Rationale: Asthma is a chronic disease that affects quality of life, productivity at work and school, and healthcare use; and it can result in death. Measuring the current economic burden of asthma provides important information on the impact of asthma on society. This information can be used to make informed decisions about allocation of limited public health resources.
Objectives: In this paper, we provide a comprehensive approach to estimating the current prevalence, medical costs, cost of absenteeism (missed work and school days), and mortality attributable to asthma from a national perspective. In addition, we estimate the association of the incremental medical cost of asthma with several important factors, including race/ethnicity, education, poverty, and insurance status.
Methods: The primary source of data was the 2008–2013 household component of the Medical Expenditure Panel Survey. We defined treated asthma as the presence of at least one medical or pharmaceutical encounter or claim associated with asthma. For the main analysis, we applied two-part regression models to estimate asthma-related annual per-person incremental medical costs and negative binomial models to estimate absenteeism associated with asthma.
Results: Of 213,994 people in the pooled sample, 10,237 persons had treated asthma (prevalence, 4.8%). The annual per-person incremental medical cost of asthma was $3,266 (in 2015 U.S. dollars), of which $1,830 was attributable to prescription medication, $640 to office visits, $529 to hospitalizations, $176 to hospital-based outpatient visits, and $105 to emergency room visits. For certain groups, the per-person incremental medical cost of asthma differed from that of the population average, namely $2,145 for uninsured persons and $3,581 for those living below the poverty line. During 2008–2013, asthma was responsible for $3 billion in losses due to missed work and school days, $29 billion due to asthma-related mortality, and $50.3 billion in medical costs. All combined, the total cost of asthma in the United States based on the pooled sample amounted to $81.9 billion in 2013.
Conclusions: Asthma places a significant economic burden on the United States, with a total cost of asthma, including costs incurred by absenteeism and mortality, of $81.9 billion in 2013.
Asthma is a chronic disease of the airways characterized by periods of reversible airflow obstruction resulting in symptoms of cough, wheeze, chest tightness, and dyspnea. In 2013, approximately 22.6 million people in the United States (7.3% of the population) had current asthma, including 6.1 million children (aged <18 yr) and 16.5 million adults (1). Asthma negatively affects quality of life, productivity at work and school, and healthcare use, and it can even result in death.
Asthma places a significant economic burden on the United States (2–6). The cost of asthma is a measure of the economic burden of the disease and represents the additional costs imposed by having asthma. Cost studies can influence public health policy decisions and help decision makers understand the scale, seriousness, and implications of the disease so that resources can be identified to improve asthma management and reduce the burden of asthma (7, 8). Reports on the cost of asthma present disease burden in monetary terms and allow reasonable comparison of the population effects of different chronic conditions (9–11).
Multiple studies on the cost of asthma in the United States (4–6, 12–16) have demonstrated that costs are affected by numerous factors, including new treatment options, federal and state policies, changes in price and the healthcare market, and increasing effectiveness of asthma control programs (1). Dissemination of the medical and economic burden of asthma can inform decisions about allocation of public health resources.
The first comprehensive study of asthma economic burden estimated the cost to society at $6.2 billion (1990 U.S. dollars) in 1990, including direct medical costs and productivity losses due to morbidity and mortality (16). The authors of that study used a gross-costing method that was based on healthcare use and average per-unit cost data (17–22). The cost of asthma-related hospitalizations, for example, was estimated by multiplying the number of asthma hospitalizations by the average cost for one hospitalization (7, 23).
Lately, in cost-of-illness studies, it is increasingly common to use regression models to isolate the effect of diseases on healthcare costs (24, 25). In 2009, Kamble and Bharmal used generalized linear regression models (GLMs) to estimate the cost of asthma using data from the 2004 Medical Expenditure Panel Survey (MEPS) (26). Those authors found that the per-person incremental medical costs of asthma (additional cost associated with having asthma) were $2,078 for adults and $1,005 for children, amounting to an estimated $37.2 billion (2007 U.S. dollars) in total medical cost associated with asthma. Using 2003 and 2005 MEPS data, Sullivan and colleagues found that adults with asthma incurred $1,907 (2008 U.S. dollars) annually in incremental medical costs (27). In 2011, Barnett and Nurmagambetov estimated the per-person incremental medical cost of asthma at $3,856 (2009 U.S. dollars) and the total national cost of asthma at $56 billion (4).
The objective of the present study was to provide current estimates of medical, absenteeism, and mortality costs of treated asthma at both the individual and national levels for the years 2008–2013. For the purposes of this paper, we define treated asthma as having had at least one medical or pharmaceutical encounter or claim associated with asthma. Our estimates also include the prevalence of treated asthma, per-person cost, and total cost of treated asthma in the United States. In addition, we examined the effects of several demographic and socioeconomic factors on asthma medical costs, including income, education, age, race/ethnicity, and insurance status.
We used data derived from MEPS for calendar years 2008–2013 (28). The survey sample of households for each year was drawn from among respondents in the previous year’s National Health Interview Survey, a nationally representative sample of the U.S. civilian noninstitutionalized population (29). MEPS collects detailed information on healthcare use, expenditures, payment source, and health insurance coverage. Cosponsored by the Agency for Healthcare Research and Quality and the National Center for Health Statistics, MEPS uses a complex survey design and provides population weights to create nationally representative estimates for the U.S. population.
The MEPS household component contains detailed self-reported information on demographics, socioeconomic status, health conditions, insurance status, healthcare use and expenditures, employment, missed work, and missed school. MEPS data cover expenditures for office-based provider visits, hospital-based outpatient visits, inpatient hospitalizations, emergency room (ER) visits, prescription medications, home health care, dental services, and vision aids. The MEPS medical provider component is a follow-up survey covering a sample of pharmacies and healthcare providers. The full 2008–2013 MEPS sample ranged from 32,846 to 38,974 persons annually, and the response rate ranged from 53.5 to 59.3%.
We merged data from the MEPS household component full-year consolidated data files with household component event files. Event files included data on office-based physician visits, hospital-based outpatient and special clinic visits, ER visits, hospital inpatient stays, and prescription medications. To eliminate missing information and to improve accuracy, MEPS collects additional information from a sample of medical providers and applies a specific imputation procedure for any remaining missing values (28). Using unique identification variables, we created a merged file of person-level data for each of the years during 2008–2013. Pooled data files from these 6 years provided a total sample size of 213,994 persons. To address the complex survey design of MEPS, we used person-level weights and survey commands in the Stata 12 software program for the analysis (30). For the remainder of this paper, all monetary values are adjusted to 2015 U.S. dollars using the Consumer Price Index and the medical care Consumer Price Index (31). We applied the Stata twopm program to run two-part regression models (TPRMs) (32).
In our analysis, we used the following definitions. Treated asthma was defined as International Classification of Diseases, Ninth Revision, Clinical Modification, diagnosis code 493 (asthma) associated with an office-based medical provider office visit, hospital-based outpatient visit, ER visit, hospital inpatient stay, or filled prescription medication for asthma. Lifetime asthma was defined as an affirmative response to the question, “Has a doctor or other health professional ever told you that you had asthma?” Current asthma was defined as having lifetime asthma plus an affirmative response to the question, “Do you still have asthma?” By these definitions, a person with treated asthma also has current asthma, and a person with current asthma also has lifetime asthma. For the remainder of this paper, asthma refers to treated asthma unless otherwise specified. Given that we used expenditure data to measure medical cost, treated asthma is the term most relevant to the discussion.
For our analysis, we used annual per-person total healthcare expenditure (or medical cost) and separate annual per-person expenditures for office visits, hospital outpatient visits, ER visits, hospital admissions, and prescription medications. MEPS defines per-person expenditure as the sum of all direct payments by all payers for care during the year, including out-of-pocket payments, payments by all public and private insurances, and other sources. Given the high proportion of zero values found in annual per-person expenditure data, reflecting the frequency of persons having no healthcare expenditures during the year, we used a binary dependent variable that identified persons with a positive healthcare expenditure. We also used two additional dependent variables, missed workdays and school days, to estimate the effect of asthma on absenteeism.
The main independent variable for the analysis was a binary variable in which 1 indicated that a person had asthma and 0 if not. Sex, age, age squared, race/ethnicity, education, marital status, income level, health insurance, U.S. Census region, and the D’Hoore adaptation of the Charlson comorbidity index were also included (33). Enrollment in a healthcare insurance plan meant continuous enrollment throughout the year; uninsured meant uninsured for the entire year.
To estimate the incremental medical costs of asthma and related absenteeism during 2008–2013, we applied regression-based techniques that take into account distribution of medical costs and missed workdays and school days. We used a TPRM to estimate the per-person annual incremental medical cost of asthma. The model produces the incremental cost of asthma or the difference between predicted annual medical expenditure of the person with asthma (the value of the variable for asthma equal to 1) and the predicted annual medical expenditure of the same person, assuming that person does not have asthma (changing the value from 1 to 0). Using a TPRM allowed us to isolate the effect of asthma on medical cost while controlling for the presence of other factors.
In the first part of the TPRM, we used logistic regression to predict the probability of any positive healthcare expenditure. In the second part, we estimated actual expenditure conditional on having a nonzero expenditure during the year. In both parts, we used the same set of independent variables. To select the appropriate model for the second part of the TPRM, we used criteria recommended by Manning and Mullahy (25). On the basis of their algorithm, in the second stage, we used a GLM with a gamma distribution and a log link to estimate per-person annual medical expenditures for all persons who had a nonzero expenditure. The TPRM generates a prediction function for per-person total medical cost, then the Stata 12 marginal effect command, applied to the asthma variable, estimates the incremental medical cost of asthma. Incremental costs of prescription medications, office-based visits, hospital-based visits, ER visits, and hospitalizations were similarly obtained.
For analysis of missed workdays and school days, we used a negative binomial model with the same independent variables used to calculate incremental medical cost. We produced two predicted values for missed days: one for persons with asthma and one for the same persons without asthma by simulating the removal of asthma. The difference between these two predicted values was the expected incremental workdays or school days lost owing to asthma.
To estimate the cost of missed workdays or school days, we used a human capital approach whereby the cost of one missed workday was equivalent to a lost daily wage (34, 35). Daily wage was estimated using actual or imputed number of hours worked per week and hourly wage. To assign the value to the missed school day, we assumed that one parent missed work to care for the child, so the value was equivalent to the day’s lost wage. For a two-parent household, we assumed the lower-earning or nonworking parent would stay home, and for the latter, the value of the missed day was based on the national minimum wage.
For mortality data, we used the Centers for Disease Control and Prevention’s CDC WONDER (Wide-ranging Online Data for Epidemiologic Research) web application, extracting cases with asthma as the underlying cause of death for years 2008–2013 (36). To assess the value of mortality, we used the value of statistical life (VSL) approach (37).
Of 213,994 people in the pooled sample, 10,237 persons (4.8%) had asthma (Table 1). During 2008–2013, the annual sample size ranged from 32,846 in 2010 to 38,974 in 2012, with prevalence ranging from 4.6% in 2012 to 4.9% in 2013. The average age in both groups was the same; however, the population with asthma had a larger proportion of children aged 5–14 years.
|Number in the pooled sample||10,237 (4.8%)||203,757|
|National estimates in millions*||15.4 (5.0%)||294.6|
|Number of children (age <18 yr), in millions||4.9 (6.4%)||71.2|
|Male (female)||42.6% (57.4%)||48.1% (51.9%)|
|Less than high school diploma||40.9%||33.5%†|
|High school graduate (or diploma)||23.4%||28.4%†|
|Income level (relative to poverty level)|
|Near poor (1.0–1.249)||7.0%||6.6%†|
|Low income (1.25–2.49)||16.2%||17.3%†|
|Middle income (2.5–4.49)||25.2%||28.7%†|
|High income (≥4.5)||22.5%||24.9%†|
|Charlson comorbidity index, mean||0.5635||0.2652†|
|Outpatient hospital visits||$471||$259†|
|Emergency room visits||$308||$147†|
|Days missed from school due to illness/injury||1.6||0.5†|
|Days missed from work due to illness/injury||2.2||1.5†|
Women and black individuals were more likely to have asthma. Married adults were less likely to have asthma. Among people with asthma, a larger proportion lived in poverty (<100% of the poverty line) or near the poverty line (from 100 to 125% of the poverty line). Persons with asthma had a significantly higher Charlson comorbidity index than did persons without asthma.
The proportion of persons covered by Medicaid was significantly higher in the asthma group (33%) than in the nonasthma group (17%). A smaller proportion of the asthma group (6%) was uninsured than in the nonasthma group (18%). Persons with asthma were also generally less educated and had lower incomes than their counterparts without asthma.
On average, the total unadjusted medical cost of people with asthma was more than twice that of people without asthma; this was also true for the remaining five categories of healthcare expenditure. On average, children and adults with asthma also missed significantly more days of school and work than those without asthma. We include more details on the methods and results for the annual estimates, variances, and confidence intervals in the online supplement.
Table 2 shows the results of the TPRM for six major medical expenditure categories for each year during 2008–2013 and for the pooled sample. The total annual per-person incremental medical cost of asthma for the pooled sample was $3,266, comprising expenditure for prescription medications of $1,830; office-based visits, $640; hospital-based outpatient visits, $176; ER visits, $105; and inpatient hospital admissions, $529. All point estimates were significant at the 95% confidence level. The results derived from the TPRM and the marginal effect analysis can also be applied to specific subpopulations of interest identified by the independent variables. For example, those living below the poverty line incur significantly higher incremental medical cost of asthma than those with higher income (Figure 1). Compared with $3,266 for the entire population, the average medical cost for women was $3,322; for children (aged <18 yr), $1,737; for black individuals, $3,145; for Hispanic individuals, $2,905; for high school graduates, $3,424; for the Medicaid population, $3,453; and for the uninsured, $2,145 (Table 3).
|Year||Total Medical Cost||Prescription Medications||Office-based Visits||Hospital-based Outpatient Visits||Emergency Room Visits||Inpatient Admissions|
|Pooled sample||$3,266* ($2,687 to $3,844)||$1,830* ($1,409 to $2,251)||$640* ($504 to $776)||$176* ($79 to $273)||$105* ($83 to $127)||$529† ($288 to $771)|
|2008||$2,698* ($1,979 to $3,417)||$1,544* ($1,290 to $1,789)||$499* ($325 to $674)||$118† ($8 to $227)||$110* ($45 to $175)||$600† ($51 to $1,148)|
|2009||$3,657* ($2,499 to $4,815)||$1,584* ($1,231 to $1,939)||$696* ($483 to $909)||$346† ($117 to $574)||$164* ($97 to $119)||$718† ($119 to $1,317)|
|2010||$3,027* ($2,189 to $3,865)||$1,585* ($1,316 to $1,854)||$513* ($316 to $710)||$9 (−$97 to $114)||$95* ($43 to $147)||$749† ($247 to $1,251)|
|2011||$4,022* ($2,512 to $5,532)||$1,805* ($1,458 to $2,152)||$768* ($515 to $1,020)||$240† ($53 to $429)||$130* ($77 to $183)||$872† ($107 to $163)|
|2012||$4,304* ($2,234 to $6,375)||$2,901* ($1,227 to $4,575)||$516* ($343 to $689)||$240 (−$46 to $526)||$71* ($28 to $115)||$456† (−$11 to $923)|
|2013||$3,728* ($2,215 to $5,240)||$2,196* ($1,564 to $2,827)||$1,010* ($452 to $1,569)||$181† ($47 to $316)||$124* ($64 to $183)||$43 (−$289 to $374)|
|Factor||Incremental Medical Cost of Asthma||95% Confidence Interval|
|All population||$3,265.6||($2,686.9 to $3,844.3)|
|Women||$3,322.4||($2,723.7 to $3,921.1)|
|Men||$3,199.8||($2,636.6 to $3,762.9)|
|Children (<18 yr)||$1,737.0||($1,443.8 to $2,030.1)|
|Adults (≥18 yr)||$3,760.8||($3,083.7 to $4,437.8)|
|Blacks||$3,144.5||($2,567.8 to $3,721.1)|
|Hispanics||$2,904.7||($2,363.5 to $3,445.9)|
|Whites||$3,323.0||($2,729.6 to $3,916.4)|
|Married||$3,346.1||($2,735.2 to $3,957.0)|
|High school diploma||$3,424.0||($2,810.8 to $4,037.1)|
|Bachelor’s degree||$3,365.2||($2,747.2 to $3,983.2)|
|Medicaid||$3,453.2||($2,776.0 to $4,130.5)|
|Medicare||$3,720.2||($3,022.8 to $4,417.6)|
|Private insurance||$3,248.5||($2,658.4 to $3,838.6)|
|Noninsured||$2,145.2||($1,767.6 to $2,522.8)|
|Poor||$3,581.3||($2,955.4 to $4,207.3)|
|Near poor||$3,274.0||($2,697.9 to $3,850.1)|
|Low income||$3,183.8||($2,615.4 to $3,721.1)|
|Middle income||$3,231.9||($2,642.5 to $3,821.2)|
|High income||$3,203.5||($2,605.4 to $3,801.6)|
During 2008–2013, the annual asthma prevalence was almost 5.0%, with the annual total medical cost nearly $50.3 billion based on the pooled sample. The prevalence of asthma in the United States ranged from 4.8% in 2008 and 2009 to 5.2% in 2011, and the total medical costs ranged from $39.3 billion in 2008 to $67.5 billion in 2012 (Table 4).
|Year||Prevalence of Asthma||Number of People with Asthma||Per-Person Incremental Medical Cost of Asthma||Total Medical Cost of Asthma (in Billions)||95% Confidence Interval|
|Pooled sample||5.0%||15,406,570||$3,266||$50.3||($32.0 to $68.7)|
|2008||4.8%||14,549,170||$2,698||$39.3||($21.8 to $56.7)|
|2009||4.8%||14,750,374||$3,657||$53.9||($25.4 to $82.5)|
|2010||5.1%||15,798,988||$3,027||$47.8||($27.3 to $68.4)|
|2011||5.2%||16,054,089||$4,022||$64.6||($46.6 to $82.5)|
|2012||5.0%||15,674,493||$4,304||$67.5||($40.9 to $94.1)|
|2013||4.9%||15,533,522||$3,728||$57.9||($28.3 to $87.6)|
Table 5 shows results of the negative binomial regression model for incremental days lost owing to asthma based on the pooled sample. Asthma was responsible for an additional 1.8 missed workdays and 2.3 missed school days per person per year. Nationally, over 8.7 million workdays and over 5.2 million school days were lost owing to asthma, amounting to a total loss of $3 billion. During 2008–2013, asthma caused, on average, 3,168 deaths, costing $29.0 billion per year (Table 6).
|Type of Days Lost||Incremental Days Lost (95% CI)||Weighted Number with Asthma||Mean Daily Wage (95% CI)||Total Value (in Billions) (95% CI)|
|Work||1.8 (1.2 to 2.4)||8,679,758||$120 ($118.2 to $122.6)||$1.9 ($1.6 to $2.1)|
|School||2.3 (1.9 to 2.7)||5,145,856||$89 ($86.4 to $91.8)||$1.1 ($0.6 to $1.5)|
|Average Annual Number of Deaths (95% CI)||Cost of Mortality (in Billions) (95% CI)|
|(3,122 to 3,347)||($28.1 to $29.9)|
To estimate the total economic impact of asthma on society, we combined medical, absenteeism, and mortality costs (Table 7). The total cost of asthma for the pooled sample was $81.9 billion.
|No. of Persons with Asthma (in Millions)||Total Cost of Asthma to Society (in Billions)||95% Confidence Interval (in Billions)|
|15.4||$81.9||($63.5 to $100.3)|
Our analysis underscores the serious and substantial economic burden of asthma on society. On the basis of the 2008–2013 pooled sample, annual per-person medical costs attributable to asthma were $3,266, and annual per-person expenditures for prescription medications exceeded the amount spent by persons without asthma by more than $1,800, amounting to 56% of total medical expenditures (Table 2). Recent studies support this finding (4, 27, 38, 39). The proportion of the combined expenditure for prescription medications and office-based visits exceeded 75%, compared with 19.4% for asthma-related (ER) visits and hospital admissions, which is also consistent with recent studies (4, 38).
Children with asthma missed 2.3 additional school days annually during 2008–2013 at a per-child cost of $207, notwithstanding loss of quality of life. This is consistent with other studies (4, 38, 40, 41). For adults, on average, asthma caused 1.8 days of missed work, resulting in almost $214 lost earnings per worker annually, which is consistent with previous studies (4, 27, 40). Our estimates of missed workdays and school days were also comparable with findings by both Wang and colleagues (41) and Sullivan and colleagues (27), respectively. Our mortality costs of asthma using the VSL method were higher than those reported by Barnett and Nurmagambetov, who used a human capital approach (4, 42).
During 2008–2013, the total cost of asthma based on the pooled sample was estimated at $81.9 billion, of which 61% was for medical costs; nearly 39% was attributable to absenteeism and mortality. These numbers are consistent with previous studies which have suggested that increased medical costs, influenced largely by costs of services and medications, are primarily responsible for increases in the total cost of asthma; alternatively, the value of missed workdays and school days is determined by wages, whereas mortality costs depend on the VSL (4, 27, 37).
Given that our analysis was based on treated asthma, the study excluded possible contributions to the costs by people with lifetime or current asthma who did not use any healthcare services in a given year (1). For example in 2013, among about 22.6 million people with current asthma, only 15.5 million had treated asthma, which means that about one in three persons with current asthma had no asthma-related encounter with a medical provider or a pharmacy in that year. Acknowledging data limitations for accurate estimation, we also did not include nonmedical costs, such as transportation, appointment wait time, presenteeism (not fully functioning at work because of illness), or intangible costs of pain and suffering. Consequently, our findings might actually underestimate the total cost of asthma.
Our results are comparable to those reported in 2013 by Jang and colleagues, who estimated trends in asthma costs from 2000 through 2009 using MEPS data (38). Those authors used lifetime asthma (vs. treated asthma), which may account for the higher cost of asthma: $47.2 billion in their study versus $39.3 billion in our study in 2008 and $69.4 billion in their study versus $53.9 billion in our study in 2009. Their prescription medication costs accounted for 44% versus 51% in our analysis. In a recent publication on healthcare expenditure in the United States, Bui and colleagues reported that in children with asthma, prescription costs account for over 47% of all medical costs associated with asthma, which is comparable to our 51% estimate (39).
Rappaport and colleagues used 2007 MEPS data to estimate direct and indirect costs of current asthma using a combination of propensity score matching and GLM (43). Their estimated $65.5 billion total cost of asthma in the United States is comparable to our estimates. Sullivan and colleagues studied adults aged 18 years and older based on 2003 and 2005 data (27). Their estimated $2,099 of per-person medical expenditure for asthma for 2005 is lower than our estimated $2,698 for 2008. Using treated asthma and the Heckman model, which differs conceptually and statistically from TPRM (32), they estimated that adults with asthma had 1.2 more missed workdays than adults without asthma. This is consistent with our results of 1.8 (95% confidence interval, 1.2–2.4) missed workdays regarding work absenteeism (Table 5).
In a series of articles (44–46), Sullivan and colleagues addressed healthcare use, absenteeism, mortality, and associated costs for school-aged children with asthma based on 2007–2013 MEPS data. They found that the total medical cost of asthma for school-aged children was almost $6 billion (in 2015 U.S. dollars). Using a human capital approach, they estimated the cost of 130 deaths at $211 million (in 2015 U.S. dollars). According to those authors, school-aged children with poor asthma control incurred $3,063 higher cost than children without asthma. Our results show that persons with no health insurance had a significantly lower incremental medical cost of asthma than the population average of $3,266, suggesting that these individuals may have either paid for their asthma care out of pocket and/or limited their care seeking compared with the population average.
Asthma also disproportionately affects people living in urban areas (47, 48). Previous studies have shown that indoor and outdoor environmental pollution are major factors contributing to higher risk for asthma attacks and higher cost of asthma. People with lower incomes often live in places with higher concentrations of environmental asthma triggers (49–52). On one hand, the results of this study suggest that poor people (with incomes <100% of the poverty threshold) have significantly higher medical costs because of asthma than those with higher incomes. On the other hand, having other levels of income (near poor, low, middle, high) does not seem to affect medical costs (Figure 1). People with very low income are also more likely to qualify for Medicaid, which essentially pays for high asthma treatment costs. Environmental interventions to reduce indoor asthma triggers for low-income families have been found to be cost-effective and are encouraged to reduce the burden of asthma (50, 52, 53).
Our results also show that black and Hispanic individuals have lower medical costs for asthma relative to the population average (Table 3). Multiple studies have demonstrated that these groups have consistently higher rates of hospitalizations and ER visits associated with asthma (54–56) but lower rates of asthma prescription medications and outpatient visits. This may explain their lower total medical cost of asthma because prescription medications and outpatient visits are the two largest contributors to total medical care costs (Table 2). Not having health insurance or high out-of-pocket costs for insured persons may preclude purchasing asthma medications, particularly long-acting antiinflammatory asthma drugs, or seeking regular outpatient care. Furthermore, language and health literacy barriers may also limit the effectiveness of asthma self-management education (57, 58). Medicaid or other health insurance coverage with lower out-of-pocket payments may improve access to routine care and prescription medications for persons with asthma in these groups.
This study suggests that the costs of prescription medications and office-based visits comprise the bulk of the medical costs of asthma. The combined costs of medical care, mortality, and absenteeism render the total cost of asthma a substantial and serious economic burden on society. These findings highlight the critical need to support and further strengthen asthma control strategies through increased provision of guideline-based care, improvements in self-management, and reduction of environmental asthma triggers to reduce ER visits, hospitalizations, absenteeism, and mortality.
|1 .||Centers for Disease Control and Prevention, National Center for Environmental Health. Data, statistics, and surveillance: asthma surveillance data. 2016 [accessed 2016 Nov 3]. Available from: http://www.cdc.gov/asthma/asthmadata.htm.|
|2 .||Carrier E, Cunningham P. Medical cost burdens among nonelderly adults with asthma. Am J Manag Care 2014;20:925–932.|
|3 .||Dashti R, Sedighy M, Pourpak Z, Ahmadiani MS, Fazlollahi MR, Bavarian B, et al. Direct economic cost of acute asthma in emergency department. Indian J Pediatr 2013;80:708–709.|
|4 .||Barnett SB, Nurmagambetov TA. Costs of asthma in the United States: 2002-2007. J Allergy Clin Immunol 2011;127:145–152.|
|5 .||Gergen PJ. Understanding the economic burden of asthma. J Allergy Clin Immunol 2001;107(5 Suppl):S445–S448.|
|6 .||Weiss KB, Sullivan SD. The health economics of asthma and rhinitis. I. Assessing the economic impact. J Allergy Clin Immunol 2001;107:3–8.|
|7 .||Haddix AC, Teutsch SM, Corso PS. Prevention effectiveness: a guide to decision analysis and economic evaluation. 2nd ed. New York: Oxford University Press; 2003.|
|8 .||Gold MR, Siegel JE, Russell LB, Weinstein MC, editors. Cost-effectiveness in health and medicine. New York: Oxford University Press; 1996.|
|9 .||Gold M. Panel on cost-effectiveness in health and medicine. Med Care 1996;34(12 Suppl):DS197–DS199.|
|10 .||Weinstein MC, Siegel JE, Gold MR, Kamlet MS, Russell LB. Recommendations of the Panel on Cost-effectiveness in Health and Medicine. JAMA 1996;276:1253–1258.|
|11 .||Russell LB, Gold MR, Siegel JE, Daniels N, Weinstein MC; Panel on Cost-Effectiveness in Health and Medicine. The role of cost-effectiveness analysis in health and medicine. JAMA 1996;276:1172–1177.|
|12 .||Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol 2000;106:493–499.|
|13 .||Lozano P, Sullivan SD, Smith DH, Weiss KB. The economic burden of asthma in US children: estimates from the National Medical Expenditure Survey. J Allergy Clin Immunol 1999;104:957–963.|
|14 .||Weiss KB, Sullivan SD. Socio-economic burden of asthma, allergy, and other atopic illnesses. Pediatr Allergy Immunol 1994;5(6 Suppl):7–12.|
|15 .||Weiss KB, Sullivan SD. The economic costs of asthma: a review and conceptual model. Pharmacoeconomics 1993;4:14–30.|
|16 .||Weiss KB, Gergen PJ, Hodgson TA. An economic evaluation of asthma in the United States. N Engl J Med 1992;326:862–866.|
|17 .||Lefrant JY, Garrigues B, Pribil C, Bardoulat I, Courtial F, Maurel F, et al.; CRREA Study Group; AzuRea Group. The daily cost of ICU patients: a micro-costing study in 23 French intensive care units. Anaesth Crit Care Pain Med. 2015;34:151–157.|
|18 .||Brennan A, Jackson A, Horgan M, Bergin CJ, Browne JP. Resource utilisation and cost of ambulatory HIV care in a regional HIV centre in Ireland: a micro-costing study. BMC Health Serv Res 2015;15:139.|
|19 .||Hendriks ME, Bolarinwa OA, Nelissen HE, Boers AC, Gomez GB, Tan SS, et al. Costs of cardiovascular disease prevention care and scenarios for cost saving: a micro-costing study from rural Nigeria. J Hypertens 2015;33:376–684.|
|20 .||O’Brien C, Fogarty E, Walsh C, Dempsey O, Barry M, Kennedy MJ, et al. The cost of the inpatient management of febrile neutropenia in cancer patients – a micro-costing study in the Irish healthcare setting. Eur J Cancer Care (Engl) 2015;24:125–132.|
|21 .||Parkinson F, Kent SJ, Aldous C, Oosthuizen G, Clarke D. The hospital cost of road traffic accidents at a South African regional trauma centre: a micro-costing study. Injury 2014;45:342–345.|
|22 .||McLaughlin AM, Hardt J, Canavan JB, Donnelly MB. Determining the economic cost of ICU treatment: a prospective “micro-costing” study. Intensive Care Med 2009;35:2135–2140.|
|23 .||Gold MR. Cost-effectiveness in health and medicine. New York: Oxford University Press; 1996.|
|24 .||Manning WG, Basu A, Mullahy J. Generalized modeling approaches to risk adjustment of skewed outcomes data. J Health Econ 2005;24:465–488.|
|25 .||Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ 2001;20:461–494.|
|26 .||Kamble S, Bharmal M. Incremental direct expenditure of treating asthma in the United States. J Asthma 2009;46:73–80.|
|27 .||Sullivan PW, Ghushchyan VH, Slejko JF, Belozeroff V, Globe DR, Lin SL. The burden of adult asthma in the United States: evidence from the Medical Expenditure Panel Survey. J Allergy Clin Immunol 2011;127:363–369.e3.|
|28 .||Agency for Healthcare Research and Quality (AHRQ). Medical expenditure panel survey. Rockville, MD: AHRQ [accessed 2017 Aug 1]. Available from: https://meps.ahrq.gov/mepsweb/.|
|29 .||Centers for Disease Control and Prevention, National Center for Health Statistics.. National Health Interview Survey. Hyattsville, MD: National Center for Health Statistics [accessed 2016 Jul 15]. Available from: http://www.cdc.gov/nchs/nhis/.|
|30 .||StataCorp. Stata: data analysis and statistical software. College Station, TX: StataCorp [accessed 2017 Nov 1]. Available from: http://www.stata.com/.|
|31 .||U.S. Department of Labor, Bureau of Labor Statistics. Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics [accessed 2016 Nov 15]. Available from: http://www.bls.gov/home.htm.|
|32 .||Belotti F, Deb P, Manning WG, Norton EC. twopm: two-part models. Stata J 2015;15:3–20.|
|33 .||D’Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson comorbidity index with administrative data bases. J Clin Epidemiol 1996;49:1429–1433.|
|34 .||Weisbrod BA. Economics of public health: measuring the impact of diseases. Philadelphia: University of Pennsylvania Press: 1961.|
|35 .||Jonsson B. Measuring economic burden in asthma. In: Weiss KB, Buist AS, Sullivan SD, editors. Asthma’s impact on society: the social and economic burden. New York: Marcel Dekker; 2000. pp. 251–267.|
|36 .||Centers for Disease Control and Prevention. CDC WONDER. Atlanta, GA: Centers for Disease Control and Prevention [accessed 2017 Nov 1]. Available from: http://wonder.cdc.gov/wonder/help/about-cdc-wonder-508.pdf.|
|37 .||Robinson LA, Hammitt JK. Valuing reduction in fatal illness risks: implications of recent research. Health Econ 2016;25:1039–1052.|
|38 .||Jang J, Gary Chan KC, Huang H, Sullivan SD. Trends in cost and outcomes among adult and pediatric patients with asthma: 2000-2009. Ann Allergy Asthma Immunol 2013;111:516–522.|
|39 .||Bui AL, Dieleman JL, Hamavid H, Birger M, Chapin A, Duber HC, et al. Spending on children’s personal health care in the United States, 1996–2013. JAMA Pediatr 2017;171:181–189.|
|40 .||Nurmagambetov T, Khavjou O, Murphy L, Orenstein D. State-level medical and absenteeism cost of asthma in the United States. J Asthma 2017;54:357–370.|
|41 .||Wang LY, Zhong Y, Wheeler L. Direct and indirect costs of asthma in school-age children. Prev Chronic Dis 2005;2(1). Available from: http://www.cdc.gov/pcd/issues/2005/jan/04_0053.htm.|
|42 .||Landefeld JS, Seskin EP. The economic value of life: linking theory to practice. Am J Public Health 1982;72:555–566.|
|43 .||Rappaport H, Bothapally V. The direct expenditures and indirect costs associated with treating asthma in the United States. J Allergy Ther 2012;3:1000118.|
|44 .||Sullivan PW, Ghushchyan V, Navaratnam P, Friedman HS, Kavati A, Ortiz B, et al. The national cost of asthma among school-aged children in the United States. Ann Allergy Asthma Immunol 2017;119:246–252.e1.|
|45 .||Sullivan PW, Ghushchyan VH, Navaratnam P, Friedman HS, Kavati A, Ortiz B, et al. National prevalence of poor asthma control and associated outcomes among school-aged children in the United States. J Allergy Clin Immunol Pract [online ahead of print] 25 Aug 2017; DOI: 10.1016/j.jaip.2017.06.039.|
|46 .||Sullivan PW, Ghushchyan VG, Navaratnam P, Friedman HS, Kavati A, Ortiz B, et al. School absence and productivity outcomes associated with childhood asthma in the USA. J Asthma [online ahead of print] 28 Apr 2017; DOI: 10.1080/02770903.2017.1313273.|
|47 .||Akinbami LJ, Moorman JE, Bailey C, Zahran HS, King M, Johnson CA, et al. Trends in asthma prevalence, health care use, and mortality in the United States, 2001–2010. NCHS Data Brief 2012;(94).|
|48 .||Aligne CA, Auinger P, Byrd RS, Weitzman M. Risk factors for pediatric asthma: contributions of poverty, race, and urban residence. Am J Respir Crit Care Med 2000;162:873–877.|
|49 .||Krieger JW. Home is where the triggers are: increasing asthma control by improving the home environment. Pediatr Allergy Immunol Pulmonol 2010;23:139–145.|
|50 .||Krieger JW, Song L, Takaro TK, Stout J. Asthma and the home environment of low-income urban children: preliminary findings from the Seattle-King County healthy homes project. J Urban Health 2000;77:50–67.|
|51 .||Alhanti BA, Chang HH, Winquist A, Mulholland JA, Darrow LA, Sarnat SE. Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age. J Expo Sci Environ Epidemiol 2016;26:180–188.|
|52 .||Kattan M, Stearns SC, Crain EF, Stout JW, Gergen PJ, Evans R III, et al. Cost-effectiveness of a home-based environmental intervention for inner-city children with asthma. J Allergy Clin Immunol 2005;116:1058–1063.|
|53 .||Sullivan SD, Weiss KB, Lynn H, Mitchell H, Kattan M, Gergen PJ, et al.; National Cooperative Inner-City Asthma Study (NCICAS) Investigators. The cost-effectiveness of an inner-city asthma intervention for children. J Allergy Clin Immunol 2002;110:576–581.|
|54 .||Pearlman DN, Zierler S, Meersman S, Kim HK, Viner-Brown SI, Caron C. Race disparities in childhood asthma: does where you live matter? J Natl Med Assoc 2006;98:239–247.|
|55 .||Akinbami LJ, Moorman JE, Garbe PL, Sondik EJ. Status of childhood asthma in the United States, 1980-2007. Pediatrics 2009;123(Suppl 3):S131–S145.|
|56 .||Ortega AN, Gergen PJ, Paltiel AD, Bauchner H, Belanger KD, Leaderer BP. Impact of site of care, race, and Hispanic ethnicity on medication use for childhood asthma. Pediatrics 2002;109:E1.|
|57 .||Le TT, Bilderback A, Bender B, Wamboldt FS, Turner CF, Rand CS, et al. Do asthma medication beliefs mediate the relationship between minority status and adherence to therapy? J Asthma 2008;45:33–37.|
|58 .||Gottlieb DJ, Beiser AS, O’Connor GT. Poverty, race, and medication use are correlates of asthma hospitalization rates: a small area analysis in Boston. Chest 1995;108:28–35.|
Supported by the Centers for Disease Control and Prevention.
The views expressed in this article do not necessarily communicate an official position of the Centers for Disease Control and Prevention.
Author Contributions: T.N.: made substantial contributions to conception and design, acquisition of data, and analysis and interpretation of data; drafted the submitted article and revised it for important intellectual content; provided final approval of the version to be submitted for publication; and is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved; R.K.: made substantial contributions to the design and interpretation of data; revised the article for important intellectual content; made substantial contributions to the text of the drafts of the paper; and provided final approval of the version to be published; and P.G.: made substantial contributions to conception and design as well as the analysis and interpretation of data; revised the article for important intellectual content; provided final approval of the version to be published; and is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org