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Gender Differences in Use of Complementary and Integrative Health by U.S. Military Veterans with Chronic Musculoskeletal Pain

Published:August 30, 2018DOI:https://doi.org/10.1016/j.whi.2018.07.003

      Abstract

      Aims

      The Veterans Health Administration promotes evidence-based complementary and integrative health (CIH) therapies as nonpharmacologic approaches for chronic pain. We aimed to examine CIH use by gender among veterans with chronic musculoskeletal pain, and variations in gender differences by race/ethnicity and age.

      Methods

      We conducted a secondary analysis of electronic health records provided by all women (n = 79,537) and men (n = 389,269) veterans age 18 to 54 years with chronic musculoskeletal pain who received Veterans Health Administration-provided care between 2010 and 2013. Using gender-stratified multivariate binary logistic regression, we examined predictors of CIH use, tested a race/ethnicity-by-age interaction term, and conducted pairwise comparisons of predicted probabilities.

      Results

      Among veterans with chronic musculoskeletal pain, more women than men use CIH (36% vs. 26%), with rates ranging from 25% to 42% among women and 15% to 29% among men, depending on race/ethnicity and age. Among women, patients under age 44 who were Hispanic, White, or patients of other race/ethnicities are similarly likely to use CIH; in contrast, Black women, regardless of age, are least likely to use CIH. Among men, White and Black patients, and especially Black men under age 44, are less likely to use CIH than men of Hispanic or other racial/ethnic identities.

      Conclusions

      Women veteran patients with chronic musculoskeletal pain are more likely than men to use CIH therapies, with variations in CIH use rates by race/ethnicity and age. Tailoring CIH therapy engagement efforts to be sensitive to gender, race/ethnicity, and age could reduce differential CIH use and thereby help to diminish existing health disparities among veterans.

      Opioid Misuse and Chronic Pain Among Women Military Veterans

      A national public health emergency, the opioid epidemic has resulted in extraordinary numbers of accidental injuries, infectious diseases, and premature deaths (
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      ), contributing to a historically unprecedented shortening of American life expectancy (
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      Mortality in the United States, 2016. NCHS Data Brief, 293.
      ). U.S. military veterans being treated for chronic pain are at a heightened risk for these adverse outcomes (
      • Bohnert A.S.
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      The association between receipt of guideline-concordant long-term opioid therapy and all-cause mortality.
      ), and women veterans may be especially impacted. Specifically, more women veterans than men are prescribed opioids for chronic pain (
      • Kroll-Desrosiers A.R.
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      Receipt of prescription opioids in a national sample of pregnant veterans receiving Veterans Health Administration care.
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      • Mosher H.J.
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      ) and women veterans are more likely to have multiple pain condition diagnoses (
      • Higgins D.M.
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      • Driscoll M.A.
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      • Kerns R.D.
      • Bair M.J.
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      Gender differences in demographic and clinical correlates among veterans with musculoskeletal disorders.
      ,
      • Weimer M.B.
      • Macey T.A.
      • Nicolaidis C.
      • Dobscha S.K.
      • Duckart J.P.
      • Morasco B.J.
      Sex differences in the medical care of VA patients with chronic non-cancer pain.
      ), self-report moderate to severe pain (
      • Higgins D.M.
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      • Driscoll M.A.
      • Heapy A.A.
      • Kerns R.D.
      • Bair M.J.
      • Goulet J.L.
      Gender differences in demographic and clinical correlates among veterans with musculoskeletal disorders.
      ), and have co-occurring mental health problems (
      • Finlay A.K.
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      • Sawh L.
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      • Rosenthal J.
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      Sex differences in mental health and substance use disorders and treatment entry among justice-involved Veterans in the Veterans Health Administration.
      ,
      • Higgins D.M.
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      • Bair M.J.
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      Gender differences in demographic and clinical correlates among veterans with musculoskeletal disorders.
      ,
      • Howe C.Q.
      • Sullivan M.D.
      The missing 'P' in pain management: How the current opioid epidemic highlights the need for psychiatric services in chronic pain care.
      ). These factors are well-established predictors of adverse health outcomes among veterans with chronic pain (
      • Dobscha S.K.
      • Morasco B.J.
      • Duckart J.P.
      • Macey T.
      • Deyo R.A.
      Correlates of prescription opioid initiation and long-term opioid use in veterans with persistent pain.
      ,
      • Edlund M.J.
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      Risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic non-cancer pain.
      ). Findings underscore a critical need to better understand the gender-specific health services needs, use, and outcomes of veterans being treated for chronic pain.

      Limited Research on Complementary and Integrative Health and Gender in Veterans

      Historically, opioid medications have been used in Veterans Healthcare Administration (VA) clinical settings despite acceptability of nonpharmacologic therapies for many pain patients (
      • Howe C.Q.
      • Sullivan M.D.
      The missing 'P' in pain management: How the current opioid epidemic highlights the need for psychiatric services in chronic pain care.
      ,
      • Simmonds M.J.
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      • Pugh M.J.
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      A qualitative study of veterans on long-term opioid analgesics: Barriers and facilitators to multimodality pain management.
      ). Signifying a major shift in practice, complementary and integrative health (CIH) therapies (e.g., meditation, yoga, acupuncture) are now being offered throughout the VA as nonpharmacologic approaches for chronic pain because they have been found to be beneficial for some types of chronic pain and its physical and mental health comorbidities (
      • Bawa F.L.
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      Does mindfulness improve outcomes in patients with chronic pain? Systematic review and meta-analysis.
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      • Goyal M.
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      Meditation programs for psychological stress and well-being: A systematic review and meta-analysis.
      ,
      • Hempel S.
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      • Beroes J.M.
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      Evidence map of mindfulness. VA-ESP Project #05-226.
      ,
      • Hilton L.
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      • Newberry S.
      • Maglione M.A.
      Mindfulness meditation for chronic pain: Systematic review and meta-analysis.
      ,
      • MacPherson H.
      • Vertosick E.A.
      • Foster N.E.
      • Lewith G.
      • Linde K.
      • Sherman K.J.
      • Vickers A.J.
      The persistence of the effects of acupuncture after a course of treatment: A meta-analysis of patients with chronic pain.
      ,
      • Miake-Lye I.M.
      • Lee J.F.
      • Luger T.
      • Taylor S.
      • Shanman R.
      • Shekelle P.G.
      Massage for Pain: An Evidence Map. VA ESP Project #05-226.
      ,
      • Nahin R.L.
      • Boineau R.
      • Khalsa P.S.
      • Stussman B.J.
      • Weber W.J.
      Evidence-based evaluation of complementary health approaches for pain management in the United States.
      ). Expansion of the provision of CIH therapies as a nonpharmacologic treatment for pain in the VA was mandated by Congress in the 2016 Comprehensive Addiction and Recovery Act (
      • U.S. Government Publishing Office
      Comprehensive Addiction and Recovery Act of 2016. House Report 114-669.
      ). However, most of what is known about use of CIH therapies has been provided by general population studies of civilians (

      Clarke, T. C., Black, L. I., Stussman, B. J., Barnes, P. M., & Nahin, R. L. (2015). Trends in the use of complementary health approaches among adults: United States, 2002-2012. National Health Statistics Report, (79), 1-16. Available from: https://nccih.nih.gov/research/statistics/NHIS. Accessed: April 1, 2018.

      ,
      • Nahin R.L.
      • Boineau R.
      • Khalsa P.S.
      • Stussman B.J.
      • Weber W.J.
      Evidence-based evaluation of complementary health approaches for pain management in the United States.
      ,

      Stussman, B. J., Black, L. I., Barnes, P. M., Clarke, T. C., & Nahin, R. L. (2015). Wellness-related use of common complementary health approaches among adults: United States, 2012. National Health Statistics Report, (85), 1-12.

      ,
      • Upchurch D.M.
      • Rainisch B.W.
      The importance of wellness among users of complementary and alternative medicine: Findings from the 2007 National Health Interview Survey.
      ). Such studies report that common correlates of CIH use are diagnosed chronic physical and mental health conditions (
      • Adams J.
      • Peng W.
      • Cramer H.
      • Sundberg T.
      • Moore C.
      • Amorin-Woods L.
      • Lauche R.
      The prevalence, patterns, and predictors of chiropractic use among US adults: Results from the 2012 National Health Interview Survey.
      ,
      • Burke A.
      • Lam C.N.
      • Stussman B.
      • Yang H.
      Prevalence and patterns of use of mantra, mindfulness and spiritual meditation among adults in the United States.
      ,
      • Cramer H.
      • Hall H.
      • Leach M.
      • Frawley J.
      • Zhang Y.
      • Leung B.
      • Adams J.
      • Lauche R.
      Prevalence, patterns, and predictors of meditation use among US adults: A nationally representative survey.
      ,
      • Cramer H.
      • Ward L.
      • Steel A.
      • Lauche R.
      • Dobos G.
      • Zhang Y.
      Prevalence, patterns, and predictors of yoga use: Results of a us nationally representative survey.
      ) and also sociodemographic characteristics (
      • Zhang Y.
      • Leach M.J.
      • Bishop F.L.
      • Leung B.A.
      Comparison of the characteristics of acupuncture- and non-acupuncture-preferred consumers: A secondary analysis of NHIS 2012 data.
      ), particularly female gender (
      • Burke A.
      • Lam C.N.
      • Stussman B.
      • Yang H.
      Prevalence and patterns of use of mantra, mindfulness and spiritual meditation among adults in the United States.
      ,
      • Cramer H.
      • Hall H.
      • Leach M.
      • Frawley J.
      • Zhang Y.
      • Leung B.
      • Adams J.
      • Lauche R.
      Prevalence, patterns, and predictors of meditation use among US adults: A nationally representative survey.
      ,
      • Cramer H.
      • Ward L.
      • Steel A.
      • Lauche R.
      • Dobos G.
      • Zhang Y.
      Prevalence, patterns, and predictors of yoga use: Results of a us nationally representative survey.
      ,
      • Park C.L.
      • Braun T.
      • Siegel T.
      Who practices yoga? A systematic review of demographic, health-related, and psychosocial factors associated with yoga practice.
      ), non-Hispanic White race/ethnicity (
      • Burke A.
      • Lam C.N.
      • Stussman B.
      • Yang H.
      Prevalence and patterns of use of mantra, mindfulness and spiritual meditation among adults in the United States.
      ,
      • Cramer H.
      • Hall H.
      • Leach M.
      • Frawley J.
      • Zhang Y.
      • Leung B.
      • Adams J.
      • Lauche R.
      Prevalence, patterns, and predictors of meditation use among US adults: A nationally representative survey.
      ,
      • Cramer H.
      • Ward L.
      • Steel A.
      • Lauche R.
      • Dobos G.
      • Zhang Y.
      Prevalence, patterns, and predictors of yoga use: Results of a us nationally representative survey.
      ), and higher socioeconomic status (
      • Cramer H.
      • Hall H.
      • Leach M.
      • Frawley J.
      • Zhang Y.
      • Leung B.
      • Adams J.
      • Lauche R.
      Prevalence, patterns, and predictors of meditation use among US adults: A nationally representative survey.
      ,
      • Cramer H.
      • Ward L.
      • Steel A.
      • Lauche R.
      • Dobos G.
      • Zhang Y.
      Prevalence, patterns, and predictors of yoga use: Results of a us nationally representative survey.
      ,
      • Park C.L.
      • Braun T.
      • Siegel T.
      Who practices yoga? A systematic review of demographic, health-related, and psychosocial factors associated with yoga practice.
      ), with mixed findings regarding age (
      • Adams J.
      • Peng W.
      • Cramer H.
      • Sundberg T.
      • Moore C.
      • Amorin-Woods L.
      • Lauche R.
      The prevalence, patterns, and predictors of chiropractic use among US adults: Results from the 2012 National Health Interview Survey.
      ,
      • Cramer H.
      • Hall H.
      • Leach M.
      • Frawley J.
      • Zhang Y.
      • Leung B.
      • Adams J.
      • Lauche R.
      Prevalence, patterns, and predictors of meditation use among US adults: A nationally representative survey.
      ,
      • Cramer H.
      • Ward L.
      • Steel A.
      • Lauche R.
      • Dobos G.
      • Zhang Y.
      Prevalence, patterns, and predictors of yoga use: Results of a us nationally representative survey.
      ).
      A critical limitation in knowledge is that gender differences in the use of CIH therapies have been understudied, particularly in veteran populations. Also of potential import but little examined is that use of CIH therapies by veteran populations may vary by race/ethnicity and age, for example, because of race/ethnic and age-related differences in factors that facilitate or impede CIH use such as economic resources and cultural/health beliefs (
      • Chao M.T.
      • Wade C.
      • Kronenberg F.
      • Kalmuss D.
      • Cushman L.F.
      Women's reasons for complementary and alternative medicine use: Racial/ethnic differences.
      ,
      • Goldstein J.N.
      • Ibrahim S.A.
      • Frankel E.S.
      • Mao J.J.
      Race, pain, and beliefs associated with interest in complementary and alternative medicine among inner city veterans.
      ,
      • Hsiao A.F.
      • Wong M.D.
      • Goldstein M.S.
      • Yu H.J.
      • Andersen R.M.
      • Brown E.R.
      • Wenger N.S.
      Variation in complementary and alternative medicine (CAM) use across racial/ethnic groups and the development of ethnic-specific measures of CAM use.
      ). We addressed these gaps by conducting secondary analyses of data from a national population study (
      • Taylor S.L.
      • Herman P.M.
      • Marshall N.J.
      • Zeng Q.
      • Yuan A.
      • Chu K.
      • Lorenz K.
      Use of complementary and integrated health: A retrospective analysis of U.S. veterans with chronic musculoskeletal pain nationally.
      ) of VA users age 18 to 54 years with chronic musculoskeletal pain to examine whether there are gender differences in the prevalence and predictors of CIH therapy use, and whether any effect of gender is further moderated by race/ethnicity and age. The results of this analysis could point to gender-specific policy and practice solutions for addressing chronic pain. As such, it could lay a foundation for strengthening the health achieving capabilities of women and men veterans with chronic pain.

      Methods

      Study Design and Participants

      We conducted secondary analyses of electronic health record (EHR) data of a retrospective cohort of veterans age 18 to 54 with chronic musculoskeletal pain receiving VA health care anytime from 2010 to 2013 (see parent study for details;
      • Taylor S.L.
      • Herman P.M.
      • Marshall N.J.
      • Zeng Q.
      • Yuan A.
      • Chu K.
      • Lorenz K.
      Use of complementary and integrated health: A retrospective analysis of U.S. veterans with chronic musculoskeletal pain nationally.
      ). Briefly, informed by prior research (
      • Goulet J.L.
      • Kerns R.D.
      • Bair M.
      • Becker W.C.
      • Brennan P.
      • Burgess D.J.
      • Brandt C.A.
      The musculoskeletal diagnosis cohort: Examining pain and pain care among veterans.
      ,
      • Tian T.Y.
      • Zlateva I.
      • Anderson D.R.
      Using electronic health records data to identify patients with chronic pain in a primary care setting.
      ) we defined chronic musculoskeletal pain as having two or more occurrences during 2010 through 2013 of either 1) any International Classification of Disease, 9th Clinical Modification (ICD-9-CM) chronic pain codes recorded at visits separated by more than 30 days within 1 year or 2) any of 201 common musculoskeletal ICD-9-CM codes and two or more patient-reported pain scores of 4 or greater (on a 0–10 scale) within a 90-day period (
      • Taylor S.L.
      • Herman P.M.
      • Marshall N.J.
      • Zeng Q.
      • Yuan A.
      • Chu K.
      • Lorenz K.
      Use of complementary and integrated health: A retrospective analysis of U.S. veterans with chronic musculoskeletal pain nationally.
      ). The use of CIH therapy was determined by analyzing structured and unstructured data fields (e.g., clinical notes;
      • Taylor S.L.
      • Herman P.M.
      • Marshall N.J.
      • Zeng Q.
      • Yuan A.
      • Chu K.
      • Lorenz K.
      Use of complementary and integrated health: A retrospective analysis of U.S. veterans with chronic musculoskeletal pain nationally.
      ). These data encompassed 79,537 women and 389,269 men. Use of these data for research purposes was approved by the VA Institutional Review Board.

      Measures

      The dependent variable is use of any CIH therapy (yes/no) during 2010 through 2013, occurring after first diagnosis of chronic musculoskeletal pain during that timeframe. CIH therapy was defined as any use of meditation, yoga, tai chi, acupuncture, chiropractic care, biofeedback, guided imagery, therapeutic massage, or hypnosis.
      The key independent variable is gender. One moderator variable is race/ethnicity, defined according to five categories: 1) Hispanic/Latino and non-Hispanic/Latino categories of 2) White, 3) Black, 4) other (i.e., Asian, Native Hawaiian, other Pacific Islander, Native American, Alaskan Native, other), and 5) missing (representing 5%). To better understand the characteristics of this group, and per other research (
      • Long J.A.
      • Bamba M.I.
      • Ling B.
      • Shea J.A.
      Missing race/ethnicity data in Veterans Health Administration based disparities research: A systematic review.
      ), we included the missing race/ethnic category. A second moderator variable is age, coded into three categories: 18 to 34, 35 to 44, and 45 to 54 years of age.
      Other patient characteristics are marital status and two proxy indicators of socioeconomic status: 1) health insurance type (e.g., Medicaid enrollees are generally poorer and sicker) and 2) copayment status, because patients with copayments tend to have more income and less disability. Patient need for care is indicated by four comorbid health conditions: depression, anxiety, post-traumatic stress disorder, and substance abuse (i.e., problematic use, not necessarily substance use disorder) or having a musculoskeletal pain diagnosis (e.g., back or neck pain, osteoarthritis, fibromyalgia, etc.; Table 1).
      Table 1Characteristics of VA Users with Chronic Musculoskeletal Pain by Gender and Use of CIH Therapies (N = 468,806)
      Women (n = 79,537)Men (n = 389,269)
      Used CIH? n (%)Used CIH? n (%)
      Yes 28,463 (35.8)No 51,074 (64.2)Yes 99,369 (25.5)No 289,900 (74.5)
      Age (y)
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
       18-348,798 (30.9)17,039 (33.4)31,583 (31.8)88,258 (30.4)
       35-449,583 (33.7)16,563 (32.4)30,971 (31.2)90,105 (31.1)
       45-5410,082 (35.4)17,472 (34.2)36,815 (37.1)111,537 (38.5)
      Race/ethnicity
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
       White, non-Hispanic15,590 (54.8)25,178 (49.3)61,101 (61.5)176,262 (60.8)
       Black, non-Hispanic8,128 (28.6)17,694 (34.6)20,192 (20.3)63,922 (22.1)
       Hispanic or Latino/a2,364 (8.3)3,682 (7.2)10,296 (10.4)24,015 (8.3)
       Other, non-Hispanic1,236 (4.3)1,931 (3.8)4,041 (4.1)9,951 (3.4)
       Missing1,145 (4.0)2,589 (5.1)3,739 (3.8)15,750 (5.4)
      Marital status
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
       Married9,120 (32.0)18,804 (36.8)43,701 (44.0)147,490 (50.9)
       Divorced/separated/widowed11,259 (39.6)18,099 (35.4)30,473 (30.7)75,296 (26.0)
       Single/never married8,043 (28.3)14,001 (27.4)25,023 (25.2)66,025 (22.8)
       Missing/unknown41 (0.14)170 (0.33)172 (0.17)1,089 (0.38)
      Copayment
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
       Exempt43,543 (85.3)25,732 (90.4)84,793 (85.3)236,812 (81.7)
       Required5,189 (10.2)1,471 (5.2)6,683 (6.7)37,851 (13.1)
       Other + missing2,342 (4.6)1,260 (4.4)7,893 (7.9)15,237 (5.3)
      Insurance status
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
       VA16,823 (59.1)30,796 (60.3)63,048 (63.5)182,435 (62.9)
       Private5,426 (19.1)12,764 (25.0)16,243 (16.4)67,145 (23.2)
       Non-VA government6,214 (21.8)7,514 (14.7)20,078 (20.2)40,320 (13.9)
      Comorbid conditions
       Depression
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      2,003 (70.3)21,549 (42.2)64,517 (64.9)96,506 (33.3)
       Substance abuse
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      11,813 (41.5)14,747 (28.9)59,971 (60.4)119,342 (41.2)
       Post-traumatic stress disorder
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      14,920 (52.4)13,321 (26.1)55,489 (55.8)84,260 (29.1)
       Anxiety
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      11,453 (40.2)11,069 (21.7)35,008 (35.2)50,023 (17.3)
       Sleep
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      7,424 (26.1)8,630 (16.9)33,198 (33.4)67,061 (23.1)
       Traumatic brain injury
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      2,288 (8.0)1,613 (3.2)19,618 (19.4)22,626 (7.8)
      Musculoskeletal pain diagnoses
       Back pain
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      13,670 (48.0)22,469 (44.0)56,589 (57.0)148,482 (51.2)
       Joint pain
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      8,941 (31.4)21,090 (41.3)32,931 (33.1)121,854 (42.0)
       Neck pain
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      6,386 (22.4)8,606 (16.9)19,235 (19.4)41,864 (14.4)
       Fibromyalgia
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      4,173 (14.7)5,506 (10.8)6,437 (6.5)16,866 (5.8)
       Osteoarthritis
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      1,662 (5.8)3,301 (6.5)7,275 (7.3)23,109 (8.0)
       Tempomandibular disorder
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      ,
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.
      334 (1.2)421 (0.8)478 (0.5)947 (0.3)
      Abbreviations: CIH, complementary and integrative health; VA, Veterans Health Administration.
      Among women, differences between CIH users and nonusers are statistically significant at p < .001.
      Among men, differences between CIH users and nonusers are statistically significant at p < .001.

      Data Analysis

      First, we examined for women and men separately the bivariate differences in characteristics between patients who used CIH therapies and those who did not. Among patients who used CIH therapies, we also examined differences by gender in use of several types of CIH therapies (e.g., meditation, yoga, acupuncture). Next, we used a multivariate binary logistic regression model for women and men separately to assess associations between use of CIH therapies (yes/no), race/ethnicity, and age, controlling for covariates. Using these models, we tested a race/ethnicity × age interaction term. We used the moderation models to calculate and graph the predicted probabilities with 95% confidence intervals for use of CIH therapies in relation to race/ethnicity and age. Finally, to assess whether moderation by race/ethnicity and age was different by gender, we used the full model and tested a three-way interaction term (gender × race/ethnicity × age). We used a two-tailed significance level at p < .05 for all statistical tests. All analyses were conducted using STATA 14.0 (STATA Corp LP, College Station, TX).

      Results

      Gender Differences in the Use of CIH Therapies

      Of VA patients with chronic musculoskeletal pain, more women than men use CIH therapies (35.8% vs. 25.5%; p < .001; Table 1). For both women and men, CIH therapy users and nonusers differ statistically for all sociodemographic and health characteristics examined. Also, given the large sample sizes, both large and small differences are statistically significant. Therefore, we summarize those patterns that differ by gender, focusing on differences that are greater than approximately 5%. Specifically, women CIH therapy users, compared with nonusers, tend to be White (54.8% vs. 49.3%) and are less likely to be Black (28.6% vs. 34.6%) and more of them have copayment requirements (10.2% vs. 5.2%); among men, fewer CIH therapy users (6.7%) than nonusers (13.1%) have copayments. The remaining sociodemographic differences between CIH therapy users and nonusers are mostly small and similar by gender.

      Gender Differences in Type of CIH Therapy Used

      The type of CIH therapy used, stratified by gender, is presented in Table 2. Of patients who use any type of CIH therapy (n = 127,832; 27.3% of total N = 468,806), more women than men use yoga (16.8% vs. 9.9%) and fewer women than men use meditation (37.2% vs. 42.0%). Gender differences are statistically significant but small for chiropractic care, guided imagery, massage, and tai chi. There are no gender differences in the proportion of patients who use acupuncture (approximately 15%), biofeedback (approximately 6%), and hypnosis (<1%).
      Table 2Type of CIH Therapies Used by Women and Men VA Users (N = 127,832)
      Women (n = 28,463), n (%)Men (n = 99,369), n (%)
      Type of CIH therapy
       Meditation
      Differences between women and men are statistically significant p < .001.
      10,594 (37.2)41,759 (42.0)
       Yoga
      Differences between women and men are statistically significant p < .001.
      4,792 (16.8)9,819 (9.9)
       Acupuncture4,287 (15.1)14,576 (14.7)
       Chiropractic
      Differences between women and men are statistically significant p < .001.
      3,987 (14.0)14,952 (15.1)
       Biofeedback1,641 (5.8)5,685 (5.7)
       Massage
      Differences between women and men are statistically significant p < .001.
      1,603 (5.6)6,203 (6.2)
       Guided imagery
      Differences between women and men are statistically significant p < .001.
      988 (3.5)4,237 (4.3)
       Tai Chi
      Differences between women and men are statistically significant p < .001.
      539 (1.9)2,268 (2.3)
       Hypnosis112 (0.4)370 (0.4)
      Abbreviations: CIH, complementary and integrative health; VA, Veterans Health Administration.
      The sample size reflects only those patients who used CIH (n = 127,832) and omits patients who did not use CIH (n = 340,974).
      Patients could have used more than one type of CIH; therefore, column totals may exceed 100%.
      Differences between women and men are statistically significant p < .001.

      Multivariate Predictors of CIH Therapy Use Among Women and Men

      Results from the gender-stratified main effects models for women and men indicated that Black women are less likely than White women (odds ratio, 0.81; 95% confidence interval [CI], 0.79–0.84; p < .001) to use CIH therapies, while Black men are more likely than White men to use CIH therapies (odds ratio, 1.04; 95% CI, 1.02–1.06; p < .001) (Table 3).
      Table 3Predictors of Patient Use of CIH Therapies Stratified by Gender
      Women (n = 79,537)Men (n = 389,269)
      Main Effects ModelMain Effects Model Plus InteractionsMain Effects ModelMain Effects Model Plus Interactions
      OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)
      Age, y (reference group: 18–34)
       35-441.18*** (1.13–1.22)1.12*** (1.06–1.18)1.20*** (1.18–1.23)1.19*** (1.16–1.22)
       45-541.16*** (1.12–1.21)1.06* (1.00–1.12)1.21*** (1.18–1.23)1.14*** (1.11–1.17)
      Race/ethnicity (reference group: White)
       Hispanic/Latina/o1.15*** (1.09–1.22)1.01 (0.92–1.10)1.39*** (1.35–1.42)1.27*** (1.22–1.33)
       Black0.81*** (0.79–0.84)0.70*** (0.65–0.75)1.04*** (1.02–1.06)0.92*** (0.88–0.96)
       Other1.13** (1.05–1.23)1.13 (0.99–1.28)1.29*** (1.24–1.34)1.29*** (1.21–1.39)
       Missing0.86*** (0.79–0.93)0.94 (0.81–1.08)0.88*** (0.84–0.91)0.89** (0.82–0.96)
      Marital status (reference group: married)
       Single/never married1.26*** (1.21–1.31)1.26*** (1.21–1.31)1.32*** (1.29–1.35)1.32*** (1.29–1.35)
       Divorced/separated/widowed1.15*** (1.11–1.19)1.15*** (1.11–1.19)1.18*** (1.15–1.20)1.18*** (1.15–1.20)
       Missing0.61** (0.43–0.88)0.61** (0.42–0.87)0.74*** (0.63–0.88)0.74*** (0.63–0.88)
      Copayment (reference group: exempt)
       Required0.60*** (0.57–0.64)0.60*** (0.57–0.64)0.64*** (0.61–0.65)0.64*** (0.62–0.65)
       Other + missing0.94 (0.88–1.02)0.95 (0.88–1.02)1.40*** (1.36–1.45)1.40*** (1.36–1.45)
      Insurance status (reference group: VA)
       Private0.89*** (0.86–0.93)0.89*** (0.86–0.93)0.86*** (0.84–0.88)0.86*** (0.84–0.87)
       Non-VA government1.31*** (1.25–1.36)1.31*** (1.25–1.36)1.26*** (1.24–1.29)1.26*** (1.24–1.29)
      Comorbid conditions
       Depression (reference group: no)2.01*** (1.95–2.09)2.02*** (1.95–2.09)2.23*** (2.19–2.27)2.23*** (2.19–2.27)
       Anxiety (reference group: no)1.49*** (1.44–1.55)1.49*** (1.44–1.54)1.52*** (1.49–1.55)1.52*** (1.49–1.55)
       Post-traumatic stress disorder (reference group: no)2.03*** (1.96–2.10)2.03*** (1.96–2.10)1.84*** (1.81–1.88)1.84*** (1.81–1.87)
       Traumatic brain injury (reference group: no)1.67*** (1.56–1.80)1.67*** (1.56–1.80)1.80*** (1.76–1.84)1.80*** (1.75–1.84)
       Substance abuse (reference group: no)1.24*** (1.20–1.28)1.24*** (1.20–1.28)1.54*** (1.51–1.56)1.54*** (1.51–1.56)
       Sleep disorder (reference group: no)1.23*** (1.18–1.27)1.23*** (1.18–1.27)1.19*** (1.16–1.21)1.19*** (1.16–1.21)
      Musculoskeletal pain diagnoses
       Back pain (reference group: no)1.16*** (1.12–1.21)1.17*** (1.12–1.21)1.15*** (1.13–1.17)1.15*** (1.13–1.17)
       Neck pain (reference group: no)1.34*** (1.28–1.40)1.34*** (1.28–1.40)1.18*** (1.16–1.21)1.18*** (1.16–1.21)
       Joint pain (reference group: no)0.83*** (0.80–0.87)0.83*** (0.80–0.87)0.80*** (0.78–0.81)0.80*** (0.78–0.81)
       Osteoarthritis (reference group: no)0.90** (0.84–0.97)0.90** (0.84–0.97)0.92*** (0.89–0.95)0.92*** (0.89–0.95)
       Tempomandibular disorder (reference group: no)1.32*** (1.12–1.54)1.31*** (1.12–1.53)1.29*** (1.14–1.45)1.29*** (1.14–1.45)
       Fibromyalgia (reference group: no)1.35*** (1.28–1.42)1.35*** (1.28–1.42)1.10*** (1.07–1.14)1.10*** (1.07–1.14)
      Interaction term: age (reference group: 18–34) × race/ethnicity (reference group: White)Omnibus test p < .001Omnibus test p < .001
       35–44 × Black1.18*** (1.08–1.29)1.05 (0.99–1.11)
       35–44 × Hispanic/Latina/o1.18* (1.03–1.36)1.12*** (1.05–1.20)
       35–44 × Other0.95 (0.78–1.14)1.02 (0.92–1.12)
       35–44 × Missing0.89 (0.73–1.08)1.01 (0.91–1.11)
       45–54 × Black1.29*** (1.18–1.41)1.26*** (1.20–1.33)
       45–54 × Hispanic/Latina/o1.37*** (1.18–1.60)1.17*** (1.09–1.25)
       45–54 × Other1.07 (0.88–1.31)0.97 (0.87–1.07)
       45–54 × Missing0.89 (0.74–1.08)0.97 (0.88–1.07)
      Abbreviations: CIH, complementary and integrative health; VA, Veterans Health Administration.
      ***p < .001.
      **p < .01.
      *p < .05.
      Other predictors of CIH therapy use are not different by gender. For both women and men, CIH therapy use is positively associated with older age (reference group: 18–34 years), Hispanic/Latino and other race/ethnicity (reference group: White), being single/never married or divorced/separated/widowed (reference group: married), having non-VA government insurance (reference group: VA), each comorbid health condition, and certain pain conditions (back pain, neck pain, tempomandibular disorder, fibromyalgia). Also, CIH therapy use is negatively associated with having a required copayment (reference group: exempt), private health insurance (reference group: VA), joint pain, and osteoarthritis.

      Differences in CIH Therapy Use by Gender, Race/Ethnicity, and Age

      An omnibus test of the race/ethnicity-by-age interaction term indicated that it was significant for women and men (p < .001). Moreover, in a model that included data for both women and men and all covariates, an omnibus test of a three-way interaction term (gender × race/ethnicity × age) indicated that it was significant at p = .03 (data not shown). Therefore, for women and men we used gender-stratified models to calculate and plot the predicted probabilities (with 95% CIs) of CIH therapy use in relation to race/ethnicity and age, adjusting for covariates (Figure 1).
      Figure thumbnail gr1
      Figure 1Predicted probabilities (with 95% confidence intervals) of CIH therapy use in relation to race/ethnicity and age, stratified by gender. Based on Table 3, main effects models plus interactions. Nonoverlapping confidence interval bars indicate that the difference in predicted probability is significant statistically. CIH, complementary and integrative health.
      Among women (Figure 1A), younger patients (age 18–34 and 35–44) who are Hispanic/Latina, White, or other race/ethnicity are each similarly likely to use CIH therapies (i.e., 34%–40%). However, among older women (age 45–54), White women are less likely than Hispanic/Latina women to use CIH therapies (37% vs. 42%; p < .001). Finally, Black women, compared with other women, are least likely to use CIH therapies regardless of age (i.e., 25%–31%). In other analysis, we found that meditation was used by more Black women (40%) than each of the other racial/ethnic groups (32%–37%), and fewer Black women used yoga (13% vs. 17–20%; data not shown).
      Among men (Figure 1B), in contrast with women and irrespective of age, White and Black veterans are less likely to use CIH therapies (i.e., 17%–24%) than Hispanic/Latino men or men of other race/ethnicities (i.e., 24%–29%). Also, among younger men (age 18–34 and 35–44), Black veterans are least likely to use CIH therapies (17% and 20%, respectively). Among 45- to 54-year-old men, this difference disappears, such that the probability of CIH therapy use among Black men matches that of White men (23%), but remains still lower than men of Hispanic/Latino (29%) or other race/ethnicity (26%).

      Discussion

      Of U.S. military veterans age 18 to 54 years with chronic musculoskeletal pain who use VA health care, women are more likely than men to use CIH therapies (36% vs. 26%). Also, there are gender differences in the type of CIH therapy they use, with more women than men using yoga, and fewer women using meditation, chiropractic care, guided imagery, massage, and tai chi. We also found variation in the rates of CIH therapy use by race/ethnicity and age, among both women and men, such that it is least likely to be used by younger Black or White veterans. Another striking result is that, among women, Black women are the least likely to use CIH therapies, irrespective of age. Finally, we found generally higher rates of CIH therapy use by Hispanic veterans of both genders with chronic musculoskeletal pain, compared with their similarly-aged White, Black, and other race/ethnicity counterparts.

      Implications for Practice and/or Policy

      Differences by gender in the prevalence and type of CIH therapy used may reflect differences in veterans’ preferences and goals regarding such use or in type and severity of underlying pain and comorbid conditions. Nevertheless, findings suggest that focusing CIH therapy engagement in ways that are tailored to gender might increase CIH therapy use. For example, more men veterans with chronic musculoskeletal pain might use CIH therapies and at an earlier stage in the course of their health conditions if engagement and therapeutic efforts specifically incorporated information regarding how CIH therapies can prevent or alleviate specific comorbid conditions (e.g., substance use disorders, sleep disorders, traumatic brain injury) and pain diagnoses (e.g., back pain).
      An implication of variation in the rates of CIH therapy use by race/ethnicity and age is that efforts to engage more veterans in use of CIH therapies should specifically target certain groups. In particular, it is especially concerning that Black women veterans with chronic musculoskeletal pain are the least likely of women veterans with this condition to use CIH therapies. Black women are a significant and growing proportion of the veteran population (
      • U.S. Department of Veterans Affairs
      Profile of Women Veterans: 2015. National Center for Veterans Analysis and Statistics.
      ). Also, Black adults are less likely to be prescribed opioids (
      • Burgess D.J.
      • Nelson D.B.
      • Gravely A.A.
      • Bair M.J.
      • Kerns R.D.
      • Higgins D.M.
      • van Ryn M.
      • Farmer M.
      • Partin M.R.
      Racial differences in prescription of opioid analgesics for chronic noncancer pain in a national sample of veterans.
      ) or monitored by a pain specialist (
      • Hausmann L.R.
      • Gao S.
      • Lee E.S.
      • Kwoh C.K.
      Racial disparities in the monitoring of patients on chronic opioid therapy.
      ). Furthermore, untreated physical pain is a critical reason why women veterans initiate illicit use of opioids and other substances (
      • Evans E.A.
      • Glover D.L.
      • Washington D.L.
      • Hamilton A.B.
      Psychosocial factors that shape substance abuse and related mental health of women military veterans who use community-based services.
      ). Therefore, Black women veterans may have significant unmet needs for pain relief services, which could elevate their risk for opioid and other substance use disorders.
      We found higher rates of CIH therapy use by Hispanic veterans. This finding is contrary to general population studies showing lower CIH therapy use overall by Hispanics (
      • Olano H.A.
      • Kachan D.
      • Tannenbaum S.L.
      • Mehta A.
      • Annane D.
      • Lee D.J.
      Engagement in mindfulness practices by U.S. adults: Sociodemographic barriers.
      ), but supports studies showing increases in use of certain CIH practices, especially yoga (

      Clarke, T. C., Black, L. I., Stussman, B. J., Barnes, P. M., & Nahin, R. L. (2015). Trends in the use of complementary health approaches among adults: United States, 2002-2012. National Health Statistics Report, (79), 1-16. Available from: https://nccih.nih.gov/research/statistics/NHIS. Accessed: April 1, 2018.

      ). Hispanic veterans could be using CIH therapies more than others because they are less likely to receive opioids (
      • Pletcher M.J.
      • Kertesz S.G.
      • Kohn M.A.
      • Gonzales R.
      Trends in opioid prescribing by race/ethnicity for patients seeking care in US emergency departments.
      ), or they have a greater willingness to try traditional healing practices or other alternative therapies for chronic pain (
      • Kennedy L.
      • Gonzales E.
      • Corbin L.
      The effect of curanderismo on chronic non-malignant pain: A case report.
      ,
      • Tafur M.M.
      • Crowe T.K.
      • Torres E.
      A review of curanderismo and healing practices among Mexicans and Mexican Americans.
      ). More research is needed to understand why Hispanic women and men veterans with chronic musculoskeletal pain are more likely to use CIH therapies than other racial/ethnic groups. Findings could inform efforts to engage other patient groups in CIH care.

      Limitations and Strengths

      Our findings are tempered by several limitations. First, we do not know if patients used CIH therapies to treat chronic musculoskeletal pain or for another reason. To address this issue, we only included CIH therapy use that began after patients’ receipt of a musculoskeletal diagnosis, and we controlled for comorbid conditions. Also, results may be impacted by omitted variable bias (i.e., we did not consider several factors that may be related to CIH therapy use and also vary by gender, race/ethnicity, and age, such as pain ascertainment and severity, and physician and patient attitudes, knowledge, and preferences regarding CIH services.) We analyzed all patients age 18 to 54 with musculoskeletal pain who accessed health care at the VA during a certain time period (2010–2013), and so findings may not be representative of military veterans with this condition who are older than age 54 or treated in other types of health care settings, or representative of how veterans are being treated more recently. Finally, our analyses relied on administrative EHR data, which are subject to reporting biases; however, these data have also been found to be valuable for investigating pain and health services use among VA patients (
      • Abel E.A.
      • Brandt C.A.
      • Czlapinski R.
      • Goulet J.L.
      Pain research using Veterans Health Administration electronic and administrative data sources.
      ,
      • Goulet J.L.
      • Kerns R.D.
      • Bair M.
      • Becker W.C.
      • Brennan P.
      • Burgess D.J.
      • Brandt C.A.
      The musculoskeletal diagnosis cohort: Examining pain and pain care among veterans.
      ). Related to this limitation, documentation of race/ethnicity relied on VA EHR data, a source that more accurately documents White and Black race/ethnicity than it does Hispanic and non-Black minority race/ethnicity (
      • Hamilton N.S.
      • Edelman D.
      • Weinberger M.
      • Jackson G.L.
      Concordance between self-reported race/ethnicity and that recorded in a Veteran Affairs electronic medical record.
      ,
      • Mor M.
      Assessing race and ethnicity in VA EATA. 2018 VIReC Databases and Methods Cyberseminar.
      ), constituting a potential source of bias in the present study. Also, approximately 5% of patients were missing race/ethnicity information. The proportion of patients missing race/ethnicity was not substantively different by gender or CIH use. Finally, we did not examine variation in availability of CIH therapies (
      • Veterans Healthcare Administration (VHA)
      • Healthcare Analysis & Information Group (HAIG)
      FY 2015 VHA complementary and integrative health (CIH) services. Report prepared by a Field Unit of the Office of Strategic Planning & Analysis, Office of the ADUSH for Policy and Planning.
      ) and its association with CIH use, underscoring an area for future research.
      This study has a number of strengths. It uses the veteran population, which is racially and ethnically diverse (e.g., approximately 40% of women veterans are racial/ethnic minorities) and, as such, facilitates the examination of racial and ethnic disparities. Also, we capitalized on the sophisticated VA EHR system, which facilitates linking diagnosis and health services use records. These data enabled us to examine all patients of the health care system and, unlike most studies of musculoskeletal pain and CIH use, not only those treated in hospitals and specialty clinics (
      • Callahan L.F.
      • Wiley-Exley E.K.
      • Mielenz T.J.
      • Brady T.J.
      • Xiao C.
      • Currey S.S.
      • Sniezek J.
      Use of complementary and alternative medicine among patients with arthritis.
      ,
      • Rhee T.G.
      • Leininger B.D.
      • Ghildayal N.
      • Evans R.L.
      • Dusek J.A.
      • Johnson P.J.
      Complementary and integrative healthcare for patients with mechanical low back pain in a U.S. hospital setting.
      ). Our work contributes new knowledge regarding the gender-specific correlates of musculoskeletal pain and the factors that might explain differences in CIH use. CIH therapies are a potentially promising practice for addressing opioid misuse among veteran populations with chronic musculoskeletal pain. A next step for research is to determine whether differences in CIH use by gender translate into similar gender-based differences in patient outcomes such as pain, mental health, and opioid misuse.

      Conclusions

      Among military veterans with chronic musculoskeletal pain we found differential use of CIH therapies by gender, race/ethnicity, and age. These differences in CIH therapy use are important because they might be partially contributing to existing disparities in pain and opioid use. Our findings suggest that VA clinicians might want to tailor their CIH engagement efforts to be sensitive to gender, race/ethnicity, and age.

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      Biography

      Elizabeth A. Evans, PhD, MA, is an Assistant Professor of Health Promotion and Policy at the University of Massachusetts Amherst. A public health scientist, she has expertise in women's health, substance use disorders, and mining electronic health records/big data to understand health services use and outcomes.
      Patricia M. Herman, PhD, ND, MS, is a senior behavioral scientist at the RAND Corporation and a member of the Pardee RAND Graduate School faculty. Her research centers on health economics, innovative care models, and overall quality of life.
      Donna L. Washington, MD, MPH, is the Women's Health Focused Research Area Lead, VA HSR&D's Center for the Study of Healthcare Innovation, Implementation and Policy; and Professor of Medicine, UCLA. Her research interests include health care needs of women and vulnerable/underserved populations.
      Karl A. Lorenz, MD, MSHS, is a general internal medicine/palliative care physician, and Section Chief, VA Palo Alto-Stanford Palliative Care Program. He has served the World Health Organization in Palliative Care for Older People and leading methods for Palliative Care Essential Medications.
      Anita Yuan, PhD, is a Research Health Scientist at the HSR&D Center for the Study of Healthcare Innovation, Implementation, and Policy (CSHIIP). She studies health services use, the impacts of services for homeless veterans, and complementary and integrative health.
      Dawn M. Upchurch, PhD, LAc, is Professor of Public Health at the UCLA Fielding School of Public Health. She studies women's health and well-being, emphasizing psychosocial stressors and lifestyle behaviors on health, and alternative and integrative medicine strategies to improve women's health.
      Nell Marshall, DrPH, MPH, is a Health Scientist at the VA Palo Alto Health Care System. Her research interests include implementation science, complementary and integrative health, and models for genetic consultation.
      Alison B. Hamilton, PhD, MPH, Associate Director for Implementation Science at the HSR&D Center for the Study of Healthcare Innovation, Implementation, and Policy (CSHIIP), is a medical anthropologist with expertise in implementation science, women's health, mental health, and qualitative methods.
      Stephanie L. Taylor, PhD, MPH, is Associate Director, VA GLA HSR&D COIN and Director, Complementary and Integrative Health Evaluation Center. She studies environmental and organizational influences on health/health care/implementation, with expertise in complementary and integrative health, patient safety, and women's health.