methods Data Source and Study Sample We collected data from the 2012 National Health Interview Survey (NHIS), which was administrated by the National Center for Health Statistics of the Centers for Disease Control and Prevention (19). The NHIS is an annual cross-sectional in-person interview survey to demonstrate healthcare trends (eg, health status and health services utilization) among non-institutionalized civilians in the United States (19). The NHIS collects comprehensive information about healthcare trends related to CHA, including patterns of use and perceived benefits for use every 5 years. The 2012 NHIS data set contains the most recent data for CHA use. The survey response rate was 61.2% in 2012 (20). Of all sampled adults [n = 34,525 unweighted (ie, raw sample size)], we included all sampled adults ages 65 and over (n = 7,382 unweighted). We excluded observations with missing values (n = 266 unweighted) in covariates (3.6%), which were missing completely at random as determined by the Little’s test (p = .057) (21), leaving the final analytic sample size of 7,116. Our study was exempted from the Institutional Review of Board review (#2000021662) at Yale University School of Medicine, as we used publicly available de-identified data from the CDC. Measures Use of CHAs The NHIS specifically asks about the use of 36 different CHA types in the past 12 months. Based on the previous CDC technical report (3) and existing studies (2,12,22–24), we identified seven most commonly used CHA types in older adults. We created seven binary variables (yes/no) for CHA use in acupuncture (n = 137 unweighted), herbal therapies, excluding vitamin or mineral supplements (n = 1,268 unweighted), chiropractic (n = 578 unweighted), massage (n = 385 unweighted), meditation (n = 168 unweighted), Tai Chi (n = 97 unweighted), and yoga (n = 250 unweighted). Perceived benefits of using CHA In the top three CHA types used, NHIS respondents were asked whether or not the CHA use provided specific benefits, such as: (a) a better sense of control over health; (b) stress reduction/relaxation; (c) better sleep; (d) feeling better emotionally; (e) made it easier to cope with health problems; (f) improved overall health/feeling better; and (g) improved relationships with others. Further, the NHIS also asked if CHA was (h) important for maintaining health and wellbeing in general. Using these questionnaire items, we created eight indicator variables (yes/no) to represent perceived benefits of CHA use in the past year. Covariates Covariates included age, sex, race/ethnicity, marital status, educational attainment, geographic region, poverty status as determined by federal poverty level (FPL) (25), self-reported health status, moderate mental distress using the Kessler’s K6 scale (26), and functional limitations. In addition, the NHIS asks whether or not each sampled adult currently has 10 chronic conditions: asthma, arthritis, cancer, diabetes, hepatitis, hypertension, chronic obstructive pulmonary disease, coronary heart disease, stroke, and/or weak or failing kidneys (15). Using this information, we constructed a categorical variable (0, 1, or ≥2 chronic conditions). Data Analysis First, we examined the extent to which socio-demographic and health-related characteristics were different among U.S. adults ages 65 and over by each CHA use. For each socio-demographic and health-related variable, we used cross-tabulations and weight-corrected Pearson’s chi-squared statistics (ie, design-based F-tests) to investigate the differences in each CHA type. Using these raw p-values, we estimated p-values using a false discovery rate method to perform multiple comparisons across different CHA types (27). Second, we estimated the odds of using any of seven CHA types using a multivariable logistic regression model. Lastly, we descriptively investigated patterns of reporting each of perceived benefit among those who reported using CHA in the past year. We conducted all analyses using Stata 13.1 (College Station, TX) (28), and accounted for NHIS’ complex survey sample design (eg, unequal probability of selection, clustering, and stratification) (19). For multiple comparisons tests, we used the MULTTEST procedure in SAS 9.4 (Cary, NC). A p-value of .05 (two-sided) was used for the level of statistical significance in our statistical analyses. Results Characteristics of the Study Sample We found that 2,034 (29.2%, weighted) of 7,116 older adults, which represent 11.7 million older adults nationally, reported using any form of seven elicited CHA types in the past year. Table 1 presents socio-demographic and health-related characteristics of the study sample. Most common CHA types used were herbal therapies (18.1%), chiropractic (8.4%), and massage (5.7%) in older adults. The majority of the study participants were adults ages 65–74 (57.2%), female (56.1%), non-Hispanic whites (79.4%), married (55.8%), and had an educational level of some college or higher (50.7%). In enabling factors, each characteristic was different by each CHA type at the level of p < .05. In need factors, the distributions of self-reported health status were different in massage, meditation, and yoga (p < .05, respectively). Similarly, the distribution of mental distress was different in massage and meditation (p = .014), respectively. Table 1. Selected Characteristics (weighted column %) of Older U.S. Adults (n = 7,116 unweighted) by Seven Major Complementary Health Approaches (CHA) Use in the Past Year, 2012 NHIS   Acupuncture  Herbal therapies  Chiropractic  Massage  Meditation  Tai Chi  Yoga      Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Overall  Sample size   Unweighted sample  137    1,268    578    385    168    97    250    7,116   Weighted U.S. population  711,739    7,289,383    3,364,536    2,306,932    980,621    484,655    1,512,889    40,245,549  Predisposing factors  Age   65–74 years  65.0%  .144  66.0%  <.001  64.2%  .004  73.6%  <.001  84.2%  <.001  72.8%  .014  83.1%  <.001  57.2%   75+ years  35.1%  34.1%  35.8%  26.4%  15.8%  27.2%  16.9%  42.8%  Sex   Female  64.6%  .202  56.5%  .772  57.8%  .586  64.0%  .053  62.9%  .357  70.5%  .053  69.0%  .008  56.1%   Male  35.4%  43.5%  42.2%  36.0%  37.1%  29.5%  31.0%  43.9%  Race/ethnicity   White, Non-Hispanic  80.5%  .003  85.5%  <.001  90.9%  <.001  84.0%  .276  82.6%  .850  71.2%  .106  82.4%  .165  79.4%   Black, Non-Hispanic  4.2%  4.6%  2.5%  5.6%  7.8%  11.7%  8.5%  8.4%   American Indian/Alaska Native  0.0%  0.2%  0.0%  0.0%  0.0%  0.0%  0.0%  0.3%   Asian  11.4%  4.1%  2.1%  4.3%  2.2%  10.8%  5.8%  3.7%   Other, Non-Hispanic  0.0%  1.3%  0.3%  0.9%  1.0%  0.2%  0.3%  0.7%   Hispanic  3.9%  4.4%  4.1%  5.3%  6.4%  6.2%  3.1%  7.4%  Marital status   Married  47.8%  .211  59.4%  .086  61.9%  .054  48.4%  .054  55.7%  .978  46.4%  .211  53.5%  .641  55.8%   Other*  52.3%  40.6%  38.1%  51.6%  44.3%  53.6%  46.5%  44.2%  Educational attainment   <High school diploma  7.5%  <.001  11.4%  <.001  9.4%  <.001  7.3%  <.001  4.4%  <.001  1.6%  <.001  2.7%  <.001  19.4%   High school diploma  20.2%  24.8%  34.2%  17.7%  5.0%  11.0%  15.2%  29.9%   Some college  30.3%  29.5%  27.7%  29.9%  25.2%  31.7%  23.9%  25.4%   ≥College degree  42.1%  34.3%  28.8%  45.2%  65.4%  55.8%  58.2%  25.3%  Enabling factors  Poverty status   Below 200% FPL†  20.0%  .013  19.6%  <.001  21.4%  <.001  18.0%  <.001  17.2%  <.001  22.3%  .019  16.9%  <.001  31.6%   200–399% of FPL  31.8%  35.8%  38.5%  28.3%  24.1%  28.9%  28.4%  34.9%   400%+ of FPL  48.2%  44.6%  40.1%  53.7%  58.7%  48.8%  54.8%  33.6%  Geographic region   Northeast  14.9%  <.001  15.0%  <.001  15.2%  <.001  17.7%  <.001  22.8%  .018  20.3%  .018  18.7%  .010  18.2%   Midwest  12.1%  28.0%  31.9%  23.3%  15.7%  13.2%  19.1%  22.2%   South  20.5%  28.3%  26.9%  26.5%  27.0%  28.5%  30.4%  38.1%   West  52.5%  28.7%  26.0%  32.5%  34.5%  38.0%  31.9%  21.6%  Need factors  Self-reported health status   <Excellent  81.6%  .486  82.5%  .119  82.0%  .156  78.7%  .033  75.9%  .040  74.9%  .102  74.1%  <.001  84.4%   Excellent health  18.5%  17.5%  18.0%  21.3%  24.1%  25.1%  25.9%  15.6%  Chronic conditions   None  18.3%  .333  11.2%  .118  15.6%  .681  10.4%  .175  10.5%  .333  14.3%  .421  17.1%  .036  14.3%   1  31.7%  26.0%  25.6%  29.4%  31.6%  32.1%  34.1%  25.2%   ≥2  50.0%  62.8%  58.8%  60.2%  57.9%  53.6%  48.9%  60.5%  Mental distress   Yes  14.7%  .703  13.7%  .703  11.3%  .416  19.9%  .014  21.0%  .014  11.0%  .703  10.2%  .416  13.3%   No  85.3%  86.3%  88.7%  80.1%  79.0%  89.0%  89.8%  86.7%  Functional limitations   Not limited  26.7%  .123  35.1%  .417  34.8%  .417  32.1%  .258  33.3%  .417  46.7%  .257  49.0%  .008  36.8%   Limited  73.3%  64.9%  65.2%  67.9%  66.7%  53.3%  51.1%  63.2%    Acupuncture  Herbal therapies  Chiropractic  Massage  Meditation  Tai Chi  Yoga      Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Overall  Sample size   Unweighted sample  137    1,268    578    385    168    97    250    7,116   Weighted U.S. population  711,739    7,289,383    3,364,536    2,306,932    980,621    484,655    1,512,889    40,245,549  Predisposing factors  Age   65–74 years  65.0%  .144  66.0%  <.001  64.2%  .004  73.6%  <.001  84.2%  <.001  72.8%  .014  83.1%  <.001  57.2%   75+ years  35.1%  34.1%  35.8%  26.4%  15.8%  27.2%  16.9%  42.8%  Sex   Female  64.6%  .202  56.5%  .772  57.8%  .586  64.0%  .053  62.9%  .357  70.5%  .053  69.0%  .008  56.1%   Male  35.4%  43.5%  42.2%  36.0%  37.1%  29.5%  31.0%  43.9%  Race/ethnicity   White, Non-Hispanic  80.5%  .003  85.5%  <.001  90.9%  <.001  84.0%  .276  82.6%  .850  71.2%  .106  82.4%  .165  79.4%   Black, Non-Hispanic  4.2%  4.6%  2.5%  5.6%  7.8%  11.7%  8.5%  8.4%   American Indian/Alaska Native  0.0%  0.2%  0.0%  0.0%  0.0%  0.0%  0.0%  0.3%   Asian  11.4%  4.1%  2.1%  4.3%  2.2%  10.8%  5.8%  3.7%   Other, Non-Hispanic  0.0%  1.3%  0.3%  0.9%  1.0%  0.2%  0.3%  0.7%   Hispanic  3.9%  4.4%  4.1%  5.3%  6.4%  6.2%  3.1%  7.4%  Marital status   Married  47.8%  .211  59.4%  .086  61.9%  .054  48.4%  .054  55.7%  .978  46.4%  .211  53.5%  .641  55.8%   Other*  52.3%  40.6%  38.1%  51.6%  44.3%  53.6%  46.5%  44.2%  Educational attainment   <High school diploma  7.5%  <.001  11.4%  <.001  9.4%  <.001  7.3%  <.001  4.4%  <.001  1.6%  <.001  2.7%  <.001  19.4%   High school diploma  20.2%  24.8%  34.2%  17.7%  5.0%  11.0%  15.2%  29.9%   Some college  30.3%  29.5%  27.7%  29.9%  25.2%  31.7%  23.9%  25.4%   ≥College degree  42.1%  34.3%  28.8%  45.2%  65.4%  55.8%  58.2%  25.3%  Enabling factors  Poverty status   Below 200% FPL†  20.0%  .013  19.6%  <.001  21.4%  <.001  18.0%  <.001  17.2%  <.001  22.3%  .019  16.9%  <.001  31.6%   200–399% of FPL  31.8%  35.8%  38.5%  28.3%  24.1%  28.9%  28.4%  34.9%   400%+ of FPL  48.2%  44.6%  40.1%  53.7%  58.7%  48.8%  54.8%  33.6%  Geographic region   Northeast  14.9%  <.001  15.0%  <.001  15.2%  <.001  17.7%  <.001  22.8%  .018  20.3%  .018  18.7%  .010  18.2%   Midwest  12.1%  28.0%  31.9%  23.3%  15.7%  13.2%  19.1%  22.2%   South  20.5%  28.3%  26.9%  26.5%  27.0%  28.5%  30.4%  38.1%   West  52.5%  28.7%  26.0%  32.5%  34.5%  38.0%  31.9%  21.6%  Need factors  Self-reported health status   <Excellent  81.6%  .486  82.5%  .119  82.0%  .156  78.7%  .033  75.9%  .040  74.9%  .102  74.1%  <.001  84.4%   Excellent health  18.5%  17.5%  18.0%  21.3%  24.1%  25.1%  25.9%  15.6%  Chronic conditions   None  18.3%  .333  11.2%  .118  15.6%  .681  10.4%  .175  10.5%  .333  14.3%  .421  17.1%  .036  14.3%   1  31.7%  26.0%  25.6%  29.4%  31.6%  32.1%  34.1%  25.2%   ≥2  50.0%  62.8%  58.8%  60.2%  57.9%  53.6%  48.9%  60.5%  Mental distress   Yes  14.7%  .703  13.7%  .703  11.3%  .416  19.9%  .014  21.0%  .014  11.0%  .703  10.2%  .416  13.3%   No  85.3%  86.3%  88.7%  80.1%  79.0%  89.0%  89.8%  86.7%  Functional limitations   Not limited  26.7%  .123  35.1%  .417  34.8%  .417  32.1%  .258  33.3%  .417  46.7%  .257  49.0%  .008  36.8%   Limited  73.3%  64.9%  65.2%  67.9%  66.7%  53.3%  51.1%  63.2%  Notes: p-values were adjusted for multiple comparisons across seven CHA modalities using the false discovery rate (FDR) method. *Includes widowed, divorced, separated, and living with a partner. †Indicates federal poverty level. View Large Table 1. Selected Characteristics (weighted column %) of Older U.S. Adults (n = 7,116 unweighted) by Seven Major Complementary Health Approaches (CHA) Use in the Past Year, 2012 NHIS   Acupuncture  Herbal therapies  Chiropractic  Massage  Meditation  Tai Chi  Yoga      Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Overall  Sample size   Unweighted sample  137    1,268    578    385    168    97    250    7,116   Weighted U.S. population  711,739    7,289,383    3,364,536    2,306,932    980,621    484,655    1,512,889    40,245,549  Predisposing factors  Age   65–74 years  65.0%  .144  66.0%  <.001  64.2%  .004  73.6%  <.001  84.2%  <.001  72.8%  .014  83.1%  <.001  57.2%   75+ years  35.1%  34.1%  35.8%  26.4%  15.8%  27.2%  16.9%  42.8%  Sex   Female  64.6%  .202  56.5%  .772  57.8%  .586  64.0%  .053  62.9%  .357  70.5%  .053  69.0%  .008  56.1%   Male  35.4%  43.5%  42.2%  36.0%  37.1%  29.5%  31.0%  43.9%  Race/ethnicity   White, Non-Hispanic  80.5%  .003  85.5%  <.001  90.9%  <.001  84.0%  .276  82.6%  .850  71.2%  .106  82.4%  .165  79.4%   Black, Non-Hispanic  4.2%  4.6%  2.5%  5.6%  7.8%  11.7%  8.5%  8.4%   American Indian/Alaska Native  0.0%  0.2%  0.0%  0.0%  0.0%  0.0%  0.0%  0.3%   Asian  11.4%  4.1%  2.1%  4.3%  2.2%  10.8%  5.8%  3.7%   Other, Non-Hispanic  0.0%  1.3%  0.3%  0.9%  1.0%  0.2%  0.3%  0.7%   Hispanic  3.9%  4.4%  4.1%  5.3%  6.4%  6.2%  3.1%  7.4%  Marital status   Married  47.8%  .211  59.4%  .086  61.9%  .054  48.4%  .054  55.7%  .978  46.4%  .211  53.5%  .641  55.8%   Other*  52.3%  40.6%  38.1%  51.6%  44.3%  53.6%  46.5%  44.2%  Educational attainment   <High school diploma  7.5%  <.001  11.4%  <.001  9.4%  <.001  7.3%  <.001  4.4%  <.001  1.6%  <.001  2.7%  <.001  19.4%   High school diploma  20.2%  24.8%  34.2%  17.7%  5.0%  11.0%  15.2%  29.9%   Some college  30.3%  29.5%  27.7%  29.9%  25.2%  31.7%  23.9%  25.4%   ≥College degree  42.1%  34.3%  28.8%  45.2%  65.4%  55.8%  58.2%  25.3%  Enabling factors  Poverty status   Below 200% FPL†  20.0%  .013  19.6%  <.001  21.4%  <.001  18.0%  <.001  17.2%  <.001  22.3%  .019  16.9%  <.001  31.6%   200–399% of FPL  31.8%  35.8%  38.5%  28.3%  24.1%  28.9%  28.4%  34.9%   400%+ of FPL  48.2%  44.6%  40.1%  53.7%  58.7%  48.8%  54.8%  33.6%  Geographic region   Northeast  14.9%  <.001  15.0%  <.001  15.2%  <.001  17.7%  <.001  22.8%  .018  20.3%  .018  18.7%  .010  18.2%   Midwest  12.1%  28.0%  31.9%  23.3%  15.7%  13.2%  19.1%  22.2%   South  20.5%  28.3%  26.9%  26.5%  27.0%  28.5%  30.4%  38.1%   West  52.5%  28.7%  26.0%  32.5%  34.5%  38.0%  31.9%  21.6%  Need factors  Self-reported health status   <Excellent  81.6%  .486  82.5%  .119  82.0%  .156  78.7%  .033  75.9%  .040  74.9%  .102  74.1%  <.001  84.4%   Excellent health  18.5%  17.5%  18.0%  21.3%  24.1%  25.1%  25.9%  15.6%  Chronic conditions   None  18.3%  .333  11.2%  .118  15.6%  .681  10.4%  .175  10.5%  .333  14.3%  .421  17.1%  .036  14.3%   1  31.7%  26.0%  25.6%  29.4%  31.6%  32.1%  34.1%  25.2%   ≥2  50.0%  62.8%  58.8%  60.2%  57.9%  53.6%  48.9%  60.5%  Mental distress   Yes  14.7%  .703  13.7%  .703  11.3%  .416  19.9%  .014  21.0%  .014  11.0%  .703  10.2%  .416  13.3%   No  85.3%  86.3%  88.7%  80.1%  79.0%  89.0%  89.8%  86.7%  Functional limitations   Not limited  26.7%  .123  35.1%  .417  34.8%  .417  32.1%  .258  33.3%  .417  46.7%  .257  49.0%  .008  36.8%   Limited  73.3%  64.9%  65.2%  67.9%  66.7%  53.3%  51.1%  63.2%    Acupuncture  Herbal therapies  Chiropractic  Massage  Meditation  Tai Chi  Yoga      Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Overall  Sample size   Unweighted sample  137    1,268    578    385    168    97    250    7,116   Weighted U.S. population  711,739    7,289,383    3,364,536    2,306,932    980,621    484,655    1,512,889    40,245,549  Predisposing factors  Age   65–74 years  65.0%  .144  66.0%  <.001  64.2%  .004  73.6%  <.001  84.2%  <.001  72.8%  .014  83.1%  <.001  57.2%   75+ years  35.1%  34.1%  35.8%  26.4%  15.8%  27.2%  16.9%  42.8%  Sex   Female  64.6%  .202  56.5%  .772  57.8%  .586  64.0%  .053  62.9%  .357  70.5%  .053  69.0%  .008  56.1%   Male  35.4%  43.5%  42.2%  36.0%  37.1%  29.5%  31.0%  43.9%  Race/ethnicity   White, Non-Hispanic  80.5%  .003  85.5%  <.001  90.9%  <.001  84.0%  .276  82.6%  .850  71.2%  .106  82.4%  .165  79.4%   Black, Non-Hispanic  4.2%  4.6%  2.5%  5.6%  7.8%  11.7%  8.5%  8.4%   American Indian/Alaska Native  0.0%  0.2%  0.0%  0.0%  0.0%  0.0%  0.0%  0.3%   Asian  11.4%  4.1%  2.1%  4.3%  2.2%  10.8%  5.8%  3.7%   Other, Non-Hispanic  0.0%  1.3%  0.3%  0.9%  1.0%  0.2%  0.3%  0.7%   Hispanic  3.9%  4.4%  4.1%  5.3%  6.4%  6.2%  3.1%  7.4%  Marital status   Married  47.8%  .211  59.4%  .086  61.9%  .054  48.4%  .054  55.7%  .978  46.4%  .211  53.5%  .641  55.8%   Other*  52.3%  40.6%  38.1%  51.6%  44.3%  53.6%  46.5%  44.2%  Educational attainment   <High school diploma  7.5%  <.001  11.4%  <.001  9.4%  <.001  7.3%  <.001  4.4%  <.001  1.6%  <.001  2.7%  <.001  19.4%   High school diploma  20.2%  24.8%  34.2%  17.7%  5.0%  11.0%  15.2%  29.9%   Some college  30.3%  29.5%  27.7%  29.9%  25.2%  31.7%  23.9%  25.4%   ≥College degree  42.1%  34.3%  28.8%  45.2%  65.4%  55.8%  58.2%  25.3%  Enabling factors  Poverty status   Below 200% FPL†  20.0%  .013  19.6%  <.001  21.4%  <.001  18.0%  <.001  17.2%  <.001  22.3%  .019  16.9%  <.001  31.6%   200–399% of FPL  31.8%  35.8%  38.5%  28.3%  24.1%  28.9%  28.4%  34.9%   400%+ of FPL  48.2%  44.6%  40.1%  53.7%  58.7%  48.8%  54.8%  33.6%  Geographic region   Northeast  14.9%  <.001  15.0%  <.001  15.2%  <.001  17.7%  <.001  22.8%  .018  20.3%  .018  18.7%  .010  18.2%   Midwest  12.1%  28.0%  31.9%  23.3%  15.7%  13.2%  19.1%  22.2%   South  20.5%  28.3%  26.9%  26.5%  27.0%  28.5%  30.4%  38.1%   West  52.5%  28.7%  26.0%  32.5%  34.5%  38.0%  31.9%  21.6%  Need factors  Self-reported health status   <Excellent  81.6%  .486  82.5%  .119  82.0%  .156  78.7%  .033  75.9%  .040  74.9%  .102  74.1%  <.001  84.4%   Excellent health  18.5%  17.5%  18.0%  21.3%  24.1%  25.1%  25.9%  15.6%  Chronic conditions   None  18.3%  .333  11.2%  .118  15.6%  .681  10.4%  .175  10.5%  .333  14.3%  .421  17.1%  .036  14.3%   1  31.7%  26.0%  25.6%  29.4%  31.6%  32.1%  34.1%  25.2%   ≥2  50.0%  62.8%  58.8%  60.2%  57.9%  53.6%  48.9%  60.5%  Mental distress   Yes  14.7%  .703  13.7%  .703  11.3%  .416  19.9%  .014  21.0%  .014  11.0%  .703  10.2%  .416  13.3%   No  85.3%  86.3%  88.7%  80.1%  79.0%  89.0%  89.8%  86.7%  Functional limitations   Not limited  26.7%  .123  35.1%  .417  34.8%  .417  32.1%  .258  33.3%  .417  46.7%  .257  49.0%  .008  36.8%   Limited  73.3%  64.9%  65.2%  67.9%  66.7%  53.3%  51.1%  63.2%  Notes: p-values were adjusted for multiple comparisons across seven CHA modalities using the false discovery rate (FDR) method. *Includes widowed, divorced, separated, and living with a partner. †Indicates federal poverty level. View Large Odds of CHA Use in Older Adults Table 2 presents adjusted odds ratios (AORs) of using any of seven major CHA types in older adults. In predisposing factors, adults ages 75 or older and male older adults had 40 and 27% lower odds of using any CHA, when compared to younger (65–74) and female older adults, respectively (p < .001). In racial/ethnic groups, non-Hispanic blacks and Hispanics had 42 and 38% lower odds of using any CHA, when compared with non-Hispanic whites (p < .001 and p = .001, respectively). For educational attainment, a higher level tended to have a greater likelihood of reporting the CHA use in the past year (p < .005). Table 2. Adjusted Odds Ratios (AORs) of Utilizing Any of Seven Major Complementary Health Approaches (CHA) in U.S. Older Adults (n = 7,116 Unweighted), 2012 NHIS (Reference Group in a Parenthesis)  AOR  95% CI  p-value  Predisposing factors  Age (65–74 years)   ≥75 years  0.60  0.51–0.69  <.001  Sex (Female)   Male  0.73  0.63–0.85  <.001  Race/ethnicity (Non-Hispanic White)   Black, Non-Hispanic  0.58  0.45–0.74  <.001   Asian  1.08  0.80–1.46  .603   Hispanic  0.62  0.46–0.82  .001   Other, Non-Hispanic*  0.94  0.50–1.75  .838  Marital status (Other†)   Married  0.96  0.82–1.12  .585  Educational attainment (<high school diploma)   High school diploma  1.45  1.15–2.83  .002   Some college  1.86  1.48–2.35  <.001   ≥ College degree  2.40  1.88–3.07  <.001  Enabling factors  Poverty status (Below 200% FPL‡)   200–399% of FPL  1.47  1.22–1.77  <.001   400%+ of FPL  1.83  1.50–2.23  <.001  Geographic region (Northeast)   Midwest  1.44  1.10–1.90  .008   South  0.83  0.65–1.05  .119   West  1.54  1.18–2.01  .001  Needs factors  Self-reported health status (excellent)   <Excellent  0.85  0.71–1.02  .080  Chronic conditions (none)   1  1.33  1.05–1.69  .018   ≥2  1.39  1.13–1.73  .002  Mental distress (no)   Yes  1.23  0.98–1.53  .068  Functional limitations (no)   Yes  1.28  1.09–1.50  .003  F-statistic§  16.85  <.001  (Reference Group in a Parenthesis)  AOR  95% CI  p-value  Predisposing factors  Age (65–74 years)   ≥75 years  0.60  0.51–0.69  <.001  Sex (Female)   Male  0.73  0.63–0.85  <.001  Race/ethnicity (Non-Hispanic White)   Black, Non-Hispanic  0.58  0.45–0.74  <.001   Asian  1.08  0.80–1.46  .603   Hispanic  0.62  0.46–0.82  .001   Other, Non-Hispanic*  0.94  0.50–1.75  .838  Marital status (Other†)   Married  0.96  0.82–1.12  .585  Educational attainment (<high school diploma)   High school diploma  1.45  1.15–2.83  .002   Some college  1.86  1.48–2.35  <.001   ≥ College degree  2.40  1.88–3.07  <.001  Enabling factors  Poverty status (Below 200% FPL‡)   200–399% of FPL  1.47  1.22–1.77  <.001   400%+ of FPL  1.83  1.50–2.23  <.001  Geographic region (Northeast)   Midwest  1.44  1.10–1.90  .008   South  0.83  0.65–1.05  .119   West  1.54  1.18–2.01  .001  Needs factors  Self-reported health status (excellent)   <Excellent  0.85  0.71–1.02  .080  Chronic conditions (none)   1  1.33  1.05–1.69  .018   ≥2  1.39  1.13–1.73  .002  Mental distress (no)   Yes  1.23  0.98–1.53  .068  Functional limitations (no)   Yes  1.28  1.09–1.50  .003  F-statistic§  16.85  <.001  Notes: *Includes American Indian, Alaska native, and those reporting multiple racial/ethnic groups. †Includes widowed, divorced, separated, and living with a partner. ‡Indicates federal poverty level. §After adjusted for complex survey design. View Large Table 2. Adjusted Odds Ratios (AORs) of Utilizing Any of Seven Major Complementary Health Approaches (CHA) in U.S. Older Adults (n = 7,116 Unweighted), 2012 NHIS (Reference Group in a Parenthesis)  AOR  95% CI  p-value  Predisposing factors  Age (65–74 years)   ≥75 years  0.60  0.51–0.69  <.001  Sex (Female)   Male  0.73  0.63–0.85  <.001  Race/ethnicity (Non-Hispanic White)   Black, Non-Hispanic  0.58  0.45–0.74  <.001   Asian  1.08  0.80–1.46  .603   Hispanic  0.62  0.46–0.82  .001   Other, Non-Hispanic*  0.94  0.50–1.75  .838  Marital status (Other†)   Married  0.96  0.82–1.12  .585  Educational attainment (<high school diploma)   High school diploma  1.45  1.15–2.83  .002   Some college  1.86  1.48–2.35  <.001   ≥ College degree  2.40  1.88–3.07  <.001  Enabling factors  Poverty status (Below 200% FPL‡)   200–399% of FPL  1.47  1.22–1.77  <.001   400%+ of FPL  1.83  1.50–2.23  <.001  Geographic region (Northeast)   Midwest  1.44  1.10–1.90  .008   South  0.83  0.65–1.05  .119   West  1.54  1.18–2.01  .001  Needs factors  Self-reported health status (excellent)   <Excellent  0.85  0.71–1.02  .080  Chronic conditions (none)   1  1.33  1.05–1.69  .018   ≥2  1.39  1.13–1.73  .002  Mental distress (no)   Yes  1.23  0.98–1.53  .068  Functional limitations (no)   Yes  1.28  1.09–1.50  .003  F-statistic§  16.85  <.001  (Reference Group in a Parenthesis)  AOR  95% CI  p-value  Predisposing factors  Age (65–74 years)   ≥75 years  0.60  0.51–0.69  <.001  Sex (Female)   Male  0.73  0.63–0.85  <.001  Race/ethnicity (Non-Hispanic White)   Black, Non-Hispanic  0.58  0.45–0.74  <.001   Asian  1.08  0.80–1.46  .603   Hispanic  0.62  0.46–0.82  .001   Other, Non-Hispanic*  0.94  0.50–1.75  .838  Marital status (Other†)   Married  0.96  0.82–1.12  .585  Educational attainment (<high school diploma)   High school diploma  1.45  1.15–2.83  .002   Some college  1.86  1.48–2.35  <.001   ≥ College degree  2.40  1.88–3.07  <.001  Enabling factors  Poverty status (Below 200% FPL‡)   200–399% of FPL  1.47  1.22–1.77  <.001   400%+ of FPL  1.83  1.50–2.23  <.001  Geographic region (Northeast)   Midwest  1.44  1.10–1.90  .008   South  0.83  0.65–1.05  .119   West  1.54  1.18–2.01  .001  Needs factors  Self-reported health status (excellent)   <Excellent  0.85  0.71–1.02  .080  Chronic conditions (none)   1  1.33  1.05–1.69  .018   ≥2  1.39  1.13–1.73  .002  Mental distress (no)   Yes  1.23  0.98–1.53  .068  Functional limitations (no)   Yes  1.28  1.09–1.50  .003  F-statistic§  16.85  <.001  Notes: *Includes American Indian, Alaska native, and those reporting multiple racial/ethnic groups. †Includes widowed, divorced, separated, and living with a partner. ‡Indicates federal poverty level. §After adjusted for complex survey design. View Large In enabling factors, individuals with poverty status of 200–399% FPL and ≥400% FPL had 1.47 and 1.83 times greater odds of reporting the CHA use when compared with those with under 200% FPL (p < .001). For geographic region, older adults residing in Midwest and West regions had 1.44 and 1.54 times greater odds of using CHA when compared to the Northeast region (p = .008 and p = .001, respectively). In need factors, those with one or ≥2 chronic conditions had 1.33 and 1.39 times greater odds of reporting the CHA use when compared to individuals with no chronic condition (p = .018 and p = .002, respectively). Finally, older adults with functional limitations had 1.28 times greater odds of using CHA than those without functional limitations (p = .003). Perceived Benefits of CHA Use Self-reported perceived benefits of utilizing CHA in older CHA users by age group are presented in Figure 1. Regardless of age groups, 52.3% of CHA users reported that CHA use improved their overall health and feeling better and 68.9% of CHA users stated that CHA use was important for them to maintain health and wellbeing. Improving relationships with others was the benefit that respondents were the least likely to receive by utilizing CHA (11.9%), regardless of age groups. Older adults ages 65–74 were more likely to report every domain of perceived benefits than those ages 75 or older, except one benefit called, ‘made it easier to cope with health problems’ (23.3 vs 27.5%). Figure 1. View largeDownload slide Self-reported perceived benefits of using any of major seven complementary health approaches (CHA) among older CHA users (n = 2,127 unweighted), 2012 NHIS. Note: Bars represent 95% confidence intervals. Figure 1. View largeDownload slide Self-reported perceived benefits of using any of major seven complementary health approaches (CHA) among older CHA users (n = 2,127 unweighted), 2012 NHIS. Note: Bars represent 95% confidence intervals. Discussion Using a population-based cross-sectional design, we investigated patterns and perceived benefits of CHA use among non-institutionalized older adults ages 65 or older in the United States. When extrapolated to the entire U.S. population, 29.2% of older adults (11.7 million) would have used some form of CHA in the past year. The most commonly used types of CHA were herbal therapies, chiropractic, and massage. Furthermore, these CHA users reported various perceived benefits, such as having better sense of control over health, feeling better emotionally, and reducing stress. We could not make a direct comparison of prevalence of CHA use in older adults with previous studies, as they had different populations of interest (eg, any adults and women) or used different inclusion criteria for CHA (29–33). However, our prevalence of CHA use in the past year and CHA use ever among older adults were similar to those of a previous study (2). For instance, a previous study reported that three most commonly used CHA types were herbal therapies (18.6%), chiropractic (8.7%), and massage (6.9%) in mid-life and older adults (2). Our findings highlight that a substantial number of older adults use CHA similar to a previous study (2). In our multivariable logistic regression analysis, we found that higher levels of education attainment and income were independently associated with CHA use in older adults. Geographically, older adults in Midwest and West regions had a higher likelihood of using CHA when compared to those in Northeastern region. In clinical characteristics, having more chronic conditions and functional limitations were also independently associated with the CHA use in older adults. These findings have major implications in the future research. First, future research should address whether CHA use helps older adults manage their multiple chronic conditions, and second, whether better socioeconomic means (eg, educational attainment and income) have mediating or moderating roles in such relationship. Addressing these gaps can help better understand the dynamic roles of CHA use in patient-centered care among older adults with multiple chronic conditions, for example. Our study highlights self-reported perceived benefits of CHA use among older adults. In particular, more than half of older CHA users reported that CHA were important for maintaining health and well-being and improved overall health and feeling better. While we do not know whether they used CHA for treatment only, for wellness and health promotion only, or both, future research should investigate roles of CHA by reason for use to better meet bio-psycho-social needs in older adults. There are several implications from our findings. First, because a substantial number of older CHA users reported benefits from utilizing CHA, more CHA research assessing diverse patient outcomes among older adults at the populational level is needed. By integrating CHA into conventional medical care, there may be value-based care, which particularly addresses psycho-social aspects of health in older adults. Second, clinicians should be aware that CHA use may be common in their older patients. They should be informed about CHA use to provide optimal patient care in their clinical practice. For example, certain herbal therapies may have drug-herb interactions, leading to serious adverse drug events (eg, kava and antidepressants). Clinicians should actively ask patients about CHA use and monitor potential interactions and side-effects. There are several limitations in this descriptive study. First, questionnaires for CHA use and its perceived benefits are self-reported, and their results are subject to recall bias. Second, the data were collected in 2012, such that patterns and perceived benefits of using CHA among older adults may be different now. Third, the current study lacks specificity in CHA use (eg, frequency and intensity). CHA use with respect to frequency and intensity should be evaluated to better understand CHA’s roles in improving perceived benefits among older adults. In conclusion, nearly one third (29.2%) of older adults used some form of CHA in the past year, and among older CHA users, they reported a wide range of perceived benefits from using CHA. Because CHA may play a crucial role in improving healthy lifestyles in older adults, further research on the effects of CHA use on patient outcomes in bio-psycho-social domains is needed to promote healthy aging in older adults. Funding T.G.R. received funding support from the National Institute on Aging (NIA) of National Institutes of Health (NIH) (#T32AG019134). P.H.V.N. received funding support from the NIA of NIH (#P30AG021342). The funding agency had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript, and decision to submit the manuscript for publication. Author Contributions Study concept and design: T.G.R. and M.E.T.; Data acquisition and statistical analyses: T.G.R.; Interpretation of data: all authors; Drafting of manuscript: T.G.R.; Critical revision of manuscript for important intellectual content: all authors. Conflict of Interest All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none were reported. Acknowledgements Data access and responsibility: T.G.R. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Disclaimers: Publicly available data were obtained from the National Center for Health Statistics (NCHS) and Centers for Disease Control and Prevention (CDC). Analyses, interpretation, and conclusions are solely those of the author and do not necessarily reflect the views of the Division of Health Interview Statistics or NCHS of the CDC. This article does not contain any studies with human participants or animals performed by the authors. All research procedures performed in this study are in accordance with the ethical standards of the Institutional Review Board at Yale University School of Medicine (#2000021662). References 1. 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Perceived benefits of utilising acupuncture by reason for use among US adults. Acupunct Med . 2017; 35: 460– 463. doi: 10.1136/acupmed-2017-011490 Google Scholar CrossRef Search ADS PubMed  13. Saydah SH, Eberhardt MS. Use of complementary and alternative medicine among adults with chronic diseases: United States 2002. J Altern Complement Med . 2006; 12: 805– 812. doi: 10.1089/acm.2006.12.805 Google Scholar CrossRef Search ADS PubMed  14. Alwhaibi M, Bhattacharya R, Sambamoorthi U. Type of multimorbidity and complementary and alternative medicine use among adults. Evid Based Complement Alternat Med . 2015; 2015: 362582. doi: 10.1155/2015/362582 Google Scholar CrossRef Search ADS PubMed  15. Ward BW, Schiller JS, Goodman RA. Multiple chronic conditions among US adults: a 2012 update. Prev Chronic Dis . 2014; 11: E62. doi: 10.5888/pcd11.130389 Google Scholar PubMed  16. Neuman T, Cubanski J, Huang J, Damico A. 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Complementary and alternative medicine in US adults with diabetes: reasons for use and perceived benefits. J Diabetes . 2018; 10: 310– 319. doi: 10.1111/1753-0407.12607 Google Scholar CrossRef Search ADS PubMed  25. Centers for Disease Control and Prevention. Imputed income files. 2013; https://www.cdc.gov/nchs/data/nhis/datdoc12.pdf. Accessed April 21, 2018. 26. Prochaska JJ, Sung HY, Max W, Shi Y, Ong M. Validity study of the K6 scale as a measure of moderate mental distress based on mental health treatment need and utilization. Int J Methods Psychiatr Res . 2012; 21: 88– 97. doi: 10.1002/mpr.1349 Google Scholar CrossRef Search ADS PubMed  27. Van Ness PH, Charpentier PA, Ip EH, et al.   Gerontologic biostatistics: the statistical challenges of clinical research with older study participants. J Am Geriatr Soc . 2010; 58: 1386– 1392. doi: 10.1111/j.1532-5415.2010.02926.x Google Scholar CrossRef Search ADS PubMed  28. Stata Corp. Survey Data Reference Manual . College Station, TX: Stata Press; 2013. 29. Arcury TA, Suerken CK, Grzywacz JG, Bell RA, Lang W, Quandt SA. Complementary and alternative medicine use among older adults: ethnic variation. Ethn Dis . 2006; 16: 723– 731. Google Scholar PubMed  30. Grzywacz JG, Lang W, Suerken C, Quandt SA, Bell RA, Arcury TA. Age, race, and ethnicity in the use of complementary and alternative medicine for health self-management: evidence from the 2002 National Health Interview Survey. J Aging Health . 2005; 17: 547– 572. doi: 10.1177/0898264305279821 Google Scholar CrossRef Search ADS PubMed  31. Davis MA, West AN, Weeks WB, Sirovich BE. Health behaviors and utilization among users of complementary and alternative medicine for treatment versus health promotion. Health Serv Res . 2011; 46: 1402– 1416. doi: 10.1111/j.1475-6773.2011.01270.x Google Scholar CrossRef Search ADS PubMed  32. Upchurch DM, Rainisch BW. The importance of wellness among users of complementary and alternative medicine: findings from the 2007 National Health Interview Survey. BMC Complement Altern Med . 2015; 15: 362. doi: 10.1186/s12906-015-0886-y Google Scholar CrossRef Search ADS PubMed  33. Rhee TG, Westberg SM, Harris IM. Use of complementary and alternative medicine in older adults with diabetes. Diabetes Care . 2018. doi: 10.2337/dc17-0682 © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences Oxford University Press

Patterns and Perceived Benefits of Utilizing Seven Major Complementary Health Approaches in U.S. Older Adults

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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10.1093/gerona/gly099
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Abstract

Abstract Objectives To examine patterns and perceived benefits of seven major complementary health approaches (CHA) among older adults in the United States. Methods Data from the 2012 National Health Interview Survey (NHIS), which represents non-institutionalized adults aged 65 or older (n = 7,116 unweighted), were used. We elicited seven most common CHA used in older adults, which are acupuncture, herbal therapies, chiropractic, massage, meditation, Tai Chi, and yoga. Survey participants were asked to self-report perceived benefits (eg, maintaining health and stress reduction) in their CHA used. We estimated prevalence and perceived benefits of CHA use. We also investigated socio-demographic and clinical factors associated with the use of any of these seven CHA. Results Overall, 29.2% of older adults used any of seven CHA in the past year. Most commonly used CHA included herbal therapies (18.1%), chiropractic (8.4%), and massage (5.7%). More than 60% of older CHA users reported that CHA were important for maintaining health and well-being. Other perceived benefits included improving overall health and feeling better (52.3%), giving a better sense of control over health (27.4%), and making it easier to cope with health problems (24.7%). Older adults with higher education and income levels, ≥2 chronic conditions, and functional limitations had greater odds of using CHA (p < .01, respectively). Conclusion A substantial number of older CHA users reported CHA-related benefits. CHA may play a crucial role in improving health status among older adults. At the population level, further research on the effects of CHA use on bio-psycho-social outcomes is needed to promote healthy aging in older adults. Complementary and alternative medicine, Complementary health approaches, Integrative medicine, Older adults, Perceived benefits Complementary health approaches (CHA) are ‘a group of diverse medical and health care systems, practices, and products that are not generally considered part of conventional medicine,’ (1) such as acupuncture, chiropractic, herbal therapies, and yoga. In 2012, the prevalence of utilizing CHA was 31% among adults ages 50 or older in the United States (2). Recent studies suggest that Americans are using CHA to improve and/or manage their health (2,3). For example, individuals have diverse motivations for using CHA from treating specific conditions, such as back and neck pain (eg, chiropractic and massage) (4–7), to improving general health and wellness (eg, herbal therapies and meditation) (2,8–12). These studies, however, lacked specificity of CHA or focused on the general U.S. population (13,14). Furthermore, these studies did not address self-reported perceived benefits of using CHA in older adults. Compared to their younger counterparts, older adults are more likely to have chronic conditions (eg, arthritis, heart and pulmonary diseases, and diabetes) (15), and the cost of living longer increases steadily from age 66 ($5,562) to age 96 ($16,145) on average per capita Medicare spending, as health services utilization increases with age in older adults (16). Yet, relatively little is known about to what extent older adults use CHA and their self-reported benefits of using CHA. CHA were previously known as complementary and alternative medicine (CAM), but the term became blurry as most people use these non-mainstream health practices in addition to their conventional medicine (17). In particular, it is important to understand why older adults use CHA in addition to their potentially burdensome and fragmented care, so that healthcare providers and public health professionals can better evaluate patient-value based care (18), by consideration of integrating CHA into conventional healthcare practices. As a result, we investigated the patterns and perceived benefits of utilizing CHA in adults aged 65 or older. We sought to answer the following questions: First, what are the prevalence of seven major CHA types, and which socio-demographic and clinical factors are associated with the use of CHA? Second, what are self-perceived benefits of using CHA in older adults? Methods Data Source and Study Sample We collected data from the 2012 National Health Interview Survey (NHIS), which was administrated by the National Center for Health Statistics of the Centers for Disease Control and Prevention (19). The NHIS is an annual cross-sectional in-person interview survey to demonstrate healthcare trends (eg, health status and health services utilization) among non-institutionalized civilians in the United States (19). The NHIS collects comprehensive information about healthcare trends related to CHA, including patterns of use and perceived benefits for use every 5 years. The 2012 NHIS data set contains the most recent data for CHA use. The survey response rate was 61.2% in 2012 (20). Of all sampled adults [n = 34,525 unweighted (ie, raw sample size)], we included all sampled adults ages 65 and over (n = 7,382 unweighted). We excluded observations with missing values (n = 266 unweighted) in covariates (3.6%), which were missing completely at random as determined by the Little’s test (p = .057) (21), leaving the final analytic sample size of 7,116. Our study was exempted from the Institutional Review of Board review (#2000021662) at Yale University School of Medicine, as we used publicly available de-identified data from the CDC. Measures Use of CHAs The NHIS specifically asks about the use of 36 different CHA types in the past 12 months. Based on the previous CDC technical report (3) and existing studies (2,12,22–24), we identified seven most commonly used CHA types in older adults. We created seven binary variables (yes/no) for CHA use in acupuncture (n = 137 unweighted), herbal therapies, excluding vitamin or mineral supplements (n = 1,268 unweighted), chiropractic (n = 578 unweighted), massage (n = 385 unweighted), meditation (n = 168 unweighted), Tai Chi (n = 97 unweighted), and yoga (n = 250 unweighted). Perceived benefits of using CHA In the top three CHA types used, NHIS respondents were asked whether or not the CHA use provided specific benefits, such as: (a) a better sense of control over health; (b) stress reduction/relaxation; (c) better sleep; (d) feeling better emotionally; (e) made it easier to cope with health problems; (f) improved overall health/feeling better; and (g) improved relationships with others. Further, the NHIS also asked if CHA was (h) important for maintaining health and wellbeing in general. Using these questionnaire items, we created eight indicator variables (yes/no) to represent perceived benefits of CHA use in the past year. Covariates Covariates included age, sex, race/ethnicity, marital status, educational attainment, geographic region, poverty status as determined by federal poverty level (FPL) (25), self-reported health status, moderate mental distress using the Kessler’s K6 scale (26), and functional limitations. In addition, the NHIS asks whether or not each sampled adult currently has 10 chronic conditions: asthma, arthritis, cancer, diabetes, hepatitis, hypertension, chronic obstructive pulmonary disease, coronary heart disease, stroke, and/or weak or failing kidneys (15). Using this information, we constructed a categorical variable (0, 1, or ≥2 chronic conditions). Data Analysis First, we examined the extent to which socio-demographic and health-related characteristics were different among U.S. adults ages 65 and over by each CHA use. For each socio-demographic and health-related variable, we used cross-tabulations and weight-corrected Pearson’s chi-squared statistics (ie, design-based F-tests) to investigate the differences in each CHA type. Using these raw p-values, we estimated p-values using a false discovery rate method to perform multiple comparisons across different CHA types (27). Second, we estimated the odds of using any of seven CHA types using a multivariable logistic regression model. Lastly, we descriptively investigated patterns of reporting each of perceived benefit among those who reported using CHA in the past year. We conducted all analyses using Stata 13.1 (College Station, TX) (28), and accounted for NHIS’ complex survey sample design (eg, unequal probability of selection, clustering, and stratification) (19). For multiple comparisons tests, we used the MULTTEST procedure in SAS 9.4 (Cary, NC). A p-value of .05 (two-sided) was used for the level of statistical significance in our statistical analyses. Results Characteristics of the Study Sample We found that 2,034 (29.2%, weighted) of 7,116 older adults, which represent 11.7 million older adults nationally, reported using any form of seven elicited CHA types in the past year. Table 1 presents socio-demographic and health-related characteristics of the study sample. Most common CHA types used were herbal therapies (18.1%), chiropractic (8.4%), and massage (5.7%) in older adults. The majority of the study participants were adults ages 65–74 (57.2%), female (56.1%), non-Hispanic whites (79.4%), married (55.8%), and had an educational level of some college or higher (50.7%). In enabling factors, each characteristic was different by each CHA type at the level of p < .05. In need factors, the distributions of self-reported health status were different in massage, meditation, and yoga (p < .05, respectively). Similarly, the distribution of mental distress was different in massage and meditation (p = .014), respectively. Table 1. Selected Characteristics (weighted column %) of Older U.S. Adults (n = 7,116 unweighted) by Seven Major Complementary Health Approaches (CHA) Use in the Past Year, 2012 NHIS   Acupuncture  Herbal therapies  Chiropractic  Massage  Meditation  Tai Chi  Yoga      Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Overall  Sample size   Unweighted sample  137    1,268    578    385    168    97    250    7,116   Weighted U.S. population  711,739    7,289,383    3,364,536    2,306,932    980,621    484,655    1,512,889    40,245,549  Predisposing factors  Age   65–74 years  65.0%  .144  66.0%  <.001  64.2%  .004  73.6%  <.001  84.2%  <.001  72.8%  .014  83.1%  <.001  57.2%   75+ years  35.1%  34.1%  35.8%  26.4%  15.8%  27.2%  16.9%  42.8%  Sex   Female  64.6%  .202  56.5%  .772  57.8%  .586  64.0%  .053  62.9%  .357  70.5%  .053  69.0%  .008  56.1%   Male  35.4%  43.5%  42.2%  36.0%  37.1%  29.5%  31.0%  43.9%  Race/ethnicity   White, Non-Hispanic  80.5%  .003  85.5%  <.001  90.9%  <.001  84.0%  .276  82.6%  .850  71.2%  .106  82.4%  .165  79.4%   Black, Non-Hispanic  4.2%  4.6%  2.5%  5.6%  7.8%  11.7%  8.5%  8.4%   American Indian/Alaska Native  0.0%  0.2%  0.0%  0.0%  0.0%  0.0%  0.0%  0.3%   Asian  11.4%  4.1%  2.1%  4.3%  2.2%  10.8%  5.8%  3.7%   Other, Non-Hispanic  0.0%  1.3%  0.3%  0.9%  1.0%  0.2%  0.3%  0.7%   Hispanic  3.9%  4.4%  4.1%  5.3%  6.4%  6.2%  3.1%  7.4%  Marital status   Married  47.8%  .211  59.4%  .086  61.9%  .054  48.4%  .054  55.7%  .978  46.4%  .211  53.5%  .641  55.8%   Other*  52.3%  40.6%  38.1%  51.6%  44.3%  53.6%  46.5%  44.2%  Educational attainment   <High school diploma  7.5%  <.001  11.4%  <.001  9.4%  <.001  7.3%  <.001  4.4%  <.001  1.6%  <.001  2.7%  <.001  19.4%   High school diploma  20.2%  24.8%  34.2%  17.7%  5.0%  11.0%  15.2%  29.9%   Some college  30.3%  29.5%  27.7%  29.9%  25.2%  31.7%  23.9%  25.4%   ≥College degree  42.1%  34.3%  28.8%  45.2%  65.4%  55.8%  58.2%  25.3%  Enabling factors  Poverty status   Below 200% FPL†  20.0%  .013  19.6%  <.001  21.4%  <.001  18.0%  <.001  17.2%  <.001  22.3%  .019  16.9%  <.001  31.6%   200–399% of FPL  31.8%  35.8%  38.5%  28.3%  24.1%  28.9%  28.4%  34.9%   400%+ of FPL  48.2%  44.6%  40.1%  53.7%  58.7%  48.8%  54.8%  33.6%  Geographic region   Northeast  14.9%  <.001  15.0%  <.001  15.2%  <.001  17.7%  <.001  22.8%  .018  20.3%  .018  18.7%  .010  18.2%   Midwest  12.1%  28.0%  31.9%  23.3%  15.7%  13.2%  19.1%  22.2%   South  20.5%  28.3%  26.9%  26.5%  27.0%  28.5%  30.4%  38.1%   West  52.5%  28.7%  26.0%  32.5%  34.5%  38.0%  31.9%  21.6%  Need factors  Self-reported health status   <Excellent  81.6%  .486  82.5%  .119  82.0%  .156  78.7%  .033  75.9%  .040  74.9%  .102  74.1%  <.001  84.4%   Excellent health  18.5%  17.5%  18.0%  21.3%  24.1%  25.1%  25.9%  15.6%  Chronic conditions   None  18.3%  .333  11.2%  .118  15.6%  .681  10.4%  .175  10.5%  .333  14.3%  .421  17.1%  .036  14.3%   1  31.7%  26.0%  25.6%  29.4%  31.6%  32.1%  34.1%  25.2%   ≥2  50.0%  62.8%  58.8%  60.2%  57.9%  53.6%  48.9%  60.5%  Mental distress   Yes  14.7%  .703  13.7%  .703  11.3%  .416  19.9%  .014  21.0%  .014  11.0%  .703  10.2%  .416  13.3%   No  85.3%  86.3%  88.7%  80.1%  79.0%  89.0%  89.8%  86.7%  Functional limitations   Not limited  26.7%  .123  35.1%  .417  34.8%  .417  32.1%  .258  33.3%  .417  46.7%  .257  49.0%  .008  36.8%   Limited  73.3%  64.9%  65.2%  67.9%  66.7%  53.3%  51.1%  63.2%    Acupuncture  Herbal therapies  Chiropractic  Massage  Meditation  Tai Chi  Yoga      Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Overall  Sample size   Unweighted sample  137    1,268    578    385    168    97    250    7,116   Weighted U.S. population  711,739    7,289,383    3,364,536    2,306,932    980,621    484,655    1,512,889    40,245,549  Predisposing factors  Age   65–74 years  65.0%  .144  66.0%  <.001  64.2%  .004  73.6%  <.001  84.2%  <.001  72.8%  .014  83.1%  <.001  57.2%   75+ years  35.1%  34.1%  35.8%  26.4%  15.8%  27.2%  16.9%  42.8%  Sex   Female  64.6%  .202  56.5%  .772  57.8%  .586  64.0%  .053  62.9%  .357  70.5%  .053  69.0%  .008  56.1%   Male  35.4%  43.5%  42.2%  36.0%  37.1%  29.5%  31.0%  43.9%  Race/ethnicity   White, Non-Hispanic  80.5%  .003  85.5%  <.001  90.9%  <.001  84.0%  .276  82.6%  .850  71.2%  .106  82.4%  .165  79.4%   Black, Non-Hispanic  4.2%  4.6%  2.5%  5.6%  7.8%  11.7%  8.5%  8.4%   American Indian/Alaska Native  0.0%  0.2%  0.0%  0.0%  0.0%  0.0%  0.0%  0.3%   Asian  11.4%  4.1%  2.1%  4.3%  2.2%  10.8%  5.8%  3.7%   Other, Non-Hispanic  0.0%  1.3%  0.3%  0.9%  1.0%  0.2%  0.3%  0.7%   Hispanic  3.9%  4.4%  4.1%  5.3%  6.4%  6.2%  3.1%  7.4%  Marital status   Married  47.8%  .211  59.4%  .086  61.9%  .054  48.4%  .054  55.7%  .978  46.4%  .211  53.5%  .641  55.8%   Other*  52.3%  40.6%  38.1%  51.6%  44.3%  53.6%  46.5%  44.2%  Educational attainment   <High school diploma  7.5%  <.001  11.4%  <.001  9.4%  <.001  7.3%  <.001  4.4%  <.001  1.6%  <.001  2.7%  <.001  19.4%   High school diploma  20.2%  24.8%  34.2%  17.7%  5.0%  11.0%  15.2%  29.9%   Some college  30.3%  29.5%  27.7%  29.9%  25.2%  31.7%  23.9%  25.4%   ≥College degree  42.1%  34.3%  28.8%  45.2%  65.4%  55.8%  58.2%  25.3%  Enabling factors  Poverty status   Below 200% FPL†  20.0%  .013  19.6%  <.001  21.4%  <.001  18.0%  <.001  17.2%  <.001  22.3%  .019  16.9%  <.001  31.6%   200–399% of FPL  31.8%  35.8%  38.5%  28.3%  24.1%  28.9%  28.4%  34.9%   400%+ of FPL  48.2%  44.6%  40.1%  53.7%  58.7%  48.8%  54.8%  33.6%  Geographic region   Northeast  14.9%  <.001  15.0%  <.001  15.2%  <.001  17.7%  <.001  22.8%  .018  20.3%  .018  18.7%  .010  18.2%   Midwest  12.1%  28.0%  31.9%  23.3%  15.7%  13.2%  19.1%  22.2%   South  20.5%  28.3%  26.9%  26.5%  27.0%  28.5%  30.4%  38.1%   West  52.5%  28.7%  26.0%  32.5%  34.5%  38.0%  31.9%  21.6%  Need factors  Self-reported health status   <Excellent  81.6%  .486  82.5%  .119  82.0%  .156  78.7%  .033  75.9%  .040  74.9%  .102  74.1%  <.001  84.4%   Excellent health  18.5%  17.5%  18.0%  21.3%  24.1%  25.1%  25.9%  15.6%  Chronic conditions   None  18.3%  .333  11.2%  .118  15.6%  .681  10.4%  .175  10.5%  .333  14.3%  .421  17.1%  .036  14.3%   1  31.7%  26.0%  25.6%  29.4%  31.6%  32.1%  34.1%  25.2%   ≥2  50.0%  62.8%  58.8%  60.2%  57.9%  53.6%  48.9%  60.5%  Mental distress   Yes  14.7%  .703  13.7%  .703  11.3%  .416  19.9%  .014  21.0%  .014  11.0%  .703  10.2%  .416  13.3%   No  85.3%  86.3%  88.7%  80.1%  79.0%  89.0%  89.8%  86.7%  Functional limitations   Not limited  26.7%  .123  35.1%  .417  34.8%  .417  32.1%  .258  33.3%  .417  46.7%  .257  49.0%  .008  36.8%   Limited  73.3%  64.9%  65.2%  67.9%  66.7%  53.3%  51.1%  63.2%  Notes: p-values were adjusted for multiple comparisons across seven CHA modalities using the false discovery rate (FDR) method. *Includes widowed, divorced, separated, and living with a partner. †Indicates federal poverty level. View Large Table 1. Selected Characteristics (weighted column %) of Older U.S. Adults (n = 7,116 unweighted) by Seven Major Complementary Health Approaches (CHA) Use in the Past Year, 2012 NHIS   Acupuncture  Herbal therapies  Chiropractic  Massage  Meditation  Tai Chi  Yoga      Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Overall  Sample size   Unweighted sample  137    1,268    578    385    168    97    250    7,116   Weighted U.S. population  711,739    7,289,383    3,364,536    2,306,932    980,621    484,655    1,512,889    40,245,549  Predisposing factors  Age   65–74 years  65.0%  .144  66.0%  <.001  64.2%  .004  73.6%  <.001  84.2%  <.001  72.8%  .014  83.1%  <.001  57.2%   75+ years  35.1%  34.1%  35.8%  26.4%  15.8%  27.2%  16.9%  42.8%  Sex   Female  64.6%  .202  56.5%  .772  57.8%  .586  64.0%  .053  62.9%  .357  70.5%  .053  69.0%  .008  56.1%   Male  35.4%  43.5%  42.2%  36.0%  37.1%  29.5%  31.0%  43.9%  Race/ethnicity   White, Non-Hispanic  80.5%  .003  85.5%  <.001  90.9%  <.001  84.0%  .276  82.6%  .850  71.2%  .106  82.4%  .165  79.4%   Black, Non-Hispanic  4.2%  4.6%  2.5%  5.6%  7.8%  11.7%  8.5%  8.4%   American Indian/Alaska Native  0.0%  0.2%  0.0%  0.0%  0.0%  0.0%  0.0%  0.3%   Asian  11.4%  4.1%  2.1%  4.3%  2.2%  10.8%  5.8%  3.7%   Other, Non-Hispanic  0.0%  1.3%  0.3%  0.9%  1.0%  0.2%  0.3%  0.7%   Hispanic  3.9%  4.4%  4.1%  5.3%  6.4%  6.2%  3.1%  7.4%  Marital status   Married  47.8%  .211  59.4%  .086  61.9%  .054  48.4%  .054  55.7%  .978  46.4%  .211  53.5%  .641  55.8%   Other*  52.3%  40.6%  38.1%  51.6%  44.3%  53.6%  46.5%  44.2%  Educational attainment   <High school diploma  7.5%  <.001  11.4%  <.001  9.4%  <.001  7.3%  <.001  4.4%  <.001  1.6%  <.001  2.7%  <.001  19.4%   High school diploma  20.2%  24.8%  34.2%  17.7%  5.0%  11.0%  15.2%  29.9%   Some college  30.3%  29.5%  27.7%  29.9%  25.2%  31.7%  23.9%  25.4%   ≥College degree  42.1%  34.3%  28.8%  45.2%  65.4%  55.8%  58.2%  25.3%  Enabling factors  Poverty status   Below 200% FPL†  20.0%  .013  19.6%  <.001  21.4%  <.001  18.0%  <.001  17.2%  <.001  22.3%  .019  16.9%  <.001  31.6%   200–399% of FPL  31.8%  35.8%  38.5%  28.3%  24.1%  28.9%  28.4%  34.9%   400%+ of FPL  48.2%  44.6%  40.1%  53.7%  58.7%  48.8%  54.8%  33.6%  Geographic region   Northeast  14.9%  <.001  15.0%  <.001  15.2%  <.001  17.7%  <.001  22.8%  .018  20.3%  .018  18.7%  .010  18.2%   Midwest  12.1%  28.0%  31.9%  23.3%  15.7%  13.2%  19.1%  22.2%   South  20.5%  28.3%  26.9%  26.5%  27.0%  28.5%  30.4%  38.1%   West  52.5%  28.7%  26.0%  32.5%  34.5%  38.0%  31.9%  21.6%  Need factors  Self-reported health status   <Excellent  81.6%  .486  82.5%  .119  82.0%  .156  78.7%  .033  75.9%  .040  74.9%  .102  74.1%  <.001  84.4%   Excellent health  18.5%  17.5%  18.0%  21.3%  24.1%  25.1%  25.9%  15.6%  Chronic conditions   None  18.3%  .333  11.2%  .118  15.6%  .681  10.4%  .175  10.5%  .333  14.3%  .421  17.1%  .036  14.3%   1  31.7%  26.0%  25.6%  29.4%  31.6%  32.1%  34.1%  25.2%   ≥2  50.0%  62.8%  58.8%  60.2%  57.9%  53.6%  48.9%  60.5%  Mental distress   Yes  14.7%  .703  13.7%  .703  11.3%  .416  19.9%  .014  21.0%  .014  11.0%  .703  10.2%  .416  13.3%   No  85.3%  86.3%  88.7%  80.1%  79.0%  89.0%  89.8%  86.7%  Functional limitations   Not limited  26.7%  .123  35.1%  .417  34.8%  .417  32.1%  .258  33.3%  .417  46.7%  .257  49.0%  .008  36.8%   Limited  73.3%  64.9%  65.2%  67.9%  66.7%  53.3%  51.1%  63.2%    Acupuncture  Herbal therapies  Chiropractic  Massage  Meditation  Tai Chi  Yoga      Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Yes  p  Overall  Sample size   Unweighted sample  137    1,268    578    385    168    97    250    7,116   Weighted U.S. population  711,739    7,289,383    3,364,536    2,306,932    980,621    484,655    1,512,889    40,245,549  Predisposing factors  Age   65–74 years  65.0%  .144  66.0%  <.001  64.2%  .004  73.6%  <.001  84.2%  <.001  72.8%  .014  83.1%  <.001  57.2%   75+ years  35.1%  34.1%  35.8%  26.4%  15.8%  27.2%  16.9%  42.8%  Sex   Female  64.6%  .202  56.5%  .772  57.8%  .586  64.0%  .053  62.9%  .357  70.5%  .053  69.0%  .008  56.1%   Male  35.4%  43.5%  42.2%  36.0%  37.1%  29.5%  31.0%  43.9%  Race/ethnicity   White, Non-Hispanic  80.5%  .003  85.5%  <.001  90.9%  <.001  84.0%  .276  82.6%  .850  71.2%  .106  82.4%  .165  79.4%   Black, Non-Hispanic  4.2%  4.6%  2.5%  5.6%  7.8%  11.7%  8.5%  8.4%   American Indian/Alaska Native  0.0%  0.2%  0.0%  0.0%  0.0%  0.0%  0.0%  0.3%   Asian  11.4%  4.1%  2.1%  4.3%  2.2%  10.8%  5.8%  3.7%   Other, Non-Hispanic  0.0%  1.3%  0.3%  0.9%  1.0%  0.2%  0.3%  0.7%   Hispanic  3.9%  4.4%  4.1%  5.3%  6.4%  6.2%  3.1%  7.4%  Marital status   Married  47.8%  .211  59.4%  .086  61.9%  .054  48.4%  .054  55.7%  .978  46.4%  .211  53.5%  .641  55.8%   Other*  52.3%  40.6%  38.1%  51.6%  44.3%  53.6%  46.5%  44.2%  Educational attainment   <High school diploma  7.5%  <.001  11.4%  <.001  9.4%  <.001  7.3%  <.001  4.4%  <.001  1.6%  <.001  2.7%  <.001  19.4%   High school diploma  20.2%  24.8%  34.2%  17.7%  5.0%  11.0%  15.2%  29.9%   Some college  30.3%  29.5%  27.7%  29.9%  25.2%  31.7%  23.9%  25.4%   ≥College degree  42.1%  34.3%  28.8%  45.2%  65.4%  55.8%  58.2%  25.3%  Enabling factors  Poverty status   Below 200% FPL†  20.0%  .013  19.6%  <.001  21.4%  <.001  18.0%  <.001  17.2%  <.001  22.3%  .019  16.9%  <.001  31.6%   200–399% of FPL  31.8%  35.8%  38.5%  28.3%  24.1%  28.9%  28.4%  34.9%   400%+ of FPL  48.2%  44.6%  40.1%  53.7%  58.7%  48.8%  54.8%  33.6%  Geographic region   Northeast  14.9%  <.001  15.0%  <.001  15.2%  <.001  17.7%  <.001  22.8%  .018  20.3%  .018  18.7%  .010  18.2%   Midwest  12.1%  28.0%  31.9%  23.3%  15.7%  13.2%  19.1%  22.2%   South  20.5%  28.3%  26.9%  26.5%  27.0%  28.5%  30.4%  38.1%   West  52.5%  28.7%  26.0%  32.5%  34.5%  38.0%  31.9%  21.6%  Need factors  Self-reported health status   <Excellent  81.6%  .486  82.5%  .119  82.0%  .156  78.7%  .033  75.9%  .040  74.9%  .102  74.1%  <.001  84.4%   Excellent health  18.5%  17.5%  18.0%  21.3%  24.1%  25.1%  25.9%  15.6%  Chronic conditions   None  18.3%  .333  11.2%  .118  15.6%  .681  10.4%  .175  10.5%  .333  14.3%  .421  17.1%  .036  14.3%   1  31.7%  26.0%  25.6%  29.4%  31.6%  32.1%  34.1%  25.2%   ≥2  50.0%  62.8%  58.8%  60.2%  57.9%  53.6%  48.9%  60.5%  Mental distress   Yes  14.7%  .703  13.7%  .703  11.3%  .416  19.9%  .014  21.0%  .014  11.0%  .703  10.2%  .416  13.3%   No  85.3%  86.3%  88.7%  80.1%  79.0%  89.0%  89.8%  86.7%  Functional limitations   Not limited  26.7%  .123  35.1%  .417  34.8%  .417  32.1%  .258  33.3%  .417  46.7%  .257  49.0%  .008  36.8%   Limited  73.3%  64.9%  65.2%  67.9%  66.7%  53.3%  51.1%  63.2%  Notes: p-values were adjusted for multiple comparisons across seven CHA modalities using the false discovery rate (FDR) method. *Includes widowed, divorced, separated, and living with a partner. †Indicates federal poverty level. View Large Odds of CHA Use in Older Adults Table 2 presents adjusted odds ratios (AORs) of using any of seven major CHA types in older adults. In predisposing factors, adults ages 75 or older and male older adults had 40 and 27% lower odds of using any CHA, when compared to younger (65–74) and female older adults, respectively (p < .001). In racial/ethnic groups, non-Hispanic blacks and Hispanics had 42 and 38% lower odds of using any CHA, when compared with non-Hispanic whites (p < .001 and p = .001, respectively). For educational attainment, a higher level tended to have a greater likelihood of reporting the CHA use in the past year (p < .005). Table 2. Adjusted Odds Ratios (AORs) of Utilizing Any of Seven Major Complementary Health Approaches (CHA) in U.S. Older Adults (n = 7,116 Unweighted), 2012 NHIS (Reference Group in a Parenthesis)  AOR  95% CI  p-value  Predisposing factors  Age (65–74 years)   ≥75 years  0.60  0.51–0.69  <.001  Sex (Female)   Male  0.73  0.63–0.85  <.001  Race/ethnicity (Non-Hispanic White)   Black, Non-Hispanic  0.58  0.45–0.74  <.001   Asian  1.08  0.80–1.46  .603   Hispanic  0.62  0.46–0.82  .001   Other, Non-Hispanic*  0.94  0.50–1.75  .838  Marital status (Other†)   Married  0.96  0.82–1.12  .585  Educational attainment (<high school diploma)   High school diploma  1.45  1.15–2.83  .002   Some college  1.86  1.48–2.35  <.001   ≥ College degree  2.40  1.88–3.07  <.001  Enabling factors  Poverty status (Below 200% FPL‡)   200–399% of FPL  1.47  1.22–1.77  <.001   400%+ of FPL  1.83  1.50–2.23  <.001  Geographic region (Northeast)   Midwest  1.44  1.10–1.90  .008   South  0.83  0.65–1.05  .119   West  1.54  1.18–2.01  .001  Needs factors  Self-reported health status (excellent)   <Excellent  0.85  0.71–1.02  .080  Chronic conditions (none)   1  1.33  1.05–1.69  .018   ≥2  1.39  1.13–1.73  .002  Mental distress (no)   Yes  1.23  0.98–1.53  .068  Functional limitations (no)   Yes  1.28  1.09–1.50  .003  F-statistic§  16.85  <.001  (Reference Group in a Parenthesis)  AOR  95% CI  p-value  Predisposing factors  Age (65–74 years)   ≥75 years  0.60  0.51–0.69  <.001  Sex (Female)   Male  0.73  0.63–0.85  <.001  Race/ethnicity (Non-Hispanic White)   Black, Non-Hispanic  0.58  0.45–0.74  <.001   Asian  1.08  0.80–1.46  .603   Hispanic  0.62  0.46–0.82  .001   Other, Non-Hispanic*  0.94  0.50–1.75  .838  Marital status (Other†)   Married  0.96  0.82–1.12  .585  Educational attainment (<high school diploma)   High school diploma  1.45  1.15–2.83  .002   Some college  1.86  1.48–2.35  <.001   ≥ College degree  2.40  1.88–3.07  <.001  Enabling factors  Poverty status (Below 200% FPL‡)   200–399% of FPL  1.47  1.22–1.77  <.001   400%+ of FPL  1.83  1.50–2.23  <.001  Geographic region (Northeast)   Midwest  1.44  1.10–1.90  .008   South  0.83  0.65–1.05  .119   West  1.54  1.18–2.01  .001  Needs factors  Self-reported health status (excellent)   <Excellent  0.85  0.71–1.02  .080  Chronic conditions (none)   1  1.33  1.05–1.69  .018   ≥2  1.39  1.13–1.73  .002  Mental distress (no)   Yes  1.23  0.98–1.53  .068  Functional limitations (no)   Yes  1.28  1.09–1.50  .003  F-statistic§  16.85  <.001  Notes: *Includes American Indian, Alaska native, and those reporting multiple racial/ethnic groups. †Includes widowed, divorced, separated, and living with a partner. ‡Indicates federal poverty level. §After adjusted for complex survey design. View Large Table 2. Adjusted Odds Ratios (AORs) of Utilizing Any of Seven Major Complementary Health Approaches (CHA) in U.S. Older Adults (n = 7,116 Unweighted), 2012 NHIS (Reference Group in a Parenthesis)  AOR  95% CI  p-value  Predisposing factors  Age (65–74 years)   ≥75 years  0.60  0.51–0.69  <.001  Sex (Female)   Male  0.73  0.63–0.85  <.001  Race/ethnicity (Non-Hispanic White)   Black, Non-Hispanic  0.58  0.45–0.74  <.001   Asian  1.08  0.80–1.46  .603   Hispanic  0.62  0.46–0.82  .001   Other, Non-Hispanic*  0.94  0.50–1.75  .838  Marital status (Other†)   Married  0.96  0.82–1.12  .585  Educational attainment (<high school diploma)   High school diploma  1.45  1.15–2.83  .002   Some college  1.86  1.48–2.35  <.001   ≥ College degree  2.40  1.88–3.07  <.001  Enabling factors  Poverty status (Below 200% FPL‡)   200–399% of FPL  1.47  1.22–1.77  <.001   400%+ of FPL  1.83  1.50–2.23  <.001  Geographic region (Northeast)   Midwest  1.44  1.10–1.90  .008   South  0.83  0.65–1.05  .119   West  1.54  1.18–2.01  .001  Needs factors  Self-reported health status (excellent)   <Excellent  0.85  0.71–1.02  .080  Chronic conditions (none)   1  1.33  1.05–1.69  .018   ≥2  1.39  1.13–1.73  .002  Mental distress (no)   Yes  1.23  0.98–1.53  .068  Functional limitations (no)   Yes  1.28  1.09–1.50  .003  F-statistic§  16.85  <.001  (Reference Group in a Parenthesis)  AOR  95% CI  p-value  Predisposing factors  Age (65–74 years)   ≥75 years  0.60  0.51–0.69  <.001  Sex (Female)   Male  0.73  0.63–0.85  <.001  Race/ethnicity (Non-Hispanic White)   Black, Non-Hispanic  0.58  0.45–0.74  <.001   Asian  1.08  0.80–1.46  .603   Hispanic  0.62  0.46–0.82  .001   Other, Non-Hispanic*  0.94  0.50–1.75  .838  Marital status (Other†)   Married  0.96  0.82–1.12  .585  Educational attainment (<high school diploma)   High school diploma  1.45  1.15–2.83  .002   Some college  1.86  1.48–2.35  <.001   ≥ College degree  2.40  1.88–3.07  <.001  Enabling factors  Poverty status (Below 200% FPL‡)   200–399% of FPL  1.47  1.22–1.77  <.001   400%+ of FPL  1.83  1.50–2.23  <.001  Geographic region (Northeast)   Midwest  1.44  1.10–1.90  .008   South  0.83  0.65–1.05  .119   West  1.54  1.18–2.01  .001  Needs factors  Self-reported health status (excellent)   <Excellent  0.85  0.71–1.02  .080  Chronic conditions (none)   1  1.33  1.05–1.69  .018   ≥2  1.39  1.13–1.73  .002  Mental distress (no)   Yes  1.23  0.98–1.53  .068  Functional limitations (no)   Yes  1.28  1.09–1.50  .003  F-statistic§  16.85  <.001  Notes: *Includes American Indian, Alaska native, and those reporting multiple racial/ethnic groups. †Includes widowed, divorced, separated, and living with a partner. ‡Indicates federal poverty level. §After adjusted for complex survey design. View Large In enabling factors, individuals with poverty status of 200–399% FPL and ≥400% FPL had 1.47 and 1.83 times greater odds of reporting the CHA use when compared with those with under 200% FPL (p < .001). For geographic region, older adults residing in Midwest and West regions had 1.44 and 1.54 times greater odds of using CHA when compared to the Northeast region (p = .008 and p = .001, respectively). In need factors, those with one or ≥2 chronic conditions had 1.33 and 1.39 times greater odds of reporting the CHA use when compared to individuals with no chronic condition (p = .018 and p = .002, respectively). Finally, older adults with functional limitations had 1.28 times greater odds of using CHA than those without functional limitations (p = .003). Perceived Benefits of CHA Use Self-reported perceived benefits of utilizing CHA in older CHA users by age group are presented in Figure 1. Regardless of age groups, 52.3% of CHA users reported that CHA use improved their overall health and feeling better and 68.9% of CHA users stated that CHA use was important for them to maintain health and wellbeing. Improving relationships with others was the benefit that respondents were the least likely to receive by utilizing CHA (11.9%), regardless of age groups. Older adults ages 65–74 were more likely to report every domain of perceived benefits than those ages 75 or older, except one benefit called, ‘made it easier to cope with health problems’ (23.3 vs 27.5%). Figure 1. View largeDownload slide Self-reported perceived benefits of using any of major seven complementary health approaches (CHA) among older CHA users (n = 2,127 unweighted), 2012 NHIS. Note: Bars represent 95% confidence intervals. Figure 1. View largeDownload slide Self-reported perceived benefits of using any of major seven complementary health approaches (CHA) among older CHA users (n = 2,127 unweighted), 2012 NHIS. Note: Bars represent 95% confidence intervals. Discussion Using a population-based cross-sectional design, we investigated patterns and perceived benefits of CHA use among non-institutionalized older adults ages 65 or older in the United States. When extrapolated to the entire U.S. population, 29.2% of older adults (11.7 million) would have used some form of CHA in the past year. The most commonly used types of CHA were herbal therapies, chiropractic, and massage. Furthermore, these CHA users reported various perceived benefits, such as having better sense of control over health, feeling better emotionally, and reducing stress. We could not make a direct comparison of prevalence of CHA use in older adults with previous studies, as they had different populations of interest (eg, any adults and women) or used different inclusion criteria for CHA (29–33). However, our prevalence of CHA use in the past year and CHA use ever among older adults were similar to those of a previous study (2). For instance, a previous study reported that three most commonly used CHA types were herbal therapies (18.6%), chiropractic (8.7%), and massage (6.9%) in mid-life and older adults (2). Our findings highlight that a substantial number of older adults use CHA similar to a previous study (2). In our multivariable logistic regression analysis, we found that higher levels of education attainment and income were independently associated with CHA use in older adults. Geographically, older adults in Midwest and West regions had a higher likelihood of using CHA when compared to those in Northeastern region. In clinical characteristics, having more chronic conditions and functional limitations were also independently associated with the CHA use in older adults. These findings have major implications in the future research. First, future research should address whether CHA use helps older adults manage their multiple chronic conditions, and second, whether better socioeconomic means (eg, educational attainment and income) have mediating or moderating roles in such relationship. Addressing these gaps can help better understand the dynamic roles of CHA use in patient-centered care among older adults with multiple chronic conditions, for example. Our study highlights self-reported perceived benefits of CHA use among older adults. In particular, more than half of older CHA users reported that CHA were important for maintaining health and well-being and improved overall health and feeling better. While we do not know whether they used CHA for treatment only, for wellness and health promotion only, or both, future research should investigate roles of CHA by reason for use to better meet bio-psycho-social needs in older adults. There are several implications from our findings. First, because a substantial number of older CHA users reported benefits from utilizing CHA, more CHA research assessing diverse patient outcomes among older adults at the populational level is needed. By integrating CHA into conventional medical care, there may be value-based care, which particularly addresses psycho-social aspects of health in older adults. Second, clinicians should be aware that CHA use may be common in their older patients. They should be informed about CHA use to provide optimal patient care in their clinical practice. For example, certain herbal therapies may have drug-herb interactions, leading to serious adverse drug events (eg, kava and antidepressants). Clinicians should actively ask patients about CHA use and monitor potential interactions and side-effects. There are several limitations in this descriptive study. First, questionnaires for CHA use and its perceived benefits are self-reported, and their results are subject to recall bias. Second, the data were collected in 2012, such that patterns and perceived benefits of using CHA among older adults may be different now. Third, the current study lacks specificity in CHA use (eg, frequency and intensity). CHA use with respect to frequency and intensity should be evaluated to better understand CHA’s roles in improving perceived benefits among older adults. In conclusion, nearly one third (29.2%) of older adults used some form of CHA in the past year, and among older CHA users, they reported a wide range of perceived benefits from using CHA. Because CHA may play a crucial role in improving healthy lifestyles in older adults, further research on the effects of CHA use on patient outcomes in bio-psycho-social domains is needed to promote healthy aging in older adults. Funding T.G.R. received funding support from the National Institute on Aging (NIA) of National Institutes of Health (NIH) (#T32AG019134). P.H.V.N. received funding support from the NIA of NIH (#P30AG021342). The funding agency had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript, and decision to submit the manuscript for publication. Author Contributions Study concept and design: T.G.R. and M.E.T.; Data acquisition and statistical analyses: T.G.R.; Interpretation of data: all authors; Drafting of manuscript: T.G.R.; Critical revision of manuscript for important intellectual content: all authors. Conflict of Interest All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none were reported. Acknowledgements Data access and responsibility: T.G.R. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Disclaimers: Publicly available data were obtained from the National Center for Health Statistics (NCHS) and Centers for Disease Control and Prevention (CDC). Analyses, interpretation, and conclusions are solely those of the author and do not necessarily reflect the views of the Division of Health Interview Statistics or NCHS of the CDC. This article does not contain any studies with human participants or animals performed by the authors. All research procedures performed in this study are in accordance with the ethical standards of the Institutional Review Board at Yale University School of Medicine (#2000021662). References 1. 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The Journals of Gerontology Series A: Biomedical Sciences and Medical SciencesOxford University Press

Published: Apr 28, 2018

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