Benjamin Gutierrez, PhD

 To assess whether increased short-acting β2-adrenergic agonist (SABA) claims are associated with asthma exacerbations and increased healthcare costs.

 Patients (N = 93,604) were health plan members aged 6-56 years with at least 2 years of enrollment between July 1, 2003, and June 30, 2007, an asthma diagnosis, and at least 1 asthma medication claim per study year. Two years of administrative claims were collected. SABA use was categorized as 0 (none), ½ to 2 (low), 2½ to 6 (moderate), 6½ to 12 (high), and more than 12 (excessive) canister equivalents per year. Multivariate analyses were adjusted for age, sex, geographic region, comorbidities, specialist consultation, controller medication use, and asthma severity.

Half of high and excessive SABA users had few or no controller claims. Compared with SABA nonusers, high and excessive SABA users had significantly higher odds (odds ratio [95% confidence interval]) of asthma-related emergency department/urgent care visits (6.47 [5.25, 7.98] and 7.68 [6.04, 9.76], respectively), hospitalizations (5.37 [6.04, 9.76]; 6.90 [4.90, 9.73]), and oral corticosteroid use (2.89 [2.72, 3.08]; 3.71 [3.41, 4.03]). Excessive SABA users had 3.0 times ($1791) and high SABA users had 2.2 times ($1326) higher asthma-related healthcare costs than low SABA users ($595). Total costs also increased with higher SABA use, but with smaller incremental differences between excessive and high SABA users and low SABA users.

 Increased SABA use is associated with higher total and asthma-related healthcare costs. Opportunity exists to lessen overreliance on SABAs.

Although National Asthma Education and Prevention Program guidelines for asthma management recommend a stepwise approach to pharmacologic therapy, short-acting b2-adrenergic agonist (SABA) rescue medications remain overused, while controller medications are underprescribed.

Overuse of SABA is a measure of poor asthma control, is associated with increased asthma severity and inappropriate or inadequate controller therapy, and is negatively associated with specialist care and comorbidity management.

Patients with increased SABA use are at increased risk for exacerbations and incur greater total and asthma-related costs.

Substantial opportunity exists to improve asthma management and lower exacerbation-related healthcare costs.

Asthma is a chronic condition that affects more than 20 million people in the United States and imposes a substantial health and economic burden, eg, 13.6 million physician office visits in 2004, 1.8 million emergency department (ED) visits annually, 504,000 hospitalizations annually from 2001 to 2003,

and in 2007, more than $14.7 billion in direct medical costs.

 Many of these costs could have been/could be prevented with appropriate asthma management.

 Guidelines for the diagnosis and management of asthma developed by the National Asthma Education and Prevention Program recommend a stepwise approach to pharmacologic therapy.

 For persistent asthma, recommended therapy includes daily use of controller medication to prevent exacerbations and minimize the need for rescue medication. Increasing use of short-acting β2-adrenergic agonists (SABAs) or use of a SABA more than twice a week to relieve symptoms “generally indicates inadequate asthma control and the need to initiate or intensify anti-inflammatory therapy.”

 Long-term daily use of SABAs is not recommended; however, controller medications remain underprescribed and rescue medications overused.

Previous research suggests that increased SABA use is associated with diminished asthma control and an elevated risk of exacerbations.

 Moreover, 3 months of elevated SABA use has been associated with higher asthma-related healthcare costs,

 although annual costs associated with SABA use are unknown. Exacerbation-related hospitalizations and ED visits are an important component of these costs. To determine whether increasing SABA use is associated with greater healthcare utilization and costs, we conducted a  retrospective study in a large population of health plan members.

Data were obtained from IMS LifeLink, a de-identified, Health Insurance Portability and Accountability Act (HIPAA)—compliant database containing enrollment and claims data for 50 million persons from approximately 70 US health plans. Eligible patients had at least 2 years of continuous enrollment in a health plan during July 1, 2003, to June 30, 2007, were aged 6 to 56 years, had a diagnosis of asthma, and had at least 1 prescription claim for an asthma medication in each of the preindex and postindex years. Asthma diagnosis criteria consisted of (1) at least 1 asthma-related ED visit, urgent care (UC) visit, or hospitalization with a primary 

International Classification of Disease, Ninth Revision, Clinical Modification

] diagnosis code of 493.xx, or (2) at least 2 asthma-related physician office visits (

 493.xx), or (3) at least 2 prescription claims for asthma medication (SABA or controller). Primary controllers included inhaled corticosteroids (ICS), ICS/long-acting β2-agonist combinations (ICS/LABA), and leukotriene modifiers (LMs). Adjunctive controllers included mast cell stabilizers, methylxanthines, and omalizumab. The upper age limit was chosen to exclude patients with a higher likelihood of undiagnosed chronic obstructive pulmonary disease (COPD) and because it is a criterion in the Health Plan Employer Data and Information Set (HEDIS) of the National Committee on Quality  Assurance.

 Exclusion criteria were a diagnosis of COPD, emphysema, chronic bronchitis, bronchiectasis, cystic fibrosis, or respiratory tract cancer, and more than 1 drug claim  for ipratropium or tiotropium (surrogate markers for COPD).

 Patients diagnosed with multiple sclerosis, inflammatory bowel disease, rheumatoid arthritis, systemic lupus erythematosus, psoriasis, polymyalgia rheumatica, and temporal arteritis were excluded because these conditions often require long-term treatment with oral corticosteroids (OCS).

Enrollment and claims data were collected for a 2-year period between July 1, 2003, and June 30, 2007. When more than one 2-year observation period was available for a patient, the most recent period was selected. The index date was defined as the date occurring 1 year after the beginning of the 2-year continuous enrollment period and did not otherwise correspond to the dispensing of any medication or any specific event. The observation period was divided into 12-month preindex and postindex periods, similar to other studies.

 Baseline characteristics of the population were identified in the preindex period, and the relationships between SABA exposure, exacerbations, and healthcare costs were analyzed in the postindex period. Data collected included demographics, outpatient prescription drug claims (National Drug Code, dispensing date, quantity dispensed, days of supply, and billed charges), and inpatient and outpatient claims (service date, 

 diagnosis codes, Current Procedural Terminology-4 procedure codes, and billed charges). Costs were imputed by IMS LifeLink for claims with missing cost information that were due to capitation arrangements negotiated with participating plans.

 Pharmacy fills for metered-dose inhaler (MDI) and nebulized SABA and other asthma medications were identified using National Drug Codes.

Asthma exacerbations were identified using primary diagnosis codes in healthcare claims, with an exacerbation defined as an asthma-related (

 493.xx) ED or UC visit; an asthma-related hospitalization; or a pharmaceutical claim for an OCS. Each OCS claim, regardless of days of supply, was counted as 1 dispensing (90% of OCS fills had a supply of 21 days or fewer). Proxy variables for asthma severity were created based on the presence of 1 or more claims in the preindex period for OCS and asthma-related ED/UC visits and hospitalizations. Healthcare costs were derived from billed charges in inpatient, outpatient, and pharmaceutical claims in the postindex period.

SABA exposure was calculated using methods similar to those previously published.

 Canister equivalents (CEs) were assigned to each dispensed SABA product to account for MDI and nebulized SABA use. A standard MDI canister size, or 1 CE, was defined as 200 metered actuations of albuterol; levalbuterol was considered to be equivalent in potency to albuterol. For pirbuterol, in which a standard dispensing contains 400 actuations, 1 canister was considered to be 2 CEs. For claims in which canister size was unclear, CEs were imputed based on days of supply. We considered a 3-mL ampule of nebulized albuterol to be equivalent to 4 actuations of albuterol from an MDI, so 50 3-mL ampules of nebulized SABA was considered 1 CE. For analysis, CEs were rounded to the nearest one-half CE. A categorical variable adapted from a previously validated risk stratification scheme

 was created to characterize SABA exposure as 0 CE (no use), ½ to 2 CEs (low use), 2½ to 6 CEs (moderate use), 6½ to 12 CEs (high use), or >12 CEs (excessive use).

Cross-sectional bivariate analyses and multivariate regression models were used to determine the relationships between SABA use, asthma exacerbations, and asthma-related and total (all-cause) healthcare costs in the postindex period. In the bivariate analyses, differences between groups were assessed using the 2-tailed t test or analysis of variance test for continuous variables and the β2 test for categorical variables. These methods also were used to evaluate the association between SABA exposure, outcomes, and covariates. Potential comorbidities for multivariate models were tested using the β2 test; comorbidities demonstrated to be significant in previous research and/or significant at the 5% level in our bivariate analyses were included. The comorbid conditions included in the multivariate regression models were allergies/rhinitis, chronic sinusitis, diabetes mellitus, gastroesophageal reflux disease, obstructive sleep apnea syndrome, tonsil and adenoid disease, acute pharyngitis, acute sinusitis, and upper respiratory infection.

Logistic regression models were used to determine adjusted associations between SABA use and exacerbations, and generalized linear models with a gamma family distribution and log-link were used to estimate asthma-related and all-cause healthcare costs by SABA strata. Cost differences were calculated between the means of the predicted costs, and 95% confidence intervals (CIs) were calculated using the percentile method with 1000 bootstrapped simulations. All analyses were conducted with SAS version 9.1.3 for Windows (SAS Institute, Cary, NC) or Stata version 10 for Windows (Stata-Corp LP, College Station, TX).

In the postindex period, 26.6% of patients had no drug claims for SABAs, 40.9% were dispensed between ½ and 2 CEs, 21.4% were dispensed between 2½ and 6 CEs, 7.8% were dispensed between 6½ and 12 CEs, and 3.4% were dispensed more than 12 CEs. Nebulized SABAs were prescribed to 12.9% of patients and were used 3 times as often in excessive SABA users (31.6%) and 2.4 times as often in high SABA users (25.9%) compared with low SABA users (10.6%) (data not shown). In general, SABA use by patients did not vary greatly from preindex year to postindex year (

Out of an initial pool of 946,859 patients with asthma, 93,604 met study eligibility criteria (

). Demographic and baseline clinical characteristics of the cohort are shown in 

. The study population was 45.2% male, and the largest proportion of patients (48.9%) resided in the midwestern United States. More than one-third of the population was pediatric, but only 22% of subjects in the high and excessive SABA use groups were children. The most frequent chronic comorbidities in the preindex or postindex periods were allergies/allergic rhinitis and chronic sinusitis. The prevalence of comorbid conditions generally decreased with increasing SABA exposure. This relationship was especially apparent for allergies/rhinitis; non-SABA users had this diagnosis 70% more often than excessive users. Specialist consultations were obtained by 30.9% of subjects, with a 33% higher occurrence in non-SABA users than excessive users. In the preindex period, 2046 members (2.2%) had an asthma-related ED/UC visit, 876  (0.9%) had an asthma-related hospitalization, and 28,892 (30.9%) had OCS treatment. The frequency of these exacerbation events increased with greater postindex SABA exposure.

Controller medication dispensed in the postindex period varied substantially by SABA exposure (

). In general, controller use (primary or any) was highest in excessive SABA users, followed closely by nonusers and high users. Excessive SABA users had nearly twice as many mean days of supply of any controller medication as low SABA users (209 days versus 111 days). Despite elevated levels of controller use as a group, almost half of high and excessive SABA users had either low (1 to 120 days of supply) or no use of any controller medication.

The most frequently prescribed controller medications at all levels of SABA use were ICS/LABAs and LMs (Table 3). In the high and excessive SABA use groups, many of the patients on an ICS (without a LABA) and LM were on monotherapy (46% of ICS users and 18% of LM users). The percentage of patients on monotherapy was 56% overall, and it was higher in the non-SABA group (78%) compared with the low (48%), moderate (51%), high (50%), and excessive (44%) SABA groups. Adjunctive controllers were used sparsely overall but most frequently in excessive SABA users, who were dispensed theophylline 7 times more often and omalizumab 8 times more often than low SABA users.

The adjusted associations between postindex SABA use and exacerbations, as measured by asthma-related ED/UC visits, hospitalizations, and OCS claims, are shown in Figure 2. Overn all, after adjustment for demographic characteristics, selected individual comorbidities, primary asthma controller medication use, and asthma severity, high and excessive SABA users had statistically significant higher odds (odds ratio [95% CI]) for an asthma-related ED/UC visit (6.47 [5.25, 7.98] and 7.68 [6.04, 9.76], respectively), hospitalizations (5.37 [3.96, 7.29] and 6.90 [4.90, 9.73], respectively), and OCS claims (2.89 [2.72, 3.08] and 3.71 [3.41, 4.03]) compared with SABA nonusers. Similar patterns of associations emerged for the pediatric and adult age strata (data not shown).

Unadjusted total asthma-related healthcare costs in the 12-month postindex period were lowest in the low SABA use group (½ to 2 CEs per year): 33% lower than the costs for the group not using a SABA (P <.05) (

). This difference was almost entirely due to asthma prescription drug costs, which were $316 lower in the low SABA use group. Mean total asthma-related costs increased almost linearly with increasing SABA exposure; compared with low SABA users, moderate users (2½ to 6 CEs per year) had 1.7 times higher costs ($988, 95% CI = $961, $1014), high users had 2.2 times higher costs ($1326, 95% CI = $1256, $1395), and excessive users had 3.0 times higher costs ($1791, 95% CI = $1670, $1913).

A comparison of mean all-cause healthcare costs between the SABA exposure groups followed the same pattern, with the low SABA use group having the lowest total all-cause costs ($4281; 95% CI = $4069, $4493) and the excessive SABA use group the highest ($5962; 95% CI = $5461, $6463) (Table 4). However, the relative cost differences between SABA use groups were much less pronounced for all-cause costs than for asthma-related costs. Differences in all-cause costs were mostly accounted for by the differences in asthma-related costs.

After adjustment for demographic characteristics, specialist consultation, asthma severity, and significant selected comorbidities, total asthma-related cost differences were much lower for those with no SABA fills compared with high and excessive users, primarily because of differences in pharmaceutical costs (Table 4). Conversely, medical costs were relatively unaffected by the adjustment. Adjustment of all-cause medical costs amplified the difference between the low and excessive SABA groups, but similar to asthma-related costs, pharmaceutical cost differences between these groups were attenuated.

Additional analyses showed that with increasing asthma controller medications dispensed, asthma-related pharmaceutical costs (data not shown) increased as expected, but medical expenses remained remarkably similar across all groups, most notably in the low ($174; 95% CI = $163, $184), moderate ($221; 95% CI = $192, $250) and high ($221; 95% CI = $205, $237) controller use groups.

This retrospective study of 93,604 asthma patients found a strong relationship between the amount of SABA dispensed annually and asthma-related and all-cause healthcare costs. Excessive SABA users had 3 times higher asthma-related healthcare costs than low SABA users. This finding was similar to that of Stempel et al, who reported that total asthmarelated costs were 3 times higher in patients with an average SABA use of more than 1 canister per month for at least 3 months compared with the “average asthmatic.”

Although much of the increased cost in the high and excessive SABA use groups was due to a higher rate of asthma exacerbations and increased asthma severity in the present study, higher costs remained after adjustment for asthma severity, albeit to a much lesser degree for pharmaceutical costs.

Schatz et al reported that use of 7 to 12 canisters and more than 12 canisters of SABA annually was associated with worsening asthma control.

 When considering the significantly higher odds for exacerbation-related events in patients with greater SABA use even after adjustment for asthma severity, it is likely that the high and excessive SABA use groups in our study had diminished asthma control. This diminished control may have been the result of several factors, including deficient specialty care and comorbidity management, increased asthma severity, and inappropriate or inadequate controller therapy. Allergy was diagnosed substantially less in these groups, consistent with a lack of allergy management because a lower rate of allergy would not be expected in those with diminished asthma control.

 More severe asthma is indicated by higher rates of preindex exacerbation events and greater postindex adjunctive controller, multiple controller, and nebulized SABA use. Nonetheless, nearly half of patients in the high and excessive SABA use groups had either suboptimal or no use of controller medication.

Increasing costs with greater SABA use likely were driven by multiple factors. Medical costs were very similar when stratifying patients by level of controller use, but not when stratifying patients by increasing SABA use, indicating that those with more severe asthma may have required more medication to prevent increased exacerbations and costs. Obviously, patients with more severe asthma tend to incur higher pharmacy costs to adequately treat their disease.

 Accordingly, after adjustment for asthma severity, differences among the SABA use groups substantially decreased for pharmaceutical costs but not for medical costs. Thus, increased total asthmarelated costs with increasing SABA use likely were associated with exacerbation management in addition to increased asthma severity.

Numerous studies have demonstrated that adherence to clinical guidelines reduces resource utilization and healthcare costs.

 Patients with high or excessive SABA use and inadequate or inappropriate controller prescribed likely were not being treated in accordance with the stepwise treatment approach recommended by current guidelines.

 The high ICS/ LABA combination use in the SABA nonusers could represent patients with either moderate to severe asthma that was well controlled or mild asthma that was overtreated, the latter situation being inconsistent with treatment guidelines, which may account for the higher cost of medication but little reduction in medical costs in SABA nonusers. Guidelines also were likely not adhered to in the large number of patients who had high or excessive SABA use and were on monotherapy with an ICS or LM.

A particular strength of this study was the incorporation of methods to standardize the measurement of SABA use, including nebulized SABA, by converting dispensed amounts to CEs.

 Additional strengths are the large sample and data capture across the complete spectrum of healthcare. This observational study used administrative data and lacked a randomized design; controlling for some confounding variables was impossible. Most important of these variables were pulmonary function test results, race/ethnicity, and smoking. It is possible that this study’s eligibility criteria might make the results less generalizable because uninsured patients or those with zero pharmacy claims for SABA and controller medication were not captured. These patients, possibly without medical care or medication, might be expected to have worse outcomes than patients in the non-SABA group in the present study who had some asthma medication use. In addition, interpretation of the results for primary controller medication use, broken out by SABA use category, may be limited, as ICS data were not stratified by dose and adjustments were not made based on differences in efficacy for ICS, ICS/LABA combination, and LMs. Finally, the amount of SABA or controller medication that was actually used could not be determined because exposure data were derived from prescription drug claims.

 have examined the association between SABA use and either asthma severity, exacerbations, or costs, to our knowledge this is the first study to show the relationship between increased annual SABA use, exacerbations, and direct asthma-related and total healthcare costs.

High and excessive SABA users likely represent patients at a higher risk for asthma exacerbations because they frequently have more severe asthma, poor control due to inadequate or inappropriate treatment, or both. These groups also could represent patients with severe asthma that is difficult to manage despite appropriate therapy, or patients without exacerbations as defined who are overreliant on SABA to manage asthma. Moreover, these patients incur much higher asthma-related and all-cause healthcare costs, even after controlling for asthma severity. Patients’ SABA claims in the preindex year and in the postindex year were strongly correlated, suggesting that the previous year’s SABA claims may be a useful marker for increased healthcare costs. These findings suggest that substantial opportunity exists to improve asthma management, reduce morbidity, and lower exacerbation-related healthcare costs in patients with elevated SABA claims.

We thank Judith Hurley, MS, who received payment from the Lovelace Respiratory Research Institute for medical writing services, and Susan Berry, MSW, of Lovelace Respiratory Research Institute, for editing assistance.

 From the Division of Clinical and Outcomes Research (HSS, CMB), Lovelace Respiratory Research Institute, Kannapolis, NC; Division of Clinical and Outcomes Research (HP, MAL), Lovelace Respiratory Research Institute, Albuquerque, NM; Division of Pharmaceutical Outcomes & Policy (CMB), University of North Carolina, Chapel Hill, NC; Clinical Research Institute (SK), Duke University, Durham, NC; Health Economics and Outcomes Research (DM, BG), AstraZeneca LP, Wilmington, DE. Dr Gutierrez is now with Bristol-Meyers Squibb, Princeton, NJ; Dr Blanchette is now with GlaxoSmithKline, RTP, NC; Dr Silver is now with the University of New Mexico, Albuquerque, NM; Dr Meddis has since retired from AstraZeneca.

Articles

Overuse of rescue medication among asthma patients is associated with increased exacerbations and higher total and asthma-related healthcare costs.


Relationship Between Short-Acting β-Adrenergic Agonist Use and Healthcare Costs

To investigate differences in demographics, physician specialty, and medication use between patients who achieve high versus low ratios of controller to total asthma medications.

We used a Health Insurance Portability and Accountability Act—compliant claims database to identify patients aged 5 to 56 years with persistent asthma during a premeasurement year and a measurement year. Based on values in the measurement year, the ratio of controller to total asthma medications ratio was defined using the following formula: (Units of Controllers) / (Units of Controllers + Relievers). Descriptive analysis and multivariate logistic regression models were used to examine patients with high and low ratios.

The final study group comprised 38,538 patients with persistent asthma; 28,496 (73.9%) had high ratios. Specialty of usual-care physician differed (P <.001), with more high-ratio patients than low-ratio patients having an allergist or pulmonologist. Patients who received combination inhaled corticosteroid—long-acting β-agonist therapy (odds ratio [OR], 2.4) or leukotriene receptor antagonist therapy (OR, 3.5) were more likely to be in the high-ratio group compared with those dispensed a single inhaled corticosteroid. High-group and low-group assignment could be calculated by partial-year data: assignment based on 1 quarter of data was concordant with assignment based on full-year ratio in 91% of cases (Pearson product moment correlation coefficient, 0.864; κ statistic, 0.761), and assignment based on 2 quarters of data was concordant with full-year results in 94% of cases (Pearson product moment correlation coefficient, 0.928; κ statistic, 0.843).

A high ratio of controller to total asthma medications is associated with greater controller adherence and with more controller fills. The ratio can be calculated using 1 or 2 quarters of pharmacy claims data, at a time when intervention may reduce asthma-related exacerbations. Interventions that may improve the ratio include changing from single inhaled corticosteroid therapy and from asthma specialist care.

The ratio of controller to total asthma medications may be a useful tool for improving quality of care.

Patients with greater adherence to controller therapy are more likely to have high ratios.

Higher ratios are seen in patients treated with medications other than single inhaled corticosteroids.

Ratios may be calculated using less than a full year of data.

The Healthcare Effectiveness Data and Information Set (HEDIS), developed by the National Committee for Quality Assurance,

 reports quality measures for various health conditions. The asthma measure assesses the proportion of patients who are prescribed a controller medication in a given year, and it has become a focus of quality improvement programs nationally.

 The current measure may not be ideal for directing improvement efforts because patients with “appropriate” care (those with >1 controller medication prescribed) may actually have an increased risk of requiring emergency hospital care.

A revision to the measure that would address this issue has been proposed.

 This proposed measure, the ratio of controller to total (controller plus rescue) asthma medications, has been shown to be a better indicator of the need for emergency hospital care than the HEDIS measure.

 A higher ratio also is significantly related to improved asthmarelated quality of life, better disease control, and reduced symptoms.

 In addition to validating its association with relevant outcomes, studies

 of the ratio have demonstrated its performance among various plans and regions, tested different inclusion criteria, and examined the ideal cutoff point for high versus low ratios. However, little information is available regarding which patient populations perform well on this measure or what individual patient characteristics indicate poor performance. The type and number of controller and reliever medications that comprise the ratio have not been studied extensively. Having a better understanding of this ratio measure would improve plans’ (and physicians’) ability to identify patients at risk of having poorly controlled asthma and might help plans, through targeted intervention, improve their compliance with the measure.

The primary objectives of this study were to investigate differences between patients who achieve high versus low ratios of controller to total asthma medications, to identify patient characteristics that are associated with high ratios, and to compare the type and number of controller and reliever medications used by patients with high versus low ratios.

We conducted a cohort study to examine the characteristics of patients with high and low ratios of controller to total asthma medications. We used the PharMetrics database, a Health Insurance Portability and Accountability Act—compliant administrative claims database (

). This database contains adjudicated pharmacy and medical claims submitted by providers, healthcare facilities, and pharmacies. The study was exempt from human subjects review.

We included patients who had persistent asthma as defined by the current HEDIS measure during a premeasurement year (October 1, 2005, to September 30, 2006) and a measurement year (October 1, 2006, to September 30, 2007) who were aged 5 to 56 years during the measurement year and who were continuously enrolled during those 2 years. Patients with emphysema or chronic obstructive pulmonary disease (COPD) (

International Classification of Diseases, Ninth Revision, Clinical Modification 

] codes 492, 518.2, 491.2, 493.2, 496, and 506.4) were excluded.

The main outcome measure was the ratio of controller to total asthma medications, as described by Schatz et al.

 Patients with a ratio of at least 0.5 were classified as high-ratio patients, and those with a ratio of less than 0.5 were classified as low-ratio patients. Based on values in the measurement year, the ratio was defined using the following formula: (Units of Controllers)/(Units of Controllers + Relievers). Controller medications included cromolyn sodium, leukotriene modifiers, nedocromil, inhaled corticosteroids (including combination inhaled corticosteroid—long-acting β-agonists), methylxanthines, and omalizumab. Reliever medications included inhaled short-acting β-agonists. Long-acting β-agonists as individual drugs or as part of combination therapy were not counted in the numerator or the denominator for the ratio calculation.

To count medication units for oral medications, 1 U was considered equivalent to 1 dispensing event (for a 30-day supply). If a patient received a 90-day supply, it was considered 3 U. For inhalers, 1 U was 1 canister. For injected medications, 1 U was 1 claim, but if a subsequent claim had a service date within 21 days, it was ignored (eg, claims on days 1 and 15 counted as 1 U).

 We used HEDIS definitions to count dispensing events.

 For injected medications (omalizumab), each claim was counted as a dispensing event. The number of canisters dispensed in each claim was determined by a ratio of quantity to package size. For example, a claim of triamcinolone acetonide inhalation aerosol, 75 mcg/actuation (20-g package size), with a quantity of 20 was interpreted as 1 canister dispensed. Any claim with a ratio of quantity to package size of less than 1 was counted as 1 canister. For claims with a ratio of quantity to package size of at least 1, we rounded the number to a whole number of canisters. For example, claims of aerosol solution, 75 mcg/actuation (20-g package size), with quantities of 35 and 25 were counted as 2 canisters (35/20 = 1.75 [rounded to 2]) and 1 canister (25/20 = 1.25 [rounded to 1]), respectively. If a claim was for more than 12 canisters, it was truncated to 12 canisters.

We evaluated patient characteristics in the premeasurement year and in the measurement year. Characteristics evaluated in the measurement year included demographics (age, sex, and geographic region of care), specialty of usual-care physician, and controller or reliever use. To determine the specialties of the patients’ usual physicians, we reviewed office visit claims for evidence of evaluation and management services (defined as those with 

codes for office or other outpatient services, office consultations, and preventive medicine services). We identified the specialty of the physician with whom each patient had the most evaluation and management visits during the measurement year and considered that the patient’s usual-care physician.

 We reported extent of controller and reliever use in the measurement year and calculated controller medication possession ratios (MPRs), defined as the sum of the “days of supply” divided by the total number of days in the measurement year (365 days). If the days of supply exceeded 365, the total number of covered days was truncated to 365 days.

We assessed asthma control in the premeasurement year with variables that reflect impairment and risk using 2 key concepts described by the National Asthma Education and Prevention Program.

 To assess impairment, we measured whether patients had 6 or more short-acting β-agonist canisters dispensed in the premeasurement year. Such use has been shown to be a marker of impairment in previous investigations.

 To assess risk, we measured whether patients had the following: (1) at least 2 oral corticosteroid dispensing events, (2) any asthma-related (with primary diagnosis of asthma [ICD-9-CM code 493]) hospitalization, or (3) any asthma-related emergency department (ED) visits.

 We assessed impairment and risk in the premeasurement year rather than in the measurement year to understand the effect of baseline control on the ratio.

To describe patient clinical characteristics and chronic disease burden, we used claims in the premeasurement year and in the measurement year to capture more completely any relevant diagnoses. We evaluated clinical characteristics using individual and grouped diagnoses. We used Clinical Classifications Software, developed by the Agency for Healthcare Research and Quality (

http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp

), to identify patients with mental disorders and disorders of the gastrointestinal system, conditions that may affect medication adherence and asthma severity.

 Individual diagnoses included reflux (ICD-9-CM codes 530.11 and 530.81), sinusitis (codes 461.x and 473.x), rhinitis (codes 477.0, 477.8, 477.9, and 472.0), acute upper respiratory tract infection (code 465.x), and nasal polyposis (code 471.x). To account for the burden of chronic health conditions, we applied the method by Hwang et al

 to assess the number of chronic health conditions each patient had.

We conducted a descriptive analysis to compare demographic and clinical characteristics, specialty of usual-care physician, and medication use between the high-ratio and low-ratio groups. In addition to high-ratio and low-ratio groups, we further divided patients into 4 groups based on their actual ratios (<0.25, 0.25 to <0.5, 0.5 to <0.75, and >0.75) and compared their characteristics. We studied quartiles for a more precise evaluation. We conducted bivariate analyses comparing selected characteristics between the high-ratio and low-ratio groups. Characteristics that were significantly associated with the ratio were included in the multivariate models as independent variables. We compared ratios calculated using the first quarter, the first 2 quarters, and the first 3 quarters of data with full-year ratios using Pearson product moment correlation coefficient and κ statistic.

We used 2 multivariate logistic regression models to estimate the association between certain characteristics and a high ratio. The first model excluded patients with a zero ratio (no controller use). It also excluded patients treated with only theophylline or cromolyn sodium, as these drugs are not commonly used as single agents in current practice (<1% of patients in the study). The second model included patients with no controller use. We reported adjusted odds ratios (ORs) and their 95% confidence intervals (CIs). All tests were 2-sided with a significance level of .05. All data transformations and statistical analyses were performed using SAS version 9.1

We identified 150,903 patients with persistent asthma in the premeasurement year and 156,649 in the measurement year. After excluding those who did not meet the age criteria, those who were not continuously enrolled, those who had evidence of emphysema or COPD, and those who did not use controllers or relievers in the measurement year, 38,538 patients remained (

Of the final study cohort, 28,496 (73.9%) were classified as having high (>0.5) ratios. The mean age did not differ between the groups with high versus low ratios. Age group, sex, and geographic region of care differed (

 <.001), with a higher proportion of high-ratio patients aged 5 to 9 years (14.1% vs 8.7%), female (55.2% vs 50.5%), and from the South (18.3% vs 13.6%). Specialty of usual-care physician in the measurement year differed (

 <.001), with a higher proportion of high-ratio than low-ratio patients having an allergist (7.6% vs 3.9%) or a pulmonologist (2.9% vs 2.2%) (

The mean MPR in the measurement year was 0.603 for high-ratio patients and 0.203 for low-ratio patients (Table 1). High-ratio patients used a mean of 10.3 U of controllers; low-ratio patients used a mean of 3.1 U (

 <.001). Controller medication use differed between groups (

 <.001): 7.7% of high-ratio patients used 0 to 3 U (lowest quartile) compared with 67.9% of low-ratio patients, and 35.3% of high-ratio patients used at least 12 U (highest quartile) compared with 4.1% of low-ratio patients. There were significant differences in reliever use during the measurement year (

 <.001): the mean number of reliever units used was 2.4 in the high-ratio group compared with 10.1 in the low-ratio group. Almost 30% of high-ratio patients had no reliever fills in the measurement year (0% of low-ratio patients had no reliever fills, as by definition having no reliever use produces a ratio of 1.0). The highest quartile of reliever use included those with 7 or more canisters used. Just over 8% of high-ratio patients and 63.3% of low-ratio patients were in this group.

There was a statistically significant difference between the groups in asthma control measures during the premeasurement year. High-ratio patients had less impairment, with only 16.4% filling 6 or more short-acting β-agonist canisters compared with 63.3% of low-ratio patients (

). High-ratio patients had lower risk than low-ratio patients in each of the areas measured (

 <.001 for each category). High-ratio patients had more total chronic health conditions (4.0 vs 3.6, 

 <.001) and more of the individual comorbidities assessed, except for mental disorders, which were more common among low-ratio patients (

). When we further classified patients into 4 ratio groups (<0.25, 0.25 to <0.5, 0.5 to <0.75, and >0.75), the results for medication use, asthma control, and comorbidities were similar (data not shown).

High-group and low-group assignment could be calculated using partial-year data. High-ratio vs low-ratio group assignment based on 1 quarter of data was concordant with group assignment based on full-year ratio in 91% of cases. Adding a second quarter of data increased concordance to 94%. Pearson product moment correlation coefficient of partial-year to full-year ratio (using a continuous [0-1.0] variable for the ratio) was 0.864 for 1 quarter and 0.928 for 2 quarters. κ statistic, calculated using the categories of high ratio and low ratio, was 0.761 for 1 quarter and 0.843 for 2 quarters (

The 2 multivariate models produced similar results. The model that excluded patients without controller use allowed us to estimate the effect of specific patterns of medication use on the ratio. In multivariate analysis of controller users, older age and male sex significantly lowered the odds of being in the high-ratio group, with an OR of 0.54 (95% CI, 0.48-0.61) among patients aged 10 to 17 years compared with patients aged 5 to 9 years and an OR of 0.69 (95% CI, 0.61-0.77) among patients aged 18 to 56 years compared with patients aged 5 to 9 years. Geographic region of care was associated with ratio group, with an OR of 1.14 (95% CI, 1.01-1.30) for the Northeast compared with the West and an OR of 1.18 (95% CI, 1.03-1.34) for the South compared with the West. Compared with patients having primary care physicians (internists and family physicians), patients having allergists as usual-care physicians had an OR of 1.35 (95% CI, 1.17-1.55) for being in the high-ratio group, and patients having pulmonologists had an OR of 1.26 (95% CI, 1.04-1.52) (

There was a statistically significant association between the type of controller used in the measurement year and the ratio group. Patients whose only controller was a combination inhaled corticosteroid—long-acting β-agonist product had 2.36 (95% CI, 2.15-2.58) times the odds of being in the high-ratio group compared with patients who used only single inhaled corticosteroids (Table 5). Similarly, patients with a leukotriene receptor antagonist as their only controller had an OR of 3.49 (95% CI, 3.15-3.87) for being in the high-ratio group compared with patients who used a single inhaled corticosteroid controller. Users of multiple controllers had an OR of 7.45 (95% CI, 6.80-8.17) for being in the high-ratio group.

Asthma control was associated with outcomes. Patients who filled 6 or more short-acting β-agonist canisters in the premeasurement year, a measure of impairment, had significantly lower odds (OR, 0.11; 95% CI, 0.10-0.12) of having high ratios than patients who filled fewer than 6 canisters (Table 5). Having any asthma-related hospitalization, any asthma-related ED visit, or having filled at least 2 oral corticosteroid prescriptions in the premeasurement year were all significantly associated with lower odds of having a high ratio. Patients with a mental disorder had lower odds of being in the high-ratio group (OR, 0.75; 95% CI, 0.70-0.81), whereas patients with rhinitis had higher odds (OR, 1.22; 95% CI, 1.14-1.31). Each additional chronic health condition increased the odds of being in the high-ratio group (OR, 1.04; 95% CI, 1.02-1.05).

Limitations and problems with the current HEDIS asthma measure have led to consideration of some modifications. One potential modification is the use of a ratio of controller to total asthma medications as the primary quality measure. Using a ratio that is associated with outcomes may result in improvements in the quality of asthma care, and the measure identifies a subpopulation that may benefit from attempts at improvements in care. In a study

 using 3 separate databases, a high ratio was consistently associated with improved outcomes. The proportion of patients with a ratio of at least 0.5 varied from 70% to 75% across the 3 databases. High ratios are also associated with improved patient-reported outcomes,

 but little has been published about specific differences between high-ratio and low-ratio patients or about the stability of the measure over time.

In the present study, 73.9% of patients had high ratios, similar to other published studies

; the mean age and the slight female preponderance were also similar to prior work. In an unadjusted analysis, children younger than 10 years, female patients, and those with allergists and pulmonologists as their usual-care physician were more likely to have high ratios, and these results persisted after adjustment for baseline characteristics. Other variables that had a statistically significant association with the high-ratio group in the multivariate analyses included lack of markers of impairment or risk, use of controllers other than single inhaled corticosteroids, absence of mental disorders, presence of rhinitis or acute upper respiratory tract infection diagnosis, and more chronic health conditions.

In multivariate analyses, the strongest variable associated with ratio group was the type of controller therapy, with single inhaled corticosteroids being associated with lower ratios than combination inhaled corticosteroid—long-acting β-agonist, leukotriene receptor antagonist, and multiple controllers. Patients are more adherent with combination inhaled corticosteroid–long-acting β-agonist inhalers than with concurrent treatment using separate inhaled corticosteroid and long-acting β-agonist inhalers.

 In addition, combination therapy is generally more effective than similar doses of inhaled corticosteroid monotherapy in relieving symptoms and improving lung function,

 so patients might be more consistent in their use of these therapies. Patients receiving long-acting β-agonist therapy may use less short-acting β-agonist, which may increase the ratio for combination products compared with single inhaled corticosteroid. However, if the reduced use of short-acting β-agonist reflects decreased symptoms, this is not an artificial increase. Adherence to leukotriene receptor antagonist therapy is also greater than adherence to inhaled corticosteroid monotherapy.

 Patients treated with multiple controllers were many times more likely to have high ratios, possibly as a result of the calculation used to determine the ratio. To be categorized as using multiple controllers, a patient needed at least 2 controller fills (eg, to identify >2 types of controllers), whereas some users of single therapies had only 1 fill, and a greater number of controller fills results in a higher ratio.

Ratios also increase with increasing controller adherence. This relationship between greater adherence as measured by the MPR and higher ratios may explain some of the associations we observed. Higher ratios were seen in patients who had allergists or pulmonologists as their usual-care physician. Provider knowledge and communication skills influence adherence to treatment, and specialist providers may be better at communicating complex regimens to their patients with asthma.

 Women had higher ratios, and some evidence suggests they may have better adherence than men.

 The presence of a mental disorder was associated with a lower ratio, and some mental disorders, including depression, decrease adherence to therapy.

 Many other factors associated with poor adherence were not captured in our study, such as patients’ education, socioeconomic status, disease understanding, and belief in treatment efficacy.

Some variables may mediate their effect on ratio group through their association with asthma control. Increasing numbers of chronic health conditions, as well as rhinitis and upper respiratory tract infections, all may identify patients with greater risk. These factors may also lead to more office visits and greater opportunities for prescribing controller medications. We used utilization-based measures to adjust our results for disease severity, but there were no clinical measures of severity in our database. This was a limitation of our analysis.

The goal of quality measures and tools for targeted interventions is to improve patient outcomes. Quality measures are provider oriented and aim to improve patient outcomes by improving provider adherence to treatment guidelines (eg, controllers for asthma). This is done by using quality measures as incentives for approaches such as public reporting and pay for performance. In contrast, population management (tools for targeted interventions) is patient oriented and aims to identify high-risk patients who need targeted interventions to improve outcomes. The ratio, which may be calculated from partial-year data, can be used as a quality measure or tool for population management to improve asthma outcomes. A plan interested in intervening for patients with low ratios could use data from 2 quarters or even 1 quarter to identify patients at risk for having a low ratio (if the quantities dispensed are in the 30-day to 60-day range). Interventions such as education, outreach, or medication counseling put in place after a partial-year

calculation of the ratio might not only increase the ratio but also (given the demonstrated link between the ratio and outcomes) reduce emergency hospital care. We found that patients who used combination inhalers versus single inhaled corticosteroid controllers and leukotriene receptor antagonist versus single inhaled corticosteroid were more likely to have high ratios. Using this information, interventions to improve patients’ ratios could be developed. For example, patients with poor control who are receiving single inhaled corticosteroids could switch to combination inhalers, and patients with poor adherence to single inhaled corticosteroids could switch to leukotriene receptor antagonists.22 Patients who received specialty care were more likely to have high ratios; therefore, it may be beneficial to refer patients with evidence of prior impairment or risk to asthma specialists. Studies to evaluate the effectiveness of these interventions should be considered.

The ratio of controller to total asthma medications may be useful as a measure of the quality of asthma care. The measure can be easily calculated using existing administrative data, and patients with higher ratios have improved asthma outcomes. Higher ratios are seen among patients with greater adherence to therapy and among patients treated with medications other than single inhaled corticosteroids. Ratios are stable; interventions focused on patients with low ratios based on 1 or 2 quarters of data may reduce asthma-related exacerbations. The ratio of controller to total asthma medications may be useful as a quality measure and as a tool for targeted intervention to improve asthma outcomes.

From the Partnership for Health Analytic Research (MSB, EC); Beverly Hills, CA; AstraZeneca, LP (BG, DM), Wilmington, DE; and Kaiser Permanente Medical Center (MS), San Diego, CA.

This study was funded by AstraZeneca, LP.

Drs Broder and Chang report receiving payment from AstraZeneca, LP for their involvement in conducting the study and preparing the manuscript. Drs Gutierrez and Meddis are employees of AstraZeneca, LP, the company that provided funding for the study. Dr Schatz reports receiving investigator-initiated grants from Aerocrine, Genentech, GlaxoSmithKline, and Merck.

Concept and design (MSB, BG, EC, DM, MS); acquisition of data (BG); analysis and interpretation of data (MSB, BG, EC, MS); drafting of the manuscript (MSB, BG, DM); critical revision of the manuscript for important intellectual content (MSB, BG, DM, MS); statistical analysis (EC); and obtaining funding (DM).

David Meddis, PhD, AstraZeneca, LP, 1800 Concord Pike, Wilmington, DE 19850. E-mail: 

1. National Committee for Quality Assurance. HEDIS 2008: Healthcare Effectiveness Data and Information Set. Vol 2, Technical Specifications. Washington, DC: National Committee for Quality Assurance (NCQA);2007:126-128. 2. Gelfand EW, Colice GL, Fromer L, Bunn WB III, Davies TJ. Use of the Health Plan Employer Data and Information Set for measuring and improving the quality of asthma care. Ann Allergy Asthma Immunol. 2006;97(3):298-305. 3. Berger WE, Legorreta AP, Blaiss MS, et al. The utility of the Health Plan Employer Data and Information Set (HEDIS) asthma measure to predict asthma-related outcomes. Ann Allergy Asthma Immunol. 2004;93(6):538-545. 4. Fuhlbrigge A, Carey VJ, Adams RJ, et al. Evaluation of asthma prescription measures and health system performance based on emergency department utilization. Med Care. 2004;42(5):465-471. 5. Schatz M, Zeiger RS, Vollmer WM, et al. The controller-to-total asthma medication ratio is associated with patient-centered as well as utilization outcomes. Chest. 2006;130(1):43-50. 6. Schatz M, Nakahiro R, Crawford W, Mendoza G, Mosen D, Stibolt TB. Asthma quality-of-care markers using administrative data. Chest. 2005;128(4):1968-1973.7. Schatz M, Stempel D; American College of Allergy, Asthma and Immunology Task Force; American Academy of Allergy, Asthma and Immunology Task Force. Asthma quality-of-care measures using administrative data: relationships to subsequent exacerbations in multiple databases. Ann Allergy Asthma Immunol. 2008;101(3):235-239. 8. OÃƒÂ¢Ã¯Â¿Â½Ã¯Â¿Â½Malley AS, Pham HH, Schrag D, Wu B, Bach PB. Potentially avoidable hospitalizations for COPD and pneumonia: the role of physician and practice characteristics. Med Care. 2007;45(6):562-570. 9. National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services. Expert Panel Report 3 (EPR3): guidelines for the diagnosis and management of asthma. Revised August 28, 2007. http://www.nhlbi.nih.gov/guidelines/ asthma/asthgdln.htm. Accessed February 17, 2009. 10. Schatz M, Zeiger RS, Vollmer WM, et al. Validation of a ÃƒÅ½Ã‚Â²-agonist long-term asthma control scale derived from computerized pharmacy data. J Allergy Clin Immunol. 2006;117(5):995-1000. 11. Schatz M, Cook EF, Nakahiro R, Petitti D. Inhaled corticosteroids and allergy specialty care reduce emergency hospital use for asthma. J Allergy Clin Immunol. 2003;111(3):503-508. 12. Schatz M, Nakahiro R, Jones CH, Roth RM, Joshua A, Petitti D. Asthma population management: development and validation of a practical 3-level risk stratification scheme. Am J Manag Care. 2004;10(1):25-32. 13. Cowen ME, Dusseau DJ, Toth BG, Guisinger C, Zodet MW, Shyr Y. Casemix adjustment of managed care claims data using the clinical classification for health policy research method. Med Care. 1998;36(7):1108-1113. 14. Hwang W, Weller W, Ireys H, Anderson G. Out-of-pocket medical spending for care of chronic conditions. Health Aff (Millwood). 2001;20(6):267-278. 15. Marceau C, LemiÃƒÆ’Ã‚Â¨re C, Berbiche D, Perreault S, Blais L. Persistence, adherence, and effectiveness of combination therapy among adult patients with asthma. J Allergy Clin Immunol. 2006;118(3):574-581. 16. Greenstone IR, Ni Chroinin MN, Masse V, et al. Combination of inhaled long-acting ÃƒÅ½Ã‚Â²2-agonists and inhaled steroids versus higher dose of inhaled steroids in children and adults with persistent asthma. Cochrane Database Syst Rev. 2005;4:CD005533. 17. Jones C, Santanello NC, Boccuzzi SJ, Wogen J, Strub P, Nelsen LM. Adherence to prescribed treatment for asthma: evidence from pharmacy benefits data. J Asthma. 2003;40(1):93-101. 18. World Health Organization. Adherence to Long-Term Therapies: Evidence for Action. Geneva, Switzerland: World Health Organization; 2003. 19. DiMatteo MR. Variations in patientsÃƒÂ¢Ã¯Â¿Â½Ã¯Â¿Â½ adherence to medical recommendations: a quantitative review of 50 years of research. Med Care. 2004;42(3):200-209. 20. DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient compliance. Arch Intern Med. 2000;160(14):2101-2107. 21. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353(5):487-497. 22. Tan H, Sarawate C, Singer J, et al. Impact of asthma controller medications on clinical, economic, and patient-reported outcomes. Mayo Clin Proc. 2009;84(8):675-684.

The ratio of controller to total asthma medications can be calculated using 1 or 2 quarters of data; high versus low ratios differentiate patient characteristics.

Ratio of Controller to Total Asthma Medications: Determinants of the Measure

To evaluate whether an assessment of subacute lack of asthma control (SALAC) predicts subsequent acute asthma exacerbation (AAE).

This retrospective administrative claims study used medical and pharmacy claims from the HealthCore Integrated Research Database to identify patients aged 6 to 64 years with asthma and having 3 years’ continuous enrollment from January 1, 2003, through December 31, 2005.

Study inclusion criteria were at least 2 outpatient visits or at least 1 hospitalization or emergency department (ED) visit with an asthma diagnosis (

International Classification of Diseases, Ninth Revision, Clinical Modification

 code 493.xx) in at least 1 of 3 years (2003-2005). SALAC was defined as more than 4 asthma outpatient visits or more than 5 short-acting β

-agonist (SABA) prescriptions per year, and AAE was defined as at least 1 hospitalization or ED visit with a primary asthma diagnosis or an oral corticosteroid burst prescription. Generalized estimating equations modeled the risk of subsequent-year AAE as a function of 2 sets of variables to determine the independent effect of prior-year SALAC and its components on subsequent-year AAE. The first set included age, sex, geographic region, prior year AAE, and prior-year SALAC. The second set included age, sex, geographic region, prior-year AAE, high prior-year SABA use, and frequent prior-year asthma outpatient visits.

Of 35,806 patients with asthma, 46.6% were male, and 35.8% were younger than 18 years. The mean annual prevalence of SALAC was 12.1%. Controlling for all other variables, the generalized estimating equation results indicate that prior-year SALAC is associated with a 60% increased risk of subsequent-year AAE (

 <.001). Increased prior-year asthma outpatient visits and SABA use are associated with 34% and 85%, respectively, greater risks of subsequent-year AAE (

SALAC and its components can aid in predicting patients at risk for AAE. 

Despite improved understanding of asthma pathogenesis, disease management, and use of recommended therapies, many patients have inadequately controlled asthma, resulting in increased health plan resource utilization and costs.

This study uses administrative claims data to identify patients who have subacute lack of asthma control (SALAC), defined as more than 4 asthma outpatient visits or more than 5 shortacting β

Patients with SALAC have a 60% higher risk of subsequent-year acute asthma exacerbation than patients without SALAC.

The use of SALAC may provide health plans with opportunities to improve members’ asthma control and to reduce healthcare utilization associated with asthma exacerbation.

Asthma, one of the most common chronic conditions in the United States, affected more than 22 million Americans (16 million adults) in 2005.

 Approximately 77% of these patients have moderate-to-severe persistent disease.

 Disease control is the primary goal of current management strategies and evidence-based consensus guidelines

 and has been differentiated from disease severity.

 Despite improved understanding of asthma pathogenesis, better disease management, and more widespread use of recommended therapies, asthma remains a frustrating disease for patients and healthcare providers because of the dynamic nature of the disease. For example, patients with asthma may experience variability in asthma control

 over time such that patients with asthma that is mild in severity

 may experience acute asthma exacerbation (AAE) requiring urgent medical care. Control is an issue that is relevant for all levels of asthma severity, but its measurement remains problematic. The wide range of estimates of the proportion of patients who are inadequately controlled (17%8 to 83%9) is primarily because of differing definitions of control.

The implications of poor asthma disease control are considerable. Approximately 4000 individuals die each year from asthma exacerbation.

 Uncontrolled disease results in a disproportionate use of emergency department (ED) and hospitalization resources

 and is a major component of the $14.7 billion attributed to asthma costs in the United States in 2002.

 estimated that costs for uncontrolled patients were more than twice those for controlled patients and were significantly higher even among patients who were receiving maximum guideline-recommended pharmacotherapy.

 consequences associated with poor asthma control, there is a need to discern valid predictive markers that may allow for early identification of the at-risk patient. However, predicting asthma symptom occurrence on a short-term basis is a well-recognized clinical challenge. Factors that have been found to be predictive of future severe acute asthma episodes,

including hospitalization and death, are repeated hospitalizations or ED visits, frequent use of short-acting β

-agonist (SABA) medications, current use of or recent withdrawal from oral corticosteroid use, and prior recent severe asthma exacerbation.

While the existing literature about the prediction of patients prone to AAE provides essential insight into disease management, there are limitations to its usefulness within typical clinical practice. Health plans and clinicians are in need of disease control markers that are valid, simple, free from the potential biases associated with patient and physician assessments, and easily attainable in a usual care setting and within most typical administrative claims databases.

 the construct of subacute lack of asthma control (SALAC) was introduced as a means of easily identifying patients with uncontrolled asthma within a managed care database. In that study, SALAC was defined as more than 4 asthma outpatient visits per year, at least 2 asthma outpatient visits per quarter, or more than 5 SABA prescriptions per year. More than two-thirds of patients experienced SALAC at some point during the 4-year analysis, and SALAC occurred almost every year in about 20% of patients. Moreover, patients with SALAC had a higher rate of subsequent asthma exacerbation (63% excess clinic visits per year). The present study was designed to assess how well an administrative claims—derived measure of SALAC consisting of high prior-year SABA use and frequent prior-year asthma outpatient visits predicts subsequent-year AAE in a managed care population. The results may assist US health plans in gauging the burden of uncontrolled asthma among their patients with asthma and in identifying markers of uncontrolled asthma that predict AAE.

This retrospective, longitudinal, cohort study used medical and pharmacy claims from 3 managed care health plans contained in the HealthCore Integrated Research Database for healthcare services incurred during the 3-year period from January 1, 2003, through December 31, 2005. All study materials were handled in compliance with the Health Insurance Portability and Accountability Act of 1996, and a limited data set was used for the analyses.

Patients aged 6 to 64 years with medical claims for at least 2 outpatient visits or at least 1 hospitalization or ED visit with a primary or secondary diagnosis code for asthma in at least 1 of 3 years (2003-2005) and with 3 years’ continuous enrollment from January 1, 2003, through December 31, 2005, were identified. An asthma diagnosis was defined as 

) code 493.xx, and the initial year of observation (calendar year 2003) was defined as the baseline year for the purpose of describing patient demographic characteristics. Patients were excluded if they had at least 1 medical claim with a primary or secondary diagnosis code for cystic fibrosis (

 code 277.xx) or chronic obstructive pulmonary disease (

 codes 490.xx-492.xx or 496.xx) at any time during the study period.

For the purpose of this study, asthma control was determined through the use of healthcare services identifying SALAC. SALAC was defined as more than 4 asthma outpatient visits or more than 5 SABA prescriptions per year.

Acute asthma exacerbation was defined as at least 1 hospitalization or ED visit with a primary asthma diagnosis or at least 1 oral corticosteroid burst prescription (<21 days’ supply) per year. This definition is based on the National Asthma Education and Prevention Program Expert Panel Report 3 guidelines,1 which classify severe exacerbation as that requiring hospitalization or ED visit or oral systemic corticosteroids. The proportion of patients with 1 or more asthma-related healthcare event was assessed for each year from 2003 through 2005. These events included AAE , SALAC, and its SALAC (ie, >4 asthma outpatient visits or >5 SABA prescriptions per year). The adjusted association between SALAC in the prior year with AAE in the subsequent year was assessed (ie, the association between the occurrence of SALAC in 2003 and the occurrence of AAE in 2004 and the occurrence of SALAC in 2004 and the occurrence of AAE in 2005 was determined). The adjusted association between increased prior-year asthma outpatient visits and SABA use and the occurrence of subsequent- year AAE was similarly determined.

Descriptive statistics were used to describe the study population and the prevalence of asthma control indicators. A generalized estimating equation (GEE) was used to model the risk of subsequent-year AAE as a function of prior-year SALAC, controlling for age, sex, geographic region, and prioryear AAE. A separate GEE model was developed to assess the risk of subsequent-year AAE as a function of high prior-year SABA use (>5 prescriptions per year) and frequent prior-year asthma outpatient visits (>4 visits per year). The binary dependent variable in each of the GEE models was the occurrence of AAE in 2004 and 2005. All statistical analyses were performed using SAS (version 9.1 for Windows; SAS Institute Inc, Cary, NC).

A total of 35,806 patients met the study criteria. As summarized in 

, 46.6% were male, the mean (SD) age was 31.6 (17.9) years, and 35.8% were younger than 18 years. Approximately three-fourths of the population were from the western United States. Preferred provider organizations were the dominant health plan type (73.0%). 

 gives data on the number of patients with AAE and SALAC. From 2003 through 2005, the mean percentage of patients per year with at least 1 AAE was 21.2%; 18.8% had at least 1 oral corticosteroid burst prescription, 4.3% had at least 1 ED visit, and 1.2% had at least 1 hospitalization. For the same period, the mean percentage of patients with SALAC having high SABA use was 5.7%, and 7.1% had frequent asthma outpatient visits. On average, 0.7% of patients had both increased SABA use and asthma outpatient visits, indicating that the 2 measures were virtually independent, and interaction between the 2 variables was not assessed. More than 12% of patients had SALAC during the 3-year study period. 

 shows the mean annual prevalence of SALAC and its components (high SABA use and frequent asthma outpatient visits) from 2003 through 2005.

 shows the percentage of patients with at least 1 AAE in the year following assessment of SALAC, stratified by SALAC status. Compared with patients without SALAC or its components, significantly more patients with SALAC or its components experience at least 1 subsequent- year AAE.

A GEE model was used to assess the association between prior-year SALAC as measured by asthma-associated resource use (ie, >4 asthma outpatient visits or >5 SABA prescriptions per year) and subsequent-year AAE (ie, 2003 compared with 2004 and 2004 compared with 2005). After controlling for covariates, patients with prior-year SALAC had a 60% higher risk of subsequent-year AAE (odds ratio [OR], 1.601; 95% confidence interval [CI], 1.514-1.692;

 <.001) compared with patients without SALAC (

). After controlling for covariates, patients having more than 4 asthma outpatient visits per year versus 4 or fewer had a 34% greater risk of subsequentyear AAE (OR, 1.340; 95% CI, 1.249-1.438;

 <.001). Similarly, patients having more than 5 SABA prescriptions per year versus 5 or fewer had an 85% increased risk of subsequent-year AAE (OR, 1.854; 95% CI, 1.719-2.000;

Regional differences were included in the model to adjust for the disproportionate representation of our patient sample from the western United States and was not meant to provide insight about the association between geographic region and AAE, as we had no prior hypothesis about this potential relationship. Therefore, the main independent covariates of interest (SALAC and its components) are not confounded (ie, remain statistically significant) by the disproportionate distribution of the patients in each of our geographic regions.

This analysis of a large demographically and geographically dispersed managed care population shows a mean annual prevalence from 2003 through 2005 of more than 12% of patients with SALAC. Based on GEE results and after controlling for covariates, patients with SALAC were 60% more likely to have subsequent-year AAE than patients without SALAC. Moreover, patients with versus without components of SALAC were 34% (for those with >4 asthma outpatient visits) and 85% (for those with >5 SABA prescriptions) more likely to have subsequent-year AAE. Although high SABA use was a significant predictor of AAE, the addition of asthma outpatient visits to the model demonstrated it to be an independently significant predictor of subsequent AAE. This suggests that SALAC may be a useful predictive measure for improving asthma control and for reducing healthcare utilization associated with asthma exacerbation.

Several studies report the disadvantages associated with patient-reported assessment of asthma control, including exacerbation

 as well as physician and patient assessments of asthma severity.

 Our study used administrative claims data and, as such, was not subject to the potential biases associated with recall or subjective assessments of disease. Because SALAC consists of easy-to-monitor components (medical claims—coded asthma outpatient visits or SABA prescription claims) that are not biased by recall, clinicians and health plans can effectively identify these potentially at-risk patients for targeting with additional monitoring and resources. SALAC is a measure based on administrative health plan data that fulfills an unmet need, as clinicians frequently fail to assess asthma control because of lack of patient inquiry or status documentation.

Subacute lack of asthma control (SALAC) predicts subsequent asthma exacerbation and may improve health plan members’ asthma control and healthcare utilization associated with asthma exacerbation.

Benjamin Gutierrez, PhD

Articles

Relationship Between Short-Acting β-Adrenergic Agonist Use and Healthcare Costs

Ratio of Controller to Total Asthma Medications: Determinants of the Measure

Subacute Lack of Asthma Control as a Predictor of Subsequent Acute Asthma Exacerbation in a Managed Care Population