Research in Social and Administrative Pharmacy j (2014) j–j

Original Research

Asthma medication use among U.S. adults 18 and older Maithili Deshpande, Ph.D.a,*, Betty Chewning, Ph.D.b, David Mott, Ph.D.b, Joshua M. Thorpe, Ph.D., M.P.H.c, Henry N. Young, Ph.D.d a Dean Clinic, Madison, WI, USA University of Wisconsin, Madison, WI, USA c University of Pittsburgh, PA, USA d University of Georgia, Athens, GA, USA

b

Abstract Background: Asthma is a chronic lung disease that currently affects an estimated 25 million Americans. One way to control the disease is by regular use of preventive asthma medications and controlled use of acute medications. However, little is known about adults with asthma and factors associated with their medication use. Objective: To identify factors associated with asthma medication use among U.S. adults aged 18 and older. Methods: Data were obtained from the 2006 to 2010 Medical Expenditure Panel Survey (MEPS). Medication use outcome variables include: a) daily use of a preventive asthma medication (yes/no) and b) overuse (3þ) of acute inhalers in last 3 months (yes/no). The Andersen Behavioral Model of Health Care was used to guide the selection of independent variables. The independent variables were categorized as predisposing, enabling and medical need factors. Logistic regression models were used to examine the relationship between asthma medication use in adults with asthma. Point estimates were weighted to the U.S. non-institutionalized population, and standard errors were adjusted to account for the complex survey design. Results: Compared to Whites, minority adults 18 and older were less likely to use preventive asthma medication daily (Hispanic-OR: 0.72, CI: 0.54–0.96; African American-OR: 0.62, CI: 0.51–0.75 respectively). Similarly, Hispanic adults age 18 and older were at a significantly higher likelihood of overusing rescue medications compared to Whites (OR: 1.47, CI: 1.03–2.11). Non-metropolitan adults age 18 and older were more likely to overuse acute asthma medications than those from Metropolitan Statistical Area (OR: 1.57, CI: 1.15–2.16). Compared to older adults age 65 and over, late mid-life 50–64 year old adults were less likely to use a daily preventive asthma medication (OR: 0.67, CI: 0.54–0.83). Conclusions: Race, rurality and age were important factors associated with poor asthma medication use in U.S. adults. Although this is a first step toward identifying factors that may influence the use of asthma medications, future studies are needed to develop and implement interventions to overcome issues to improve asthma care. Ó 2014 Elsevier Inc. All rights reserved. Keywords: Disparities; Asthma; Adults

* Corresponding author. N6959 Rock Lake Rd, Lake Mills, WI 53551, USA. Tel.: þ1 608 628 9358. E-mail address: [email protected] (M. Deshpande). 1551-7411/$ - see front matter Ó 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.sapharm.2014.02.006

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Background

Objectives

Asthma is a chronic lung disease that causes recurring periods of wheezing, chest tightness, shortness of breath, and coughing.1 An estimated 25 million Americans are currently diagnosed with asthma. It affects patients and their families’ quality of life, productivity at work and school, health care utilization and can even result in death.2 Since asthma currently cannot be cured, the goal of any asthma therapy is to control the disease. According to the Expert Panel Report 3 (EPR3), “Guidelines for the Diagnosis and Management of Asthma,” appropriate medication therapy including regular use of preventive asthma medications (i.e., long-term controllers) and controlled use of acute asthma medications (i.e., rescue medications) are a cornerstone of successful asthma therapy.3 While a range of asthma medications exist, some studies have documented racial disparities in access to health care providers, access to and use of asthma medications and asthma-related outcomes.4–7 For example, Krishnan et al found that African Americans were less likely to report specialist care than Whites.4 Compared to Whites, African Americans also reported lower daily use of inhaled corticosteroids, education to avoid triggers, and use of specialist care.4 However, previous research examining the use of asthma medications predominately focuses on children/ adolescents and insured populations or provides combined child and adult estimates of medication use.4,5,8,9 Little is known about asthma medication use in the general adult population and disparities in groups such as older adults and rural residents.10 The present study uses a nationally representative sample and theory to guide the examination of factors that may impact asthma medication use in U.S. adults. This study used the Andersen’s Behavioral Model of Health Services Utilization in order to identify the factors associated with asthma medication use among U.S. adults.11 The Andersen model has been widely used to identify factors associated with health services use including medication use.12–20 Andersen’s model suggests that patients’ use of health services such as medications is based upon their predisposition to use services, factors which enable use and their need for care. Predisposing, enabling and need variables were hypothesized to affect the medication use behaviors among adults (Fig. 1.)

The objectives of this study are to (1) examine the use of preventive and acute asthma medications and (2) identify predisposing, enabling and need factors associated with asthma medication use in U.S. adults.

Methods A cross-sectional, retrospective study was designed utilizing the data from 2006 to 2010 Medical Expenditure Panel Survey21 (MEPS) for asthmatic patients 18 years or older. MEPS is a nationally representative survey that collects data on health and health care utilization for the civilian, non-institutionalized U.S. population through an overlapping panel design. The MEPS sample is drawn from a nationally representative sub-sample of households that participated in the previous year’s National Health Interview Survey (NHIS) and accounts for an oversample of Hispanics, African Americans, Asians and low-income families. Detailed information about MEPS can be found at its website http://meps.ahrq.gov/mepsweb. This study was granted Institutional Review Board approval from the University of Wisconsin–Madison. Sample selection Current asthma status of a person was determined using self-report. MEPS respondents are asked to self-identify if they were ever diagnosed with asthma. Those who respond positively are then asked if they “still have asthma” and if they had “experienced an episode of asthma or an asthma attack in the past 12 months.” Those who responded positively to either of these two questions were classified as having “current asthma.” Further, only those 18 years of age or over were retained in the final data set. Dependent variables According to the National Heart Lung and Blood Institute (NHLBI) guidelines, a long-term controller medication (preventive medication) is recommended for treatment of persistent asthma, with no overuse (3þ canisters) of acute “rescue” medications as an indicator of asthma control.3 For the preventive medication use variable, those who respond “Yes” to “is now taking preventive medication daily or almost daily” were classified as current users of preventive asthma medications.

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Fig. 1. A conceptual model of utilization of asthma medications based on Andersen Behavioral Model of Health Services Use.

For overuse of acute medications, respondents who responded “Yes” to “used more than three canisters of this type of inhaler in the past 3 months” were classified as having overused acute asthma medications. In order to validate selfreported asthma diagnosis, MEPS administrators contact a sample of medical providers by telephone to obtain information on diagnosis and procedure codes. Additionally, we used the Prescribed Medications File in order to further validate self-reported medication use. MEPS keeps a record of medication utilization in the Prescribed Medications file and each record on this event file represents a unique prescribed medicine event; that is, a prescribed medicine reported as being purchased or otherwise obtained by the household respondent. We identified asthma patients with an ICD-9 code of 493 (Asthma). Respondents’ preventive asthma medications were identified on a case-by-case basis. Coding of the preventive asthma medications

was assessed by two researchers to ensure reliability. A count variable was developed based on the number of prescribed events for each individual in the final dataset. This count of prescribed preventive asthma medications provides a cross check for the commonly used self-reported logistic regression models. Independent variables The Andersen Behavioral Model of Health Services Utilization was used to guide the selection of independent variables.11 Predisposing factors included age, race/ ethnicity, sex and marital status. Age was categorized as 18–24 years old, 25–35 years old, 36–49 years old, 50–64 years old and 65 and older. Race was defined as white, black, Hispanic and other race, and ethnicity was measured as Hispanic or non-Hispanic. Marital status included never married, married, divorced, separated or widowed. We also included two questions related to

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patient attitude, “I can overcome illness without medical help” and “I am more likely to take risks” on a 5-point Likert scale of Strongly Agree to Strongly Disagree. The enabling factors included were federal poverty status, region of country, education level, health and prescription insurance status, and rurality. Poverty status was categorized based on Federal Poverty Level (FPL) as poor/near poor (less than 125% of FPL), low income (125% to less than 200% of FPL), middle income (200% to less than 400% of FPL) and high income (R to 400% of FPL). Region of country included South, Midwest, Northeast and West while education levels included no degree, high school/GED/other degree, Bachelors/Masters/ Ph.D. Individuals who had insurance that provides coverage for hospital and physician care (other than Medicare, Medicaid, or other public programs) were classified as having private insurance. Individuals were considered to have public coverage if they were not covered by private insurance and they were covered by one of the following: Medicare, Medicaid, or other public hospital/physician coverage. The uninsured were defined as people with no reported coverage for the entire period. Prescription drug coverage was dichotomized as ever having prescription drug coverage or not having any coverage during the study period. Rurality was measured as a dichotomous variable indicating residence in a Metropolitan Statistical Area (MSA; more urban) or non-MSA (more rural) area. Usual source of health care was defined as having or not having a usual source of care. Two follow-up questions to those who had usual source of care were also included. These asked if the usual source of care asked the person to help make decisions between a choice of treatments and if the usual source of care usually asked about prescription medications and treatments other doctors may have given them. These were classified as yes/no/no usual source of care. Two other access to care variables included in the study were: (a) access to medical treatment, “I was unable to get necessary medical care” (yes (1)/no (0)) and (b) access to prescription medication, “I was unable to get prescription care” (yes (1)/no (0)). Lastly, Health Professional Shortage Areas (HPSA) usually have poor access to services. HPSA data was obtained at the MEPS data center, Rockville, MD. HPSA were classified as none of the county is designated as a shortage area (yes (1)/no (0)), one or more parts of the county are designated as

a shortage area (yes (1)/no (0)) and whole county was designated as a shortage area (yes (1)/no (0)). The medical need factors include the SF-36 Physical Component Summary (PCS) and Mental Component Summary (MCS), current smoker (yes (1)/no (0)), overweight/obese (yes (1)/no (0)) and the Charlson Comorbidity Index based on the comorbid conditions available in the MEPS household component file. The PCS and MCS are available as continuous variables in the data set. Comorbid conditions, identified through the MEPS medical condition file, were assigned a score according to the adapted Charlson Comorbidity Index22 described by D’Hoore et al.23 Overweight or obese was defined as body mass index over 25.0.24 We also included perceived physical and perceived mental health status as need variables. Both variables were coded as Excellent (1), Very good (2), Good (3) and Fair/poor (4). Another need variable that serves as a proxy to asthma severity is asthma specific ER visits. We counted the number of asthma specific ER visits using the Emergency Room Visits File using the ICD-9 code for asthma (493). Data analysis Data were analyzed using STATA version 12.25 Less than 10% of the data were missing. To address missing data, we used multiple imputation to obtain 10 complete data sets.26 In order to account for the complex survey design, person weight, stratum and primary sampling unit variables were specified in the data analysis to obtain appropriate standard errors.27 Demographic characteristics of the sample including means and frequencies were calculated and bivariate logistic regression was used to identify key independent variables predictive of use of preventive asthma medications and overuse of acute asthma medications.28 Multivariate logistic and zero-inflated negative binomial regression analyses were conducted to identify the relationships between predisposing, enabling and need variables and both medication use behaviors.

Results Table 1 describes the characteristics of the study sample. A total of 5308 (weighted sample of 57,366,827) self-reported asthmatics age 18 and older were identified and included in this analysis. About 65% were women and about 48% were married. Seventy-one percent were

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non-Hispanic White, 13% were African American and 10% were Hispanic. About 18% of the adults lived in a non-MSA area. Only 28% of the asthmatic population was using a preventive medication on a daily basis for their asthma, while 9% reported overusing acute care medications (Table 1). Table 2 describes the unadjusted bivariate relationships between the two dependent variables and the Andersen Model variables. Race, age and rurality relationships had notable disparities with both medication use behaviors. African American and Hispanic adults were found to be significantly less likely to use a preventive medication daily than Whites (OR: 0.59, CI: 0.49–0.72 and OR: 0.61, CI: 0.49–0.75 respectively). They also were more likely to overuse acute medications than Whites (OR: 1.3, CI 1: 10–1.94 and 1.46, CI 1: 01–1.65 respectively). All younger adults were less likely to use preventive medication daily and less likely to overuse acute asthma medications compared to adults age 65 and older. Rural adults were significantly more likely to overuse acute asthma medications than urban adults (OR: 1.74, CI: 1.3–2.3). We also found that rural adults were less likely to use preventive asthma medication daily, but not significantly so (OR: 0.95, CI: 0.78–1.15) (Table 2). Multivariate logistic regression models found that predisposing factors such as race and age, enabling factors such as income and rurality and need factors such as smoking, comorbidities, ER visits and perceived health status were associated with asthma medication use in adults age 18 and older (Table 3). African American adults age 18 and older were 38% less likely to be currently using any preventive asthma medication than Whites age 18 and older after adjusting for predisposing, enabling and need variables (OR: 0.62, CI: 0.51–0.75). Hispanic adults age 18 and older were 28% less likely to be currently using any preventive asthma medication than Whites age 18 and older (OR: 0.72, CI: 0.54–0.96). Hispanics also were at a significantly higher likelihood of overusing acute medications compared to Whites after controlling for predisposing, enabling and medical need factors (OR: 1.47, CI: 1.03–2.11). Compared to adults 65 and older, all younger populations were found to be less likely to use a preventive asthma medication daily as well as less likely to overuse an acute medication. We also found that adults in non-metropolitan areas were more likely to overuse asthma medication than those in metropolitan areas (OR: 1.57, CI: 1.15–2.16).

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Adults who were current smokers were found to be significantly less likely to use a preventive medication daily (OR: 0.71, CI: 0.58–0.86). A higher ER count was associated with significantly higher likelihood of using a preventive asthma medication daily as well as overusing acute asthma medication (OR: 1.28, CI: 1.06–1.54; OR: 1.84, CI: 1.30–2.61 respectively). Lastly, adults with a higher comorbidity score were more likely to use a preventive asthma medication daily as well as overuse acute medications (OR: 1.18, CI: 1.11–1.24; OR: 1.13, CI: 1.05–1.21 respectively). The results of zero-inflated negative binomial regression model for the count of preventive asthma medications were also found to be similar (results not shown). Discussion Previous studies on adults with asthma provide asthma care estimates that combine adolescents/ children and adults, focus on asthma related ER visits, hospitalizations and quality of life or are limited to insured populations.4,5,8,9,29 This study solely focuses on adults and their use of asthma medication. Results from this nationally representative sample of adults age 18 and older suggests that asthma medication use among adults may be problematic. The national guidelines advocate for regular use of preventive asthma medication and controlled use of acute asthma medications in order to control asthma.3 Previous studies have found 11% overuse of acute asthma medications in a 1996–2000 MEPS study that included 5 and older asthmatics9 to 16% overuse in a managed care population study that included adult asthmatics.30 However, this study found that only 28% of adults age 18 and older use preventive asthma medications while 9% overuse acute asthma medication. The Andersen Model predisposing, enabling and need factors were found to be significantly associated with both medication use behaviors. More specifically, the predisposing factors race/ ethnicity and age were found to be important factors associated with preventive asthma medication use and overuse of acute medications in this population. Although our study controlled for a number of factors including predisposition of the population to use medical care, access to care and attitudes about the health system and the physician, race-related disparities still persisted. Similar to previous research, we also found that

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Table 1 Description of sample: U.S. adults 18 and older Study variables

U.S. adults 18 and older Unweighted n ¼ 5308 Weighted n ¼ 57,366,827 Weighted mean or weighted percent (standard error)

Predisposing characteristics Age, years 46.2 (0.29) Male, % 35.3 Race/ethnicity, % Hispanic 9.9 Black, non-Hispanic 12.7 Asian/other, 6.2 non-Hispanic White, non-Hispanic 71.2 Marital status, % Married 47.9 Widow/divorced/ 24.1 separated Single-unmarried 27.9 More likely to take risks than the average person, % Disagree strongly 42.6 Disagree somewhat 21.6 Uncertain 14.3 Agree somewhat 16.5 Agree strongly 4.9 Enabling characteristics Education, % No degree 17.6 HS/GED/other 57.6 BS/MS/PhD 24.8 Poverty status, % Poor/near poor 21.1 Low income 13.9 Middle income 29.1 High income 35.9 Health insurance status, % Any private 65.4 Public 23.7 Uninsured 10.9 Prescription insurance, % 76.3 Region, % Northeast 20.1 Midwest 22.4 South 33.8 West 23.6 Non-metropolitan 17.7 statistical area, % Unable to get medical 6.2 care, % Unable to get 6.1 prescription medications, % (Continued)

Table 1 (Continued ) Study variables

U.S. adults 18 and older Unweighted n ¼ 5308 Weighted n ¼ 57,366,827 Weighted mean or weighted percent (standard error)

Has usual source of care 84.1 (USC), % USC asks about 67.1 prescription medications and treatments other doctors may give them, % USC explores treatment 76.6 options with the patient, % Need characteristics Smoking, % 21.9 Overweight/obese, % 70.6 Mean adapted Charlson 1.6 (0.05) comorbidity score aggregate ER count 0 97.5 ER count 1 1.8 ER count R 2 0.7 Mean physical 44.5 (0.29) component summary Mean mental component 48.4 (0.13) summary Perceived physical health status, % Excellent 27.9 Very good 30.2 Good 26.9 Poor/fair 14.9 Perceived mental health status, % Excellent 32.6 Very good 26.9 Good 27.2 Poor/fair 13.2 Dependent variables, % Use of preventive 27.5 medication Overuse of rescue 9.2 medication

minority populations were at risk for worse asthma medication use.31,32 Consistent with other studies,9,29,30 this study also found that minorities were less likely to use a preventive asthma medication and more likely to overuse an acute medication. These results may indicate the need to explore other avenues that may contribute to

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Table 2 Objective 1: U.S. adults 18 and older: bivariate analysis Preventive medications

Race/ethnicity African American Hispanic Asian/other White Rurality Non-MSA MSA Age 18–24 25–35 36–49 50–64 65 and older

Overuse of acute medications 95% CI

Odds ratioa

95% CI

0.59*** 0.61*** 0.49*** Reference

0.49–0.72 0.49–0.75 0.33–0.72 Reference

1.29* 1.46** 1.00 Reference

1.10–1.94 1.01–1.65 0.67–1.49 Reference

0.95 Reference

0.78–1.15 Reference

1.74*** Reference

1.31–2.33 Reference

0.18*** 0.28*** 0.43*** 0.55*** Reference

0.13–0.24 0.21–0.35 0.34–0.53 0.46–0.66 Reference

0.21*** 0.25*** 0.50*** 0.63** Reference

0.14–0.33 0.17–0.36 0.37–0.68 0.47–0.86 Reference

Odds ratio

a

*P ! 0.05, **P ! 0.01, ***P ! 0.001. a Unadjusted odds ratio.

these racial disparities in adults. Previous research has identified 4 domains that may be potential drivers of racial/ethnic disparities in pediatric asthma. These include lower expectations about treatment and control,33 parental concern about adverse effects from anti-inflammatory medications,34 positive interactions with providers,35 and competing family priorities that are common barriers to the use of preventive health care services for children.36 The underlying mechanism to introduce behavioral change for improved disease management in minority adults is poorly understood. Rigorous studies of asthma management interventions in adult minority populations are also lacking. Therefore, research examining the patterns, reasons and barriers to the appropriate use of preventive medication in adult populations is needed to discern the root causes of disparities in an effort to develop interventions to overcome such disparities. Our study also found that all adults younger than 65 years of age were less likely to use a preventive asthma medication. Hong et al analyzed MEPS data for asthmatics age 5 and older, and found that adults less than 65 years of age used fewer preventive asthma medications.9 This age disparity becomes an important issue for adults closest to Medicare eligibility (50–64 years old) due to their increased likelihood of comorbid conditions and reduced access to care in comparison to younger adults.37 We also found that all adults younger than 65 years of age were

less likely to overuse acute asthma medications. One plausible explanation for this finding is Chronic Obstructive Pulmonary Disease (COPD) diagnosis and treatment in older adults. COPD is usually seen in older adults and has constant and progressive symptoms similar to asthma.38 Treatment of COPD with bronchodilator medications is fundamental for symptomatic management of the disease and is prescribed on an as-needed basis for patients.38 It may be possible that the overuse of bronchodilator medications in older adults may be due to COPD treatment. The enabling factor – rurality was found to be significantly associated with asthma medication use in adults 18 and older. Adults age 18 and older from rural areas were more likely to overuse acute asthma medications than those from urban areas. This is troubling because overuse of acute asthma medications can result in poor asthma control, exacerbation, ER utilization and hospitalizations.5,9,11 Previous studies in urban populations have found that race/ethnicity, cost of treatment and quick symptom relief are associated with the overuse of acute asthma medications.29,39 For example, Cole et al conducted a qualitative study of bronchodilator use in young adults aged 20–32 years. Results from their study indicated that easy access to medications (due to low costs) and quick relief of symptoms contributed to the overuse of acute asthma medications.39 However, scant research has examined the overuse of acute asthma medications in rural populations.

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Table 3 Objective 1: U.S. adults 18 and older: multivariate logistic regression Study variables

Predisposing characteristics Age 18–24 25–36 36–49 50–64 65 and older Male Race/ethnicity Hispanic African American Asian/other White More likely to take risks than the average person Marital status Single-unmarried Widow/divorced/separated Married Enabling characteristics Education status HS/GED/other BS/MS/PhD No degree Poverty status Low income Middle income High income Poor/near poor Health insurance status Public Any private Uninsured Prescription insurance Region Northeast Midwest West South Unable to get medical care Unable to get prescription medications Has usual source of care (USC) USC asks about prescription medications and treatments other doctors may give them USC does not ask about prescription medications and treatments other doctors may give them No care/USC USC asks the person to help make decisions between a choice of treatments USC doesn’t ask the person to help make decisions between a choice of treatments No care/USC

Odds ratio (95% CI) Use of preventive medications

Overuse of acute medications

0.30*** (0.20–0.45) 0.48*** (0.36–0.63) 0.64** (0.50–0.83) 0.67** (0.54–0.83) Reference 0.99 (0.85–1.16)

0.33*** (0.19–0.56) 0.45*** (0.30–0.69) 0.71 (0.48–1.03) 0.73* (0.52–0.97) Reference 1.5*** (1.14–1.94)

0.72** (0.54–0.96) 0.62*** (0.51–0.75) 0.54** (0.35–0.81) Reference 0.95 (0.89–1.03)

1.47* (1.03–2.11) 1.13 (0.86–1.48) 1.07 (0.73–1.58) Reference 1.03 (0.94–1.12)

1.05 (0.78–1.41) 1.07 (0.91–1.26) Reference

1.34 (0.82–2.19) 1.05 (0.75–1.48) Reference

0.91 (0.74–1.12) 0.93 (0.71–1.22) Reference

0.85 (0.64–1.14) 0.60** (0.41–0.88) Reference

0.99 (0.78–1.25) 0.91 (0.71–1.15) 1.20 (0.90–1.59) Reference

1.09 (0.74–1.60) 0.99 (0.70–1.41) 0.78 (0.47–1.30) Reference

1.71** (1.18–2.47) 1.55* (1.06–2.56) Reference 1.22 (0.95–1.58)

1.26 (0.79–2.02) 1.08 (0.61–1.91) Reference 1.25 (0.91–1.73)

1.10 (0.84–1.44) 1.09 (0.88–1.34) 0.75** (0.61–0.94) Reference 0.62* (0.43–0.92) 1.52** (1.08–2.14) 1.37 (0.80–2.35) 0.99 (0.63–1.59)

1.08 (0.73–1.59) 0.77 (0.55–1.06) 0.86 (0.64–1.15) Reference 0.71 (0.42–1.21) 1.28 (0.82–1.98) 1.69 (0.66–4.32) 0.82 (0.34–2.01)

1.14 (0.70–1.84)

0.90 (0.36–2.23)

Reference 0.94 (0.60–1.48)

Reference 1.22 (0.54–2.71)

0.99 (0.67–1.45)

0.93 (0.53–1.65)

Reference

Reference (Continued)

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Table 3 (Continued ) Study variables

Rurality – MSA classification Non-metropolitan statistical area Metropolitan statistical area Health provider shortage area (HPSA) No shortage Whole county shortage Partial county shortage Need characteristics Current smoker Overweight/obese Charlson comorbidity score ER visits Perceived physical health Perceived mental health Physical component summary Mental component summary

Odds ratio (95% CI) Use of preventive medications

Overuse of acute medications

0.86 (0.69–1.04) Reference

1.57* (1.15–2.16) Reference

1.11 (0.74–1.67) 0.89 (0.76–1.01) Reference

1.14 (0.82–1.58) 0.98 (0.75–1.29) Reference

0.71*** 0.78 1.18*** 1.28* 1.14 0.87* 0.99 1.00

1.13 0.94 1.13** 1.84*** 1.28** 1.07 0.98** 0.99

(0.58–0.86) (0.62–1.00) (1.11–1.24) (1.06–1.54) (0.96–1.36) (0.79–0.95) (0.98–1.03) (0.99–1.01)

(0.85–1.51) (0.70–1.24) (1.05–1.21) (1.30–2.61) (1.10–1.48) (0.93–1.22) (0.97–0.99) (0.98–1.02)

*P ! 0.05, **P ! 0.01, ***P ! 0.001. OR, odds ratio; CI, 95% confidence interval; HS, High school; BS, Bachelor of science; MS, Master of science; PhD, Doctor of philosophy.

Lastly, the presence of multiple comorbidities also was related to asthma medication use. Multiple comorbidities were positively associated with the likelihood of being on a preventive asthma medication as well as a higher likelihood of overusing acute asthma medications. In general, those who have multiple chronic conditions use health services more frequently and have to manage multiple medications.40 Patients who are prescribed multiple medications may suffer increased risk for adverse drug events.40–42 In addition, patients with multiple chronic conditions may encounter greater health care quality problems.43 Given these complex relationships, adults with asthma may need additional help to manage their asthma as well as their other conditions.

Limitations As with any non-experimental study, it is difficult to make any causal conclusions from this study. Second, due to the non-specificity of the survey, not all asthma-specific variables are available. Proxy variables such as Charlson comorbidity index and asthma related ER visits may not adequately capture disease severity. Third, MEPS diagnosis codes are based on patient self-reports and may be inaccurate. The accuracy

of self-reported diagnosis and medication use is often not directly verifiable by medical providers in MEPS. Although this study does not explicitly identify persistent asthmatics, a vast majority of asthmatics (about 77%) have moderate to severe persistent asthma.44 Fourth, our sample consisted of unique responses of study participants (i.e., non-overlapping panels). It is possible that some independent variables may have changed over time and could have impacted the medication use behavior. Lastly, this study fails to describe the reasons that lead to the report of under or overuse of medications (e.g., lack of a prescription, the prescription was not filled, etc.).

Conclusion Daily use of preventive asthma medications and controlled use of acute asthma medications is a major component of asthma care. However, it is apparent that preventive asthma medications are largely underused and there is some overuse of acute asthma medications among U.S. adults. This study identified various factors that were associated with asthma medication use behavior among U.S. adults. Rural adults, late mid-life adults and minorities were vulnerable to poor asthma medication use. Although this is a first step toward identifying factors that influence

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asthma medication use among adults, future studies are needed to develop and implement interventions to overcome issues to improve asthma care. Acknowledgments The research in this paper was conducted at the CFACT Data Center and the support of AHRQ is acknowledged. The results and conclusions in this paper are those of the author and do not indicate concurrence by AHRQ or the Department of Health and Human Services.

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