How Much Money Is Spent A Year On Finding A Cure For Heart Disease

ACKNOWLEDGMENT

This enquiry was funded by the Institute of Medicine Committee on Advancing Pain Research, Care, and Education. The authors are grateful for insights and commentary provided by the committee. Besides, we give thanks Nancy Richard for her able help in compiling tables for this manuscript.

SUMMARY

Background

In 2008, according to the Medical Expenditure Console Survey (MEPS), nearly 100 million adults in the United States were affected by chronic pain, including joint pain or arthritis. For those who suffer hurting, 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 besides complicates medical care for other ailments, and it hinders one's power to work and function in society.

Objective

Nosotros estimated (1) the annual economic costs of pain in the U.s. and (two) the almanac costs of treating patients with a primary diagnosis of pain.

Data

We used the 2008 MEPS to compute the economical 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 almanac economical cost of hurting, we defined persons with hurting as those who reported having "severe pain," "moderate pain," "joint pain," "arthritis," or functional limitation that restricted their power to work. To compute the cost of medical care for patients with a primary diagnosis of hurting, nosotros examined adults who were treated for headache, abdominal pain, chest hurting, and back hurting in 2008.

Methodology

The almanac economic costs of pain can be divided into two components: (1) the incremental costs of medical care due to hurting, and (two) the indirect costs of pain due to lower economic productivity associated with lost wages, disability days, and fewer hours worked. Nosotros estimated the incremental and indirect costs using two-role models consisting of logistic regression models and generalized linear models. Nosotros besides 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 encounters for headache, abdominal pain, breast pain, and back pain. Nosotros converted the cost estimates into 2010 dollars using the Medical Intendance Inflation Alphabetize of the Consumer Toll Index (CPI) for medical costs and the General CPI for wages.

Results

We found that the full incremental cost of wellness care due to hurting ranged from $261 to $300 billion. The value of lost productivity is based on three estimates: days of piece of work missed (ranging from $11.half dozen to $12.7 billion), hours of work lost (from $95.2 to $96.5 billion), and lower wages (from $190.6 to $226.3 billion). Thus, the total financial cost of pain to society, which combines the health intendance cost estimates and the iii productivity estimates, ranges from $560 to $635 billion. All estimates are in 2010 dollars.

Conclusion

Nosotros found that the annual price of pain was greater than the annual costs in 2010 dollars of heart affliction ($309 billion), cancer ($243 billion), and diabetes ($188 billion) and nearly 30 percentage 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 two percentage to 40 pct, 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 having a migraine or severe headache, 15 pct reported having hurting in the cervix area, 28 per centum reported having pain in the lower dorsum, and 5 per centum reported having pain in the face or jaw area. For those who have persistent pain, information technology limits their functional status and adversely impacts their quality of life. Consequently, pain can be costly to the nation considering it requires medical treatment, complicates medical treatment for other weather, and hinders people's ability to work and function in guild.

Several studies accept examined the economic costs of pain. The U.S. Agency of the Census (1996) reported the total costs of chronic noncancer pain to be $150 billion annually. In 1999, a written 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 hurting direction was $16.four billion, and the cost of lumbar surgeries was $two.9 billion. Their estimates of the indirect costs of pain were $18.9 billion for inability bounty and $6.nine billion for productivity loss. Researchers have estimated the annual costs of migraines and rheumatoid arthritis at $14 billion each (Hu et al., 1999; Lubeck, 2001). Stewart and colleagues (2003) estimated that common pain weather (i.due east., arthritis, back, headache, and other musculoskeletal) result in $61.2 billion in lower productivity for U.S. workers. The evidence leaves no doubtfulness 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 report. For example, Turk and Theodore (2011) identified per patient costs of treating hurting based on information from the U.Due south. Workers' Compensation database and the Centers for Medicare and Medicaid Services. They computed indirect costs using data on inability compensation and estimates of lost work time for specific pain atmospheric condition from the literature. Our report is more comprehensive because our measures of pain conditions, health care costs, and indirect costs (such as missed piece of work days and hours and wages) were drawn more rigorously from the same sample population. We used nationally representative information sets and standard econometric techniques to address sample selection issues. Our measures of pain too capture people with chronic and persistent pain that is non formally diagnosed past a doc.

Nosotros estimated the annual economic costs of pain in the United States and the almanac costs of treating patients with a main diagnosis of hurting. The annual economic costs of pain tin can be divided into 2 components: (one) the incremental costs of medical care due to pain and (two) 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 infirmary stays for which the primary diagnosis was pain and the costs of medications used to manage hurting. This is a subset of the costs of medical care due to hurting because unlike cancer, eye disease, and diabetes, persistent pain is not always a diagnosed condition. The medical costs for other conditions are college for individuals who are experiencing persistent hurting. These costs are not captured in the almanac costs of treating patients with a primary diagnosis of hurting only are captured in the incremental costs of medical care due to hurting.

DATA

Sample

Nosotros used the 2008 MEPS to examine the economic brunt of hurting in the U.s.a.. Cosponsored past the Bureau for Health care Inquiry and Quality and the National Center for Health Statistics, the MEPS is a nationally representative longitudinal survey that covers the U.South. noncombatant 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 collects data on demographic characteristics, health expenditures, wellness conditions, health status, utilization of medical services, access to care, health insurance coverage, 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 straight medical costs. The analytic sample for the analysis of incremental health care costs was restricted to xx,214 individuals aged 18 or older. This sample is representative of all noninstitutionalized civilian adults in the United states of america. The analytic sample for the analysis of indirect costs was restricted to 15,945 individuals aged 24–65 to capture the active labor strength in the United states. The assay of straight medical costs was conducted at the event level. We scanned the Effect 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 hurting, nonspecific chest hurting, and back pain that occurred in several settings, including physician and nonphysician office-based visits, infirmary outpatient visits, emergency department visits, and hospital inpatient stays. Nosotros likewise identified expenditures associated with prescription drugs. We summed the costs of medical encounters for these diagnoses and the costs of medications used to treat hurting.

Key Independent Variables

Nosotros divers persons with pain as those who reported that they experienced hurting that limited their ability to work, that they were diagnosed with joint hurting or arthritis, or that they had a disability that express their ability to work. The SF-12 pain question of the MEPS asked the respondent whether, during the past iv weeks, hurting 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 articulation in the last 12 months. The question for arthritis determined whether the person had e'er been diagnosed with arthritis. The question about functional disability inquired whether the person had any work or housework limitation. We explored whether nosotros 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 hurting in the population. Rather, nosotros expected that persons suffering from these hurting weather would report having moderate or severe pain on the SF-12.

Dependent Variables

We used full expenditures equally the dependent variable to predict the incremental costs of treat individuals with selected hurting weather condition 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 both facility and separately billed md services. Full expenditures include inpatient, emergency room, outpatient (hospital, clinic, and office-based visits), prescription drugs, and other (e.g., home wellness 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 weather, the almanac number of hours of work missed because of pain conditions, and hourly wages every bit dependent variables to predict the productivity loss associated with the different pain atmospheric condition. Variations in the annual number of days of work missed measure workers' decisions to use ill days. Variations in the almanac number of hours worked measure out 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 tin perform in an hr.

Command Variables

Nosotros used a modified version of Aday and Andersen's (1974) behavioral health model of health services to estimate straight medical costs for patients with hurting compared with those without any hurting. This model hypothesizes that wellness expenditures depend on predisposing, enabling, and health demand factors. In this conceptual framework, hurting is a health need gene. Nosotros estimated the association between pain and health care expenditures. We predicted health care expenditures using demographic, socioeconomic status, wellness behavior, location, and health demand measures. The demographic factors were age, gender, race, and marital status. The socioeconomic factors were education, income, and wellness insurance status. To measure wellness behaviors, we used whether respondents smoked or exercised and their obesity status. Census region and urban/rural residence were used to mensurate location. To mensurate 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 considering they may complicate the treatment of other weather, and we did not want to attribute these costs to the incremental medical costs of hurting. We excluded other chronic weather condition, including hypertension, heart disease, emphysema, and stroke because we were concerned most the potential correlation betwixt 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 near parsimonious models that fairly controlled for health needs.

The lost productivity computation was based on the homo capital letter arroyo of estimating labor supply and earning models (Becker, 1973, 1974; Killingsworth, 1983). Theoretically, hours worked, wages, and labor forcefulness participation are based on a set of factors, including age, sex, race, ethnicity, education, health status, and location. Nosotros as well included the size of the family unit the person lives with to capture some of the household characteristics that are associated with labor market place outcomes.

ESTIMATION STRATEGY

Equally stated above, we estimated two types of costs: (i) the incremental costs of health care due to pain, computed by estimating the bear upon of chronic hurting 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 hurting weather condition noted to a higher place compared with those without whatever hurting (Manning, 1998; Mullahy, 1998; Manning and Mullahy, 2001; Buntin and Zaslavsky, 2004; Deb et al., 2006; Cameron and Trivedi, 2008). The get-go function of the model consisted of estimating logistic regression models to estimate the probability of having any blazon of wellness care expenditures. The second 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 accost the skew in the expenditure information. We eliminated outliers, i.due east., observations with expenditures greater than $100,000. Nosotros 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 regression procedures in STATA 11, which appropriately incorporates the pattern factors and sample weights.

We developed three models to predict full health care expenditures and conduct sensitivity analyses for robustness, varying the degree to which we controlled for health status. In the offset model, we measured hurting with indicators for moderate pain, astringent hurting, joint hurting, and arthritis. We controlled for health status using only cocky-reported general wellness status and trunk mass alphabetize. 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 bachelor in the MEPS and found that diabetes and asthma were pregnant predictors of expenditures independently of the hurting measures. We estimated models with and without an indicator for functional disability. Nosotros were concerned that persons with a functional disability who had chronic hurting might not be captured past the other pain measures; yet, we were too enlightened that the functional disability variable might capture people with a functional disability merely no chronic pain. By conducting the computation both ways, we could see whether including functional disability in our definition of pain weather 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 take hurting (Deb et al., 2006). To perform this calculation, the probabilities of having health care costs for persons with and without hurting must be taken into account. Nosotros computed unconditional levels of health care expenditures by multiplying the probabilities obtained from the first function of the model past predicted levels of expenditures from the second part of the model for individuals with and without hurting. Subsequently, we computed the incremental values for each type of pain condition by taking the difference betwixt those with and without pain. We converted the toll estimates into 2010 dollars using the medical care index of the CPI.

Nosotros 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: (ane) out-of-pocket payments by users of care or family; (2) Medicare; (3) Medicaid; (4) private insurance; (5) the VA, excluding CHAMPVA; (6) TRICARE; (seven) 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 departments, and state programs other than Medicaid); (nine) workers' bounty; (x) other unclassified sources (includes such sources equally automobile, dwelling house-owner's, and liability insurance and other miscellaneous or unknown sources); (11) other private (any blazon of private insurance payments); and (12) other public. For each payer category, we computed its proportion of total health care expenditures. Nosotros multiplied our gauge of total incremental health care costs due to hurting by these proportions to estimate the affect on each payer.

Indirect Price Models

As with the health care expenditure models, we used ii-function models to estimate the indirect costs of pain. The structure of the models depended upon the dependent variables. For missed days of work, nosotros estimated the probability of missing a piece of work mean solar day equally a result of selected hurting 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 hurting on the probability that a person is working. The 2nd equation estimated the bear on of hurting on the number of annual work hours and hourly wages. Combining the results from these different parts of the models, nosotros computed the productivity costs associated with chronic pain for each of the conditions noted above. We used a standard two-footstep estimator for labor supply to predict lost productivity due to hurting (Greene, 2005; Cameron and Trivedi, 2008). Every bit with the incremental price models, we multiplied the probabilities obtained from the start 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, nosotros multiplied the days missed by 8 hours times the predicted hourly wage rate for individuals with the pain status. To compute the total cost of reduction of hours worked, we multiplied the full of annual hours missed by the predicted hourly wage charge per unit for individuals with the hurting condition. To compute the total toll due to a reduction in hourly wages, we multiplied the predicted hourly wage reduction by the predicted annual hours lost for individuals with the hurting 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 similar to the utilize of Heckman selection models, but tin be used in the absenteeism of the identifying variables required by Heckman pick models and other limited dependent variables models, such every bit the Tobit (see Heckman, 1979; Ettner, 1995). Additionally, nosotros conducted a series of tests to determine the appropriate distribution 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 Intendance

Table C-one displays the dependent and contained variables used in the analysis of the incremental costs of health intendance. The sample includes 20,214 individuals aged eighteen and older, representing 210.vii 1000000 adults in the United States as of 2008. The mean health intendance expenditures were $4,475, and 85 percentage of adults had a positive expenditure. The prevalence estimates for selected pain conditions were 10 pct for moderate pain, xi percent for severe pain, 33 percentage for joint pain, 25 percent for arthritis, and 12 percent for functional disability.

TABLE C-1

Dependent and Contained Variables Used in the Incremental Price Models for Patients Anile xviii or Older for Selected Hurting Conditions (N = xx,214, US$2010).

Adults with pain reported college health intendance expenditures than adults without hurting (see Table C-2). Based on the SF-12 pain measures, a person with moderate pain had health care expenditures $four,516 higher than those of someone with no hurting. Persons with severe pain had health care expenditures $3,210 higher than those of a person with moderate pain. We found like differences for persons with joint pain ($iv,048), arthritis ($5,838), and a functional inability ($9,680) compared with persons without these conditions. All of these differences were statistically significant (p < 0.001).

Table C-two

Ways of Unadjusted Expenditures for Patients Aged 18 or Older for Selected Pain Conditions (U.s.$2010).

The regression results of the logistic regression models and generalized linear models point that moderate hurting, severe pain, joint pain, arthritis, and functional disability were strongly associated with an increased probability of having a health care expenditure and with higher expenditures (come across Table C-three). The coefficients were all statistically significant and positive predictors of whether a person had a wellness intendance expenditure and the amount of that expenditure. The coefficients were relatively stable beyond the iii models. The magnitude of the coefficients declined as we included functional disability, asthma, and diabetes in the models.

Table C-3

Results of Two-Part Full Expenditure Models for Patients Anile 18 or Older for Selected Hurting Weather.

To interpret the coefficients on pain conditions, we exponentiated the coefficients in the logistic models to compute the odds ratio (OR) of having a wellness care expenditure for a person with pain relative to a person without hurting. For case, the odds of having a health intendance expenditure increased by 70 percent for persons with articulation pain relative to persons without joint hurting (OR = i.70) according to Model 1. Similarly, because the link function in the generalized linear model is a log, nosotros exponentiated the coefficients on the pain variables to compute the per centum increase in health care expenditure for a person with hurting relative to a person without pain. For example, among persons with a health care expenditure, spending for persons with joint pain was xvi.ii pct 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 take a wellness intendance expenditure and a higher expenditure than men. The likelihood of an expenditure and the level of expenditures increased with age. Blacks, Hispanics, and Asians were less likely than whites to have a wellness care expenditure and had lower expenditures. Socioeconomic and wellness factors had the expected bear on. As instruction, income, and health insurance condition increased, wellness care spending besides increased. Health intendance 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 full incremental costs of the selected hurting conditions (see Tables C-4 and C-5). The average incremental costs of health care for selected pain conditions ranged from $854 for articulation pain to $3,957 for severe hurting according to Model 1. When functional disability was included in the model, its incremental costs were $three,787, while the estimates for the other pain conditions declined, particularly for astringent hurting, which cruel to $2,573 in Model 2. We estimated that approximately 100 one thousand thousand persons had at least i of the pain conditions based on the 2008 MEPS. Our gauge correlates well with the national estimate of at least 116 million persons given in the chief study because the MEPS excludes persons in nursing homes, prisons, and the armed forces. The most prevalent condition was joint pain, affecting more 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 almost expensive pain condition was severe hurting at $89.4 billion annually. However, functional disability was the most expensive when we included it in the model—$93.v billion in Model two. I interesting observation is that the incremental costs of severe pain declined to $58 billion when nosotros included functional disability.

TABLE C-4

Average Incremental Costs of Medical Expenditures for Selected Pain Weather condition (United states of america$2010).

TABLE C-5

Full Incremental Costs of Medical Expenditures for Selected Hurting Weather (in millions of The states$2010 and millions of persons).

Table C-6 shows the distribution of the incremental costs by source of payment. We estimated that individual insurers paid the largest share of incremental 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 intendance expenditures due to persistent pain. Medicaid paid nearly viii pct of the incremental costs of pain, ranging from $20 billion to $23 billion.

TABLE C-vi

Distribution of Total Incremental Costs of Medical Expenditures by Source of Payment (in millions of US$2010).

Indirect Costs

Table C-7 shows the dependent and contained variables for the analysis of incremental indirect costs. The sample was 15,945 persons ages 24 to 64, representing 156 1000000 working-age adults. The hateful number of piece of work days missed was 2.14, and 46 percent of adults missed at least one twenty-four hours of piece of work. The average number of hours the sample worked annually was ane,601, with 81 percent of adults working. The average hourly wage was $fourteen.xix. Among working-age adults, nine percent reported having moderate pain, 10 pct severe pain, 31 percent joint hurting, 21 percent arthritis, and 10 pct a functional disability.

Tabular array C-7

Dependent and Independent Variables Used in the Indirect Price Models for Patients Aged 24–64 for Selected Hurting Weather condition (N = 15,945).

Adults with hurting reported missing more days of work than adults without hurting (see Tabular array C-8). A person with moderate pain, based on the SF-12 pain measures, missed 2.1 days more than someone with no hurting. Adults with severe pain missed 2.half dozen days more than those with moderate hurting. The differences for joint pain, arthritis, and functional disability were i.three days, 1.three days, and 3.3 days, respectively. Hurting was associated with fewer annual hours worked (run into Table C-9). Persons with functional disability had the largest departure, working ane,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 hurting 717 fewer hours. We constitute like differences in hours for joint pain (220) and arthritis (384). Wages were lower for persons with pain (run across Table C-10). The largest difference was for persons with functional disability, followed by severe pain, moderate hurting, arthritis pain, and joint hurting. Persons with functional inability earned $xi an hour less than persons without functional disability.

TABLE C-8

Means of Unadjusted Number of Piece of work Days Missed for Adults Aged 24–64 with Selected Pain Conditions.

TABLE C-ix

Ways of Unadjusted Number of Hours Worked for Adults Anile 24–64 with Selected Pain Weather.

Tabular array C-10

Means of Unadjusted Number of Hourly Wages for Adults Anile 24–64 with Selected Hurting Atmospheric condition (US$2010).

The regression results for the indirect cost analysis are reported in Tables C-11, C-12, and C-13. As with the wellness care cost models, we interpreted the coefficients on the pain measures by exponentiating them. The first step models were logistic regressions, then 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 dependent variables. For example, in Tabular array C-11, Model 1, the coefficients on moderate hurting 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 to the lowest degree one missed day of work during the yr, and having moderate pain increased the number of days missed past 63 percent. Tables C-12 and C-13 brandish the impact of hurting atmospheric condition on the likelihood of working, the number of hours worked, and hourly wages. The hurting conditions had a significant negative touch on on the likelihood of working. The touch on hours worked and wages was negative but modest and in several cases insignificant. This means that the negative impact of hurting conditions on hours worked and wages occurred largely through the decision to work or non. Persons with pain were less likely to work than persons without pain.

TABLE C-11

Results of Two-Function Missed Days Models for Persons Aged 24–64 for Selected Pain Conditions.

Tabular array C-12

Results of Two-Part Missed Hours Models for Persons Anile 24–64 for Selected Hurting Conditions.

Table C-xiii

Results of Two-Part Logistic Regression and Generalized Linear Hourly Wages Models for Adults Aged 24–64 for Selected Pain Weather condition.

The calculated incremental costs are reported in Tables C-14 to C-19. The average incremental number of days of work missed was greatest for severe pain, with estimates ranging from 5.0 to five.nine days. Arthritis caused the fewest days of work missed—0.1 to 0.3. Nigh 70 million working adults reported having one of the pain conditions. The annual costs for the number of days missed ranged from $11.half dozen to $12.7 billion. More than persons reported joint hurting, but severe pain was more than costly. Including functional disability in these models did non affect the estimates for the other pain conditions.

TABLE C-xiv

Average Incremental Number of Days of Piece of work Missed Because of Selected Pain Conditions.

Table C-15

Total Incremental Costs of Number of Days of Work Missed Because of Selected Pain Conditions (in millions of United states of america$2010 and millions of persons).

Tabular array C-16

Average Incremental Number of Hours of Work Lost Considering of Selected Pain Conditions.

TABLE C-17

Total Incremental Costs of Number of Hours of Work Missed Considering of Selected Pain Conditions (in millions of US$2010 and millions of persons).

Tabular array C-18

Boilerplate Incremental Reduction in Hourly Wages Due to Selected Hurting Conditions (United states$2010).

TABLE C-19

Full Indirect Costs Associated with Reductions in Wages Due to Selected Pain Conditions (in millions of US$2010 and millions of persons).

Pain besides was associated with fewer annual hours worked. For Model 1, astringent pain was associated with the largest reduction, 204 hours. Even so, when we included functional inability in the model, the impact of severe pain fell to 30 hours, while the reduction associated with having a functional inability was 740 hours. While the inclusion of functional disability changed the distribution of the costs, information technology did not alter the overall estimate of the costs associated with fewer almanac hours worked, which totaled about $95 to $96 billion.

The average reduction in hourly wages for selected pain conditions ranged from $0.26 an hr for joint hurting to $3.76 an hour for severe pain according to Model 1. Including functional disability in the models changed the estimates substantially for the other pain weather condition—from $0.05 an hour for articulation pain to $1.66 an 60 minutes for astringent pain. Functional disability was associated with a large reduction in wages ($9.36 an 60 minutes), which did impact the total estimate of the costs due to wage reductions. The indirect price associated with reduced wages was $191 billion for Model one but $226 and $217 billion for Models 2 and 3, respectively.

Total Directly Cost for Medical Care for Pain Diagnoses

The direct price of medical handling for pain diagnoses was almost $47 billion (meet Table C-20). The bulk of these costs was for dorsum hurting ($34 billion). Role-based services and hospital stays accounted for 36 percent and 33 percentage of the total costs, respectively. The difference betwixt the full directly price and the total incremental health care costs was $214 to $246 billion. This indicates that near of the health intendance costs were attributable non to a direct diagnosis of hurting but to the impact of pain on the treatment of other conditions.

Table C-20

Full Direct Costs for Selected Pain Conditions (in millions of US$2010).

In summary, we found that the full incremental costs of health intendance due to pain ranged from $261 to $300 billion. The value of lost productivity ranged from $11.6 to $12.7 billion for days of work missed, from $95.2 to $96.5 billion for hours of piece of work lost, and from $190.6 to $226.3 billion for lower wages. The total annual costs ranged from $560 to $635 billion.

DISCUSSION

Persistent pain impacts 100 one thousand thousand adults and costs from $560 to $635 billion annually. Based on statistics published by the National Institutes of Wellness (NIH), the costs of persistent pain exceed the economic costs of the half-dozen most plush major diagnoses—cardiovascular diseases ($309 billion); neoplasms ($243 billion); injury and poisoning ($205 billion); endocrine, nutritional, and metabolic diseases ($127 billion); digestive arrangement diseases ($112 billion); and respiratory system diseases ($112 billion) (National Center, Lung, and Claret Constitute, 2011) (nosotros have converted these costs into 2010 dollars). These cost-of-condition estimates differ from our cost-of-hurting gauge. NIH combined personal wellness care costs reported in the MEPS and the costs of premature death due to these conditions; yet, the NIH estimates practise not include lost productivity. We do not consider the costs of premature death due to pain because pain is not considered a direct cause of death as are centre disease, cancer, and stoke. The American Diabetes Association reported that in 2007, diabetes cost $174 billion, including $116 billion in excess medical expenditures and $58 billion in reduced productivity (ADA, 2008). (This is equivalent to $188 billion in 2010 U.S. dollars.) Dissimilar these diagnosed conditions, pain affects a much larger number of people, past a factor of nearly four compared with middle disease and diabetes and a factor of nine compared with cancer. Thus, the per person cost of hurting is lower than that of the other atmospheric condition, just the total cost of pain is higher.

Our judge of the toll of chronic hurting is bourgeois for several reasons. First, we did not business relationship for the price of hurting for institutionalized and noncivilian populations. In detail, the incremental health care costs for nursing home residents, military personnel, and prison inmates with pain were not included and may be substantial. 2nd, we did not include the costs of pain for persons nether age eighteen. Third, we did not include the cost of hurting to caregivers. For example, nosotros did not consider time a spouse or adult child might lose from work to care for a loved 1 with chronic pain. Fourth, we considered the indirect costs of pain only for working-age adults. Nosotros did not gauge these costs for working persons over the historic period of 65 or nether the age of 24. While there are persons in these age categories who are retired or continuing their pedagogy, there also are persons in both historic period categories who are working or willing to work. We did not capture the value of their lost productivity. Fifth, we also did not include the value of time lost for other, non-work-related activities. Sixth, nosotros did non include other indirect costs—lost taxation acquirement, costs for replacement workers, legal fees, and transportation costs for patients to achieve providers. Finally, in our cost estimates we did not try to measure the psychological or emotional cost of chronic pain. The presence of chronic pain can lower a person'southward quality of life and diminish the person'due south enjoyment of other aspects of life.

Our analysis has a few limitations. Get-go, it is a cantankerous-sectional analysis, so we cannot infer causality. 2d, our measures of pain are limited. We cannot estimate the touch of pain associated with musculoskeletal conditions or cancer. 3rd, our functional inability may include persons who do non have chronic pain. Finally, we used two-office models to command for unobserved differences between persons with pain and persons without hurting. However, we recognize that the two-part approach may not fully capture the unobserved differences between the 2 groups and if so, our estimates of costs associated with pain will be likewise big.

In general, given the magnitude of the economic costs of pain, gild should consider investing in research, education, and care designed to reduce the impact of pain. Eliminating hurting may exist impossible, just helping people alive ameliorate with pain may exist achievable.

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Source: https://www.ncbi.nlm.nih.gov/books/NBK92521/

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