Use of Complementary and Integrative Health Therapies and Chiropractic Care Among Patients Receiving Long-Term Opioid Therapy in the Department of Veterans Affairs

Abstract

Background:

Use of complementary and integrative health (CIH) therapies has increased over the past two decades. Accumulating evidence supports the effectiveness of CIH therapies for a range of health outcomes and an association between CIH therapy use and reduced use of opioid therapy.

Objective:

To describe differences in the use of evidence-based CIH therapies among individuals with chronic pain on long-term opioid therapy (LTOT) by sociodemographic and clinical characteristics.

Design:

This was an observational study using a national retrospective cohort of patients developed from Veterans Affairs (VA) electronic health record data.

Sample:

The sample comprised 281,903 patients receiving LTOT upon cohort entry between July 1, 2017, and December 31, 2021, with a health care encounter in one of 54 VA medical centers.

Main Measures:

CIH therapy use was defined as chart-documented use of any of eight priority CIH therapies or chiropractic care in the year prior to cohort entry. LTOT was defined as having a prescribed opioid analgesic for ≥90 consecutive days, allowing ≤30 days between fills. Sociodemographic and clinical characteristic variables were assessed as covariates of CIH therapy use.

Key Results:

Among the full cohort, the CIH therapy use rate was 9.5%. Use increased with each cohort entry year relative to 2017. In a multivariable model, variables associated with greater odds of CIH therapy use included being female, having a musculoskeletal pain condition, obesity, opioid use disorder, anxiety, depression, post-traumatic stress disorder, and receiving services in a flagship site. Lower odds of use were associated with being older, unmarried, prescribed higher opioid doses, smoking, and rural residence.

Conclusions:

CIH therapy use has increased over time, consistent with national trends; however, differential patterns of exposure suggest the need to explore pathways to use. These may elucidate opportunities to broaden access to these evidence-based therapies for patients with chronic pain.

Keywords

CIH opioid therapy veterans whole health chronic pain

Introduction

Research describing the use and impact of complementary and integrative health (CIH) therapies has grown exponentially over the past two decades,¹ mirroring the expansion of these therapies nationally and the prioritization of clinical research in this area.² The broad expansion of CIH therapies reflects a turn-of-century shift in the United States away from a disease-focused medical model of health care in favor of a more holistic approach with emphasis on well-being. National clinical practice guidelines for the management of pain^3–5 and other health conditions,^6–8 and growing evidence for the safety and effectiveness of select CIH therapies support increased CIH utilization.^9,10 Among patients with chronic pain, the transition toward greater CIH use parallels the national movement away from long-term opioid therapy (LTOT) as a first-line treatment for chronic pain, given growing evidence of opioid-related risks and equivocal evidence for long-term benefits.^11–13

The Department of Veterans Affairs (VA) has been a leader among health care systems for over a decade in advancing a holistic approach to care through its transition to a patient-centered Whole Health System of Care (WHS).^14,15 Led by the Office of Patient-Centered Care and Cultural Transformation, this transition includes a VA directive supporting the expansion of eight evidence-based CIH therapies as part of standard medical care.^16–19 These priority therapies include acupuncture (traditional and Battlefield), biofeedback, clinical hypnosis, guided imagery, therapeutic massage, meditation, Tai Chi/qigong, and yoga. The effectiveness of these therapies has been demonstrated across a wide range of pain and pain-related outcomes including physical function, balance, headache, sleep, and mental health symptoms.^3,20,21 The most prominent recent VA effort to support the provision of these CIH therapies began in 18 “flagship” VA medical centers in 2018.²² Patients receiving health care in VA flagship sites experienced 46% growth in CIH therapy use within 2 years of the program’s initiation compared to a 16.5% growth among patients at other VA facilities.²³ In a national cohort of patients with chronic musculoskeletal pain using VA services between 2016 and 2019, 14% had used one of these CIH therapies or chiropractic care (not a CIH therapy by VA classification, but typically grouped with CIH therapies in VA research as an additional evidence-based nonpharmacologic therapy) by 2019, making them the subgroup with the highest rate of CIH use.²⁴

Analyses into patterns of CIH use (including chiropractic care) in the VA have identified differences by patient sociodemographic and clinical characteristics. In fiscal year 2020, patients with a greater likelihood of using any CIH therapy or chiropractic care were women, younger, Asian, Native Hawaiian or Other Pacific Islander, and American Indian or Alaska Native, Latinx, not married, and living in urban settings. Patients who used CIH therapies in that year were also more likely to have chronic pain, a history of opioid use, a mental health diagnosis, and obesity. The therapies used most by patients in the VA were acupuncture and chiropractic care, whereas those used least were biofeedback, guided imagery, and clinical hypnosis.²⁵

Preliminary evidence supports an association between CIH therapy use and reduced opioid use for chronic pain management. For example, in a national retrospective musculoskeletal diagnosis cohort, patients who used CIH therapies compared to propensity score-matched patients with no CIH therapy use had a 55% lower likelihood of receiving an opioid prescription. In the same study, among patients prescribed an opioid, those with CIH therapy use had a lower mean prescribed dose and fewer days of opioid prescription.²⁶ In a separate retrospective cohort of patients within the 18 VA flagship sites who were prescribed LTOT, having any CIH therapy use was associated with faster opioid tapering rates, controlling for variables associated with opioid dose and CIH therapy use.²⁷ Among patients with chronic pain in the same 18 sites, those who used any CIH therapy had larger propensity score-adjusted reductions in mean opioid dose over an 18-month period compared to patients using conventional health care only.²⁸ In contrast, one study assessing CIH therapy use between 2010 and 2013 among younger patients with chronic musculoskeletal disorders found, using propensity score-adjusted modeling, slightly higher rates of opioid use among patients who used CIH therapies compared to those who did not.²⁹

The potential for CIH therapies to alter opioid exposure patterns in the context of chronic pain underscores the importance of understanding differences in CIH therapy use among individuals on LTOT, including veterans. Such insight is critical to informing analyses of causal mechanisms underlying the association between opioid use and use of CIH therapies and could inform health care systems’ efforts to broaden access and uptake of CIH therapies. Therefore, in the current study, we assessed rates and covariates of CIH therapy use among patients on LTOT who received services within 54 VA sites spanning the 18 veterans Integrated Service Networks nationwide.

Materials and Methods

Cohort

Using data from VA’s electronic health record (EHR), we created a retrospective cohort of patients who received LTOT and used services at one of 54 VA medical centers between July 1, 2017, and December 31, 2021. Patients entered the cohort on the day after meeting criteria for LTOT, which we defined using published methods as having a prescribed opioid analgesic for ≥90 consecutive days, allowing up to 30 days between fills.³⁰ In cases where patients had multiple discrete episodes of LTOT, we selected the earliest within the study period as the qualifying episode. Medical centers of focus for this study included the 18 WHS flagship sites matched 1:2 to non-flagship sites on facility complexity, region, rurality, and CIH therapy provision. VA facility complexity was defined on a 5-point ordinal scale according to the VA facility complexity model, with facilities ranging from “highest complexity” (i.e., facilities with high volume, patients with high-risk conditions, most complex clinical programs, and large research and teaching programs) to “low complexity” (i.e., facilities with low volume, low-risk patients, few or no complex clinical programs, and small or no research and teaching programs).³¹ Facility region was defined as Northeast, South, Midwest, and West, corresponding to regions used to describe national variation in opioid prescribing.³² Urban versus rural or highly rural facility location was determined based on rural–urban commuting area using recorded facility classifications within the VA administrative data.^33,34 CIH implementation level was a variable created for this study based on the VA WHS dashboard-recorded percentage of each facility’s patient population that used any CIH by year. Facilities were categorized as high or low based on their population percentage of CIH therapy use relative to the median facility population percentage for each year over the study period.

CIH therapies and chiropractic care use

Patients’ use of any of the eight priority CIH therapies or chiropractic care (hereafter referred to collectively as “any CIH therapy use”) in the year prior to cohort entry was determined by VA EHR data using methods described by VA’s Complementary and Integrative Health Evaluation Center,²⁵ considering a combination of structured and semi-structured data. For this study, we counted any CIH therapy use within the VA and/or VA-purchased community-based use of acupuncture, chiropractic care, or therapeutic massage.

Sociodemographic characteristics

Sociodemographic characteristics of interest were identified via the VA EHR using information recorded in the year prior to cohort entry. For each characteristic, the status recorded closest to the cohort entry date was assigned. Focus characteristics included sex, age at time of cohort entry in intervals (18–39, 40–49, 50–59, 60–69, 70+), race, ethnicity, marital status, and urban (vs. rural/highly rural) residence. Race and marital status included “unknown” categories as recorded in the EHR.

Health conditions

All participants were determined to have chronic pain due to receipt of LTOT. Specific health conditions, including clusters of pain disorders, were identified by the International Classification of Diseases, Tenth Revision (ICD-10) codes recorded in the VA EHR. Informed by methods of the musculoskeletal diagnosis cohort,³⁵ medical conditions were considered present if the same ICD-10 code was recorded twice in association with outpatient encounters or once with an inpatient encounter. Conditions that did not meet these criteria were considered absent. We identified 12 common pain-related diagnostic clusters, which we categorized as representing musculoskeletal pain (back pain, limb/extremity pain, joint pain, musculoskeletal chest pain, and neck pain) versus other chronic pain conditions (abdominal pain; fibromyalgia; headache; neuropathy; orofacial, ear, and temporomandibular disorder pain; systemic disorders or diseases causing pain; urogenital, pelvic, and menstrual pain; and other painful conditions). Due to their chronicity, any diagnostic cluster recorded in the 10 years prior to cohort entry was recorded. Pain-related covariates of interest including anxiety, depressive disorders, post-traumatic stress disorder (PTSD), cardiovascular disease (CVD), diabetes, obesity, current smoking status, and opioid use disorder (OUD) were recorded within 1 year prior to cohort entry.

Analyses

We restricted analyses to patients in the cohort who received services in one of the 54 focus VA medical facilities in the year prior to cohort entry. Informed by correlates of CIH therapy use in prior studies, we calculated rates of any CIH therapy use in the year prior to cohort entry by sociodemographic and health conditions of interest, as well as by mean baseline opioid dose (measured as milligram morphine equivalents [MME]) and receipt of health care services in a flagship versus non-flagship medical center. Given the expansion of Whole Health in VA over the study period and the mixture of prevalent and incident LTOT receipt associated with year of cohort entry, percentages were calculated by calendar year of cohort entry and overall. We then conducted simple and multivariable hierarchical logistic regression modeling, averaging across years, to estimate unconditional and conditional effects of predictors of interest, respectively (Table 1).

Table 1.

Rates of Any Complementary and Integrative Health Therapy Use by Cohort Entry Year and Patient Characteristics

Characteristic			Cohort entry year
Characteristic	N	All years	2017	2018	2019	2020	2021
N		281,903	203,230	34,827	20,217	15,021	8608
Overall	281,903	9.50%	8.36%	10.59%	14.22%	13.01%	14.93%
Sex
Male (REF)	259,687	8.93%	7.87%	10.03%	13.35%	12.09%	13.89%
Female	22,216	16.22%	14.33%	16.57%	23.52%	22.61%	24.06%
Age
18–39 (REF)	15,480	17.47%	15.32%	18.38%	25.50%	25.53%	24.89%
40–49	21,701	15.73%	13.79%	17.13%	22.22%	23.99%	24.71%
50–59	52,239	12.21%	10.71%	14.26%	18.71%	16.98%	19.82%
60–69	105,046	8.24%	7.35%	9.12%	13.14%	11.71%	14.09%
70+	87,437	6.46%	5.51%	7.03%	9.51%	8.34%	10.37%
Race
White (REF)	212,129	9.50%	8.38%	10.52%	14.23%	12.99%	15.12%
Black	45,035	9.31%	8.25%	10.22%	14.15%	12.31%	12.27%
Asian	1059	17.19%	15.63%	18.79%	16.04%	21.18%	29.55%
NHOPI	2055	11.00%	9.21%	13.19%	14.74%	17.65%	24.14%
AIAN	2349	9.62%	7.63%	14.98%	13.69%	13.43%	19.74%
>One race	2546	11.86%	10.39%	13.76%	15.31%	20.14%	15.29%
Unknown	16,730	9.09%	7.74%	10.46%	13.92%	12.68%	16.89%
Ethnicity
Hispanic	12,577	11.20%	9.44%	13.07%	16.20%	14.82%	19.54%
Not Hispanic (REF)	269,326	9.43%	8.31%	10.45%	14.11%	12.92%	14.68%
Currently married
Yes (REF)	147,328	9.49%	8.36%	10.61%	13.85%	12.47%	15.17%
No	133,806	9.51%	8.36%	10.56%	14.59%	13.68%	14.55%
Unknown	769	11.57%	8.29%	10.71%	20.00%	10.26%	22.03%
Rural
Rural/Highly rural	115,857	8.60%	7.50%	9.90%	13.18%	11.96%	14.06%
Urban (REF)	166,046	10.13%	8.98%	11.03%	14.91%	13.70%	15.50%
Musculoskeletal pain diagnosis
Yes	273,887	9.72%	8.51%	10.96%	14.85%	13.58%	15.52%
No (REF)	8016	2.06%	1.33%	2.75%	3.12%	2.36%	3.33%
Diabetes
Yes	92,405	8.69%	7.59%	9.59%	13.00%	11.99%	14.23%
No (REF)	189,498	9.90%	8.73%	11.09%	14.84%	13.51%	15.29%
Obesity
Yes	139,200	10.45%	9.09%	11.79%	16.27%	15.20%	16.68%
No (REF)	142,703	8.58%	7.64%	9.42%	12.33%	11.04%	13.46%
CVD
Yes	11,841	9.20%	7.79%	11.97%	11.96%	10.53%	15.23%
No (REF)	270,062	9.52%	8.38%	10.52%	14.34%	13.15%	14.91%
Anxiety
Yes	33,230	15.17%	13.20%	17.59%	21.48%	19.22%	20.44%
No (REF)	248,673	8.75%	7.76%	9.59%	13.08%	11.94%	13.84%
Depression
Yes	66,487	14.76%	13.02%	16.56%	20.98%	19.25%	21.02%
No (REF)	215,416	7.88%	6.98%	8.66%	11.87%	10.77%	12.56%
PTSD
Yes	54,669	14.79%	12.94%	17.19%	20.46%	19.93%	22.29%
No (REF)	227,234	8.23%	7.29%	8.97%	12.58%	11.11%	12.85%
Current tobacco use
Yes	126,021	8.75%	7.74%	10.20%	13.60%	12.13%	12.81%
No (REF)	155,882	10.11%	8.90%	10.85%	14.65%	13.60%	16.35%
Opioid use disorder
Yes	4749	19.54%	18.50%	19.64%	28.68%	16.48%	23.27%
No (REF)	277,154	9.33%	8.20%	10.44%	13.93%	12.93%	14.73%
Opioid dose (MME)
<10 (REF)	52,874	10.42%	7.99%	10.78%	14.38%	14.61%	15.19%
10–19	87,103	9.54%	8.08%	10.60%	14.24%	12.97%	15.57%
20–49	97,669	8.80%	8.05%	10.35%	14.34%	12.26%	14.65%
50–89	24,247	9.73%	9.54%	9.34%	13.47%	8.62%	13.18%
>90	20,010	10.10%	9.91%	12.12%	12.70%	8.49%	11.94%
Flagship site
Yes	135,791	9.58%	8.15%	11.06%	15.07%	13.55%	15.40%
No (REF)	146,112	9.44%	8.56%	10.16%	13.38%	12.42%	14.38%

AIAN, American Indian or Alaskan Native; CVD, cardiovascular disease; MME, milligram morphine equivalent; NHOPI, Native Hawaiian or Pacific Islander; PTSD, post-traumatic stress disorder; REF, reference group in regression models.

Results

Of 314,661 patients on LTOT during the study period, 281,903 (89.6%) had a recorded encounter in one of the 54 facilities and were included in analyses. Most (72.1%) entered the cohort in 2017, representing patients with prevalent LTOT. Most patients in the cohort (92%) were male; two-thirds were 60 years of age or older, most identified as White, non-Hispanic, and approximately half were married. A total of 49% had obesity, 45% had current tobacco use, and 33% had diabetes. The vast majority of patients (97.2%) had at least one documented musculoskeletal pain condition, and most (65.5%) were prescribed a mean opioid daily dose between 10 mg and 49 mg morphine equivalents (Table 1). Across cohort entry years, the overall rate of any CIH therapy use was 9.5%. CIH therapy use rates increased with each year of cohort entry, from 8.4% in 2017 to 14.9% in 2021.

In simple regression models, variables associated with significantly greater odds of any CIH therapy use were being female, Asian, Black, Pacific Islander, or identifying as >1 race (relative to White), Hispanic, having anxiety, depression, PTSD, a musculoskeletal disorder, obesity, OUD, and receiving services in a flagship site. Variables associated with significantly lower odds of using a CIH therapy included older age groups (relative to being 18–39), receiving a higher baseline opioid dose (relative to 0–<10 mg MME), current smoking, having diabetes, and living in a rural location (Table 2, Fig. 1). In the multivariable model, variables that remained significantly associated with greater odds of CIH therapy use were being female, Asian, having anxiety, depression, PTSD, a musculoskeletal disorder, obesity, OUD, and receiving services in a flagship site. In addition, having CVD emerged as significantly associated with any CIH therapy use. Variables that remained significantly associated with lower odds of use were older age groups, higher baseline opioid doses, current smoking, and living in a rural setting. Being unmarried emerged as significantly associated with lower odds of use (Table 2, Fig. 2).

Table 2.

Unconditional and Conditional Odds Ratios and 95% Confidence Interval Limits

		95% CI limits			95% CI limits
Effect	UC OR	LL	UL	Conditional OR	LL	UL
Age 40–49 (REF = 18–39)	0.90	0.85	0.96	0.90	0.85	0.96
Age 50–59 (REF = 18–39)	0.66	0.63	0.70	0.72	0.69	0.77
Age 60–69 (REF = 18–39)	0.42	0.40	0.44	0.49	0.47	0.52
Age 70+ (REF = 18–39)	0.31	0.30	0.33	0.38	0.36	0.40
Anxiety	1.87	1.81	1.94	1.28	1.23	1.33
BL MME 10–19 (REF ≤ 10)	0.92	0.89	0.96	0.86	0.81	0.92
BL MME 20–49 (REF ≤ 10)	0.85	0.82	0.89	0.91	0.88	0.95
BL MME 50–89 (REF ≤ 10)	0.92	0.87	0.97	0.83	0.79	0.86
BL MME ≥90 (REF ≤ 10)	0.94	0.89	0.99	0.87	0.82	0.92
Current smoking	0.88	0.85	0.90	0.78	0.76	0.81
CVD	0.96	0.90	1.03	1.11	1.03	1.19
Depression	2.07	2.01	2.13	1.54	1.49	1.59
Diabetes	0.88	0.85	0.91	0.99	0.96	1.02
Female (REF = Male)	2.03	1.95	2.11	1.43	1.37	1.49
Flagship site	1.22	1.13	1.32	1.10	1.02	1.19
Hispanic	1.24	1.17	1.33	1.02	0.95	1.09
Married: No (REF = Yes)	0.98	0.95	1.00	0.95	0.92	0.98
Married: Unknown (REF = Yes)	1.36	1.08	1.71	1.18	0.94	1.50
MSD pain (REF = Other pain)	5.10	4.35	5.98	4.47	3.81	5.24
Obese	1.25	1.22	1.29	1.11	1.08	1.14
OUD	2.17	2.01	2.35	1.74	1.60	1.89
PTSD	1.97	1.91	2.03	1.45	1.40	1.50
Race: American Indian (REF = White)	0.97	0.83	1.12	0.88	0.76	1.03
Race: Asian (REF = White)	1.77	1.49	2.10	1.31	1.10	1.56
Race: Black (REF = White)	1.10	1.06	1.14	0.98	0.94	1.02
Race: Multiple race (REF = White)	1.32	1.16	1.50	1.12	0.98	1.28
Race: Pacific Islander (REF = White)	1.19	1.02	1.37	1.07	0.92	1.24
Race: Unknown (REF = White)	0.97	0.91	1.03	0.94	0.89	1.00
Rural	0.86	0.84	0.89	0.90	0.87	0.93

BL, baseline; CI, confidence interval; LL, lower limit; MME, milligram morphine equivalent; MSD, musculoskeletal disorder; OR, odds ratio; OUD, opioid use disorder; UC, unconditional; UL, upper limit.

FIG. 1.

Unconditional odds ratios (ORs) with 95% confidence intervals (CIs) are shown for patient-level variables predicting any complementary and integrative health therapy in simple regression models. The vertical line at OR = 1 indicates no association. Points to the right of the line indicate greater odds; points to the left indicate lower odds. Error bars represent 95% CIs. BL, baseline; CVD, cardiovascular disease; MSD, musculoskeletal disorder; OUD, opioid use disorder; PTSD, post-traumatic stress disorder.

FIG. 2.

Conditional ORs with 95% CIs are shown for patient-level variables predicting any complementary and integrative health therapy in a multivariable regression model. The vertical line at OR = 1 indicates no association. Points to the right of the line indicate greater odds; points to the left indicate lower odds. Error bars represent 95% CIs.

Discussion

Patients on LTOT face risks of OUD and overdose in addition to other opioid-related adverse effects and poorly managed chronic pain, all of which impact quality of life. There is promising accumulating evidence supporting a role for CIH therapies as an opioid-sparing approach to chronic pain management. In this large national cohort of patients on LTOT between 2017 and 2021, increasing rates of CIH use across years of cohort entry reflect national trends, clinical practice guidelines, and ongoing VA implementation efforts to support CIH therapy access. Such efforts include broadening the provision of virtual CIH therapies, live group classes, and recorded sessions. The mean CIH therapy use rate of 14%−15% in years 2019 and 2021 (excluding 2020 due to potential COVID-19 interference) in this cohort with primarily musculoskeletal pain disorders is similar to 2019 rates described among all veterans with musculoskeletal disorders,²⁵ suggesting LTOT did not substantially alter therapy use rates. As described among the broader VA population of patients, as well as some national samples of patients with chronic pain.^13,36 CIH therapy use in the current cohort varied by sociodemographic and clinical characteristics. Averaging across cohort entry years and controlling for other covariates, women, younger, and Asian patients each had greater rates of any CIH therapy use than their respective comparison groups. Patients with a musculoskeletal pain condition, those with a mental health disorder, obesity, and OUD all used CIH therapies at rates higher than their comparison groups. Indeed, an alternate explanation for the observed overall increase in CIH therapy use by year is the simultaneous yearly increase in the prevalence of patients with greater probability of use, including women (increase of 2.65% prevalence by 2021) and patients with a mental health disorder (increased prevalence of 3.06%, 5.46%, and 5.14% for PTSD, anxiety, and depression, respectively). In contrast to population-level trends, unmarried patients in the cohort were significantly less likely than married patients to have used a CIH therapy. The finding of lower rates of CIH therapy use by patients prescribed higher opioid doses is consistent with literature describing lower and declining rates of opioid use among patients exposed to CIH therapies. This highlights the important question of causality that remains a focus for future research.

The processes underlying subgroup differences in CIH therapy use remain underexplored, including whether differences are driven by patterns of referral, referral uptake, accessibility of services, or other factors. Furthermore, although increased CIH therapy use rates over time are promising, there is a need to address remaining barriers to use among individuals prescribed opioids for chronic pain, ensuring equitable access to these evidence-based therapies. A recent survey among patients who were regular health care users within the 18 flagship sites suggested that patients would use CIH therapies if accessible.³⁷

Limitations

Results of the current study should be interpreted in light of several limitations, including our reliance on EHR data to determine CIH therapy use, a practice consistent with VA-wide Whole Health evaluations, but likely to underestimate actual CIH exposure due to the exclusion of home use and community-based care not purchased by VA. CIH use was limited to the year prior to patients’ cohort entry, reflecting different contexts of use for patients with prevalent versus incident LTOT. As a result, year-to-year rates should be compared with this caveat in mind. Despite some similarity of our study results to those involving other samples, our study’s focus on patients receiving care within the VA may limit its generalizability to veterans not engaged with the VA and patients prescribed LTOT for chronic pain more broadly. Finally, given the study’s focus on health record data from 2016 to 2021, patterns emerging after 2021 are not described.

Conclusion

Research has established that individuals prescribed LTOT for chronic pain face the risk of opioid-related harms and likely limited improvement in pain and physical function. CIH therapies offer a safe, evidence-based option for managing chronic pain. Within the VA, the percentage of patients prescribed LTOT who used CIH therapies increased significantly over recent years. In the context of prevalent chronic pain conditions and clinical guidelines de-emphasizing the use of LTOT, expanded access to CIH therapies is essential. By identifying differences in patterns of CIH therapy use among patients on LTOT, the current study represents a fundamental step toward understanding the complex association between CIH therapy use and use of opioid therapy among individuals with chronic pain. Future research assessing the causal relationships among these variables should account for patient characteristics associated with CIH therapy use in this population.

Authors’ Contributions

A.C.B.: Conceptualization, project administration, resources, supervision, methodology, formal analysis, writing—original draft, funding acquisition, and investigation. S.B.Z.: Conceptualization, project administration, resources, supervision, methodology, investigation, visualization, and writing—critical review and editing. R.D.K. and A.A.H.: Methodology, investigation, visualization, and writing—critical review and editing. J.H.D., H.R., and M.K.: Data curation, validation, visualization, and writing—critical review and editing. E.A.A.: Methodology, data curation, validation, investigation, visualization, and writing—critical review and editing. S.L.T.: Visualization and writing—critical review and editing. W.C.B.: Conceptualization, project administration, resources, supervision, formal analysis, writing—original draft, funding acquisition, and investigation. All named authors meet the International Committee of Medical Journal Editors criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Footnotes

Author Disclosure Statement

The authors declare there are no competing interests.

Funding Information

The study was funded by the Department of Veterans Affairs, Health Services Research & Development, I01HX003412 (A.C.B., W.C.B.). The funders had no role in the conduct of this study or preparation of this article. The views expressed herein are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the U.S. government.

Supplemental Material

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