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Appendix C
The Economic Costs of Pain
in the United States
Darrell J. Gaskin, Ph.D.
Associate Professor of Health Economics
Department of Health Policy and Management
Johns Hopkins Bloomberg School of Public Health
Patrick Richard, Ph.D., M.A.
Assistant Research Professor of Health Economics and Policy
Department of Health Policy
The George Washington University
School of Public Health and Health Services
ACKNOWLEDGMENT
This research was funded by the Institute of Medicine Committee on Ad-
vancing Pain Research, Care, and Education. The authors are grateful for insights
and commentary provided by the committee. Also, we thank Nancy Richard for
her able assistance in compiling tables for this manuscript.
301
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302 RELIEVING PAIN IN AMERICA
SUMMARY
Background
In 2008, according to the Medical Expenditure Panel Survey (MEPS), about
100 million adults in the United States were affected by chronic pain, including
joint pain or arthritis. For those who suffer pain, it limits their functional status
and adversely impacts their quality of life. Pain is costly to the nation because
it sometimes requires medical treatment. Pain also complicates medical care for
other ailments, and it hinders one’s ability to work and function in society.
Objective
We estimated (1) the annual economic costs of pain in the United States and
(2) the annual costs of treating patients with a primary diagnosis of pain.
Data
We used the 2008 MEPS to compute the economic costs of pain in the United
States. The analytic sample was restricted to adults, ages 18 years or older, who
were civilians and noninstitutionalized. To compute the annual economic cost of
pain, we defined persons with pain as those who reported having “severe pain,”
“moderate pain,” “joint pain,” “arthritis,” or functional limitation that restricted
their ability to work. To compute the cost of medical care for patients with a
primary diagnosis of pain, we examined adults who were treated for headache,
abdominal pain, chest pain, and back pain in 2008.
Methodology
The annual economic costs of pain can be divided into two components:
(1) the incremental costs of medical care due to pain, and (2) the indirect costs
of pain due to lower economic productivity associated with lost wages, disability
days, and fewer hours worked. We estimated the incremental and indirect costs
using two-part models consisting of logistic regression models and generalized
linear models. We also used different model specifications for sensitivity analysis
and robustness. To compute the annual costs of medical treatment for patients
with a primary diagnosis of pain, we summed the expenditures for medical en -
counters for headache, abdominal pain, chest pain, and back pain. We converted
the cost estimates into 2010 dollars using the Medical Care Inflation Index of the
Consumer Price Index (CPI) for medical costs and the General CPI for wages.
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APPENDIX C
Results
We found that the total incremental cost of health care due to pain ranged
from $261 to $300 billion. The value of lost productivity is based on three esti-
mates: days of work missed (ranging from $11.6 to $12.7 billion), hours of work
lost (from $95.2 to $96.5 billion), and lower wages (from $190.6 to $226.3 bil -
lion). Thus, the total financial cost of pain to society, which combines the health
care cost estimates and the three productivity estimates, ranges from $560 to
$635 billion. All estimates are in 2010 dollars.
Conclusion
We found that the annual cost of pain was greater than the annual costs in
2010 dollars of heart disease ($309 billion), cancer ($243 billion), and diabetes
($188 billion) and nearly 30 percent higher than the combined cost of cancer and
diabetes.
INTRODUCTION
Millions of Americans experience persistent pain. A review of 15 studies of
chronic pain among adults found that prevalence estimates ranged from 2 percent
to 40 percent, with a median of 15 percent (Verhaak et al., 1998; Turk, 2002;
Manchikanti et al., 2009). Data from the 2009 National Health Interview Survey
(NHIS) indicate that during a 3-month period, 16 percent of adults reported hav -
ing a migraine or severe headache, 15 percent reported having pain in the neck
area, 28 percent reported having pain in the lower back, and 5 percent reported
having pain in the face or jaw area. For those who have persistent pain, it limits
their functional status and adversely impacts their quality of life. Consequently,
pain can be costly to the nation because it requires medical treatment, complicates
medical treatment for other conditions, and hinders people’s ability to work and
function in society.
Several studies have examined the economic costs of pain. The U.S. Bureau
of the Census (1996) reported the total costs of chronic noncancer pain to be
$150 billion annually. In 1999, a report issued by the American Academy of
Orthopedic Surgeons estimated the total cost of musculoskeletal disorders at
$215.5 billion in 1995 (Praemer et al., 1999). In 2001, the National Research
Council and the Institute of Medicine (IOM) reported that the economic cost
of musculoskeletal disorders, in terms of lost productivity, was $45-54 billion
(NRC and IOM, 2001). Turk and Theodore (2011) reported that the annual cost of
pharmaceuticals for pain management was $16.4 billion, and the cost of lumbar
surgeries was $2.9 billion. Their estimates of the indirect costs of pain were
$18.9 billion for disability compensation and $6.9 billion for productivity loss.
Researchers have estimated the annual costs of migraines and rheumatoid arthritis
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at $14 billion each (Hu et al., 1999; Lubeck, 2001). Stewart and colleagues (2003)
estimated that common pain conditions (i.e., arthritis, back, headache, and other
musculoskeletal) result in $61.2 billion in lower productivity for U.S. workers.
The evidence leaves no doubt that the cost of treating pain can be high.
These studies used a more exacting, piecemeal approach to compute the
cost of pain than that used for our study. For example, Turk and Theodore (2011)
identified per patient costs of treating pain based on information from the U.S.
Workers’ Compensation database and the Centers for Medicare and Medicaid
Services. They computed indirect costs using data on disability compensation
and estimates of lost work time for specific pain conditions from the literature.
Our study is more comprehensive because our measures of pain conditions,
health care costs, and indirect costs (such as missed work days and hours and
wages) were drawn more rigorously from the same sample population. We used
nationally representative data sets and standard econometric techniques to address
sample selection issues. Our measures of pain also capture people with chronic
and persistent pain that is not formally diagnosed by a physician.
We estimated the annual economic costs of pain in the United States and the
annual costs of treating patients with a primary diagnosis of pain. The annual
economic costs of pain can be divided into two components: (1) the incremental
costs of medical care due to pain and (2) the indirect costs of pain due to lower
productivity associated with lost days and hours of work and lower wages. The
annual costs of treating patients with a primary diagnosis of pain are the sum of
the costs of provider visits and hospital stays for which the primary diagnosis
was pain and the costs of medications used to manage pain. This is a subset of
the costs of medical care due to pain because unlike cancer, heart disease, and
diabetes, persistent pain is not always a diagnosed condition. The medical costs
for other conditions are higher for individuals who are experiencing persistent
pain. These costs are not captured in the annual costs of treating patients with a
primary diagnosis of pain but are captured in the incremental costs of medical
care due to pain.
DATA
Sample
We used the 2008 MEPS to examine the economic burden of pain in the
United States. Cosponsored by the Agency for Health care Research and Quality
and the National Center for Health Statistics, the MEPS is a nationally repre-
sentative longitudinal survey that covers the U.S. civilian noninstitutionalized
population (Cohen et al., 1996-1997). For this analysis, we used the Household
Component (HC) file of the MEPS—the core component of the survey that col -
lects data on demographic characteristics, health expenditures, health conditions,
health status, utilization of medical services, access to care, health insurance cov-
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APPENDIX C
erage, and income for each person surveyed. We combined data from the HC file
with data from the Condition and Event files of the MEPS to capture the different
pain management services used and associated direct medical costs. The analytic
sample for the analysis of incremental health care costs was restricted to 20,214
individuals aged 18 or older. This sample is representative of all noninstitution-
alized civilian adults in the United States. The analytic sample for the analysis
of indirect costs was restricted to 15,945 individuals aged 24-65 to capture the
active labor force in the United States. The analysis of direct medical costs was
conducted at the event level. We scanned the Event files for diagnosis of pain and
the Prescribed Medicine file for pharmaceuticals used to treat pain. Specifically,
we identified medical expenditures associated with headache, abdominal pain,
nonspecific chest pain, and back pain that occurred in several settings, includ-
ing physician and nonphysician office-based visits, hospital outpatient visits,
emergency department visits, and hospital inpatient stays. We also identified
expenditures associated with prescription drugs. We summed the costs of medical
encounters for these diagnoses and the costs of medications used to treat pain.
Key Independent Variables
We defined persons with pain as those who reported that they experienced
pain that limited their ability to work, that they were diagnosed with joint pain
or arthritis, or that they had a disability that limited their ability to work. The
SF-12 pain question of the MEPS asked the respondent whether, during the past
4 weeks, pain interfered with normal work outside the home and housework. The
joint pain question inquired whether the person had experienced pain, swelling, or
stiffness around a joint in the last 12 months. The question for arthritis determined
whether the person had ever been diagnosed with arthritis. The question about
functional disability inquired whether the person had any work or housework
limitation. We explored whether we could use information from the Event files
on persons who were diagnosed with a headache, abdominal pain, chest pain,
or back pain. We identified relatively few persons who had medical encounters
in which pain was the primary diagnosis. Consequently, we decided not to use
the Event files to determine the prevalence of pain in the population. Rather, we
expected that persons suffering from these pain conditions would report having
moderate or severe pain on the SF-12.
Dependent Variables
We used total expenditures as the dependent variable to predict the incremen-
tal costs of care for individuals with selected pain conditions compared with those
without these conditions. Total expenditures in the MEPS include both out-of-
pocket and third-party payments to health care providers but do not include health
insurance premiums. Expenditures for hospital-based services include those for
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both facility and separately billed physician services. Total expenditures include
inpatient, emergency room, outpatient (hospital, clinic, and office-based visits),
prescription drugs, and other (e.g., home health services, vision care services,
dental care, ambulance services, diagnostic services, medical equipment). The
expenditures do not include over-the-counter purchases.
For the analysis of indirect costs, we used the annual number of days of work
missed because of pain conditions, the annual number of hours of work missed
because of pain conditions, and hourly wages as dependent variables to predict
the productivity loss associated with the different pain conditions. Variations in
the annual number of days of work missed measure workers’ decisions to use sick
days. Variations in the annual number of hours worked measure workers’ decisions
whether to work full time, part time, or overtime. Variations in the hourly earnings
measure the value of the amount of work workers can perform in an hour.
Control Variables
We used a modified version of Aday and Andersen’s (1974) behavioral
health model of health services to estimate direct medical costs for patients with
pain compared with those without any pain. This model hypothesizes that health
expenditures depend on predisposing, enabling, and health need factors. In this
conceptual framework, pain is a health need factor. We estimated the association
between pain and health care expenditures. We predicted health care expenditures
using demographic, socioeconomic status, health behavior, location, and health
need measures. The demographic factors were age, gender, race, and marital
status. The socioeconomic factors were education, income, and health insurance
status. To measure health behaviors, we used whether respondents smoked or
exercised and their obesity status. Census region and urban/rural residence were
used to measure location. To measure health needs, we used whether respondents
reported they were in fair or poor health and whether they had been diagnosed
with diabetes or asthma. Diabetes and asthma were included because they may
complicate the treatment of other conditions, and we did not want to attribute
these costs to the incremental medical costs of pain. We excluded other chronic
conditions, including hypertension, heart disease, emphysema, and stroke because
we were concerned about the potential correlation between these other chronic
conditions and the SF-12 measures of pain. We estimated preliminary models
with the full complement of chronic conditions; however, some conditions were
statistically insignificant. Therefore, we elected to use the most parsimonious
models that adequately controlled for health needs.
The lost productivity computation was based on the human capital ap-
proach of estimating labor supply and earning models (Becker, 1973, 1974;
Killingsworth, 1983). Theoretically, hours worked, wages, and labor force partici-
pation are based on a set of factors, including age, sex, race, ethnicity, education,
health status, and location. We also included the size of the family the person
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APPENDIX C
lives with to capture some of the household characteristics that are associated
with labor market outcomes.
ESTIMATION STRATEGY
As stated above, we estimated two types of costs: (1) the incremental costs
of health care due to pain, computed by estimating the impact of chronic pain on
the annual cost of medical care; and (2) the indirect costs of pain due to lower
economic productivity associated with disability days, lost hours worked, and
lost wages.
Health Care Expenditure Models
We estimated a standard two-part expenditure model to address issues of
sample selection and heterogeneity and computed the economic burden for
patients with the different types of pain conditions noted above compared with
those without any pain (Manning, 1998; Mullahy, 1998; Manning and Mullahy,
2001; Buntin and Zaslavsky, 2004; Deb et al., 2006; Cameron and Trivedi, 2008).
The first part of the model consisted of estimating logistic regression models to
estimate the probability of having any type of health care expenditures. The sec-
ond part consisted of using generalized linear models with log link and gamma
distribution to predict levels of direct expenditures conditional on individuals
with positive expenditures. We used a log link and gamma distribution to ad-
dress the skew in the expenditure data. We eliminated outliers, i.e., observations
with expenditures greater than $100,000. We conducted the different diagnostic
and specification tests recommended by Manning (1998), Mullahy (1998), and
Manning and Mullahy (2001). We estimated the models using the survey regres-
sion procedures in STATA 11, which appropriately incorporates the design factors
and sample weights.
We developed three models to predict total health care expenditures and
conduct sensitivity analyses for robustness, varying the degree to which we
controlled for health status. In the first model, we measured pain with indicators
for moderate pain, severe pain, joint pain, and arthritis. We controlled for health
status using only self-reported general health status and body mass index. In the
second model, we added functional disability to our pain measures. In the third
model, we included diabetes and asthma in our measures of health status. We
conducted sensitivity analyses using several of the chronic condition indicators
available in the MEPS and found that diabetes and asthma were significant pre-
dictors of expenditures independently of the pain measures. We estimated models
with and without an indicator for functional disability. We were concerned that
persons with a functional disability who had chronic pain might not be captured
by the other pain measures; however, we were also aware that the functional dis -
ability variable might capture people with a functional disability but no chronic
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pain. By conducting the computation both ways, we could see whether including
functional disability in our definition of pain conditions mattered.
We computed the incremental costs of pain by using our model to predict
health care costs if a person has any type of pain and subtracting the predicted
health care costs if a person does not have pain (Deb et al., 2006). To perform
this calculation, the probabilities of having health care costs for persons with
and without pain must be taken into account. We computed unconditional levels
of health care expenditures by multiplying the probabilities obtained from the
first part of the model by predicted levels of expenditures from the second part
of the model for individuals with and without pain. Subsequently, we computed
the incremental values for each type of pain condition by taking the difference
between those with and without pain. We converted the cost estimates into 2010
dollars using the medical care index of the CPI.
We computed the impact of the incremental costs of selected pain conditions
on the various payers for health care services. The HC file from the MEPS contains
12 categories of direct payment for care provided during 2008: (1) out-of-pocket
payments by users of care or family; (2) Medicare; (3) Medicaid; (4) private
insurance; (5) the VA, excluding CHAMPVA; (6) TRICARE; (7) other federal
sources (includes the Indian Health Service, military treatment facilities, and
other care provided by the federal government); (8) other state and local sources
(includes community and neighborhood clinics, state and local health depart -
ments, and state programs other than Medicaid); (9) workers’ compensation;
(10) other unclassified sources (includes such sources as automobile, home-
owner’s, and liability insurance and other miscellaneous or unknown sources);
(11) other private (any type of private insurance payments); and (12) other public.
For each payer category, we computed its proportion of total health care expen -
ditures. We multiplied our estimate of total incremental health care costs due to
pain by these proportions to estimate the impact on each payer.
Indirect Cost Models
As with the health care expenditure models, we used two-part models to
estimate the indirect costs of pain. The structure of the models depended upon
the dependent variables. For missed days of work, we estimated the probability
of missing a work day as a result of selected pain conditions during the year.
Second, we estimated a log linear regression model in which the dependent
variable was the log of the number of disability days for those adults who had
positive disability days.
For hours worked and wages, the first equation estimated the impact of pain
on the probability that a person is working. The second equation estimated the
impact of pain on the number of annual work hours and hourly wages. Combining
the results from these different parts of the models, we computed the productivity
costs associated with chronic pain for each of the conditions noted above. We
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APPENDIX C
used a standard two-step estimator for labor supply to predict lost productivity
due to pain (Greene, 2005; Cameron and Trivedi, 2008). As with the incremental
cost models, we multiplied the probabilities obtained from the first part of the
model by predicted levels of work days missed, lost work hours, or lost wages
from the second part of the model for individuals with and without pain. To
compute the total cost of missed days, we multiplied the days missed by 8 hours
times the predicted hourly wage rate for individuals with the pain condition. To
compute the total cost of reduction of hours worked, we multiplied the total of
annual hours missed by the predicted hourly wage rate for individuals with the
pain condition. To compute the total cost due to a reduction in hourly wages, we
multiplied the predicted hourly wage reduction by the predicted annual hours lost
for individuals with the pain condition. We converted the cost estimates into 2010
dollars using the general CPI.
The approach of using a two-part model to estimate lost productivity is simi -
lar to the use of Heckman selection models, but can be used in the absence of the
identifying variables required by Heckman selection models and other limited
dependent variables models, such as the Tobit (see Heckman, 1979; Ettner, 1995).
Additionally, we conducted a series of tests to determine the appropriate distri -
bution for each of these models. For instance, we used a log link with Gaussian
distribution to estimate the models for hours worked.
RESULTS
Incremental Costs of Health Care
Table C-1 displays the dependent and independent variables used in the
analysis of the incremental costs of health care. The sample includes 20,214 indi-
viduals aged 18 and older, representing 210.7 million adults in the United States
as of 2008. The mean health care expenditures were $4,475, and 85 percent of
adults had a positive expenditure. The prevalence estimates for selected pain con-
ditions were 10 percent for moderate pain, 11 percent for severe pain, 33 percent
for joint pain, 25 percent for arthritis, and 12 percent for functional disability.
Adults with pain reported higher health care expenditures than adults without
pain (see Table C-2). Based on the SF-12 pain measures, a person with moderate
pain had health care expenditures $4,516 higher than those of someone with no
pain. Persons with severe pain had health care expenditures $3,210 higher than
those of a person with moderate pain. We found similar differences for persons
with joint pain ($4,048), arthritis ($5,838), and a functional disability ($9,680)
compared with persons without these conditions. All of these differences were
statistically significant (p < 0.001).
The regression results of the logistic regression models and generalized lin-
ear models indicate that moderate pain, severe pain, joint pain, arthritis, and func-
tional disability were strongly associated with an increased probability of having
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a health care expenditure and with higher expenditures (see Table C-3). The
coefficients were all statistically significant and positive predictors of whether
a person had a health care expenditure and the amount of that expenditure. The
coefficients were relatively stable across the three models. The magnitude of the
coefficients declined as we included functional disability, asthma, and diabetes
in the models.
To interpret the coefficients on pain conditions, we exponentiated the
coefficients in the logistic models to compute the odds ratio (OR) of having a
health care expenditure for a person with pain relative to a person without pain.
For example, the odds of having a health care expenditure increased by 70 percent
for persons with joint pain relative to persons without joint pain (OR = 1.70) ac -
cording to Model 1. Similarly, because the link function in the generalized linear
model is a log, we exponentiated the coefficients on the pain variables to compute
the percentage increase in health care expenditure for a person with pain relative
to a person without pain. For example, among persons with a health care expen -
diture, spending for persons with joint pain was 16.2 percent higher than that for
persons without joint pain based on Model 1.
The coefficients on the control variables had the expected signs. Women
were more likely to have a health care expenditure and a higher expenditure
than men. The likelihood of an expenditure and the level of expenditures in-
creased with age. Blacks, Hispanics, and Asians were less likely than whites to
have a health care expenditure and had lower expenditures. Socioeconomic and
health factors had the expected impact. As education, income, and health insur-
ance status increased, health care spending also increased. Health care spending
increased for persons who were obese, who reported they were in fair or poor
health, who had asthma, and who had diabetes.
We computed the average and total incremental costs of the selected pain
conditions (see Tables C-4 and C-5). The average incremental costs of health
care for selected pain conditions ranged from $854 for joint pain to $3,957 for
severe pain according to Model 1. When functional disability was included in the
model, its incremental costs were $3,787, while the estimates for the other pain
conditions declined, particularly for severe pain, which fell to $2,573 in Model 2.
We estimated that approximately 100 million persons had at least one of the pain
conditions based on the 2008 MEPS. The most prevalent condition was joint pain,
affecting more than 70 million adults. We estimated that the incremental costs of
health care for these selected pain conditions ranged from $261 billion to $293
billion annually. The most expensive pain condition was severe pain at $89.4
billion annually. However, functional disability was the most expensive when
we included it in the model—$93.5 billion in Model 2. One interesting observa -
tion is that the incremental costs of severe pain declined to $58 billion when we
included functional disability.
Table C-6 shows the distribution of the incremental costs by source of pay-
ment. We estimated that private insurers paid the largest share of incremental
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APPENDIX C
costs, ranging from $112 billion to $129 billion. Medicare bore 25 percent of the
incremental costs due to pain, ranging from $66 billion to $76 billion. Individuals
paid an additional $44 billion to $51 billion in out-of-pocket health care expen -
ditures due to persistent pain. Medicaid paid about 8 percent of the incremental
costs of pain, ranging from $20 billion to $23 billion.
Indirect Costs
Table C-7 shows the dependent and independent variables for the analysis of
incremental indirect costs. The sample was 15,945 persons ages 24 to 64, repre -
senting 156 million working-age adults. The mean number of work days missed
was 2.14, and 46 percent of adults missed at least one day of work. The average
number of hours the sample worked annually was 1,601, with 81 percent of adults
working. The average hourly wage was $14.19. Among working-age adults,
9 percent reported having moderate pain, 10 percent severe pain, 31 percent joint
pain, 21 percent arthritis, and 10 percent a functional disability.
Adults with pain reported missing more days of work than adults without
pain (see Table C-8). A person with moderate pain, based on the SF-12 pain
measures, missed 2.1 days more than someone with no pain. Adults with severe
pain missed 2.6 days more than those with moderate pain. The differences
for joint pain, arthritis, and functional disability were 1.3 days, 1.3 days, and
3.3 days, respectively. Pain was associated with fewer annual hours worked (see
Table C-9). Persons with functional disability had the largest difference, work-
ing 1,203 fewer hours than persons with no functional disability. Compared
with persons with no pain, persons with moderate pain worked 291 fewer hours,
and persons with severe pain 717 fewer hours. We found similar differences in
hours for joint pain (220) and arthritis (384). Wages were lower for persons with
pain (see Table C-10). The largest difference was for persons with functional
disability, followed by severe pain, moderate pain, arthritis pain, and joint pain.
Persons with functional disability earned $11 an hour less than persons without
functional disability.
The regression results for the indirect cost analysis are reported in Tables C-11,
C-12, and C-13. As with the health care cost models, we interpreted the coef-
ficients on the pain measures by exponentiating them. The first step models were
logistic regressions, so the exponentiated coefficients on the indicator variables
were ORs. The second step models were log-linear using the generalized linear
model. Thus, the exponentiated coefficients were percent changes in the depen -
dent variables. For example, in Table C-11, Model 1, the coefficients on moderate
pain were 0.5 in the logistic model and 0.49 in the generalized linear model. We
interpreted these coefficients as follows. Compared with a person with no pain,
someone with moderate pain had 64 percent greater odds of having at least one
missed day of work during the year, and having moderate pain increased the
number of days missed by 63 percent. Tables C-12 and C-13 display the impact
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328 RELIEVING PAIN IN AMERICA
TABLE C-11 Results of Two-Part Missed Days Models for Persons
Aged 24-64 for Selected Pain Conditions
Model 1 Model 2 Model 3
Categories Logit GLM Logit GLM Logit GLM
Moderate Pain 0.50*** 0.49*** 0.43*** 0.48*** 0.43*** 0.48***
(0.09) (0.12) (0.09) (0.12) (0.09) (0.12)
Severe Pain 0.79*** 0.81*** 0.57*** 0.80*** 0.56*** 0.80***
(0.09) (0.1) (0.09) (0.11) (0.09) (0.11)
Joint Pain 0.25*** 0.08 0.23*** 0.06 0.23*** 0.06
(0.05) (0.07) (0.05) (0.07) (0.05) (0.07)
Arthritis 0.20*** –0.07 0.15*** –0.06 0.13** –0.06
(0.06) (0.08) (0.06) (0.08) (0.06) (0.08)
Female 0.51*** –0.05 0.52*** –0.04 0.51*** –0.04
(0.04) (0.06) (0.04) (0.06) (0.04) (0.06)
Family Size –0.01 –0.01 0 –0.02 0 –0.02
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Age 35-44 –0.22*** –0.02 –0.23*** –0.01 –0.23*** –0.01
(0.06) (0.1) (0.06) (0.09) (0.06) (0.1)
Age 45-54 –0.31*** –0.04 –0.34*** –0.05 –0.34*** –0.06
(0.06) (0.1) (0.06) (0.1) (0.06) (0.1)
Age 55-64 –0.09 –0.17* –0.12 –0.17* –0.12 –0.17*
(0.08) (0.1) (0.08) (0.1) (0.08) (0.1)
Black –0.14** 0.19** –0.13** 0.22*** –0.13** 0.22***
(0.06) (0.08) (0.06) (0.08) (0.06) (0.08)
Hispanic –0.26*** 0.22** –0.22*** 0.23** –0.22*** 0.23**
(0.06) (0.11) (0.06) (0.11) (0.06) (0.11)
Asian –0.27*** –0.29** –0.26*** –0.28** –0.25** –0.28**
0.1 0.11 0.1 0.11 0.1 0.11
High School Degree 0.05 0.13* 0.05 0.13* 0.05 0.13*
(0.05) (0.07) (0.05) (0.07) (0.05) (0.07)
College Degree 0.03 0.05 0.02 0.05 0.02 0.05
(0.06) (0.1) (0.06) (0.09) (0.06) (0.09)
Graduate Degree 0 0.11 –0.01 0.1 –0.01 0.11
(0.08) (0.12) (0.08) (0.12) (0.08) (0.12)
Divorced –0.02 0.03 –0.03 0.01 –0.04 0.01
(0.08) (0.1) (0.08) (0.1) (0.08) (0.1)
Widow –0.07 –0.04 –0.09 –0.08 –0.08 –0.07
(0.13) (0.22) (0.14) (0.21) (0.14) (0.21)
Separated 0 –0.17 –0.02 –0.21 –0.03 –0.22
(0.15) (0.19) (0.15) (0.2) (0.15) (0.2)
200-400% of Federal Poverty –0.31*** 0.04 –0.29*** 0.05 –0.29*** 0.05
Level (FPL) (0.06) (0.09) (0.06) (0.09) (0.06) (0.09)
More Than 400% of FPL –0.36*** 0.02 –0.33*** 0.02 –0.33*** 0.02
(0.07) (0.09) (0.07) (0.09) (0.07) (0.09)
Public Insurance 0.54*** –0.64*** 0.30*** –0.65*** 0.30*** –0.65***
(0.08) (0.1) (0.09) (0.1) (0.09) (0.1)
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APPENDIX C
TABLE C-11 Continued
Model 1 Model 2 Model 3
Categories Logit GLM Logit GLM Logit GLM
Uninsured –0.01 –0.37*** –0.04 –0.38***
–0.03 –0.38***
(0.07) (0.09) (0.07) (0.09)
(0.07) (0.09)
Never Married –0.09 –0.17** –0.1 –0.20**
–0.1 –0.20**
(0.06) (0.08) (0.06) (0.08)
(0.06) (0.08)
Fair/Poor Health 0.63*** 0.25*** 0.46*** 0.25***
0.43*** 0.24***
(0.07) (0.08) (0.08) (0.09)
(0.08) (0.09)
Midwest 0.05 0.04 0.07 0.02
0.07 –0.02
(0.08) (0.09) (0.09) (0.08)
(0.09) (0.08)
South 0 –0.12 0.02 –0.12
0.03 –0.12
(0.08) (0.08) (0.08) (0.08)
(0.08) (0.08)
West 0.08 –0.14 0.1 –0.13
0.1 –0.13
(0.08) (0.1) (0.08) (0.1)
(0.08) (0.1)
Metropolitan Statistical Area 0.1 0.11 0.11 0.13
0.11 0.13
(0.08) (0.08) (0.08) (0.08)
(0.08) (0.08)
Functional Disability 1.09*** 0.1
1.07*** 0.1
(0.1) (0.13)
(0.1) (0.13)
Diabetes 0.1 0.04
(0.08) (0.11)
Asthma 0.27*** –0.01
(0.08) (0.09)
Constant –0.42*** 1.36*** –0.47*** 1.35*** –0.50*** 1.35***
(0.14) (0.17) (0.15) (0.17) (0.15) (0.17)
NOTES: Total expenditures include inpatient, emergency room, and outpatient services (hospital,
clinic and office-based visits); prescription drugs; and other (e.g., home health services, vision care
services, dental care, ambulance services, medical equipment). The expenditures do not include over-
the-counter purchases. Linearized standard errors are in parentheses. * p <.10, ** p <0.05, *** p <.01.
SOURCE: Based on the 2008 Medical Expenditure Panel Survey.
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330 RELIEVING PAIN IN AMERICA
TABLE C-12 Results of Two-Part Missed Hours Models for Persons
Aged 24-64 for Selected Pain Conditions
Model 1 Model 2 Model 3
Categories Logit GLM Logit GLM Logit GLM
Moderate Pain –0.33*** 0.00 –0.15 0.01 –0.14 0.01
(0.10) (0.01) (0.10) (0.01) (0.10) (0.01)
Severe Pain –0.81*** 0.00 –0.31*** 0.02 –0.31*** 0.02
(0.09) (0.01) (0.11) (0.01) (0.11) (0.01)
Joint Pain 0.02 –0.02*** 0.11 –0.02** 0.11 –0.02**
(0.07) (0.01) (0.07) (0.01) (0.08) (0.01)
Arthritis –0.32*** –0.01 –0.21*** 0.00 –0.20*** 0.00
(0.07) (0.01) (0.07) (0.01) (0.07) (0.01)
Female –0.82*** –0.14*** –0.91*** –0.14*** –0.92*** –0.14***
(0.06) (0.01) (0.07) (0.01) (0.07) (0.01)
Family Size –0.02 0.00 –0.05** 0.00 –0.05** 0.00
(0.02) (0.00) (0.02) (0.00) (0.02) (0.00)
Age 35-44 0.29*** 0.02** 0.32*** 0.02** 0.33*** 0.02**
(0.08) (0.01) (0.08) (0.01) (0.08) (0.01)
Age 45-54 0.00 0.03*** 0.07 0.03*** 0.08 0.03***
(0.09) (0.01) (0.09) (0.01) (0.09) (0.01)
Age 55-64 –0.82*** –0.02* –0.81*** –0.02* –0.78*** –0.02
(0.10) (0.01) (0.10) (0.01) (0.10) (0.01)
Black 0.26*** 0.02** 0.22** 0.02** 0.23** 0.02**
(0.08) (0.01) (0.09) (0.01) (0.09) (0.01)
Hispanic 0.26*** 0.03*** 0.14* 0.03*** 0.16* 0.03***
(0.08) (0.01) (0.08) (0.01) (0.08) (0.01)
Asian –0.32** 0.03* –0.36*** 0.02* –0.35*** 0.02*
(0.12) (0.01) (0.12) (0.01) (0.12) (0.01)
High School Degree 0.03 –0.01 0.05 –0.01 0.05 –0.01
(0.08) (0.01) (0.08) (0.01) (0.08) (0.01)
College Degree –0.02 0.00 0.00 0.00 0.00 0.00
(0.10) (0.01) (0.10) (0.01) (0.10) (0.01)
Graduate Degree 0.26* 0.01 0.29** 0.01 0.29** 0.01
(0.14) (0.01) (0.14) (0.01) (0.14) (0.01)
Divorced 0.69*** 0.02** 0.80*** 0.03** 0.80*** 0.03**
(0.10) (0.01) (0.11) (0.01) (0.11) (0.01)
Widow 0.04 0.02 0.05 0.03 0.06 0.03
(0.20) (0.03) (0.21) (0.03) (0.21) (0.03)
Separated 0.78*** 0.04** 0.83*** 0.03* 0.82*** 0.03*
(0.18) (0.02) (0.18) (0.02) (0.18) (0.02)
200-400% of Federal Poverty 0.63*** 0.06*** 0.61*** 0.06*** 0.61*** 0.06***
Level (FPL) (0.08) (0.01) (0.09) (0.01) (0.09) (0.01)
More Than 400% of FPL 0.97*** 0.10*** 0.95*** 0.10*** 0.95*** 0.10***
(0.09) (0.01) (0.09) (0.01) (0.09) (0.01)
Public Insurance –2.01*** –0.17*** –1.67*** –0.17*** –1.67*** –0.17***
(0.10) (0.02) (0.10) (0.02) (0.10) (0.02)
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APPENDIX C
TABLE C-12 Continued
Model 1 Model 2 Model 3
Categories Logit GLM Logit GLM Logit GLM
Uninsured –0.81*** –0.08*** –0.78*** –0.08*** –0.79*** –0.08***
(0.08) (0.01) (0.09) (0.01) (0.09) (0.01)
Never married ed 0.01 0.53*** 0.01 0.53*** 0.01
0.45***
(0.10) (0.01) (0.10) (0.01)
(0.10) (0.01)
Fair/Poor Health –0.69*** –0.02* –0.28*** 0.00
–0.27*** 0.00
(0.08) (0.01) (0.09) (0.01)
(0.08) (0.01)
Midwest 0.12 0.02 0.12 0.02
0.11 0.02
(0.12) (0.01) (0.12) (0.01)
(0.12) (0.01)
South –0.07 0.03*** –0.11 0.03***
–0.11 0.03***
(0.09) (0.01) (0.10) (0.01)
(0.10) (0.01)
West –0.04 0.01 –0.06 0.00
–0.06 0.00
(0.10) (0.01) (0.10) (0.01)
(0.10) (0.01)
Metropolitan Statistical Area –0.07 –0.03** –0.04 –0.02**
–0.04 –0.02**
(0.09) (0.01) (0.09) (0.01)
(0.09) (0.01)
Functional Disability –1.98*** –0.12***
–1.98*** –0.12***
(0.10) (0.03)
(0.10) (0.03)
Diabetes –0.20* 0.00
(0.11) (0.01)
Asthma 0.12 0.00
(0.11) (0.01)
Constant 2.16*** 7.60*** 2.26*** 7.60*** 2.26*** 7.60***
(0.20) (0.02) (0.21) (0.02) (0.21) (0.02)
NOTES: Total expenditures include inpatient, emergency room, and outpatient services (hospital,
clinic and office-based visits); prescription drugs; and other (e.g., home health services, vision care
services, dental care, ambulance services, medical equipment). The expenditures do not include
over-the-counter purchases. Linearized standard errors are in parentheses. GLM = generalized linear
model. * p <.10, ** p <0.05, *** p <.01.
SOURCE: Based on the 2008 Medical Expenditure Panel Survey.
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332 RELIEVING PAIN IN AMERICA
TABLE C-13 Results of Two-Part Logistic Regression and Generalized Linear
Hourly Wages Models for Adults Aged 24-64 for Selected Pain Conditions
Model 1 Model 2 Model 3
Categories Logit GLM Logit GLM Logit GLM
Moderate Pain –0.29*** –0.05** –0.12 –0.05* –0.12 –0.05*
(0.08) (0.03) (0.09) (0.03) (0.09) (0.03)
Severe Pain –0.77*** –0.09*** –0.26*** –0.07** –0.26*** –0.07**
(0.08) (0.03) (0.09) (0.03) (0.09) (0.03)
Joint Pain –0.04 –0.01 0.03 0.00 0.02 0.00
(0.06) (0.01) (0.06) (0.02) (0.06) (0.02)
Arthritis –0.18*** –0.03* –0.06 –0.03 –0.06 –0.02
(0.06) (0.02) (0.07) (0.02) (0.06) (0.02)
Female –0.17*** –0.19*** –0.17*** –0.19*** –0.17*** –0.19***
(0.05) (0.01) (0.05) (0.01) (0.05) (0.01)
Family Size –0.08*** –0.02*** –0.09*** –0.03*** –0.09*** –0.03***
(0.02) (0.00) (0.02) (0.00) (0.02) (0.00)
Age 35-44 –0.07 0.14*** –0.07 0.14*** –0.07 0.14***
(0.07) (0.02) (0.07) (0.02) (0.07) (0.02)
Age 45-54 –0.22*** 0.19*** –0.20*** 0.19*** –0.20*** 0.19***
(0.07) (0.02) (0.08) (0.02) (0.08) (0.02)
Age 55-64 –0.82*** 0.17*** –0.83*** 0.17*** –0.82*** 0.17***
(0.08) (0.02) (0.08) (0.02) (0.08) (0.02)
Black 0.16*** –0.10*** 0.19*** –0.11*** 0.19*** –0.10***
(0.06) (0.02) (0.07) (0.02) (0.06) (0.02)
Hispanic 0.07 –0.19*** 0.01 –0.20*** 0.01 –0.20***
(0.07) (0.02) (0.07) (0.02) (0.07) (0.02)
Asian –0.23** –0.05* –0.26** –0.05* –0.25** –0.05*
(0.10) (0.03) (0.10) (0.03) (0.10) (0.03)
High School Degree 0.13** 0.00 0.14** 0.00 0.14** 0.00
(0.06) (0.02) (0.06) (0.02) (0.06) (0.02)
College Degree 0.21*** 0.41*** 0.18** 0.41*** 0.18** 0.41***
(0.07) (0.02) (0.07) (0.02) (0.07) (0.02)
Graduate Degree 0.11 0.59*** 0.07 0.59*** 0.07 0.59***
(0.10) (0.02) (0.11) (0.02) (0.11) (0.02)
Divorced 0.20*** –0.06*** 0.33*** –0.06*** 0.32*** –0.06***
(0.07) (0.02) (0.07) (0.02) (0.07) (0.02)
Widow –0.46*** –0.11** –0.35* –0.10** –0.34* –0.10**
(0.18) (0.05) (0.18) (0.05) (0.18) (0.05)
Separated 0.14 –0.21*** 0.25* –0.21*** 0.25* –0.21***
(0.12) (0.04) (0.13) (0.04) (0.13) (0.04)
Never Married 0.10 –0.14*** 0.21*** –0.14*** 0.21*** –0.14***
(0.07) (0.02) (0.08) (0.02) (0.08) (0.02)
Fair/Poor Health –0.71*** –0.10*** –0.28*** –0.09*** –0.28*** –0.08***
(0.07) (0.02) (0.07) (0.02) (0.07) (0.02)
Midwest 0.13 –0.08*** 0.12 –0.08*** 0.12 –0.08***
(0.10) (0.03) (0.10) (0.03) (0.10) (0.03)
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APPENDIX C
TABLE C-13 Continued
Model 1 Model 2 Model 3
Categories Logit GLM Logit GLM Logit GLM
South –0.04 –0.09*** –0.09 –0.10***
–0.09 –0.10***
(0.08) (0.02) (0.09) (0.02)
(0.09) (0.02)
West –0.16* 0.04 –0.20** 0.04
–0.20** 0.04
(0.09) (0.03) (0.10) (0.03)
(0.10) (0.03)
Metropolitan Statistical Area 0.16* 0.15*** 0.18** 0.15***
0.17** 0.15***
(0.08) (0.02) (0.09) (0.02)
(0.08) (0.02)
Functional Disability –1.95*** –0.13***
–1.95*** –0.13***
(0.09) (0.04)
(0.09) (0.04)
Diabetes –0.07 –0.05*
(0.08) (0.02)
Asthma 0.13 –0.01
(0.08) (0.02)
Constant 1.46*** 2.88*** 1.51*** 2.89*** 1.50*** 2.89***
(0.15) (0.04) (0.16) (0.04) (0.16) (0.04)
NOTES: Total expenditures include inpatient, emergency room, and outpatient services (hospital,
clinic and office-based visits); prescription drugs; and other (e.g., home health services, vision care
services, dental care, ambulance services, medical equipment). The expenditures do not include
over-the-counter purchases. Linearized standard errors are in parentheses. GLM = generalized linear
model. * p <.10, ** p <0.05, *** p <.01.
SOURCE: Based on the 2008 Medical Expenditure Panel Survey.
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334 RELIEVING PAIN IN AMERICA
TABLE C-14 Average Incremental Number of Days of Work Missed Because
of Selected Pain Conditions
Conditions Model 1 Model 2 Model 3
Moderate Pain 1.87 1.70 1.70
Severe Pain 5.92 5.01 4.99
Joint Pain 0.44 0.36 0.35
Arthritis 0.03 0.01 0.01
Functional Disability — 1.38 1.35
NOTES: This analysis is based on the total noninstitutionalized adult subpopulation of the United
States for individuals aged 24-64, who represented 156 million individuals as of 2008. Model 2 in-
cludes functional disability in addition to all the other control variables. Model 3 includes functional
disability, asthma, and diabetes in addition to all the other control variables.
SOURCE: Based on authors’ calculations using the 2008 Medical Expenditure Panel Survey.
TABLE C-15 Total Incremental Costs of Number of Days of Work Missed
Because of Selected Pain Conditions (in millions of US$2010 and
millions of persons)
Conditions Population Model 1 Model 2 Model 3
Moderate Pain 14.1 $2,643 $2,541 $2,540
Severe Pain 15.6 6,476 7,330 7,196
Joint Pain 49.1 2,401 1,999 1,983
Arthritis 32.9 105 40 19
Functional Disability 14.9 — 919 898
Total 69.8 $11,625 $12,728 $12,635
NOTES: Dollar amounts were adjusted for inflation as of 2010 using the General Consumer Price
Index. This analysis is based on the total noninstitutionalized adult subpopulation of the United States
for individuals aged 24-64, who represented 156 million individuals as of 2008. Model 2 includes
functional disability in addition to all the other control variables. Model 3 includes functional dis -
ability, asthma, and diabetes in addition to all the other control variables. To compute the total cost,
we multiplied days missed by 8 hours times the predicted hourly wage rate for individuals with the
pain condition. A total of 69.8 million persons had at least one of the pain conditions studied. The
population totals for the selected pain conditions do not sum to 69.8 million because some persons
have multiple conditions.
SOURCE: Based on authors’ calculations using the 2008 Medical Expenditure Panel Survey.
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335
APPENDIX C
TABLE C-16 Average Incremental Number of Hours of Work Lost Because of
Selected Pain Conditions
Conditions Model 1 Model 2 Model 3
Moderate Pain 64.43 15.28 14.03
Severe Pain 204.27 30.06 30.33
Joint Pain 28.73 7.80 7.58
Arthritis 85.74 45.48 44.45
Functional Disability — 739.61 744.85
NOTES: This analysis is based on the total noninstitutionalized adult subpopulation of the United
States for individuals aged 24-64, who represented 156 million individuals as of 2008. Model 2 in-
cludes functional disability in addition to all the other control variables. Model 3 includes functional
disability, asthma, and diabetes in addition to all the other control variables.
SOURCE: Based on authors’ calculations using the 2008 Medical Expenditure Panel Survey.
TABLE C-17 Total Incremental Costs of Number of Hours of Work Missed
Because of Selected Pain Conditions (in millions of US$2010 and millions of
persons)
Conditions Population Model 1 Model 2 Model 3
Moderate Pain 14.1 $11,380 $2,846 $2,618
Severe Pain 15.6 27,939 5,422 5,472
Joint Pain 49.1 19,750 5,550 5,296
Arthritis 32.9 37,472 20,530 20,090
Functional Disability 14.9 — 61,495 61,742
Total 69.8 $96,542 $95,744 $95,217
NOTES: Dollar amounts were adjusted for inflation as of 2010 using the General Consumer Price
Index. This analysis is based on the total noninstitutionalized adult subpopulation of the United States
for individuals aged 24-64, who represented 156 million individuals as of 2008. Model 2 includes
functional disability in addition to all the other control variables. Model 3 includes functional disabil -
ity, asthma, and diabetes in addition to all the other control variables. To compute the total cost, we
multiplied the total of annual hours of work missed by the predicted hourly wage rate for individuals
with the pain condition. A total of 69.8 million persons had at least one of the pain conditions stud -
ied. The population totals for the selected pain conditions do not sum to 69.8 million because some
persons have multiple conditions.
SOURCE: Based on authors’ calculations using the 2008 Medical Expenditure Panel Survey.
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336 RELIEVING PAIN IN AMERICA
TABLE C-18 Average Incremental Reduction in Hourly Wages Due to
Selected Pain Conditions (US$2010)
Conditions Model 1 Model 2 Model 3
Moderate Pain $1.65 $0.99 $0.97
Severe Pain 3.76 1.65 1.66
Joint Pain 0.26 0.05 0.05
Arthritis 1.12 0.59 0.57
Functional Disability — 9.36 9.37
NOTES: Dollar amounts were adjusted for inflation as of 2010 using the General Consumer Price
Index. This analysis is based on the total noninstitutionalized adult subpopulation of the United States
for individuals aged 24-64, who represented 156 million individuals as of 2008. Model 2 includes
functional disability in addition to all the other control variables. Model 3 includes functional dis -
ability, asthma, and diabetes in addition to all the other control variables.
SOURCE: Based on authors’ calculations using the 2008 Medical Expenditure Panel Survey.
TABLE C-19 Total Indirect Costs Associated with Reductions in Wages Due
to Selected Pain Conditions (in millions of US$2010 and millions of persons)
Conditions Population Model 1 Model 2 Model 3
Moderate Pain 14.1 $35,795 $22,114 $21,791
Severe Pain 15.6 78,214 40,173 40,453
Joint Pain 49.1 19,959 3,709 4,293
Arthritis 32.9 56,657 30,340 29,396
Functional Disability 14.9 — 130,029 129,577
Total 69.8 $190,625 $226,365 $216,924
NOTES: Dollar amounts were adjusted for inflation as of 2010 using the General Consumer Price
Index. This analysis is based on the total noninstitutionalized adult subpopulation of the United States
for individuals aged 24-64, who represented 156 million individuals as of 2008. Model 2 includes
functional disability in addition to all the other control variables. Model 3 includes functional disabil -
ity, asthma, and diabetes in addition to all the other control variables. To compute the total cost due
to a reduction in hourly wages, we multiplied the predicted change in hourly wages by the predicted
annual hours of work for individuals with each of the pain condition by the total population affected
by the condition. A total of 69.8 million persons had at least one of the pain conditions studied. The
population totals for the selected pain conditions do not sum to 69.8 million because some persons
have multiple conditions.
SOURCE: Based on authors’ calculations using the 2008 Medical Expenditure Panel Survey.
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APPENDIX C
TABLE C-20 Total Direct Costs for Selected Pain Conditions (in millions of
US$2010)
Office- Hospital Emergency Hospital Prescription
Conditions based Outpatients Services Inpatients Drugs Total
Headache 1,350 434 958 147 3,730 6,619
Nonspecific Chest Pain 596 1,040 948 1,930 62 4,576
Abdominal Pain 689 305 438 128 38 1,598
Back Pain 14,400 3,000 607 13,500 2,660 34,167
Total 17,035 4,779 2,951 15,705 6,490 46,960
NOTE: Dollar amounts were adjusted for inflation as of 2010 using the Medical Care Inflation Index
of the Consumer Price Index.
SOURCE: Based on authors’ calculations using the 2008 Medical Expenditure Panel Survey.
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