J General Internal Medicine 2020 (Mar); 35 (3): 775–783 ~ FULL TEXT
Esther L. Meerwijk, PhD, MSN , Mary Jo Larson, PhD, MPA, Eric M. Schmidt, PhD,
Rachel Sayko Adams, PhD, MPH, Mark R. Bauer, MD, Grant A. Ritter, PhD,
Chester Buckenmaier III, MD, and Alex H. S. Harris, PhD, MS
VA Health Services Research & Development,
Center for Innovation to Implementation (Ci2i),
VA Palo Alto Health Care System,
Menlo Park, CA, USA.
Nahin ~ Pain 2017
BACKGROUND: Potential protective effects of
nonpharmacological treatments (NPT) against long-term pain-related adverse outcomes have not been examined.
OBJECTIVE: To compare active duty U.S. Army service members with chronic pain who did/did not receive NPT in the Military Health System (MHS) and describe the association between receiving NPT and adverse outcomes after transitioning to the Veterans Health Administration (VHA).
DESIGN AND PARTICIPANTS: A longitudinal cohort study of active duty Army service members whose MHS healthcare records indicated presence of chronic pain after an index deployment to Iraq or Afghanistan in the years 2008-2014 (N = 142,539). Propensity score-weighted multivariable Cox proportional hazard models tested for differences in adverse outcomes between the NPT group and No-NPT group.
EXPOSURES: NPT received in the MHS included acupuncture/dry needling, biofeedback, chiropractic care, massage, exercise therapy, cold laser therapy, osteopathic spinal manipulation, transcutaneous electrical nerve stimulation and other electrical manipulation, ultrasonography, superficial heat treatment, traction, and lumbar supports.
MAIN MEASURES: Primary outcomes were propensity score-weighted proportional hazards for the following adverse outcomes: (a) diagnoses of alcohol and/or drug disorders; (b) poisoning with opioids, related narcotics, barbiturates, or sedatives; (c) suicide ideation; and (d) self-inflicted injuries including suicide attempts. Outcomes were determined based on ICD-9 and ICD-10 diagnoses recorded in VHA healthcare records from the start of utilization until fiscal year 2018.
KEY RESULTS: The propensity score-weighted proportional hazards for the NPT group compared to the No-NPT group were 0.92 (95% CI 0.90-0.94, P < 0.001) for alcohol and/or drug use disorders; 0.65 (95% CI 0.51-0.83, P < 0.001) for accidental poisoning with opioids, related narcotics, barbiturates, or sedatives; 0.88 (95% CI 0.84-0.91, P < 0.001) for suicide ideation; and 0.83 (95% CI 0.77-0.90, P < 0.001) for self-inflicted injuries including suicide attempts.
CONCLUSIONS: Nonpharmacological treatments (NPT) provided in the Military Health System (MHS) to service members with chronic pain may reduce risk of long-term adverse outcomes.
KEYWORDS: adverse outcomes; chronic pain; nonpharmacological treatment; opioids; veterans
From the FULL TEXT Article:
Chronic pain is a costly public health issue that is associated
with many adverse outcomes including chronic opioid use and
suicide.  Deployment to conflict zones places military service
members at risk for chronic pain, which often persists after
they leave military service and transition their healthcare from
the Military Health System (MHS) to the Veterans Health
Administration (VHA). [2, 3] Twenty-nine percent to 44% of active duty service members reported chronic pain after deployment to conflict zones in Iraq or Afghanistan, and 48 to 60% of VHA primary care patients reported chronic pain. [4–7]
Chronic pain is a well-established risk factor for suicide ideation and suicide attempts, as well as for opioid use disorder and opioid-related overdose, especially in the presence of already existing substance use disorder. [8, 9]
Chronic pain is often managed with prescription opioids
which, especially at higher doses and/or longer duration of
use, have been associated with increased risk for substance use
disorders, opioid-related overdose, self-inflicted injuries, and
suicide attempts. [10–16] In addition to opioids, chronic pain can
be managed with
nonpharmacological treatments (NPT). [17–19]
These include treatments like exercise therapy and chiropractic
manipulation, as well as less common treatments, like
yoga, massage, and acupuncture.  Compelling evidence for
a moderate effect on clinical outcomes was found for exercise
and spinal manipulation in nonmilitary samples with chronic
low back pain, although the effect on pain intensity was small
to moderate and mostly short-term. [21, 22] Recent research in
active duty service members showed that early NPT was
associated with a lower risk of military duty limitations, and
facilities where NPT was more common were less likely to
initiate long-term opioid treatment for their patients. [23, 34]
If NPT is used to manage chronic pain, in addition to or
instead of opioids, thismay not only have an effect on pain and
functional status, but also on adverse outcomes that are associated
with chronic pain and opioid use, such as substance use
disorders, drug overdose, and self-inflicted injuries. The potential
long-term protective effect of NPT against adverse
outcomes has not been examined. The purpose of the current
analyses was to compare active duty U.S. Army service members
with chronic pain who did and did not receive NPT in the
MHS and describe the association between receiving NPT in
theMHS and adverse outcomes observed after transition to the
VHA, specifically alcohol and drug abuse or dependence,
accidental or intentional drug poisoning, suicide ideation,
and self-inflicted injuries.
Studying these outcomes broadens
our knowledge of the potential impacts of NPT beyond their
effect on pain and provides valuable information to support
clinical decision-making regarding chronic pain management.
Studying outcomes after transition to VHA allows for longterm
observation and highlights the potential cross-system
impacts of NPT. We hypothesized that the use of NPT in the
MHS would be associated with a lower likelihood of adverse
outcomes in the VHA. These analyses are part of the Substance
Use and Psychological Injury Combat Study (SUPIC),
the largest longitudinal, observational study to date of pain
management and behavioral health conditions using MHS
data from U.S. Army service members returning from deployments
in support of Operations Enduring Freedom (OEF),
Iraqi Freedom (OIF), and New Dawn (OND). 
The current study used a longitudinal cohort design where
active duty service members with chronic pain were identified
through their healthcare records in the military data repository
and other DoD sources compiled by SUPIC.  MHS data up
to the end of 2015 were included. Quasi-experimental
methods were used to determine outcomes for identified
service members who enrolled in VHA and based on
clinical encounters registered in the VHA corporate data
warehouse. Healthcare records related to outpatient visits
and inpatient stays were used.
Active duty Army service members with chronic pain after an
index OEF/OIF/OND deployment ending between October 1,
2007, and September 30, 2014, were included (N = 286,885).
The relative timing of deployment end date, chronic pain
diagnosis, NPT treatment, and VHA outcome measurement
is shown in Figure 1. Chronic pain was operationalized as a
recurrence at least 90 days apart within any of ten clusters of
International Classification of Diseases (ICD-9) diagnoses
known to be associated with pain (e.g., nontraumatic joint
disorders, musculoskeletal disorders). Similar diagnosis clusters
have been previously used to identify chronic pain in
health record data. [26, 27] The specific codes used for this study
are described in detail elsewhere.  In addition to diagnoses in
a service member’s health records, any 60-days supply of
opioids prescribed in a 3-month period or a 90-days supply
in 12 months was taken as an indication of chronic pain.  We
determined days supply across non-injectable opioids prescribed
in the MHS including codeine, dihydrocodeine, fentanyl,
hydrocodone, hydromorphone, meperidine, methadone,
morphine, oxycodone, oxymorphone, and tapentadol.
We excluded servicemembers who were discharged from the
military for reasons we classified as misconduct and service
members who died while receiving care in the MHS, as they
were unlikely to have substantial VHA records. To avoid cases
where events occurred in reverse order, we also excluded service
members who received VHA care before receiving NPT in
the military. After applying exclusion criteria, 275,820 Army
service members with chronic pain remained: 142,539 who
received care in VHA and 133,281 who did not (see Fig. 2).
Independent Variable: Receipt of NPT in the MHS
For each servicemember, we determined if they received any
NPT (yes/no) in the MHS after their index deployment. NPT
were identified in the MHS data repository using ICD-9 diagnosis
codes, Current Procedural Terminology (CPT) codes,
and Healthcare Common Procedure Coding System (HCPCS)
codes. NPT were defined as acupuncture/dry needling, biofeedback,
chiropractic care, massage, exercise therapy, cold
laser therapy, osteopathic spinal manipulation, transcutaneous
electrical nerve stimulation (TENS) and other electrical manipulation,
ultrasonography, superficial heat treatment, traction,
other physical therapy, and lumbar supports. The specific
codes used are described in detail elsewhere. 
Dependent Variables: Adverse Outcomes in the VHA
Main outcomes after service members enrolled in VHA were
determined based on ICD-9 and ICD-10 diagnosis codes
recorded in VHA healthcare records until fiscal year 2018 (see
eTable 1 in the Supplement). Outcomes included diagnoses of
alcohol and/or drug disorders (yes/no), poisoning with opioids,
related narcotics, barbiturates, or sedatives (yes/no, separately
for accidental and intentional poisoning), suicide ideation (yes/
no), and self-inflicted injuries including suicide attempts (yes/
no). The outcome for alcohol and/or drug disorders combined
abuse and dependence and excluded tobacco use disorder.
Propensity Score Weighting
To account for differences between service members with
chronic pain who received NPT in the MHS and those who
did not, we used propensity score-weighted analyses.
Propensity scores represent the probability of group membership,
in our studymembership of the group who receivedNPT,
and were estimated using the following demographic, clinical,
and military service characteristics recorded between the end
of the index deployment and the last quarter of MHS utilization
or the end of 2015, whichever came first: age, gender,
race, marital status, rank/pay grade, fiscal year of index deployment,
total days being deployed, days of deployment as of
the index deployment, length of observation in the MHS,
presence of any diagnoses for the following disorders: adjustment,
depression, anxiety, or posttraumatic stress (PTSD),
traumatic brain injury (TBI), alcohol use disorder (AUD), or
substance use disorder (SUD), whether specialty services were
received for mental health or substance use, use of prescription
opioids (average dailymorphine equivalents and days supply),
sum of appointment days with low pain, sum of appointment
days with moderate pain, sum of appointment days with
severe pain, and number of inpatient days and hospital discharges.
Characteristics that increase with increasing MHS
observation time (e.g., days supply opioids, number of inpatient
days) were normalized by the length of a service member’s
observation in the MHS. Propensity scores were then
used to determine inverse probability of treatment weights
(IPTW), which were used in our final analyses to balance
group differences.  To avoid undue influence from extreme
weights, we truncated the weights to 10, as 90% of our cohort
had a weight less than 10.  To account for potential differences
between soldiers who do and do not enroll in VHA, we
used a multinomial propensity score model to determine
IPTW for four groups: (1) NPT and enrolled in VHA; (2)
No-NPT and enrolled in VHA; (3) NPT and not enrolled in
VHA; and (4) No-NPT and not enrolled in VHA.
Primary statistical analysis involved time-to-event analysis
comparing the two groups that enrolled in VHA (NPT vs.
No-NPT). Data were right censored. For each of the outcomes,
we report a propensity score-weighted log-rank test for differences
in the Kaplan-Meier survival curves and propensity
score-weighted multivariable Cox proportional hazard
models. We assessed both weighted and unweighted Kaplan-
Meier survival curves (see Supplement) and found the weighted
curves to support the proportional hazards assumption. The
log-rank test combines results of χ2 tests of the probability of
an event of interest between two groups across time.  Cox
proportional hazard models estimate the relative difference
(NPT vs. No-NPT) in rates at which events occur across time,
while accounting for covariates. Because the No-NPT group is
weighted to balance the NPT group, the estimated model
coefficient for the group variable represents the average
adjusted difference among those exposed to NPT. As the
NPT group and the No-NPT group were still significantly
different after applying IPTW in age, the length of observation
in the MHS, presence of TBI, and the number of
inpatient days and the sums of appointment days with low,
moderate, and severe pain (see Table 1), we included
those variables as covariates in our final analyses, following
Ridgeway et al.  As MHS alcohol and drug use
diagnoses were available, we limited analyses for that
particular outcome to only those service members who
had not been diagnosed with alcohol and/or drug abuse
or dependence while in the MHS, essentially focusing on
new-onset alcohol and drug use disorders in VHA (n =
86,773 with NPT, n = 18,789 No-NPT).
Descriptive statistics showed that 26,103 (9.5%) active duty
service members with chronic pain received NPT before they
were diagnosed with chronic pain. Running our primary analyses
without these service members did not substantially
change our results (data not shown), and these service members
were retained in our analytic cohort.
As a secondary analysis and to address potential alternative
interpretations of our results, we added additional covariates
from each service member’s VHA healthcare records to the
Cox proportional hazard models. Specifically, we added
length of observation in the VHA, exposure to NPT in the
VHA, and days supply of opioids in the VHA, based on the
same specifications that were used to determine these variables
in the MHS.
All analyses were done in R version 3.5.3 and IPTW were
determined with the function ‘mnps’ from the R package
‘twang’.  All propensity score-weighted analyses were done
with functions from the ‘Survey’ package and P < 0.05 was
considered statistically significant. Approval for this study
was granted by the Brandeis University Committee for Protection
of Human Subjects, the Stanford University and VA
Palo Alto Health Care System Institutional Review Boards,
and the Human Research Protection Program at the Office of
the Assistant Secretary of Defense for Health Affairs/ Defense
Health Agency (OASD/DHA). The DHA Privacy and Civil
Liberties Office executed an annual Data Sharing Agreement.
Table 1 shows the demographic and military variables as well
as the clinical history based on MHS records of service members
in our cohort. The table also shows that propensity score
weighting was successful in balancing the NPT and the No-
NPT groups on most variables. The median age in the cohort
was 26 (range 18–67), and most service members were married
(63.2%) and ranked as enlisted (91.7%). The median total
duration of deployments was 446 days (range 30–3856) and
the median duration of observation in theMHS after the index
deployment was 1274 days (range 0–2912). Table 2 shows the
distribution of diagnoses associated with pain and exposure to
NPT for each cluster of diagnoses, and Table 3 shows the rate
of each NPT modality.
Alcohol and/or drug use disorders were the most frequent
adverse outcome (n = 28,614; 20.1%; median time to event
264 days), followed by suicide ideation (n = 7648; 5.4%;
median time to event 581 days) and self-inflicted injuries
including suicide attempts (n = 1621; 1.1%; median time to
event 621 days). Poisoning with opioids, related narcotics,
barbiturates, or sedatives was least frequent (intentional poisoning:
n = 270, 0.2%, median time to event 786 days;
accidental poisoning: n = 147, 0.1%, median time to event
1045 days). Table 4 shows the results of the time-to-event
analyses for the models with and without additional VHA
covariates. The propensity score-weighted log-rank test
showed significant differences in the Kaplan-Meier survival
curves between the NPTand No-NPT groups for all outcomes,
except for intentional poisoning with opioids, related narcotics,
barbiturates, or sedatives. The Cox proportional hazard
models showed that, while accounting for covariates, the
relative rate at which events occurred (NPT vs. No-NPT)
was significantly less than 1.0. The proportional hazard for
the NPT group was 0.92 for alcohol and/or drug use disorders,
0.65 and 0.82 for accidental and intentional poisoning with
opioids, related narcotics, barbiturates, or sedatives, 0.88 for
suicide ideation, and 0.83 for self-inflicted injuries including
suicide attempts, compared to the No-NPT group. The Cox
proportional hazard models additionally adjusted for VHA
covariates showed only marginally lower proportions,
compared to the models without VHA covariates. We also
did propensity score-weighted logistic regressions for these
adverse outcomes and found essentially the same results (see
eTable 2 in the Supplement)
We ran a chi-square analysis on the proportion of service
members who died after transition to the VHA before
any of our outcomes of interest occurred and found that
slightly more service members died in the No-NPT group
compared to the NPT group (0.5% vs. 0.4%, χ2 = 11.16,
P < 0.001).
The purpose of this study was to compare active duty U.S.
Army service members with chronic pain who did and did not
receive NPT in theMHS and describe the association between
receiving NPT in the MHS and adverse outcomes observed in
the VHA. The results corroborated our hypothesis that use of
NPT in the MHS would be inversely associated with adverse
outcomes in the VHA. Active duty service members with
chronic pain who received NPT in the MHS were at significantly
lower risk in the VHA for new-onset alcohol and/or
drug use disorder, poisoning with opioids, related narcotics,
barbiturates, or sedatives, suicide ideation, and self-inflicted
injuries including suicide attempts. These results were observed
when we used propensity score-weighted Cox proportional
hazardmodels and were further supported by propensity
score-weighted logistic regression.
Note that we did not study
individual NPT modalities. If some modalities did not protect
against adverse outcomes, our resultsmay understate the effect
for NPT modalities that did protect against adverse outcomes.
It is well-known that ICD codes for suicide ideation, suicide
attempts, and other self-inflicted injuries are underreported in
electronic medical records. [35–37] As such, the rates of these
adverse outcomes based on our data may be lower than the
actual rates. However, we have no reason to believe that the
amount of underreporting differed between those who did and
did not receive NPT. Consequently, we think it unlikely that
relying on ICD codes has affected our findings for suicide
ideation and self-inflicted injuries.
From other research into NPT, it might be inferred that
service members who use NPT may be healthier than those
who do not use NPT and as such might be expected to be at
lower risk for adverse outcomes.  However, we did not
observe this pattern in our data. Service members who received
NPT in the military more often reported their highest
pain level as low, moderate, or severe, were more often hospitalized
and had longer inpatient stays, and were more likely
to be diagnosed with mental disorders (except alcohol use
disorder) than service members who had not received NPT.
A study by Han et al. also found higher pain intensity and a
higher likelihood of mental disorders in veterans who received
NPT in the VHA, compared to those who did not receive
NPT.  The proportion of service members who received
NPT and who died before an adverse event occurred was
slightly lower than for those who had not received NPT. This
reinforces our confidence in the favorable effect of NPT, as
proportionally more who received NPT remained at risk.
We are not aware of other studies of chronic pain and NPT
that examined the transition from the MHS to the VHA. By
combining data from two healthcare systems, we could study
long-term outcomes and examine the cross-system impacts of
NPT. The use of propensity score weights and additional VHA
covariates in the analyses accounted for group differences and
increased our confidence that the observed protective associations
with adverse outcomes in the VHA are indeed due to
NPT received in the MHS. While our analysis methods were
rigorous, there are several unobserved factors that we could
not control for and should be considered limitations of the
study. The vastmajority of NPTwas administered after service
members were diagnosed with chronic pain; however, we do
not know whether NPT was specifically provided to treat
It could also be that some service members in
our cohort used other pain treatments of a psychobehavioral
nature (e.g., cognitive behavioral therapy for chronic pain) or
of a pharmacological nature (e.g., NSAIDs, acetaminophen,
gabapentinoids, serotonin-norepinephrine reuptake inhibitors),
or any self-administered treatment not captured in the
medical record. If these other pain treatments decreased pain
levels or the number of days with pain, it would be reasonable
to conclude that at least some of the observed effects were
caused by these other pain treatments. Yet, the NPT group
reported more days with pain, regardless of pain level, weakening
the argument that other pain treatments may have been
responsible for the observed effects. Also, service members
with more combat exposure during their deployments are at
higher risk for suicidal behavior. [39, 40]
While our analyses
accounted for covariates of combat exposure (e.g., TBI and
PTSD), we did not control for combat exposure itself. It is
possible that combat exposure was not balanced between the
NPTand No-NPT group, thus potentially affecting the observed
associations between NPT and suicide ideation, and self-inflicted
injuries in our study. As the prevalence of individual
NPTmodalities in the MHS varied widely, it is possible that our
results were driven by some NPT modalities more than others,
or the dose in which they were received, and that the protective
association of NPT may be more apparent for some chronic
pain conditions than for others. We were unable to control for
the severity of any of the mental health conditions that we
included as covariates, nor did we control for physical health
conditions associated with adverse outcomes (see, for example,
Ahmedani et al.),  although these aspects may have been
reflected in the number of hospitalizations and number of
inpatient days. Overall, we do not expect that an imbalance of
physical health conditions is likely to have affected our results,
as our cohort was relatively young (median age 26) and healthy
(they had been deployed to Iraq or Afghanistan).
Our results suggest that nonpharmacological treatments (NPT) provided to active duty service
members with chronic pain may reduce their odds of longterm
adverse outcomes. Given known associations of these
adverse outcomes with morbidity and mortality, providing
NPT to service members with chronic pain could potentially
save lives. Our results provide further support for the role of
NPT as a risk mitigation strategy when long-term opioid
therapy is initiated, which is only briefly mentioned in the
VA/DoD Clinical Practice Guideline for Opioid Therapy for Chronic Pain.  Given that our findings may have been driven
by some NPT modalities more than others, the dose in which
these modalities were received, or unmeasured confounding,
more research is needed to clarify these effects. As confounders
may change during NPT (e.g., daily dose of opioids),
it may be important to include time-varying covariates in
eFigure 1. Trends in Self-Reported Prediabetes by Age (Panel A),
Body Mass Index Category (Panel B), and
Race/Ethnicity Group (Panel C)
eTable 1. Demographic and Clinical Characteristics of U.S. Adults with
Self-Reported Prediabetes by Race/Ethnicity,
We thank Richard Gromadzki and Andrea
Linton with AXIOM Resource Management, Inc., who compiled the
DoD data used in these analyses, and the Defense Health Agency’s
Privacy and Civil Liberties Office, which provided access to the DoD
This research was supported by grants NIDA
R01DA030150 and NCCIH R01AT008404 from the National Institutes
of Health (M.J. Larson) and a Research Career Scientist Award
RCS-14-232 from the Veterans Health Administration Health Services
Research and Development Service (A.H. Harris).
Compliance with Ethical Standards:
Approval for this study was granted by the Brandeis University
Committee for Protection of Human Subjects, the Stanford University
and VA Palo Alto Health Care System Institutional Review Boards,
and the Human Research Protection Program at the Office of the
Assistant Secretary of Defense for Health Affairs/ Defense Health
Conflict of Interest:
The authors declare that they do not have a
conflict of interest.
Institute of Medicine.
Relieving Pain in America: A Blueprint for Transforming Prevention,
Care, Education, and Research
Washington, DC: The National Academies Press, 2011.
Spelman JF, Hunt SC, Seal KH, Burgo-Black AL.
Post deployment care for returning combat veterans.
J Gen Intern Med 2012;27(9):1200-9.
Seal KH, Bertenthal D, Barnes DE, Byers AL, Strigo I, Yaffe K, et al.
Association of Traumatic Brain Injury With Chronic Pain in Iraq and Afghanistan Veterans: Effect of Comorbid Mental Health Conditions.
Arch Phys Med Rehabil. 2017;98(8):1636-45. https://doi.org/10.1016/j.apmr.2017.03.026.
Helmer DA, Chandler HK, Quigley KS, Blatt M, Teichman R, Lange G.
Chronic widespread pain, mental health, and physical role function in OEF/OIF veterans.
Pain Med. 2009;10(7):1174-82. https://doi.org/10.1111/j.1526-4637.2009.00723.x.
Toblin RL, Quartana PJ, Riviere LA, Walper KC, Hoge CW.
Chronic pain and opioid use in US soldiers after combat deployment.
JAMA. 2014;174(8):1400-1. https://doi.org/10.1001/jamainternmed.2014.2726.
Crosby FE, Colestro J, Ventura MR, Graham K.
Survey of pain among veterans in Western New York.
Pain Manag Nurs. 2006;7(1):12-22. https://doi.org/10.1016/j.pmn.2005.12.001.
Kerns RD, Otis J, Rosenberg R, Reid MC.
Veterans’ reports of pain and associations with ratings of health, health-risk behaviors, affective distress, and use of the healthcare system.
J Rehabil Res Dev. 2003;40(5):371-9.
Chronic pain and suicide risk: A comprehensive review.
Prog. Neuropsychopharmacol. Biol Psychiatry. 2018;87(Pt B):269–4LL
Kaye AD, Jones MR, Kaye AM, Ripoll JG, Galan V, Beakley BD, et al.
Prescription Opioid Abuse in Chronic Pain: An Updated Review of Opioid Abuse Predictors and Strategies to Curb Opioid Abuse: Part 1.
Pain Physician. 2017;20(2S):S93-S109.
Paulozzi LJ, Zhang K, Jones CM, Mack KA.
Risk of adverse health outcomes with increasing duration and regularity of opioid therapy.
J Am Board Fam Med. 2014;27(3):329-38. https://doi.org/10.3122/jabfm.2014.03.130290.
Morasco BJ, O'Hearn D, Turk DC, Dobscha SK.
Associations between prescription opioid use and sleep impairment among veterans with chronic pain.
Pain Med. 2014;15(11):1902-10. https://doi.org/10.1111/pme.12472.
Seal KH, Shi Y, Cohen G, Cohen BE, Maguen S, Krebs EE, et al.
Association of mental health disorders with prescription opioids and high-risk opioid use in US veterans of Iraq and Afghanistan.
JAMA. 2012;307(9):940-7. https://doi.org/10.1001/jama.2012.234.
Barth KS, Guille C, McCauley J, Brady KT.
Targeting practitioners: A review of guidelines, training, and policy in pain management.
Drug Alcohol Depend. 2017;173 Suppl 1:S22-s30
Macey TA, Morasco BJ, Duckart JP, Dobscha SK.
Patterns and correlates of prescription opioid use in OEF/OIF veterans with chronic noncancer pain.
Pain Med. 2011;12(10):1502-9
The Opioid Therapy for Chronic Pain
Healthcare Inspection - VA Patterns of Dispensing Take-Home Opioids and Monitoring Patients on Opioid Therapy:
Department of Veterans Affairs - Office of Inspector General,, I
nspections OoH;2014 May 14, 2014.
Report No.: Report No. 14-00895-163.
U.S. Department of Veterans Affairs - Office of Inspector General
VA/DoD Clinical Practice Guideline for Opioid Therapy for Chronic Pain
U.S. Department of Veterans Affairs - Department of Defense.
Version 3.0 – 2017
Han L, Goulet JL, Skanderson M, Bathulapalli H, Luther SL, Kerns RD, et al.
Evaluation of Complementary and Integrative Health Approaches Among US Veterans
with Musculoskeletal Pain Using Propensity Score Methods.
Pain Med. 2018. https://doi.org/10. 1093/pm/pny027.
The Diagnosis and Treatment of Low Back Pain Work Group.
VA/DoD Clinical Practice Guideline for Diagnosis and Treatment of Low Back Pain
Washington, DC: The Office of Quality, Safety and Value, VA, &
Office of Evidence Based Practice, U.S. Army MedicalCommand, 2017, Version 2.0.
Buckenmaier CC, 3rd, Galloway KT, Polomano RC, Deuster PA.
Pain as a Barrier to Human Performance: A Focus on Function for Self-Reporting Pain With the Defense Veterans Pain Rating Scale
J Spec Oper Med. 2016;16(2):82-7.
National Center for Complementary and Integrative Health.
Complementary, Alternative, or Integrative Health: What’s In a Name?
Chou R, Deyo R, Friedly J, Skelly A, Hashimoto R, Weimer M, et al.
Nonpharmacologic Therapies for Low Back Pain: A Systematic Review for an
American College of Physicians Clinical Practice Guideline
Annals of Internal Medicine 2017 (Apr 4); 166 (7): 493–505
Chou R, Huffman LH; American Pain Society.
Nonpharmacologic Therapies for Acute and Chronic Low Back Pain:
A Review of the Evidence for an American Pain Society/
American College of Physicians Clinical Practice Guideline
Annals of Internal Medicine 2007 (Oct 2); 147 (7): 492–504
Larson MJ, Adams RS, Ritter GA, Linton A, Williams TV, Saadoun M, et al.
Associations of Early Treatments for Low-Back Pain with Military Readiness Outcomes.
J Altern Complement Med. 2018;24(7):666-76
Carey EP, Nolan C, Kerns RD, Ho PM, Frank JW.
Association Between Facility-Level Utilization of Non-pharmacologic Chronic Pain Treatment and Subsequent Initiation of Long-Term Opioid Therapy.
J Gen Intern Med. 2018;33(Suppl 1):38-45
Larson MJ, Adams RS, Mohr BA, Harris AH, Merrick EL, Funk W, et al.
Rationale and methods of the Substance Use and Psychological Injury Combat Study (SUPIC): a longitudinal study of Army service members returning from deployment in FY2008-2011.
Subst Use Misuse. 2013;48(10):863-79
Reif S, Adams RS, Ritter GA, Williams TV, Larson MJ.
Prevalence of Pain Diagnoses and Burden of Pain
Among Active Duty Soldiers, FY2012
Military Medicine 2018 (Sep 1); 183 (9-10): e330–e337
Dobscha SK, Morasco BJ, Kovas AE, Peters DM, Hart K, McFarland BH.
Short-term variability in outpatient pain intensity scores in a national sample of older veterans with chronic pain.
Pain Med 2015;16(5):855-65.
Tian TY, Zlateva I, Anderson DR.
Using electronic health records data to identify patients with chronic pain in a primary care setting.
J Am Med Inform Assoc 2013;20(e2):e275-80.
VannemanME, LarsonMJ, Chen C, Adams RS,Williams TV, Meerwijk E, et al.
Treatment of Low Back Pain With Opioids and Nonpharmacologic Treatment Modalities for Army Veterans.
Med Care. 2018.
Linden A, Adams JL.
Using propensity score-based weighting in the evaluation of health management programme effectiveness.
J Eval Clin Pract. 2010;16(1):175-9.
Austin PC, Stuart EA.
Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies.
Stat Med. 2015;34(28):3661-79.
Rich JT, Neely JG, Paniello RC, Voelker CC, Nussenbaum B, Wang EW.
A practical guide to understanding Kaplan-Meier curves.
Otolaryngol Head Neck Surg. 2010;143(3):331-6.
Ridgeway G, McCaffrey D, Morral A, Burgette L, Griffin BA.
Toolkit for Weighting and Analysis of Nonequivalent Groups: A tutorial for the twang package. 2017.
R Core Team. R:
A language and environment for statistical computing.
Vienna: R Foundation for Statistical Computing: 2018.
Anderson HD, Pace WD, Brandt E, Nielsen RD, Allen RR, Libby AM, et al.
Monitoring suicidal patients in primary care using electronic health records.
J Am Board Fam Med. 2015;28(1):65-71.
Hedegaard H, Schoenbaum M, Claassen C, Crosby A, Holland K, Proescholdbell S.
Issues in Developing a Surveillance Case Definition for Nonfatal Suicide Attempt and Intentional Self-harm Using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) Coded Data.
Natl Health Stat Rep. 2018(108):1-19.
Hoffmire C, Stephens B, Morley S, Thompson C, Kemp J, Bossarte RM. VA
Suicide Prevention Applications Network: A National Health Care System-Based Suicide Event Tracking System.
Public Health Rep. 2016;131(6):816-21.
Micek MA, Bradley KA, Braddock CH, 3rd, Maynard C, McDonell M, Fihn SD.
Complementary and alternative medicine use among Veterans Affairs outpatients.
J Altern Complement Med. 2007;13(2):190-3.
Dillon KH, CunninghamKC, Neal JM, Wilson SM, Dedert EA, Elbogen EB, et al.
Examination of the indirect effects of combat exposure on suicidal behavior in veterans.
J Affect Disord. 2018;235:407-13.
Cesur R, Chesney A, Sabia JJ.
Combat Exposure, Cigarette Consumption, and Substance Use.
Econ Inq. 2016;54(3):1705-26.
Ahmedani BK, Peterson EL, Hu Y, Rossom RC, Lynch F, Lu CY, et al.
Major Physical Health Conditions and Risk of Suicide.
Am J Prev Med. 2017;53(3):308-15.
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