Cost burden of chronic pain in an integrated delivery system in the US.

1. ORIGINAL ARTICLE
Cost Burden of Chronic Pain Patients in a Large
Integrated Delivery System in the United States
Peter W. Park, PhD*; Richard D. Dryer, MD†
; Rozelle Hegeman-Dingle, PharmD*;
Jack Mardekian, PhD*; Gergana Zlateva, PhD*; Greg G. Wolff, MPH‡
;
Lois E. Lamerato, PhD‡
*Pfizer Inc., New York, New York ; †
Department of Internal Medicine, Henry Ford Health
System, Detroit, Michigan ; ‡
Department of Public Health Sciences, Henry Ford Health System,
Detroit, Michigan, U.S.A.
& Abstract
Objectives: To estimate all-cause healthcare resource
utilization and costs among chronic pain patients within an
integrated healthcare delivery system in the United States.
Methods: Electronic medical records and health claims data
from the Henry Ford Health System were used to determine
healthcare resource utilization and costs for patients with 24
chronic pain conditions. Patients were identified by ≥ 2 ICD-
9-CM codes ≥ 30 days apart from January to December, 2010;
the first ICD-9 code was the index event. Continuous cover-
age for 12 months pre- and postindex was required. All-cause
direct medical costs were determined from billing data.
Results: A total of 12,165 patients were identified for the
analysis. After pharmacy, the most used resource was outpa-
tient visits, with a mean of 18.8 (SD 13.2) visits per patient for
the postindex period; specialty visits accounted for 59.0% of
outpatient visits. Imaging was utilized with a mean of 5.2
(SD 5.5) discrete tests per patient, and opioids were the most
commonly prescribed medication (38.7%). Annual direct
total costs for all conditions were $386 million ($31,692 per
patient; a 40% increase from the pre-index). Pharmacy costs
comprised 14.3% of total costs, and outpatient visits were the
primary cost driver.
Conclusions: Chronic pain conditions impose a substantial
burden on the healthcare system, with musculoskeletal
conditions associated with the highest overall costs. Costs
appeared to be primarily related to use of outpatient
services. This type of research supports integrated delivery
systems as a source for assessing opportunities to improve
patient outcomes and lower the costs for chronic pain
patients. &
Key Words: healthcare costs, musculoskeletal conditions,
chronic pain, outpatient services, resource utilization,
economic burden
INTRODUCTION
Chronic pain, which often results from or is associated
with a variety of disease states, remains one of the most
prevalent and burdensome medical conditions. Esti-
mates suggest that approximately 100 million United
States adults suffer from chronic pain,1
almost one-third
of the population, and in the EU, the prevalence estimate
is similar, 27%.2
The burden of chronic pain is substantial and is
manifested in patients by reductions in function and
quality of life,3,4
with an economic impact that results
from direct costs associated with medical care as well as
indirect costs related to lost productivity.1,5
The annual
Address correspondence and reprint requests to: Peter W. Park, PhD,
Pfizer Inc., 235 East 42nd Street, New York, NY 10017, U.S.A. E-mail:
peter.park@pfizer.com.
Disclosures: Peter W. Park, Rozelle Hegeman-Dingle, Jack Mardekian,
and Gergana Zlateva are employees of Pfizer Inc., the sponsor of this study.
Richard D. Dryer, Greg G. Wolff, and Lois E. Lamerato are employees of the
Henry Ford Health System and were not financially compensated for their
collaboration on this project or for the development of this article.
Submitted: February 12, 2015; Revision accepted: June 27, 2015
DOI. 10.1111/papr.12357
© 2015 World Institute of Pain, 1530-7085/15/$15.00
Pain Practice, Volume , Issue , 2015 –

2. cost of chronic pain in the United States was estimated to
be in the range of $560 to $635 billion for calendar year
2010 based on a report by the Institute of Medicine.5
Whileindirectcostshavebeensuggestedtobetheprimary
driver of costs in chronic pain,1,5–7
direct medical costs
impact the healthcare system and their characterization is
importantindeterminingwhatdrivesthesecostsandhow
they can be reduced. The Institute of Medicine report
utilized population health data from the National Health
Interview Survey, an ongoing, cross-sectional household
interview survey of approximately 35,000 U.S. house-
holds and the National Health and Nutrition Examina-
tion Survey, which collects data through in-person
interviews and physical examinations of a representative
sample of about 5,000 Americans annually.5
While individual studies have estimated the direct
medical costs for a variety of specific chronic pain
conditions, to the best of our knowledge, there are no
cost studies of a comprehensive set of chronic pain
conditions from large, single integrated healthcare
systems. The Henry Ford Health System (HFHS) pro-
vided an opportunity to use its available administrative
data to evaluate the economic burden among insured
patients with such conditions within a large integrated
healthcare delivery system in the U.S.A. Therefore, the
objectives of this analysis were to estimate the annual
total and per-patient all-cause medical costs for patients
with each condition, including identification of pain
conditions that are significant cost drivers, and to
provide initial characterization of variables that may
contribute to these costs.
METHODS
Data Source
The data source for this retrospective, observational
study was electronic medical record (EMR) and health
claims data from the HFHS, a vertically integrated
healthcare delivery system that provides clinical services
in the metropolitan Detroit region. Specifically, data
were from patients enrolled in the Health Alliance Plan
(HAP), a subsidiary of the HFHS that has an enrolled
population of approximately 500,000 members; most
are commercially insured, and of the HAP members
covered by Medicare, coverage is only by Medicare
Advantage. Clinical and resource utilization data for all
care within the network are contained within the health
system’s integrated administrative databases, which also
contain information on claims external to the HFHS.
The EMR system is searchable for each patient. All data
are quality-controlled,
de-identified, and fully compliant with the Health
Insurance Portability and Accountability Act; the study
was approved by the HFHS Institutional Review Board.
Population
Identification of patients has been previously described as
part of an epidemiologic evaluation of chronic pain,8
but
in brief, patients were identified based on the presence of
≥ 2 ICD-9-CM codes at least 30 days apart for the
conditions of interest during the study period of January
1, 2010 to December 31, 2010. Choice of conditions was
derived from a study by Davis et al.9
supplemented by
conditions additionally identified as being of interest by
the investigators, for a total of 24 chronic pain condi-
tions. The first date of the ICD-9 code was the index date,
and patients were required to have continuous HAP
coverage for 12 months pre- and postindex.
Outcomes and Analyses
Data on all-cause resource utilization were obtained for
the 12-month pre- and postindex periods for all chronic
pain conditions identified in a chronic pain prevalence
study except for postherpetic pain, cancer, muscular
dystrophies, and spinal cord pain, as few patients with
these conditions were identified.10
Resource categories
include emergency room visits; outpatient office visits
stratified by primary care, specialty care, other, and
total; hospital admissions and length of stay; imaging;
and prescriptions, also stratified by pain medication
usage. All-cause direct medical costs were determined
from billing data, which reflect the charged or requested
amount that is billed by the provider and was used as a
proxy for actual costs. Charges were aggregated for the
pre- and postindex periods and stratified by external
claims, HFHS charges, and prescriptions.
As imaging may be a component of managing several
chronic pain conditions in addition to those associated
with musculoskeletal pain, utilization of imaging tech-
niques was evaluated separately. Imaging was catego-
rized as X-rays, computed tomography scans, magnetic
resonance imaging, and other imaging techniques that
included ultrasound, fluoroscopy, and other special
techniques; a unit represents a discrete imaging test.
To explore the relationships between total costs and
other variables, postindex utilization was stratified by
cost quartiles of $8,000, $8,000 to $16,000, $16,000
2 PARK ET AL.

3. to $35,000 and $35,000. The quartile range of values
was based on arithmetic stratification of the overall
range into four even groups. Relationships that were
evaluated in this manner included demographic and
clinical factors such as age, BMI, Charlson comorbidi-
ties, and resource use. The Charlson Comorbidity Index
is a popular method of predicting mortality by classifi-
cation of a patient population’s comorbid conditions. It
is extensively used in outcomes research as a measure of
disease burden. Published studies in various patient
populations consistently show that the Charlson index is
a valid prognostic indicator for mortality.11
Summary statistics, means and standard deviations for
continuous measures or frequencies and percentages for
categorical variables, were used to characterize resource
utilization and costs. Paired t-tests were used to compare
pre- and postindex outcomes to determine year-to-year
changes in cost burden. Per-patient and total direct
medical costs were determined for each condition, with
the total costs calculated based on the cost of the
condition multiplied by the condition prevalence as
previously reported.10
The overall burden was estimated
by multiplying the number of unique patients by the
mean per-patient cost for all the conditions.
All analyses were performed using SAS version 9.2
(SAS Institute Inc., Cary, NC, USA) and SPSS version 19
(SPSS, Inc. IBM Corporation, Chicago, IL); a P-value
0.05 was considered statistically significant, and
Bonferroni correction was applied to adjust for multiple
comparisons.
RESULTS
Patients
Table 1 presents the sample attrition and shows that a
total of 13,082 patients were included in the study cohort
identifiedinthepreviouslyreportedprevalenceanalysis.10
This number was slightly less than the 14,784 patients
with chronic pain conditions identified based only on the
≥ 2 ICD-9 codes ≥ 30 days apart and that reflected an
11.6%prevalenceofchronicpainconditions.Asreported
in that study, musculoskeletal disorders were the most
common chronic pain conditions, comprising 75% of all
pain diagnoses, and 23% of patients had more than one
chronic pain condition. While demographic characteris-
tics varied among the pain conditions, often reflecting the
type of condition, patients were generally older, female,
and tended toward obesity. Of the patients in the
prevalence study, 917 had HIV, stroke, or cancer and
were excluded from the current healthcare resource
utilizationanalysis,astheserepresenthighcostconditions
and may skew the estimate of costs, which were based on
all-causeresourceuse.Thus,thisanalysisincluded12,165
unique patients (Table 1).
Healthcare Resource Utilization
As shown in Table 2, there was substantial resource
utilization during both the pre- and postindex periods,
with significantly higher postindex utilization across all
resource categories (P 0.001). All-cause pharmacy use
was the most heavily used resource category, with a
mean (SD) of 29.7 (28.2) prescriptions per patient in the
pre-index period and 32.6 (28.0) in the postindex period
(P 0.001), followed by outpatient visits with means
(SD) of 14.2 (13.0) and 18.8 (13.2) visits per patient in
the pre- and postindex periods, respectively (P 0.001).
Specialty visits accounted for the majority of outpatient
visits during both evaluation periods, 56.3% and
59.0%, respectively.
Imaging appeared to be heavily utilized during both
periods, and use was significantly greater during the
postindex period, overall as well as for each of the
individual imaging tests (Table 2). Among the tests,
X-ray imaging was the technique used most frequently,
accounting for more than half the tests pre- (51.6%) and
postindex (55.4%). The chronic pain conditions with
the highest units of imaging tests per patient postindex
were abdominal pain (6.8 [SD 7.4]), osteoarthritis (6.7
[SD 5.9], cervical radiculopathy (6.2 [SD 5.8]), neural-
gia (6.2 [SD 8.7]), and joint pain (6.1 [SD 5.7]), with
multiple sclerosis associated with the least imaging (3.8
[SD 5.2]) (data not shown).
When medication utilization was stratified by drug
class (Table 3), opioids were observed to be the most
Table 1. Sample Attrition
Attrition Criterion N %
Total number of adult patients in
the HFHS Health Alliance Plan
127,317 100
Total number of adult patients in
HAP with ≥ 2 ICD-9-CM codes for
pain conditions ≥ 30 days apart*
14,784 11.6
Not continuously enrolled
in the HFHS Health Alliance Plan
for ≥ 12 months pre- and postindex
date†
(exclusion criteria)
1,702 1.3
Total number of patients in the prevalence study 13,082 10.3
HIV, stroke, or cancer (exclusion criteria) 917 0.7
Total number of patients in utilization/cost analysis 12,165 9.6
HAP, Health Alliance Plan; HFHS, Henry Ford Health System.
*Had to be the same condition from among any of the conditions of interest.
†
Index date is the first date of service (ICD-9-CM code) for the pain condition.
Chronic Pain Cost Burden in the United States 3

4. commonly prescribed pain-related medication, and
antihypertensives were the most frequently prescribed
medication overall. Among the pain-related medica-
tions, significant differences (P 0.05) in use were
observed postindex relative to the pre-index period
for all medications except antidepressants and topical
agents approved for neuropathic pain (Table 3).
While the differences showed an increase for opioids
(to 3.01 from 2.73; P 0.001) and anticonvulsants
(to 1.55 from 1.36; P 0.001), there was a decrease
in use of COX-2 inhibitors and nonselective NSAIDs
(P 0.05) (Table 3). On a proportional basis, opioids
accounted for more than one-third of pain-related
medication prescriptions in both the pre- (37.0%) and
postindex (38.7%) periods (data not shown). In the
postindex period among the individual conditions,
opioids as a proportion of pain medication prescrip-
tions ranged from 11.8% (multiple sclerosis) to
46.9% (arthropathy) and were the most frequently
prescribed medication class across conditions except
for headache, migraine, multiple sclerosis, and neu-
ralgia, for which anticonvulsants were most fre-
quently prescribed (data not shown); opioids and
anticonvulsants were prescribed with a similar fre-
quency in diabetic neuropathy, 32.1% and 32.3%,
respectively.
Healthcare Resource Costs
Per-patient annual healthcare resource utilization costs
during the pre-index period were $22,639 and increased
to $31,692 during the postindex period (P 0.001)
(Figure 1). Pharmacy costs were significantly increased,
by 11.0%, during the postindex relative to the pre-index
period (P 0.001). However, pharmacy costs com-
prised only 17.9% and 14.3% of the total costs during
the pre- and postindex periods, respectively (Figure 1).
Costs of medications by class showed no significant
differences between the pre- and postindex periods
except for hypoglycemic agents, which significantly
increased from $174 (SD ($853) per patient to $381
(SD $1673) per patient despite the slight decrease in use
of these medications (Table 3). Antihypertensives,
hypoglycemic agents, and statins were the drug classes
associated with the highest per-patient costs, and among
the pain-related medications, anticonvulsants and opi-
oids had the highest costs (Table 3).
The conditions with the highest total costs were
related to musculoskeletal pain (Table 4) and included
osteoarthritis ($100,772,576), back pain
($95,775,664), limb pain ($86,404,781), and joint pain
($82,615,062), all of which had costs that were
approximately twice as high as the next most costly
condition, abdominal pain ($45,420,627). The chronic
pain conditions with the highest mean per-patient costs
were multiple sclerosis ($54,061 [SD $54,449]), diabetic
neuropathy ($49,107 [SD $76,536]), and neuropathy
($44,908 [SD $58,320]).
Cost Relationships
Evaluation of cost relationships, stratified by cost
quartiles for the total population, suggested that all-
cause direct medical costs were associated with several
Table 2. Healthcare Resource Utilization Among Patients with Chronic Pain Conditions During the 12-month Pre- and
Postindex Periods (N = 12,165)
Resource Category
Units of Resource
P-value
post- vs.
pre-index
Pre-index Postindex
Total Per Patient, Mean (SD) Total Per Patient, Mean (SD)
Emergency department 16,100 1.3 (3.1) 18,949 1.6 (3.8) 0.001
Outpatient visits
All 172,103 14.2 (13.0) 228,188 18.8 (13.2) 0.001
Primary care 59,271 4.9 (4.8) 74,628 6.1 (5.5) 0.001
Specialty 97,225 8.0 (9.0) 135,428 11.1 (10.0) 0.001
Other 15,607 1.3 (3.8) 18,132 1.5 (3.3) 0.001
Hospitalizations
Admissions 2,148 0.2 (0.6) 3,369 0.3 (0.7) 0.001
Inpatient days 9,334 0.8 (3.7) 13,975 1.2 (4.2) 0.001
Pharmacy 360,768 29.7 (28.2) 396,291 32.6 (28.0) 0.001
Imaging 46,873 3.9 (4.8) 62,931 5.2 (5.5) 0.001
X-ray 24,163 2.0 (3.1) 34,856 2.9 (3.6) 0.001
Computed tomography 6,711 0.6 (1.3) 9,099 0.8 (1.7) 0.001
Magnetic resonance 3,447 0.3 (0.7) 5,416 (8.6) 0.5 (0.9) 0.001
Other 12,552 1.0 (1.5) 13,560 1.1 (1.6) 0.001
4 PARK ET AL.

5. demographic (Figure 2) and resource variables (Fig-
ure 3), including a shift toward a greater proportion of
patients with a higher number of Charlson comorbidi-
ties across the quartiles (Figure 2A). In particular, there
was a trend toward increasing comorbid conditions in
Quartile 4, which was characterized by the highest
proportion of patients with 2 (35%) and ≥ 3 (56%)
comorbid conditions. In contrast, Quartile 1 showed the
reverse trend, having the highest proportion of patients
with no Charlson comorbidities and decreasing propor-
tions at each subsequent level of comorbidity.
Although all age categories were represented by
similar proportions in quartiles 2 and 3, the age
distribution in quartiles 1 and 4 suggested an inverse
relationship between age and costs (Figure 2B). This
relationship was indicated by higher proportions of the
younger age groups in Quartile 1 relative to Quartile 4,
whereas Quartile 4 had progressively higher proportions
of patients as the age category increased.
Table 3. Prescription Medication
Utilization Among Patients with
Chronic Pain Conditions During
the 12-month Pre- and Postindex
Periods (N = 12,165)
Medication Class
Fills Per Patient, Mean (SD) Cost Per Patient, $, Mean (SD)
Pre-index Postindex
P-value
post- vs.
pre-index Pre-index Postindex
P-value
post- vs.
pre-index
Pain-related
Opioids 2.73 (5.26) 3.01 (5.00) 0.001 98 (982) 207 (2253) 0.1760
COX-2 inhibitors 0.08 (0.87) 0.07 (0.72) 0.0280 7 (71) 12 (127) 0.8200
Nonselective NSAIDs 1.26 (2.40) 1.21 (2.25) 0.0010 51 (917) 79 (1135) 0.2230
Antidepressants 1.63 (4.27) 1.62 (3.97) 0.6890 81 (1156) 150 (1000) 0.5610
Anticonvulsants 1.36 (3.95) 1.55 (3.94) 0.001 113 (2661) 218 (1831) 0.8910
Miscellaneous
analgesics*
0.16 (1.13) 0.17 (1.09) 0.0060 16 (174) 34 (321) 0.3780
Topical agents
approved for
neuropathic pain
0.15 (0.80) 0.15 (0.78) 0.8930 22 (185) 50 (571) 0.2530
Other medications
Topical corticosteroids 0.41 (1.32) 0.37 (1.20) 0.001 23 (413) 51 (712) 0.6540
Corticosteroids 0.95 (2.36) 0.98 (2.30) 0.0200 29 (271) 65 (453) 0.0950
Benzodiazepines 0.69 (2.58) 0.73 (2.49) 0.0010 14 (383) 19 (102) 0.1990
Sedative/hypnotics 0.12 (0.96) 0.11 (0.85) 0.0250 3 (40) 29 (1848) 0.1540
Muscle relaxants 0.57 (1.99) 0.60 (1.96) 0.0720 29 (1731) 77 (4290) 0.0620
Anxiolytics 1.39 (3.58) 1.36 (3.39) 0.1720 74 (1152) 136 (903) 0.5240
Sleep medications 0.40 (1.92) 0.41 (1.85) 0.6050 15 (156) 34 (674) 0.4100
Hypoglycemic agents 2.18 (5.98) 2.07 (5.47) 0.001 174 (853) 381 (1673) 0.0110
Antihypertensives 5.86 (7.93) 5.85 (7.54) 0.9810 234 (1115) 503 (3873) 0.2470
Statins 1.54 (2.53) 1.55 (2.38) 0.5140 147 (2470) 340 (6263) 0.3240
*Includes acetaminophen, butorphanol, nalbuphine, pentazocine.
$0
$5,000
$10,000
$15,000
$20,000
$25,000
$30,000
$35,000
xedni-tsoPxedni-erP
External claims HFHS charges Prescription drugs
$22,639 ($42,560)
$31,692 ($50,188)
$4,545 ($17,192)
$24,387 ($40,031)
$2,759 ($13,319)
$4,044 ($15,758)
$16,474 ($33,754)
$2,120 ($10,332)
Perpatientcost,$,mean(standarddeviation)
*
*
*
*
Figure 1. Annual per-patient pre- and postindex direct medical
costs among patients with chronic pain conditions. Index date
was the first date of the ICD-9 code for the chronic pain condition
during 2010. *P 0.001 vs. pre-index.
Table 4. Total Annualized, All-cause, Direct Medical
Costs Among Adults with Chronic Pain Conditions Based
on the Postindex Period (N = 12,165)
Condition Total Cost, $
Cost Per Patient, $,
Mean (SD)
Total costs (N = 12,165)* 385,541,521 31,692 (50,188)
Joint pain (n = 2773) 82,615,062 29,793 (48,941)
Limb pain (n = 2732) 86,404,781 31,627 (50,324)
Back pain (n = 2648) 95,775,664 36,169 (50,779)
Osteoarthritis (n = 2545) 100,772,576 39,296 (46,042)
Abdominal pain (n = 1143) 45,420,627 39,738 (65,766)
Headache (n = 570) 14,985,417 26,290 (35,458)
Cervical radiculopathy (n = 584) 22,180,018 37,979 (61,647)
Arthropathy (n = 614) 19,809,382 32,263 (52,248)
Rheumatoid arthritis (n = 422) 15,307,138 36,273 (49,821)
Diabetic neuropathy (n = 316) 15,517,941 49,107 (76,536)
Neuropathy (n = 315) 14,146,087 44,908 (58,320)
Migraine (n = 164) 5,424,324 33,075 (63,776)
Fibromyalgia (n = 166) 5,084,143 30,627 (34,051)
Multiple sclerosis (n = 149) 8,055,115 54,061 (54,449)
Genitourinary pain (n = 67) 1,954,697 29,175 (33,448)
Neuralgia (n = 79) 3,074,516 38,918 (86,327)
Gout (n = 65) 2,373,336 36,513 (82,642)
*Sum of patients for individual conditions exceeds the total number of unique patients
as some patients had more than one condition.
Chronic Pain Cost Burden in the United States 5

6. BMI calculations showed a predominance of under-
weight patients (ie, BMI 18.5 kg/m2
) in the higher
cost quartiles (Figure 2C), although this was a small
proportion of the overall study population (0.7%).
Patients in the other BMI categories were generally
evenly distributed across quartiles.
As shown in Figure 3A, which presents the relation-
ship between cost quartiles and utilization of the
different healthcare resource categories, there appeared
to be a direct relationship between outpatient visits and
cost; number of outpatient visits per patient was higher
at each increasing cost quartile. A direct relationship
with costs was also observed for emergency department
visits, which increased across quartiles, but hospitaliza-
tions occurred almost exclusively among patients in
Quartile 4. Imaging showed a similar trend toward
greater use in higher cost quartiles (Figure 3B). The
volume of imaging exams was approximately fivefold
higher for the highest cost quartile compared with the
lowest, which appeared to be primarily driven by the
30%
25% 24%
21%
17%
24%
31%
28%
10%
24%
32%
35%
4%
13%
26%
56%
0%
20%
40%
60%
Quartile 1 Quartile 2 Quartile 3 Quartile 4
0 comorbidities (n=8425)
1 comorbidities (n=2532)
2 comorbidities (n=772)
≥ 3 comorbidities (n=436)
42%
24%
22%
12%
36%
27%
21%
16%
30%
26%
24%
20%
23% 24%
27% 27%
18%
24%
29% 29%
0%
10%
20%
30%
40%
50%
Quartile 1 Quartile 2 Quartile 3 Quartile 4
18 - 34 (n=630) 35 - 44 (n=1180) 45 - 54 (n=2391)
55 - 64 (n=3014) ≥ 65 (n=4950)
15%
12%
35%
38%
26% 26%
24% 24%25% 24%
26% 25%
21%
25%
28%
26%
0%
10%
20%
30%
40%
Quartile 1 Quartile 2 Quartile 3 Quartile 4
18.5 (n=82) 18.5 - 25 (n=1770) 25 - 30 (n=3314) ≥ 30 (n=5568)
A Number of comorbid conditions
($0-$8,000) ($8,000-$16,000) ($16,000-$35,000) (≥ $35,000)
($0-$8,000) ($8,000-$16,000) ($16,000-$35,000) (≥ $35,000)
($0-$8,000) ($8,000-$16,000) ($16,000-$35,000) (≥ $35,000)
PercentofsubjectsPercentofsubjectsPercentofsubjects
B Age
C Body Mass Index
Figure 2. Relationship between total annual direct medical costs, stratified by quartiles, and patient characteristics among patients
with chronic pain conditions. (A) Comorbid conditions, (B) age, and (C) body mass index.
6 PARK ET AL.

7. increased use of X-ray imaging across the quartiles.
Pharmacy costs increased proportionately as total costs
increased across the quartiles (Figure 3C), with an
approximately 10-fold difference in costs between
Quartile 4 ($10,097) and Quartile 1 ($973).
DISCUSSION
This analysis, the first to evaluate a set of chronic pain
conditions from a single integrated healthcare system
database, provides real-world data on healthcare
resource utilization and its associated economic burden
across these conditions. In the regional database repre-
sented by the HFHS, all-cause direct medical costs for the
12-month period following the index diagnosis pain
conditions were estimated to range from $26,290 per
patient for headache to $54,061 per patient for multiple
sclerosis, with an overall average of $31,692, providing a
total of $385,541,521 for all identified patients. The
postindex costs were significantly higher than the com-
0.7 1.2 1.7 2.6
0.0 0.0 0.1
1.0
0.0 0.0 0.2
4.4
9.0
15.0
21.0
29.0
0
5
10
15
20
25
30
Quartile 1 Quartile 2 Quartile 3 Quartile 4
Emergency department Hospitalizations
Inpatient days Outpatient visits
1.2
1.8
2.5
6.0
0.1 0.3
0.7
1.8
0.1 0.3 0.6 0.8
0.3
0.8
1.3
2.01.7
3.2
5.1
10.6
0
2
4
6
8
10
12
Quartile 1 Quartile 2 Quartile 3 Quartile 4
X-ray CT scans MRI Other imaging Total imaging
$973
$2,412
$4,792
$10,097
$0
$2,000
$4,000
$6,000
$8,000
$10,000
$12,000
Quartile 1 Quartile 2 Quartile 3 Quartile 4
A Resource Categories
($0-$8,000) ($8,000-$16,000) ($16,000-$35,000) (≥ $35,000)
($0-$8,000) ($8,000-$16,000) ($16,000-$35,000) (≥ $35,000)
($0-$8,000) ($8,000-$16,000) ($16,000-$35,000) (≥ $35,000)
UnitspersubjectCostpersubjectUnitspersubject
B Imaging
C Pharmacy Costs
Figure 3. Relationship between total annual direct medical costs, stratified by quartiles, and resource use among patients with chronic
pain conditions. (A) Resource categories, (B) imaging, and (C) pharmacy.
Chronic Pain Cost Burden in the United States 7

8. parable pre-index period and resulted from the signifi-
cant increase in use of all healthcare resource categories
that was also observed from the pre- to postindex
periods. These increases may reflect either greater
resource use associated with the diagnosis and subse-
quent management of these conditions or an incremental
increase in resource over time even among patients
already diagnosed. Notably, in both periods, pharmacy
costs only accounted for 20% of total costs despite the
high number of prescriptions ordered. Although hospi-
talizations are generally associated with the highest cost
per event, the rate of hospitalizations was low. In
contrast, outpatient visits were high and averaged more
than one visit per month per patient, with the majority of
these (59.0%) coded as specialist visits. These data
suggest that the overall cost driver among patients with
chronic pain conditions may be outpatient visits, as also
supported by the quartile analysis. However, it should be
noted that the primary driver of costs could not be
determined with certainty, and the cost driver may
actually vary among the individual conditions; hospital-
izations have previously been reported as the cost driver
of diabetic neuropathy.12,13
The most costly pain conditions with respect to total
all-cause costs were associated with musculoskeletal
pain. These high costs were not a result of high per-
patient costs, but rather reflect the fact that muscu-
loskeletal conditions were the most prevalent chronic
pain conditions in this database,10
consistent with what
is known regarding the prevalence of chronic pain.1,9
Interestingly, the most costly condition on a per-patient
basis was multiple sclerosis. Although multiple sclerosis
is not generally classified as a chronic pain condition,
chronic pain, and neuropathic pain in particular, is
under-recognized and may be present in at least 50% of
individuals with this disease.14,15
While the per-patient cost of multiple sclerosis,
$54,061, is within the range of $8528 to $54,244 per
patient per year that has been reported for multiple
sclerosis in a systematic review,16
the per-patient costs of
other conditions were two- to fourfold higher than
reported in previous studies. These conditions include
diabetic neuropathy ($49,107), with costs that have
ranged from $14,06212
to $40,70513
; neuropathy
($44,908), relative to $17,355 across a variety of painful
neuropathic conditions17
; osteoarthritis ($39,296), with
reported direct costs of approximately $11,000 to
$13,00018–20
; fibromyalgia ($30,627), with ranges of
approximately $7,000 to $11,00021–24
; rheumatoid
arthritis($36,273)relative to almost $11,00019
; andgout
($36,513), with recent estimates of up to $25,000
depending on severity.25,26
However, it should be noted
that there are several factors that likely account for these
differences such as different data sources and methodolo-
gies including our use of charges rather than actual costs
and that we did not adjust for other factors such as
comorbidities and age.
There was high prescribing of opioids, with these
drugs often being the primary pain-related medication
overall and among most individual conditions. Such
high use of opioids is consistent with described pre-
scribing patterns especially among musculoskeletal and
neuropathic pain conditions.19,20,22,23,27–31
This high
opioid prescribing is despite the fact that these drugs
may not necessarily be appropriate for all patients,
especially an older population in whom opioid use may
be challenging because of side effects, drug interactions
due to polypharmacy, and the risk of dependence.32
Many of the chronic disease conditions in this database
were characteristically represented by an older demo-
graphic, including diabetic neuropathy and many of the
musculoskeletal conditions (eg, osteoarthritis, limb
pain, rheumatoid arthritis, and gout).10
The quartile analysis not surprisingly suggested the
relationships of costs with increasing comorbidities and
age. However, the predominance of underweight
patients in the higher cost quartile was surprising. While
the reason and implications of this observation are not
clear, it may in part reflect that not only was the overall
mean BMI indicative of obesity (31.3 kg/m2
), but that
all chronic pain conditions were characterized by mean
and median BMI values that were at least at the upper
end of the range of overweight, with many in the range
indicating obesity.10
The quartile analysis further supported outpatient
visits as the potential driver of overall costs and showed
relationships between overall costs and both pharmacy
costs and imaging utilization. These observations sug-
gest potential sources for cost reduction through more
effective management. In particular, it should be con-
sidered whether the high use of component resources
such as outpatient visits and imaging, which amounted
to 29 visits per subject and 10.6 units per subject during
the 12-month follow-up period in the highest cost
quartile, respectively, represents potential sources of
cost savings. Most outpatient visits were specialist visits,
which are generally associated with higher costs than
primary care visits, although it could not be determined
whether these specialist visits were referrals or routine/
preventive care for patients already under the care of the
8 PARK ET AL.

9. specialist physician. It can be speculated that at least
some of the specialist visits may be referrals, which may
result in a referral burden that increases overall costs
resulting from the two outpatient visits (primary care
and specialist) as well as potentially overlapping or
redundant diagnostic tests ordered by the physicians.
Studies have suggested trends toward greater referrals to
specialists33
as well as specialists increasingly function-
ing as primary care providers.34
Nevertheless, placing a
greater emphasis on the role and functions of primary
care may result in cost savings.
Imaging is also important for follow-up in specific
diseases such as musculoskeletal conditions. However,
the reasons for the high use of this resource (eg, disease
severity) and whether patient subsets with high utiliza-
tion could be identified for increasing the management
efficiency through reduction of the need for resource use
warrants further evaluation.
LIMITATIONS
The interpretation and generalizability of these results
are dependent on the strengths and limitations of the
study. The main strength is the use of EMR data from a
single integrated healthcare system with links to pri-
mary, ancillary, and rehabilitation centers. However,
the database does not have broad geographic represen-
tation, reducing generalizability. Furthermore, this
analysis relied heavily on coding used within the
database and, with all such studies, represents a limita-
tion in that the data are subject to potential errors
leading to misclassification of diagnosis or miscoding.
Another limitation was the inability to disaggregate
healthcare resource use category costs, and thus, it is not
possible to determine the specific resource category that
represents the driver of direct medical costs. Similarly,
because of the nature of the diversity of costs associated
with imaging, it was not possible to differentiate these
resource utilization costs. In regard to costs, the overall
direct medical cost burden, represented by the total cost
among all patients with chronic pain conditions, is likely
to be overestimated; all-cause costs were estimated, and
as some patients had more than one chronic pain
condition, the same costs were included in multiple
conditions. Costs were also based on charges rather than
actual costs, which may further overestimate the eco-
nomic impact, as charges have greater variability and
may not necessarily be related to the amount paid.
Although resource use and costs were determined pre-
and postindex, the population does not necessarily
represent an incident cohort, as no exclusion criteria
were applied based on the presence of ICD-9 codes prior
to 2010, the year for which the data were collected. Thus,
it is possible that these conditions may have been
preexisting in a proportion of patients, and no inferences
can be made with respect to causality regarding pre- and
postindex differences in resource use or costs. Similarly,
as all-cause resource utilization and costs were evaluated,
no inferences can be made regarding the specific contri-
bution of pain to these outcomes. Furthermore, many of
the statistically significant differences observed between
the pre- and postindex periods may be due to the large
sample and may not necessarily be clinically relevant.
Another limitation of this analysis is the inability to
distinguish resource utilization related to pain only vs.
all-cause resource utilization. We recommend that such
analysis be attempted in the future, when EMR captures
more consistently pain symptoms and can be linked to
medical claims data in order to create cohorts of patients
with pain symptoms vs. those without.
In conclusion, this analysis not only confirms the
substantial economic burden associated with chronic
pain patients, but suggests the overall magnitude of the
burden and the contribution of specific conditions. In
particular, while patients with musculoskeletal condi-
tions are the most costly overall because of their higher
prevalence relative to the other chronic pain conditions,
the data suggest that patients with other chronic
pain conditions may have with substantially higher per-
patient costs than those with more prevalent conditions.
Regardless of the condition, these results highlight the
need for more effective chronic pain management strate-
gies and provide a basis for further characterization of the
economic burden of patients with chronic pain condi-
tions and identification of cost drivers, especially those
that could be modified to reduce costs. Additionally, to
the best of our knowledge, this is the first time an
assessment of medical spending for pain conditions has
been carried out at a system level. This type of research
suggests that integrated delivery systems, such as the
Henry Ford Healthcare System, may be useful in assess-
ing potential opportunities to improve patient outcomes
and to lower the cost of care for large segments of patients
including those with chronic pain.
ACKNOWLEDGEMENTS
The study was sponsored by Pfizer Inc. Editorial support
was provided by E. Jay Bienen and was funded by Pfizer
Inc.
Chronic Pain Cost Burden in the United States 9

10. REFERENCES
1. Institute of Medicine. Relieving pain in America: a
blueprint for transforming prevention, care, education, and
research. The National Academies Press. http://www.-
nap.edu/catalog.php?record_id=13172#toc (accessed Febru-
ary 13, 2013).
2. Leadley RM, Armstrong N, Lee YC, Allen A, Kleijnen
J. Chronic diseases in the European Union: the prevalence and
health cost implications of chronic pain. J Pain Palliat Care
Pharmacother. 2012;26:310–325.
3. Breivik H, Collett B, Ventafridda V, Cohen R, Gallacher
D. Survey of chronic pain in Europe: prevalence, impact on
daily life, and treatment. Eur J Pain. 2006;10:287–333.
4. McCarberg BH, Nicholson BD, Todd KH, Palmer T,
Penles L. The impact of pain on quality of life and the unmet
needs of pain management: results from pain sufferers and
physicians participating in an Internet survey. Am J Ther.
2008;15:312–320.
5. Gaskin DJ, Richard P. The economic costs of pain in
the United States. J Pain. 2012;13:715–724.
6. Patel AS, Farquharson R, Carroll D, et al. The impact
and burden of chronic pain in the workplace: a qualitative
systematic review. Pain Pract. 2012;12:578–589.
7. Stewart WF, Ricci JA, Chee E, Morganstein D,
Lipton R. Lost productive time and cost due to common
pain conditions in the US workforce. JAMA. 2003;290:
2443–2454.
8. Lamerato LE, Dryer RD, Wolff GG, Hegeman-Dingle
R, Mardekian J, Park PW, Zlateva G. Prevalence of Chronic
Pain in a Large Integrated Healthcare Delivery System in the
U.S.A. Pain Pract. 2015; doi: 10.1111/papr.12334. [Epub
ahead of print].
9. Davis JA, Robinson RL, Le TK, Xie J. Incidence and
impact of pain conditions and comorbid illness. J Pain Res.
2011;4:331–345.
10. Lamerato LE, Dryer RD, et al. Cost Burden of Chronic
Pain in a Large Integrated Delivery System in the US Poster
Presentation at the International Society of Pharmacoeco-
nomics and Outcomes Research, Montreal, QC, Canada 2014.
11. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A
new method of classifying prognostic comorbidity in longitu-
dinal studies: development and validation. J Chronic Dis.
1987;40:373–383.
12. Ritzwoller DP, Ellis JL, Korner EJ, Hartsfield CL,
Sadosky A. Comorbidities, healthcare service utilization and
costs for patients identified with painful DPN in a managed-
care setting. Curr Med Res Opin. 2009;25:1319–1328.
13. Dworkin RH, Malone DC, Panarites CJ, Armstrong
EP, Pham SV. Impact of postherpetic neuralgia and painful
diabetic peripheral neuropathy on health care costs. J Pain.
2010;11:360–368.
14. O’Connor AB, Schwid SR, Hermann DN, Markman
JD, Dworkin RH. Pain associated with multiple sclerosis:
systemic review and proposed classification. Pain. 2008;
137:96–111.
15. Solaro C, Uccelli MM. Management of pain in multiple
sclerosis: a pharmacological approach. Nat Rev Neurol.
2011;7:519–527.
16. Adelman G, Rane SG, Villa KF. The cost burden of
multiple sclerosis in the United States: a systematic review of
the literature. J Med Econ. 2013;16:639–647.
17. Berger A, Hartrick C, Edelsberg J, Sadosky A, Oster G.
Direct and indirect economic costs among private-sector
employees with osteoarthritis. J Occup Environ Med.
2011;53:1228–1235.
18. White AG, Birnbaum HG, Janagap CC, Buteau L,
Schein J. Direct and indirect costs of pain therapy for
osteoarthritis in an insured population in the United States.
J Occup Environ Med. 2008;50:998–1005.
19. Silverman S, Dukes EM, Johnston SS, Brandenburg
NA, Sadosky A, Huse DM. The economic burden of
fibromyalgia: comparative analysis with rheumatoid arthritis.
Curr Med Res Opin. 2009;25:829–840.
20. Gore M, Sadosky A, Stacey B, Leslie D. Clinical
comorbidities, treatment patterns, and healthcare costs of
patients with osteoarthritis in usual care: a database analysis.
J Med Econ. 2011;14:497–507.
21. White LA, Robinson RL, Yu AP, et al. Comparison of
health care use and costs in newly diagnosed and established
patients with fibromyalgia. J Pain. 2009;10:976–983.
22. Palacio A, Uribe CL, Li H, et al. Financial and clinical
characteristics of fibromyalgia: a case-control comparison. Am
J Manag Care. 2010;16:S118–S125.
23. Palacio AM, Uribe CL, Li H, et al. Healthcare utiliza-
tion and costs for insured patients with fibromyalgia. Am J
Pharm Benefits. 2011;3:212–220.
24. Berger A, Sadosky A, Dukes EM, Edelsberg J, Zlateva
G, Oster G. Patterns of healthcare utilization and cost in
patients with newly diagnosed fibromyalgia. Am J Manag
Care. 2010;16:S126–S137.
25. Park H, Rascati KL, Prasla K, McBayne T. Evaluation
of health care costs and utilization patterns for patients with
gout. Clin Ther. 2012;34:640–652.
26. Wertheimer A, Morlock R, Becker MA. A revised
estimate of the burden of illness of gout. Curr Ther Res Clin
Exp. 2013;75:1–4.
27. Dunn JD, Pill MW. A claims-based view of health care
charges and utilization for commercially insured patients with
osteoarthritis. Manag Care. 2009;18:44–50.
28. Gore M, Dukes E, Rowbotham DJ, Tai K-S, Leslie DL.
Clinical characteristics and pain management among patients
with painful peripheral neuropathic disorders in general
practice settings. Eur J Pain. 2007;11:652–664.
29. Dworkin RH, Panarites CJ, Armstrong EP, Malone
DC, Pham SV. Healthcare utilization in people with posther-
petic neuralgia and painful diabetic peripheral neuropathy.
J Am Geriatr Soc. 2011;59:827–836.
30. Gore M, Sadosky A, Stacey BR, Tai K-S, Leslie D. The
burden of chronic low back pain: clinical comorbidities,
treatment patterns, and healthcare costs in usual care settings.
Spine. 2012;37:E668–E677.
10 PARK ET AL.

11. 31. Berger A, Dukes EM, Oster G. Clinical characteristics
and economic costs of patients with painful neuropathic
disorders. J Pain. 2004;5:143–149.
32. Smith H, Bruckenthal P. Implications of opioid anal-
gesia for medically complicated patients. Drugs Aging.
2010;27:417–433.
33. Barnett ML, Song Z, Landon BE. Trends in physician
referrals in the United States, 1999–2009. Arch Intern Med.
2012;172:163–170.
34. Valderas JM, Starfield B, Forrest CB, Sibbald B, Roland
M. Ambulatory care provided by office-based specialists in the
United States. Ann Fam Med. 2009;7:104–111.
Chronic Pain Cost Burden in the United States 11