J Gen Intern Med. 2018 (Sep); 33 (9): 1469–1477 ~ FULL TEXT
Charles Elder, MD MPH, Lynn DeBar, PhD MPH, Cheryl Ritenbaugh, PhD MPH, John Dickerson, PhD, William M. Vollmer, PhD, Richard A. Deyo, MD MPH, Eric S. Johnson, PhD, and Mitchell Haas, DC MA
Kaiser Permanente Center for Health Research,
Portland, OR, USA.
BACKGROUND: Chiropractic care is a popular alternative for back and neck pain, with efficacy comparable to usual care in randomized trials. However, the effectiveness of chiropractic care as delivered through conventional care settings remains largely unexplored.
OBJECTIVE: To evaluate the comparative effectiveness of usual care with or without chiropractic care for patients with chronic recurrent musculoskeletal back and neck pain.
STUDY DESIGN: Prospective cohort study using propensity score-matched controls.
PARTICIPANTS: Using retrospective electronic health record data, we developed a propensity score model predicting likelihood of chiropractic referral. Eligible patients with back or neck pain were then contacted upon referral for chiropractic care and enrolled in a prospective study. For each referred patient, two propensity score-matched non-referred patients were contacted and enrolled. We followed the participants prospectively for 6 months.
MAIN MEASURES: Main outcomes included pain severity, interference, and symptom bothersomeness. Secondary outcomes included expenditures for pain-related health care.
KEY RESULTS: Both groups' (N = 70 referred, 139 non-referred) pain scores improved significantly over the first 3 months, with less change between months 3 and 6. No significant between-group difference was observed. (severity – 0.10 (95% CI – 0.30, 0.10), interference – 0.07 (– 0.31, 0.16), bothersomeness – 0.1 (– 0.39, 0.19)). After controlling for variances in baseline costs, total costs during the 6–month post-enrollment follow-up were significantly higher on average in the non-referred versus referred group ($1,996 [SD = 3874] vs $1,086 [SD = 1212], p = .034). Adjusting for differences in age, gender, and Charlson comorbidity index attenuated this finding, which was no longer statistically significant (p = .072).
CONCLUSIONS: We found no statistically significant difference between the two groups in either patient-reported or economic outcomes. As clinical outcomes were similar, and the provision of chiropractic care did not increase costs, making chiropractic services available provided an additional viable option for patients who prefer this type of care, at no additional expense.
KEYWORDS: alternative medicine; back pain; chiropractic; chronic musculoskeletal pain; comparative effectiveness; complementary and integrative medicine; managed care; neck pain; primary care; propensity scoring; spinal manipulation
From the FULL TEXT Article:
Chronic musculoskeletal pain remains a substantial clinical
and public health challenge. [1, 2] In 2008, the total financial cost
of pain to society, including both health care costs and lost
productivity, was estimated at $560 to $635 billion.  Spinal
disorders are the fourth most common primary diagnosis for
office visits in the USA,  and are reported by over a third of
patients presenting with musculoskeletal complaints.  Conventional
management commonly includes nonsteroidal antiinflammatory
drugs, skeletal muscle relaxants, and opioids,
which are of modest benefit and are associated with serious
toxicities. [6, 7]
Chiropractic care is popular among patients, [8, 9] with efficacy
for treating back and neck pain comparable to usual care in
experimental randomized controlled trial (RCT) settings. [10–12]
However, the effectiveness of chiropractic care as actually
delivered in routine conventional and integrative medicine
practice remains largely unexplored. Rigorous evaluation of
such routine care is complicated by numerous methodological
challenges. In comparative effectiveness research of this type,
RCT study designs may be logistically difficult or even infeasible,
where the requirements for informed consent, the mechanics
of the randomization process, the protocols for
blinding, and other experimental constraints often directly
conflict with the flow of routine office care. Further, even if
feasible, imposing these research constraints is likely to alter
care as actually delivered in everyday clinical settings. Prospective
cohort studies provide a compelling alternative, but
introduce challenges in identifying an appropriate control
group that minimizes confounding or bias.
Propensity scores represent one viable approach to controlling
for confounding in observational studies. However,
propensity scores are typically applied in retrospective analyses
and, to our knowledge, have not been previously used
to recruit and match subjects on an ongoing basis in prospective
cohort studies requiring the collection of patient-reported
outcomes.  We sought to evaluate the comparative
effectiveness of usual care with or without chiropractic
care as provided to patients in an established health maintenance
organization (HMO), using novel and scientifically
rigorous methods. Specifically, we used data from retrospective
electronic health record (EHR) and administrative
databases to develop a propensity score model describing
the likelihood of a patient’s being referred for chiropractic
care,  and then implemented a prospective cohort study
comparing patients with chronic musculoskeletal pain who
were referred for chiropractic care with propensity score-matched
controls who were not.
The Relief project is a multi-phased study evaluating acupuncture
and chiropractic care for patients with chronic
musculoskeletal pain in an HMO setting. Full descriptions
of our study design and propensity score methodology have
been previously published. [13, 14] In brief, the project featured
two prospective cohort studies evaluating the effectiveness
of usual care with or without acupuncture, and usual care
with or without chiropractic care. This paper presents data
from the chiropractic study; results of the acupuncture study
will be published separately.
The study was conducted at Kaiser Permanente Northwest
(KPNW), an HMO serving approximately 550,000 members
in the metropolitan Portland area. KPNW provides
chiropractic care to patients through a contracted network
of chiropractors at Complementary Health Plans (CHP).
Most KPNW members are eligible for referral to a CHP
chiropractor by a KPNW clinician for a limited number of
visits. Over the period of this project, KPNW policy
allowed patient referral for chiropractic care in the setting
of acute (3 months or less) non-radicular back or neck
pain. Importantly, those with an acute exacerbation of a
chronic back or neck pain syndrome were eligible for
We developed a study-specific chronic pain registry
employing a comprehensive International Classification of
Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)
code list to identify patients whose pattern of clinical diagnoses
in the EHR suggested chronic musculoskeletal pain. 
(Appendix 1) The sample was operationally defined
as including members at least 18 years old with ≥ 3 outpatient
pain-related encounters evident in the EHR, spanning at least
180 days but no more than 18 months.
During the course of the study (August 1, 2013 to August
31, 2015), we used the EHR to electronically monitor the
routine care delivered at KPNW to registry patients. We
contacted and invited patients to enroll in the study immediately
upon chiropractic referral. As this was an observational
study, the decision to initiate chiropractic referral, or not, was
made between physician and patient in the context of the
delivery of routine care in the clinics. Exclusion criteria included
a baseline score of < 4 on the (0–10) scale for pain
bothersomeness, non-persistent pain, current or recent (last
6 months) chiropractic care, pregnancy, or plans to move out
of the area. Given both the study sample definition and the
KPNW medical necessity criteria for chiropractic care, most
eligible patients had chronic pain with acute exacerbations of
back or neck pain. The patients were screened and provided
written consent, and baseline measures were collected.
For each referred patient enrolled, we targeted enrollment of
two control patients with chronic musculoskeletal pain who
had a recent (previous 10 days) office visit for back or neck
pain but were not referred for chiropractic care. These patients
were likewise identified through EHR monitoring, contacted,
and invited to enroll.We matched patients in the control arm to
referred patients based upon gender, pain bothersomeness
(difference within ± 2 points), and propensity to be referred
for chiropractic care. The latter was based upon a propensity
score model developed using retrospective EHR data from
2010, and then validated on 2011 data, before implementation
for matching in the 2013 prospective cohort study. The rationale
andmethodology for development of the propensity score
model were identical to those for the Relief acupuncture cohort,
and are described elsewhere.13 Baseline predictive factors
ultimately included in the calculation of the propensity score were
age; opioid and pain medication use; number of outpatient visits;
physical therapy utilization; diagnoses of nonspecific chronic
pain, sleep disorders, substance abuse, and anxiety; tobacco
abuse; procedures for diagnosing and treating pain; ambulatory
Charlson score; andmonths since cohort entry (Appendix 2). Over the course of their study participation, both referred and
non-referred patients continued to receive usual care as deemed
appropriate by their primary care physicians.
We collected patient-reported measures at baseline and at
months 1, 3, and 6.
Main Outcome Measures.
Two subscales (the four-item
pain severity and the seven-item pain interference subscales)
from the short form of the Brief Pain Inventory
(BPI-SF) [15–17] were used to assess pain and related disability.
The BPI has sound psychometrics and has been
widely adopted. We also included a measure of how
bothered the participants were by their pain. This instrument
uses a 0 to 10 scale of Bsymptom bothersomeness,^
where 0 represents Bnot at all bothersome^ and 10 is
Bextremely bothersome.^ This question has been frequently
used in studies of back pain [18, 19] and shown to have
adequate construct validity. 
Secondary Outcome Measures, Clinical/Patient Reported.
Quality of sleep was measured using the five-item Insomnia
Severity Index (ISI) [21–23] We used the Personal Health Questionnaire
(PHQ-8)  to evaluate depression severity. The
PHQ-8 is established as a valid severity measure for depressive
disorders in large clinical studies. [24–26] We used the sevenitem
Generalized Anxiety Disorder Scale (GAD-7)  to screen
for anxiety disorders. Health-related quality of life (QOL) was
assessed using the five-level EuroQol instrument (EQ-5D). 
Secondary Outcome Measures, Health Care Costs. We
monitored the cost of pain-related outpatient visits (including
both conventional and chiropractic care), inpatient hospitalizations,
and drugs dispensed for the 6 months prior to
and following study entry. We estimated health plan costs
(in 2014 US dollars) by applying internal unit costs (developed
and tested in previous studies) [29–31] to patient-level
utilization measures, with the final cost variable acting as
a proxy for HMO resource cost.
On each of the assessment surveys, we asked
the participants to report any adverse events associated with
We collected patient-reported outcomes and adverse events
over the phone or online. (Appendix 3) 
We used EHR data to determine pain-related and comorbid
diagnoses, estimate pain-related health care costs, and calculate
Charlson comorbidity index scores.  We obtained patient data
for the 180 days before and after patient enrollment. The data
included ICD-9-CM diagnostic codes and Current Procedural
Terminology (CPT) codes for procedures and filled prescription
data associated with chronic pain.
Finally, we mailed a questionnaire to chiropractors who
cared for study participants to collect information on the
treatments they provided (session frequency, reason for referral,
and use of specific treatment approaches including joint
manipulative procedures, soft tissue manipulative procedures,
physical modalities, prescriptions/devices, rehabilitative exercises,
and self-care/lifestyle recommendations).
Sample size calculations demonstrated that 100 study participants
per group would yield statistical power of approximately
0.90 to detect standardized effect sizes of 0.50 or greater. 
Using an alpha of 0.025, and given the number of participants
actually enrolled, we have statistical power of 0.72 and 0.61 to
detect standardized effect sizes of 0.50 or greater for BPI pain
interference and severity, respectively. Using a 0.05 alpha, the
power calculations are 0.81 for pain interference and 0.71 for
pain severity, respectively.
For pain scores, our primary
analytic model was a piecewise continuous, segmented
regression model that allowed us to estimate shorter-term (first
3 months, which, given the benefit structure, was the expected
maximum duration of chiropractic treatment) and longer-term
(second 3 months) changes in pain scores. The model was fit
using the mixed procedure in Stata (version 13.1) in order to
account for clustering of observations within patients and
propensity score deciles, and adjusted for age, gender, and
baseline Charlson comorbidity index (dichotomized as 0,
Charlson = 0, and 1, Charlson = 1+). We included this parsimonious
set of additional control variables because they were
considered the factors most likely to confound the relationship
between chiropractic referrals and patient outcomes. Similar
models were used to analyze the secondary clinical outcomes.
We also conducted analyses using only the baseline and 6–
month data to assess the net impact of the chiropractic referrals
6 months after enrollment. Here, we used simple linear regression
models to predict 6–month outcomes as a function of
referral status, again adjusting for the baseline level of the
outcome measure and age, gender, and baseline Charlson
comorbidity index. Finally, we performed sensitivity analyses
which retained only referred patients who actually received
chiropractic care and non-referred patients who actually did
not receive such care.
We modeled health care costs using generalized
linear models with gamma specification and log link to
accommodate the distributional properties of cost data and
to avoid interpretation issues associated with backtransformation
from transformation models. Model-based
cost estimates were presented for the Btypical^ study participant
where relevant,  and models were adjusted for age,
gender, baseline Charlson comorbidity index, and baseline
Each analysis was performed on participants
with available data. Multilevel estimation procedures
allowed all participants to contribute to estimates if they
had at least one observation for primary and secondary
outcomes. Models estimating the net impact of the
chiropractic referrals 6 months after enrollment required
completion of the 6–month follow-up assessment. Costs
analyses extracted data from the EHR and thus required
continuous health plan enrollment during the 6–month follow-
up. Results using multiple imputation with chained
equations yielded similar results.
Among potential referred participants, 264 completed
screening, 94 screened eligible, and 70 consented and were
enrolled (Figure 1). The most common reason for screening
ineligibility was undocumented chiropractic care within the
preceding 6 months (N = 127). Among potential controls,
717 completed screening, 422 screened eligible, and 139
consented and were enrolled. The most common reasons for
screening ineligibility were similarly undocumented
chiropractic care within the preceding 6 months (N = 162)
and low baseline pain scores (N = 151).
Baseline patient characteristics
The participants were predominantly Caucasian (90.7%)
and female (66.0%), with a mean age of 48.0 years
(Table 1). Nearly all had back and/or neck pain. Despite
propensity score matching, the patients referred for chiropractic
care were less likely than non-referred patients
to be involved with litigation, to have depressive symptoms,
or to have received physical therapy, spinal injections,
or pain clinic specialty care. At 6 months, 89% of
the referred patients and 86% of the controls provided
Among the referred patients, the mean number of visits with
the chiropractor, based upon EHR data, was 4.0 (SD 4.4;
median = 3, IQR= 0–7).
Based upon both EHR data and participant self-report, 73%
of those referred for chiropractic care actually received such
care, while in the non-referred group, 16% sought and received
chiropractic care on their own.
Forty chiropractors returned questionnaire responses describing
the care for 43 referred patients. Regarding spinal
care, 80% of responses reported providing thrust adjustment,
51% segmental mobilization, 31% instrument adjustment, and
21% traction/distraction. For soft tissue manipulative procedures,
52% of responses reported using massage, 42% muscle
stretching, 39% point-pressure techniques, and 13% the
Graston technique (a method using metal instruments to rub
patient muscles). Regarding physical modalities, 60% reported
electrical stimulation, 60% hot/cold packs, and 24% ultrasound.
For home care, 81% of the responses recommended
stretching, 41% core stabilization, 33% resistance strengthening,
11% McKenzie exercises, and 11% proprioceptive drills.
Regarding lifestyle recommendations, 61% provided exercise
handouts, 33% advised stress reduction, 24% coached regarding
injury prevention, and 12%provided dietary and nutritional
For bothersomeness, pain
severity, and pain interference, both groups improved
significantly over the first 3 months, with much less change
between months 3 and 6. None of these changes differed
significantly between the referred and non-referred groups in
either adjusted (Table 2), unadjusted, or sensitivity analyses.
Table 3 presents mean pain scores at the 6–month visit. Although
these tended to be lower for the referred than for the
non-referred groups, again none of the differences were statistically
We likewise found no significant difference between groups
for any secondary clinical outcome measure (Table 2). Similarly,
there was no significant difference between groups for
any of these outcome measures in terms of the percentage of
patients showing clinically significant improvement at
As shown in Tables 4 and 5, total costs
during the 6–month post-enrollment follow-up were significantly
higher on average in the non-referred versus referred
group ($1,996 [SD = 3,874] vs $1,086 [SD = 1,212], p = .034)
after controlling for differences in baseline costs. Sensitivity
analyses demonstrated similar results during the 6–month
follow-up, with non-referred patients having higher costs
than referred patients ($2,272 [SD = 4,545] vs $819 [SD =
882], p = .020). However, adjusting for differences in age,
gender, and Charlson comorbidity index attenuated these
differences, which were no longer statistically significant.
A total of 20 participants reported an adverse event: 14, or
10%, of the participants from the non-referred group and six,
or 8.5%, of those referred for chiropractic care. Among the
non-referred patients, four (3%) reported an adverse event
attributable to medications, four (3%) to physical activity,
one (< 1%) to acupuncture treatments, and five (4%) to other
factors. Among those referred for chiropractic care, three (4%)
reported an adverse event attributable to physical activity and
three (4%) potentially to chiropractic care. Of these, one
participant noted at 6–month follow-up that although neck pain
improved with chiropractic care, hip pain worsened. A second
participant indicated worsening pain at 3–month follow-up,
but expressed uncertainty as to whether this was attributable
to chiropractic treatments or to their discontinuation. A third
participant specified chiropractic care as a cause of worsening
symptoms at 1–month follow-up, without further details.
No serious study-related adverse events were reported.
In this prospective cohort study, the patients referred for chiropractic
care showed statistically significant improvement comparable
to the propensity score-matched non-referred patients
for clinical pain-related outcomes. After adjusting for differences
in baseline costs, total pain-related health care expenditures
during the 6–month post-enrollment follow-up were significantly
higher on average in the non-referred versus referred
group. Although this result persisted in a sensitivity analysis,
and is consistent with previously published retrospective analyses,  it did not maintain statistical significance after adjustment
for differences in age, gender, and Charlson comorbidity
index. As clinical outcomes were generally similar, however,
and the provision of chiropractic care clearly did not increase
costs, chiropractic care may have to some degree substituted for
conventional care, rather than just adding to it. Chiropractic care
thus provided an additional clinically viable option for patients
who prefer this type of care, at no additional expense.
There were no serious adverse events associated with chiropractic
care. Chiropractors reported considerable heterogeneity
in the types of modalities offered to patients, and chiropractic
care commonly included a substantial component of
Strengths of this study include the prospective cohort design,
incorporating patient-reported outcomes, and employing a novel
approach to matching referred patients with controls at baseline
using propensity score modeling. The study setting provided
a unique opportunity to describe and evaluate chiropractic
benefits as actually provided in a conventional HMO setting.
Study limitations include a relatively small sample size, as
we did not achieve our targeted enrollment. This was largely
due to many patients having recently used chiropractic care
undetected by the EHR, thus rendering them ineligible for the
study. Excluding such individuals from the study may have
removed those most likely to use chiropractic care routinely
from consideration, thereby limiting generalizability of our
findings. Further, our analyses do not distinguish between
those with neck pain and back pain, which may likewise limit
generalizability. In addition, not everyone who was referred
actually sought chiropractic care, while some participants in
the non-referred group did receive such care. This would
generally blunt real differences that might exist, likely biasing
our results toward the null.
Even so, the study provides important insight. We found
that referred and non-referred participants had comparable
clinical outcomes and that chiropractic referral neither added
to health care costs nor introduced significant safety concerns.
Data suggest that although two thirds of primary care physicians
have recommended chiropractic care to their patients, 
lack of communication remains a major barrier to care coordination. [37, 38] Better integration of chiropractors into conventional
care spine management algorithms could represent a
sensible approach to enhancing patient-centered care for patients
with chronic musculoskeletal pain. Finally, the project
establishes the feasibility of a methodologic alternative for
prospectively evaluating the comparative effectiveness of clinical
interventions in routine settings for chronic musculoskeletal
Sources of Funding:
This project was funded by a grant from the
National Institutes of Health, Center for Complementary and Integrative
Health (R01 AT005896).
An earlier version of these data were presented as part of a poster
presentation at the International Congress on Integrative Medicine and
Health in Las Vegas, in 2016.
Compliance with Ethical Standards:
The Institutional Review Board at Kaiser Permanente Northwest
approved all study procedures.
Conflict of Interest:
Dr. Deyo reports royalties from UpToDate for
authoring topics on low back pain, an endowment from Kaiser
Permanente to Oregon Health and Science University, and a financial
gift from NuVasive as part of a lifetime achievement award from the
International Society for Study of the Lumbar Spine. For the remaining
authors, no conflicts of interest were declared.
Hoy, D., L. March, P. Brooks, F. Blyth, A. Woolf, et al.
The Global Burden of Low Back Pain: Estimates from the Global Burden of Disease 2010 study
Ann Rheum Dis. 2014 (Jun); 73 (6): 968–974
Hoy D, March L, Woolf A, et al.
The Global Burden of Neck Pain: Estimates From the Global Burden of Disease 2010 Study
Ann Rheum Dis. 2014 (Jul); 73 (7): 1309–1315
Institute of Medicine.
Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research
Washington, DC: The National Academies Press, 2011.
Centers for Disease Control and Prevention.
National Ambulatory Medical Care Survey: 2010 Summary Tables.
Wiitavaara B, Fahlstrom M, Djupsjobacka M.
Prevalence, diagnostics and management of musculoskeletal disorders in primary health care in
Sweden—an investigation of 2000 randomly selected patient records.
J Eval Clin Pract 2017;23(2):325-332.
Beebe FA, Barkin RL, Barkin S.
A clinical and pharmacologic review of skeletal muscle relaxants for musculoskeletal conditions.
Am J Ther 2005;12(2):151-171.
Von Korff M, Kolodny A, Deyo RA, Chou R.
Long-term opioid therapy reconsidered.
Ann Intern Med 2011;155(5):325-328.
Barnes, PM and Bloom, BS.
Complementary and Alternative Medicine Use Among Adults and Children: United States, 2007
US Department of Health and Human Services,
Centers for Disease Control and Prevention,
National Center for Health Statistics, Hyattsville, MD, 2008.
Elder C, DeBar L, Ritenbaugh C, et al.
Acupuncture and Chiropractic Care: Utilization and Electronic Medical Record Capture
American J Managed Care 2015 (Jul 1); 21 (7): e414–421
Bronfort G, Haas M, Evans R, Leininger B, Triano J.
Effectiveness of Manual Therapies: The UK Evidence Report
Chiropractic & Osteopathy 2010 (Feb 25); 18 (1): 3
The Role of Spinal Manipulation in the Treatment of Low Back Pain
JAMA. 2017 (Apr 11); 317 (14): 1418–1419
Paige NM, Myiake-Lye IM, Booth MS, et al.
Association of Spinal Manipulative Therapy With Clinical Benefit and Harm
for Acute Low Back Pain: Systematic Review and Meta-analysis
JAMA. 2017 (Apr 11); 317 (14): 1451–1460
Johnson ES, Dickerson JF, Vollmer WM, et al.
The feasibility of matching on a propensity score for acupuncture in a prospective cohort study of patients with chronic pain.
BMC Med Res Methodol 2017;17(1):42.
DeBar LL, Elder C, Ritenbaugh C, et al.
Acupuncture and chiropractic care for chronic pain in an integrated health plan: a mixed methods study.
BMC Complement Altern Med 2011;11:118.
Cleeland CS, Ryan KM.
Pain assessment: global use of the Brief Pain Inventory.
Ann Acad Med Singap 1994;23(2):129-138.
Keller S, Bann CM, Dodd SL, Schein J, Mendoza TR, Cleeland CS.
Validity of the brief pain inventory for use in documenting the outcomes of patients with noncancer pain.
Clin J Pain 2004;20(5):309-318.
Tan G, Jensen MP, Thornby JI, Shanti BF.
Validation of the brief pain inventory for chronic nonmalignant pain.
J Pain 2004;5(2):133-137.
Cherkin, DC, Deyo, RA, Battie, M, Street, J, and Barlow, W.
A Comparison of Physical Therapy, Chiropractic Manipulation, and Provision of an Educational Booklet
for the Treatment of Patients with Low Back Pain
New England Journal of Medicine 1998 (Oct 8); 339 (15): 1021-1029
Patrick DL, Deyo RA, Atlas SJ, Singer DE, Chapin A, Keller RB.
Assessing health-related quality of life in patients with sciatica.
Spine 1995;20(17):1899-1908; discussion 1909.
Dunn KM. Croft PR.
Classification of Low Back Pain in Primary Care: Using "Bothersomeness" to Identify the Most Severe Cases
Spine (Phila Pa 1976). 2005 (Aug 15); 30 (16): 1887–1892
Bastien CH, Vallieres A, Morin CM.
Validation of the Insomnia Severity Index as an outcome measure for insomnia research.
Sleep Med 2001;2(4):297-307.
Morin CM, Belleville G, Belanger L, Ivers H.
The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response.
Thorndike FP, Ritterband LM, Saylor DK, Magee JC, Gonder-Frederick LA.
Validation of the insomnia severity index as a web-based measure.
Behav Sleep Med 2011;9(4):216-223.
Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH.
The PHQ-8 as a measure of current depression in the general population.
J Affect Disord 2009;114(1-3):163-173.
Lowe B, Unutzer J, Callahan CM, Perkins AJ, Kroenke K.
Monitoring depression treatment outcomes with the patient health questionnaire-9.
Med Care 2004;42(12):1194-1201.
Lowe B, Kroenke K, Herzog W, Grafe K.
Measuring depression outcome with a brief self-report instrument: sensitivity to change of the Patient
Health Questionnaire (PHQ-9).
J Affect Disord 2004;81(1):61-66.
Kroenke K, Spitzer RL, Williams JB, Monahan PO, Lowe B.
Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection.
Ann Intern Med 2007;146(5):317-325.
EuroQol: the current state of play.
Health Policy 1996;37(1):53-72.
Hornbrook MC, Goodman MJ.
Chronic disease, functional health status, and demographics: a multi-dimensional approach to risk adjustment.
Health Serv Res 1996;31(3):283-307.
Lynch FL, Hornbrook M, Clarke GN, et al.
Cost-effectiveness of an intervention to prevent depression in at-risk teens.
Arch Gen Psychiatry 2005;62(11):1241-1248.
Smith DH, O'Keeffe-Rosetti M, Owen-Smith AA, et al.
Improving adherence to cardiovascular therapies: an economic evaluation of a randomized pragmatic trial.
Value Health 2016;19(2):176-184.
Rutherford C, Costa D, Mercieca-Bebber R, Rice H, Gabb L, King M.
Mode of administration does not cause bias in patient-reported outcome results: a meta-analysis.
Qual Life Res 2016;25(3):559-574.
Charlson ME, Pompei P, Ales KL, MacKenzie CR.
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
J Chronic Dis 1987;40(5):373-383.
Mihaylova B, Briggs A, O'Hagan A, Thompson SG.
Review of statistical methods for analysing healthcare resources and costs.
Health Econ 2011;20(8):897-916.
Martin BI, Gerkovich MM, Deyo RA, et al.
The Association of Complementary and Alternative Medicine Use
Health Care Expenditures for Back and Neck Problems
Med Care. 2012 (Dec); 50 (12): 1029–1036
Greene BR, Smith M, Allareddy V, Haas M.
Referral patterns and attitudes of primary care physicians towards chiropractors.
BMC Complement Altern Med 2006;6:5.
Allareddy V, Greene BR, Smith M, Haas M, Liao J.
Facilitators and barriers to improving interprofessional referral relationships between primary care physicians and chiropractors.
J Ambul Care Manage 2007;30(4):347-354.
Penney LS, Ritenbaugh C, Elder C, Schneider J, Deyo RA, DeBar LL.
Primary Care Physicians, Acupuncture and Chiropractic Clinicians,
Pain Patients: A Qualitative Analysis of Communication and Care Coordination Patterns
BMC Complement Altern Med. 2016 (Jan 25); 16: 30
Return to the COST-EFFECTIVENESS Page
Return to the CHRONIC NECK PAIN AND CHIROPRACTIC Page