Michael Schatz, MD, MS

To assess the predictive ability of the ratio of controller-to-total asthma medication in commercially insured and Medicaid patients.

Medical and pharmacy claims were used to identify asthma patients between 2004 and 2006. Ratios were computed during 3-, 6-, and 12-month assessment periods and asthma exacerbations were assessed during a subsequent 12-month follow-up period. Receiver operating characteristic curve analyses and logistic regression were used to select optimal ratio number, assessment time period, and incremental ratio analysis.

The ratio significantly predicted future asthma exacerbations. An optimal value of >0.7 was identified in pediatric and adult Medicaid patients with a shorter assessment period in adults (3 months) than in children (6 months). In commercially insured patients, an optimal value of >0.5 during a 6-month assessment period was identified for children and adults. In commercially insured patients, a 0.1-unit increase in the ratio below the 0.5 value resulted in a 72% (odds ratio [OR] 0.28; 95% confidence interval [CI] 0.13-0.57) and 80% (OR 0.20; 95% CI 0.12-0.33) risk reduction among pediatric and adult patients, respectively. Similarly, a 0.1-unit increase in the ratio below the 0.7 optimal value in the Medicaid population resulted in significant risk reduction in the pediatric (OR 0.65; 95% CI 0.43-0.97) but not the adult cohort.

The ratio is a significant predictive risk marker in commercially insured and Medicaid asthma populations. Incremental risk reductions can be realized by unit increases in the ratio up to the identified optimal value.

This study examined the predictive ability of the controller-to-total asthma medication ratio in pediatric and adult commercially insured and Medicaid patients.

 The ratio significantly predicted future asthma exacerbations in both populations.

Incremental risk reductions could be realized by unit increases in the ratio up to the identified optimal value.

Asthma represents a significant burden to the healthcare system. In 2008, almost half of the total current asthma population in the United States (12.7 of 23.3 million) experienced an asthma exacerbation, adversely affecting patients’ quality of life and increasing the likelihood of hospitalizations and emergency department (ED) visits.

 Consequently, efforts have been made to determine quality-of-care markers that can predict future exacerbations, particularly those requiring hospital or ED visits, in order to improve asthma management and reduce this burden.

One such measure, the Healthcare Effectiveness Data and Information Set (HEDIS), is based on the proportion of health plan members with persistent asthma who are appropriately prescribed long-term control medication at least once during a given year.

 Although adherence to the HEDIS measure has been associated with a lower risk of hospital and ED visits,

 it has shown to be an inconsistent predictor of asthma outcomes.

Therefore, alternate measures have been proposed that more effectively assess asthma quality of care and that can more reliably predict the occurrence of exacerbations. One measure in particular, the ratio of controller-to-total asthma medication (ratio), has been shown to be a strong predictor of patient outcomes, asthma-related hospitalizations, and ED utilization,

 and a better quality-of-care marker than the HEDIS measure.

 Studies examining the predictive ability of this marker incorporate a 1-year measurement period that is consistent with the HEDIS performance measure. This period is valid given that the ratio might not be appropriate for patients who do not have persistent asthma. However, a 1-year measurement period prevents rapid interventional adjustments. It is not known whether a shorter measurement period would affect the predictive ability of the ratio. Because of the restrictive nature of the persistent asthma measure and the need for defining a population in a study with shorter observation times, an asthma population at risk for uncontrolled asthma or asthma exacerbations was defined for this study. At-risk patients were defined as those that meet the treatment and symptom (albuterol use as the proxy) criterion of having at least mild persistent asthma according to National Asthma Education and Prevention Program guidelines.

Most of the studies on the ratio have been performed in persistent asthma patients from commercially insured databases across age groups. Studies in children and those from the Medicaid population are limited. It is important to study these populations because Medicaid patients are representative of a population that is more likely to have asthma and asthmarelated adverse events, specifically individuals who are younger, are female, and have lower socioeconomic status.

 Among those insured by Medicaid, a higher percentage of patients have asthma and poorer asthma control compared with those who have private insurance or are uninsured.

Medicaid coverage was found to be independently associated with poor asthma control in children, and controller medication underuse among Medicaid- covered children is widespread.

 Studies have shown that children in Medicaid were more likely to be admitted to the ED or hospital for asthma compared with privately insured children.

In this study we examined the predictive ability of the ratio in pediatric and adult populations at risk for asthma exacerbations using both commercially insured and Medicaid databases. The primary goal was to determine an optimal value for the ratio that is associated with a significant difference in predicting future exacerbations. We also examined the correlation between incremental unit changes in the ratio and the risk of having an exacerbation. Lastly, we identified an optimal time frame for measuring the ratio to accurately predict the risk of future exacerbations.

This study was a retrospective analysis of 2 databases, the Ingenix Impact National Managed Care Database (commercially insured) and the MarketScan Medicaid Database. The data used were from January 1, 2003, to either June 30, 2007 (Medicaid), or June 30, 2008 (commercially insured). The commercially insured database utilized the integrated medical and pharmacy administrative claims of more than 98 million lives spanning 9 census regions. The Medicaid database utilized nearly 22 million enrollees across 6 states. The initial study population identified included patients with at least 1 pharmacy claim for any type of asthma medication excluding oral corticosteroids (OCSs) during the enrollment period from January 1, 2004, to either June 30, 2005 (Medicaid), or June 30, 2006 (commercially insured). The 1-year period before the enrollment period (preindex period) was used for baseline assessment and to ensure the presence of asthma. The postindex period had 2 parts: 1 to calculate the ratio (the optimal assessment period) and a subsequent 12-month follow-up period. The optimal assessment period was varied using 3-, 6-, and 12-month periods to allow for sufficient time to accurately calculate the ratio.

All patients identified as the initial study population were also required to meet the following study criteria: have at least 1 medical claim with an asthma diagnosis (

International Classification of Diseases, Ninth Revision, Clinical Modification

] code 493.xx) in any diagnosis field during the preindex period or 30 days after the index date; be continuously eligible to receive medical and pharmacy services in the 1-year preindex period and 24-month postindex period; be at least 4 years of age at the index date; and have no diagnosis of chronic obstructive pulmonary disease (codes 491.xx, 492.xx, 496.xx) anytime during the preindex and postindex periods. Patients at risk for asthma exacerbation constituted a subset of this initial study population meeting all study criteria. The definition of “at risk for asthma exacerbation” for this study was based on medication use and differed based on the length of the assessment period. Accordingly, the number of patients identified as being at risk for asthma exacerbation differed for each assessment period. Patients were considered to be at risk for asthma exacerbation if they had 

1 dispensing event for an asthma controller medication or 

2 short-acting beta-agonist (SABA) canisters for the 3-month period; 

4 SABA canisters for the 6-month period; or 

5 SABA canisters for the 12-month period. By requiring patients to have at least 1 controller medication or SABA use during the assessment periods, we reduced the likelihood of having missing ratio values (ie, no controller or SABA use) or many zero values (ie, no controller but only SABA use). Including patients with missing ratio values or many zero values would have increased the likelihood of including patients with intermittent asthma. Since the ratio is only reliable in patients with persistent asthma, inclusion of patients with intermittent asthma would affect the overall reliability of the marker.

The controller-to-total asthma medication ratio was calculated as the ratio of the units of controller medications used divided by the sum of the units of the controller medications, plus the units of inhaled SABAs. Controller medications included inhaled corticosteroids (ICSs), ICS and long-acting beta-agonist (LABA) combination products, leukotriene modifiers, cromolyn sodium, nedocromil, methylxanthines, and omalizumab. The ratio ranged from 0 (no controller + SABA) to 1 (controller + no SABA). Patients using no medications or using only LABAs during the assessment periods would have had missing ratio values and were excluded. As previously described,

 1 unit of medication was defined as (1) 1 canister of an inhaler, (2) a 30-day supply of an oral medication, or (3) 1 dispensing of a nebulized or injected medication.

The primary outcome was an asthma exacerbation requiring an inpatient hospitalization or ED visit with a primary discharge diagnosis of asthma (

 code 493.xx) during the 12-month follow-up period. We selected the optimal ratio value, the assessment period, and the incremental risk-change analysis using this primary outcome. We evaluated the impact of the optimal ratio value and assessment period on exacerbations, individual components of the exacerbations (ie, inpatient hospitalization, ED visits), and OCS-dispensing events.

The final study sample was split into pediatric (4-17 years) and adult (

18 years) cohorts. The same analysis was conducted on both cohorts. A receiver operating characteristic (ROC) curve analysis was done to select the optimal cutoff of the ratio. The ROC analysis was done only with the primary outcome of exacerbations during a 12-month follow-up period. The cutoff values for the ratio ranged from

0.9 at 0.1 intervals. The ROC analysis compared the areas under the curve (AU Cs) obtained from the C statistic of the logistic regression model that was run for each cutoff value. All logistic regression models controlled for baseline characteristics during the preindex period (age, sex, Charlson Comorbidity Index,

 number of unique diagnosis codes, number of unique prescription drug categories, number of unique prescriptions filled) and asthma severity (presence of asthma-related hospitalizations or ED visits, number of OCS-dispensing events) during the assessment period. To select the optimal cutoff value within an assessment period, we considered the model that had the highest AU C and that showed a statistically significant effect of the ratio on the outcome. After selecting the optimal cutoff within each assessment period, the choice of an assessment period was based on a statistical comparison of the AU Cs of the models selected for each of the 3 assessment periods. The following steps were used for this comparison. The highest AU C was compared with the lowest. If the difference was not statistically significant, then the shorter assessment period was chosen with its respective cutoff value. If, however, the difference was statistically significant, then the comparison was repeated with the next-lowest AU C.

For the incremental risk-change analysis, the presence of a linear relationship was first demonstrated with a significant linear trend statistical test result and a graphical analysis showing an increasing or decreasing odds ratio (OR) with an increase in the ratio values.

 If a linear relationship was found, the ratio was entered into a logistic regression model as a continuous variable, adjusting for baseline characteristics and asthma severity as described above. The OR was then interpreted as the percent decrease or increase in risk of the outcome with each unit increase in the ratio value.

After selection of the optimal cutoff value and assessment period, multivariate logistic regression models were developed to predict the likelihood of the other outcomes during the follow-up period as a function of the optimal cutoff of the ratio, while controlling for baseline characteristics during the preindex period and asthma severity during the assessment period as described above. Adjusted ORs with corresponding 95% confidence intervals (CIs) are presented.

The initial study population included 101,437 patients (41,753 children and 59,684 adults) from the commercially insured database and 33,793 patients (25,048 children and 8745 adults) from the Medicaid database that met the study criteria. A demographic description of the study sample is provided in 

. Of the initial study population that met all the criteria, at least 45% of children and adults in the Medicaid population and at least 50% of children and adults in the commercially insured population were identified as being at risk for asthma exacerbation, depending on the assessment period. In the sample that was at risk for asthma exacerbation, the majority achieved a ratio of

). Adults were more likely than children to have a ratio of 

0.5 in both the Medicaid and commercially insured populations. Similarly, a higher proportion of commercially insured patients (range 71% to 85%) had a ratio of 

0.5 compared with Medicaid patients (range 58% to 73%).

Receiver Operating Characteristic Curve Analysis The AU Cs predicted by the ratio ranged from 0.68 to 0.73 for children and 0.71 to 0.74 for adults (

). The optimal ratio cutoffs within each assessment period (boldface entries in Table 2) were identified as those with the highest AU C values, and generally were at least 

0.5 for both children and adults in both study populations. Statistical comparison of the AU Cs then identified the optimal assessment period. For example, in the pediatric commercially insured population, a statistically significant difference between AU Cs for the 3- and 12-month assessment periods (P = .005) was found. The subsequent comparison between the 6- and 12-month AU Cs was not significant (P = .114); hence, 6 months was chosen as the optimal assessment period, with >0.5 designated as the optimal cutoff. Thus, in both children and adults at risk for asthma exacerbation, the ratio of 

0.5 achieved during a 6-month assessment period was optimal in predicting an asthma exacerbation in the following year. In Medicaid patients, however, a higher optimal cutoff of 

0.7 was found for both children and adults, but the optimal assessment period was shorter for adults (3 months) than for children (6 months).

An incremental effect on the risk of an exacerbation was observed for both children and adults in the commercially insured population, with the effect leveling after the optimal cutoff value of 0.5 (

). However, each 0.1-unit increase in the ratio up to the optimal value of 0.5 resulted in a significantly reduced risk of exacerbations in pediatric and adult commercially insured patients: 72% (OR 0.28; 95% CI 0.13-0.57) and 80% (OR 0.20; 95% CI 0.12-0.33), respectively. Similar to what happened in the commercially insured population, the risk reduction leveled off after the optimal cutoff of 

). However, each 0.1-unit increase in the ratio up to the optimal cutoff of 0.7 resulted in a significant reduction only for children (OR 0.65; 95% CI 0.43-0.97) and not adults.

Predictive Ability of the Ratio for Outcomes

The optimal ratio significantly predicted reductions in the risk of exacerbations in the 12-month follow-up period in all cohorts. Pediatric Medicaid patients with a ratio of 

0.7 in a 6-month assessment period and pediatric commercially insured patients with a ratio of >0.5 in a 6-month assessment period had a 31% (OR 0.69; 95% CI 0.59-0.81) and 33% (OR 0.67; 95% CI 0.56-0.80) lower risk of exacerbations, respectively, in the 12-month follow-up period compared with those who had lower ratios (

). Adult Medicaid patients with a ratio of 

0.7 in a 3-month assessment period and adult commercially insured patients with a ratio of >0.5 in a 6-month assessment period had a 31% (OR 0.69; 95% CI 0.55-0.86) and 48% (OR 0.52; 95% CI 0.45-0.60) lower risk of exacerbations, respectively, in the 12-month follow-up period compared with those who had lower ratios. The reduction in risk was primarily driven by the reduced risk of an ED visit. The ratio was not useful for predicting OCS-dispensing events in the Medicaid population. However, the ratio was associated with a reduction in OCS-dispensing events in the commercially insured cohort, with the difference being statistically significant only in adults (OR 0.81; 95% CI 0.76-0.88).

The present study has provided valuable information on the optimal value of the controller-to-total medication ratio, as measured during defined time frames, for prediction of asthma events in patients receiving asthma pharmacotherapy. The ratio was found to be a significant predictor of subsequent exacerbations and OCS-dispensing events in both children and adults from databases containing Medicaid and commercially insured patients whom the study defined as being at risk for asthma exacerbation. The results are consistent with previous studies using HEDIS-defined persistent asthma populations in 12-month assessment periods,

even though this study defines a broaderasthma subset that uses shorter measurement periods. However, use of the population at risk for asthma exacerbation broadens the applicability of the results to a wider asthma population.

A recent study showed a high concordance between ratio values computed using 6 months and 12 months of data in 94% of patients,

which is consistent with our finding that similar proportions of both child and adult patients had a value of 

0.5 in the 6-month and 12-month assessment periods (Figure 1). However, to our knowledge, this study is the first to demonstrate that the ratio measured during an assessment period shorter than 12 months can still serve as a valid quality-of-care marker. The optimal cutoff values were found to be 

0.5 in 6 months (commercially insured children), and 

0.5 in 6 months (commercially insured adults). This finding has important implications for monitoring asthma patients, in whom changes in therapy can be instituted much earlier with the goal of increasing the ratio.

Previous studies on the ratio in HEDIS-defined persistent asthma populations have shown the optimal cutoff to be

 The optimal cutoff value was reported as high as 

0.9 in 1 study, but exacerbations were defined as emergency hospital care and OCS-dispensing combined, and significant risk reductions were noted at 

 Unlike the commercially insured cohort, a higher optimal ratio of 

0.7 was found for the Medicaid cohort in both children and adults. The reason for this higher value is not entirely clear, but may be related to the higher severity level or lower adherence of the Medicaid population (Table 1).

The optimal ratio values selected resulted in a 31% to 48% reduction in the risk of exacerbations in a 12-month followup period, which is within the range found in prior studies (23%-60%).

 The optimal ratio values were less able to predict OCS-dispensing events, with a significant effect found only in the adult commercially insured population.

This study provides another valuable piece of information that helps to inform use of the medication ratio to monitor therapy: demonstration of an incremental risk reduction with 0.1-unit increases in the ratio. This finding helps patients and caregivers by showing that increasing the medication ratio is valuable, even before the ultimate goal (optimal ratio) is achieved. From a managed care standpoint, it may allow for cost-benefit analyses of performance measures to determine optimal treatment strategies.

Several study limitations need to be considered. The studydefined population at risk for asthma exacerbation was not validated using clinical criteria. As such, we cannot exclude the possibility that intermittent asthma patients were included. However, at least 75% of the patients at risk for asthma exacerbation had a nonzero value for the ratio (Figure 2), suggesting a low proportion of patients with intermittent asthma. Increasing the number of controller medications or reducing SABA use will increase the ratio. The manner in which the ratio is increased cannot be determined from the study, but rather would depend on the treatment requirements of the individual patient. However, considering that controller medications are preventive treatment for symptoms, the increased use of controllers should reduce the need for albuterol. Prescription claims were used and a prescription that was filled was assumed to be taken. That may not be the case for drugs used on an as-needed basis (eg, SABAs). In addition, the possibility of miscoding or undercoding of diagnoses on claims may have resulted in underestimation of the prevalence of the main outcome of hospital and ED visits. However, this underestimation, if any, would be similar across comparison groups.

Articles

The controller-to-total asthma medication ratio was a significant predictor of exacerbations in pediatric and adult commercially insured and Medicaid patients.

Predicting Asthma Outcomes in Commercially Insured and Medicaid Populations

 To determine whether step-up care recommended by national asthma guidelines improves asthma control in a large managed care organization.

 A cohort with uncontrolled asthma, defined as impairment or risk in a 1-year consecutive period, was identified using electronic medical records among continuously enrolled patients with asthma aged 12 to 56 years. Guideline-based step level of care was determined 3 months before and 3 months after the index event of uncontrolled asthma according to an algorithm of asthma medication dispensing using electronic pharmacy data. Impairment based on short-acting b-agonist canisters dispensed and risk based on asthma emergency or hospital care were compared during the following year in patients with vs without step-up care, adjusting for demographics, prior utilizations, asthma risk, and comorbidities.

 Uncontrolled asthma was identified in 7694 eligible patients (mean [SD] age, 35.7 [14.4] years; 54.0% female). Step-up care during the 3-month period after the uncontrolled asthma event was seen in 2160 of 7177 patients (30.1%) with classifiable step care. Step-up care was associated with significant reductions in impairment, with adjusted relative risks of 0.78 (95% confidence interval [CI], 0.73-0.85) during the first 6 months and 0.84 (95% CI, 0.78-0.90) during the full 12-month follow-up period. Emergency or hospital care was unaffected by step-up care. Subgroups with uncontrolled asthma defined by impairment only and risk only showed outcomes generally similar to those of the entire cohort.

Guideline-based step-up care in uncontrolled asthma was determined by administrative data and was shown to be associated with significant and clinically meaningful improvements in asthma impairment but not risk. 

Determination of national asthma guideline-based step-up care in uncontrolled asthma by administrative data may be useful for improving quality of care in managed care organizations.

Step-up care after determination of uncontrolled asthma is documented in a minority of patients. 

Greater improvement in the impairment domain (short-acting b-agonist canisters dispensed) of asthma control is seen in patients with uncontrolled asthma receiving step-up asthma care compared with those not receiving step-up asthma care. 

Real-time determination of uncontrolled asthma by administrative data is recommended to identify patients in whom step-up asthma care is needed to improve asthma control.

Poor asthma control impairs health-related quality of life, increases resource utilization and cost, and persists as a major public health issue affecting more than 23 million individuals in the United States.

 In 2007, direct and indirect costs for asthma totaled $19.7 billion, with the cost of prescription drugs accounting for most of the direct medical expenses.

 The National Asthma Education and Prevention Program

 (http://www.ginasthma.com) developed guidelines to improve asthma control and to reduce burden. These guidelines emphasize 2 major domains of asthma control: impairment and risk. Impairment refers to rescue medication use, symptoms, nocturnal awakenings, school and work absences, exercise problems, and pulmonary function. Risk addresses frequency and severity of exacerbations, loss of lung function, and adverse effects of medication. In addition, the guidelines established 6 step levels of treatment to implement based on a patient’s asthma severity determined before treatment or level of control determined after therapy is instituted. Additional evidence is needed to determine whether appropriate step-up care in patients with uncontrolled asthma improves asthma  control in large managed care organizations.

 This study used administrative data to identify patients with uncontrolled asthma and their step level of care to determine whether step-up care is associated with improvements in subsequent asthma control. We hypothesized that step-up care instituted after a defined uncontrolled asthma event is associated with improvements in asthma control.

 This institutional review board—approved retrospective cohort study was conducted using administrative pharmacy and utilization databases of health plan members of the Kaiser Permanente Southern California Region. The study design is shown in 

 Patients were identified as having asthma if they met at least 1 of the following criteria

 in both 2005 and 2006: (1) any discharge diagnosis of asthma in the health plan and contracting hospital databases (

International Classification of Diseases, Ninth Revision,

] code 493.xx);(2) emergency department or outpatient asthma-related diagnosis; or (3) at least 2 asthmarelated drug dispensings (excluding oral corticosteroids) in the pharmacy database, including β-agonists (excluding oral terbutaline sulfate) and all asthma controller drugs.

Determination of the Cohort With Uncontrolled Asthma.

 Uncontrolled asthma was defined as impairment or risk in a 1-year consecutive period within the first 18 months (January 1, 2005, to June 30, 2006) of the entire 36-month study period (Figure 1).

 Impairment was defined as at least 7 short-acting β-agonist (SABA) canisters dispensed in 1 year. In a clinical setting, asthma impairment is defined based on rescue therapy, symptoms, functional limitations, and pulmonary function

; however, only rescue therapy use can be obtained in administrative data. We validated the number of SABA canisters dispensed in 1 year as a long-term measure of asthma control because it correlates with patient-reported measures of asthma impairment.

 The SABA cut point of at least 7 canisters dispensed per year defined impairment because at least 70% more asthma control problems

 occurred in patients receiving at least 7 vs 6 or fewer canisters dispensed per year.

 Risk was captured by at least 2 unique exacerbations within a year. These included asthma-related emergency department visits or hospitalizations (emergency or hospital care was defined as a primary diagnosis of asthma [

 code 493.xx] or as a secondary diagnosis of asthma when the primary diagnosis was related to a respiratory disease) or oral corticosteroid courses with a provider visit for asthma within 2 days of dispensing.

 The “index date” of uncontrolled asthma for impairment was the date of the seventh SABA canister dispensing, and the index date for risk was the date of the second exacerbation event during a consecutive 1-year interval within the first 18 months of the study. For patients with both impairment and risk events within the 1-year interval, the date of the last event was used as the index date to determine asthma step care (Figure 1).

 Eligible study patients were health plan members aged 12 to 56 years who met the study definition of having uncontrolled asthma and from 2005 to 2007 were continuously enrolled and had health plan drug benefit. The eligible cohort is shown in 

 Asthma drug dispensing was determined using the health plan pharmacy information management system,

 in which the prescription directions for use, specific drug class and name, and quantities dispensed are available. Daily dosage of inhaled corticosteroid was calculated as the following: ([No. of Canisters] × [No. of Puffs per Canister] × [Strength in Micrograms per Puff]) / No. of Days’ Supply).

 Inhaled corticosteroids were categorized as low dose, medium dose, or high dose based on the calculated daily dosage and on their individual potency using the estimated comparative daily dosages recommended by National Heart, Lung, and Blood Institute guidelines

 for persons 12 years or older. Fluticasone propionate plus salmeterol (Advair) was the only combination inhaled corticosteroid and long-acting β-agonist therapy dispensed during step care determination. Strengths of Advair Diskus 100/50, 250/50, and 500/50 were categorized as low-dose, medium-dose, and high-dose inhaled corticosteroid, respectively.

 Step level of care was determined using drug dispensing 3 months before and 3 months after the uncontrolled asthma index date based on guidelines for patients 12 years or older as follows

: (step 1) no drugs or only SABA dispensed; (step 2) monotherapy low-dose inhaled corticosteroid, leukotriene modifier, mast cell stabilizer, or theophylline; (step 3) (a) combination low-dose inhaled corticosteroid with longacting β-agonist, leukotriene modifier, mast cell stabilizer, or theophylline or (b) monotherapy medium-dose inhaled  corticosteroid; (step 4) (a) combination medium-dose inhaled corticosteroid with long-acting β-agonist, leukotriene modifier, mast cell stabilizer, or theophylline or (b) monotherapy high-dose inhaled corticosteroid; (step 5) high-dose inhaled corticosteroid plus long-acting β-agonist with or without omalizumab; and (step 6) high-dose inhaled corticosteroid plus long-acting β-agonist plus oral corticosteroid with or without omalizumab. Electronic medical records of patients in step 6 were reviewed to determine that the oral corticosteroid  dispensed was for maintenance therapy and not for acute use only. Patients were then divided into categories of step-up care or no step-up care based on whether the post—step care was at least 1 step level higher than the pre–step care level. Patients were considered unclassifiable and were excluded from the final analyses when pharmacy data did not allow step classification because of incomplete or missing information. Immunotherapy could not be captured accurately by our electronic databases during the time of the study. Only 4 patients were taking omalizumab during the study and therefore were of inadequate sample size to allow separate analysis.

 Patient characteristics were captured by administrative data (

). Census block group levels of family household income and educational attainment estimates in 2006 were supplied by Nielsen Claritas (http://www.claritas.com) and were obtained for more than 95% of members through geocoding, a technique that links members’ addresses to census geographic areas.

 Databases did not capture smoking history or race/ethnicity during the study period.

Outcomes (Comparisons Between Step-Up Care vs No Step-Up Care).

 Impairment was defined as at least 7 SABA canisters dispensed in the full year

 or as at least 4 SABA canisters dispensed in months 0 through 6 after the 3-month post—step care period. Risk was defined as a severe asthma exacerbation requiring asthma emergency or hospital care during the first 6 months and entire year after the 3-month post–step care period. Asthma emergency or hospital care was selected as the risk outcome because it is more specific for asthma risk and represents more severe exacerbations than oral corticosteroid dispensing; furthermore, the latter is affected by its frequent prophylactic dispensing during routine visits to be used for future worsening asthma.

 Descriptive analyses for individual resource use items entered as a dichotomous variable (eg, ≥7 SABA canisters dispensed [yes or no]) or as a count variable (eg, the number of SABA canisters dispensed) were conducted to illustrate their association with step care. Counts and proportions were obtained for categorical variables, and the mean (SD) and median (range) were used for continuous variables. Bivariate associations were assessed as appropriate by t test, analysis of variance, χ2 test, or nonparametric methods (eg, Wilcoxon rank sum test and Kruskal-Wallis analysis of variance). Significance was set at 2-tailed 

 Univariate and multivariate Poisson regression analyses with a robust error variance (SAS/GENMOD) were constructed to calculate the unadjusted and adjusted relative risk (RR) and the 95% confidence interval [CI] for the outcome variables, as this type of regression model provides better estimates in health investigations involving common binary outcomes (prevalence rate >10%) than logistic regression.

 All point estimates and 95% CIs were generated using SAS software (version 9.1 for Windows; SAS Institute, Cary, North Carolina).

 The following covariates were available for selection for the multivariate models: (1) baseline variables, including age at the time of the uncontrolled asthma index event, sex, and geocoded educational and income attainment; (2) prior-year variables, including number of SABA canisters dispensed, asthma exacerbations (any asthma emergency or hospital care or any asthma oral corticosteroid dispensing determined

), total nonasthma emergency department visits and outpatient visits,

 and comorbidities (gastroesophageal reflux disease, sinusitis, and nasal polyposis); and (3) the pre—index date step level of care. Covariates were selected according to Akaike  information criterion by means of stepAIC function in R statistical software version 2.9.0 (http://www.r-project.org).

 A total of 7694 patients aged 12 to 56 years with uncontrolled asthma were eligible for the study based on asthma impairment or risk definitions (Figure 2). Of these patients with uncontrolled asthma, 5529 (71.9%) were identified as having asthma impairment only, 1355 (17.6%) as having asthma risk only, and 810 (10.5%) as having both asthma impairment and risk.

The mean patient age was 35.7 years of age, 54.0% were female, and patients were health plan members on average for more than a decade (Table 1). About three-quarters had more than a high school education, and the cohort was middle class by mean income. Compared with the impairment-only subgroup, the risk-only and risk and impairment subgroups had significantly more women, less education and income, and more subsidized insurance.

 Among the cohort, half of the patients met criteria for medium-to-high risk stratification asthma categories,

 and slightly less were in step 2 or higher care. During the year before the uncontrolled asthma index date, frequencies were 13.6% for asthma emergency or hospital care, 38.6% for oral corticosteroid courses, and 12.5% for 15 or more SABA canisters dispensed9 (Table 1). Compared with the impairment-only subgroup, the risk-only and risk and impairment subgroups had statistically more asthma burden.

 Respiratory and allergy comorbidities were common, with frequencies of 20.5% for allergic rhinitis, 15.4% for sinusitis, and 8.2% for gastroesophageal reflux disease (

). Most respiratory comorbidities occurred significantly less frequently in the impairment-only subgroup than in the risk-only and risk and impairment subgroups.

Significant clinically meaningful improvements in asthma impairment are documented by administrative data for 1 year after initiation of step-up care in patients with uncontrolled asthma.

Step-Up Care Improves Impairment in Uncontrolled Asthma: An Administrative Data Study

To examine the association of race/ ethnicity with medication use, emergency hospital care (EHC) utilization, and quality-of-life outcomes in a population with persistent asthma and to determine if factors related to severity of illness, treatment characteristics, and demographic, socioeconomic, and smoking status explain differences in study outcomes.

We examined survey and administrative data for 974 adults with persistent asthma enrolled in a group-model health maintenance organization. Patients with persistent asthma were identified in 1999 using Healthcare Effectiveness Data and Information Set inclusion criteria. In 2000, the same patients were surveyed regarding quality of life using the Mini Asthma Quality of Life Questionnaire. In 2001, the use of controller medications, the ratio of controller medications to rescue medications, and EHC utilization were identified by electronic medical record. Multiple logistic regression and linear regression analyses were used to evaluate the independent association of race/ethnicity with study outcomes after adjusting for severity of illness, treatment characteristics, and demographic, socioeconomic, and smoking status.

Compared with whites, African Americans (standardized ß coefficient, -0.12) and Native Americans/Aleutians/Eskimos (standardized ß coefficient, -0.14) had lower Mini Asthma Quality of Life Questionnaire scores (P <.05 for both). African Americans were significantly (P <.05) more likely to report EHC utilization (odds ratio, 5.2; 95% confidence interval, 2.6-10.3).

Disparities existed in 2 outcome measures, Mini Asthma Quality of Life Questionnaire scores and EHC utilization. A concerning finding is that African Americans were at least 5 times more likely to report higher EHC utilization, even after adjusting for factors such as income and education.

Significant racial/ethnic disparities in emergency hospital care utilization and quality of life exist among adults with persistent asthma.

African Americans were more likely to have asthma-specific emergency hospital care utilization compared with whites even after adjusting for various explanatory factors. 

African Americans and Native Americans/Aleutians/Eskimos were more likely to report lower asthma-specific quality-of-life scores compared with whites. 

These disparities existed even though there were no reported differences in controller medication use among racial/ethnic groups.

Asthma affects more than 20 million American adults; 7.7% of American adults (22.2 million) had asthma in 2005.

 The 2003 National Health Interview Survey found that 29.8 million Americans had been diagnosed as having asthma at some point in their lifetime.

 The disease accounts for about 3400 deaths a year

 and is associated with substantial morbidity.

 Moreover, asthma attacks are responsible for more than 130 million days of restricted activity each year among persons of all ages with asthma (Healthy People 20103), including 10.1 million missed workdays among adults.

 Asthma results in significant healthcare utilization and costs. In 2004, asthma was associated with 14.7 million outpatient visits and with 2 million emergency department (ED) visits.

 Furthermore, asthma is responsible for more than 500,000 hospitalizations annually.

 The Agency for Healthcare Research and Quality4 estimates that total 2004 asthma-related medical costs were more than $16.1 billion, including $11.5 billion in direct medical costs.

 Among persons with asthma, significant disparities exist in rates of morbidity, mortality, and other outcomes.

 Age-adjusted asthma mortality is significantly higher among non-Hispanic blacks (3.3 deaths per 100,000 persons) compared with non-Hispanic whites (1.1 deaths per 100,000 persons).

 found that African American patients with asthma were more likely than their white counterparts to have severe asthma, poor quality of life, more asthma control problems, and increased ED visits. They were also more likely to be treated with 3 or more long-term controller medications.

 Disparities are also seen in asthma-related ED visits and hospitalizations, particularly race/ethnicity—based differences, which are well documented.

 In 2005, African Americans had 201 asthma-related ED visits per 10,000 persons compared with 44 asthma-related ED visits per 10,000 whites (age-adjusted, including all ages).1 Asthma-related ED visits declined 25% among whites between 1998 and 2005 but remained stable among African Americans.

 Similar differences are seen for asthma-related hospitalizations,

 with 2005 rates of 10 per 10,000 whites and 34 per 10,000 African Americans.

 Although evidence documenting disparities in asthma prevalence and outcomes is plentiful, the reasons behind these differences are poorly understood and do not seem to be fully explained by differences in severity or comorbid conditions.

 Socioeconomic status (SES) and the disproportionate burden of poverty among racial/ethnic minorities in the United States likely influence asthma-related disparities. Research suggests that SES affects race/ethnicity—based disparities in asthma-related ED use.

 Genetic makeup, environmental risk factors, and differences in health behaviors may also contribute to observed racial/ethnic differences in asthma prevalence and health outcomes, yet none of these fully explain asthma disparities.

 Research findings are equivocal as to the extent to which asthma disparities are related to differences in quality of care, and factors contributing to disparities in asthma care are not well understood.

 report race/ethnicity—based disparities in asthma care received, demonstrating that members of minority groups are less likely to receive recommended elements of care. However, other research suggests that disparities in asthma care do not fully explain differences in asthma outcomes.

 With this background in mind, this study had the following 2 objectives: (1) to examine the association of race/ethnicity with dispensed medication use, emergency hospital care (EHC) utilization, and quality-of-life outcomes in a managed care population with persistent asthma and (2) to determine the extent to which severity of illness, treatment characteristics, and demographic, socioeconomic, and smoking status explain race/ethnicity differences in study outcomes.

 Patients in this study have been previously described.

 Surveys were sent from Kaiser Permanente’s Care Management Institute, Oakland, California, in August 2000, to a random sample of Kaiser Permanente Medical Care Program adult members aged 18 to 56 years from the Northern California (n = 3072) and Northwest (n = 543) regions who were diagnosed as having persistent asthma in 1999 based on Healthcare Effectiveness and Data Information Set (HEDIS) criteria.

 The HEDIS criteria for persistent asthma comprise the following administrative database observations in a 12-month period: (1) 4 or more asthma medication dispensings, (2) 1 or more ED visits or hospitalizations with a primary diagnosis of asthma, or (3) 3 or more asthma outpatient visits with 2 or more asthma medication dispensings. Completed surveys were returned by 2219 members (61.4%), of whom 1998 (90.0%) had a confirmed diagnosis of physician-diagnosed asthma. The present study was restricted to 1100 respondents who met the HEDIS criteria for persistent asthma in 2000 and in 1999 and had electronic healthcare utilization information available for 2001. Survey information did not statistically (P <.05) differ by race/ethnicity or by demographic, socioeconomic, or smoking status among the total population with completed survey information (n = 2219) or by the subset with electronic healthcare utilization information available (n = 1100). Of these 1100 patients, 974 had complete race/ethnicity information and are the subject of this analysis. The study was approved by the Northern California Region and the Northwest Region Kaiser Permanente institutional review boards.

The primary predictor variable, namely, race/ethnicity (white, African American, Hispanic, Asian/Pacific Islander, or Native American/Aleutian/Eskimo), was obtained from patient surveys. Secondary survey-based predictor measures included 2 demographic variables (age and sex), 2 SES measures (highest grade in school completed and annual household income), and smoking status. All 3 survey-based measures were ordinal, including highest grade in school completed (eighth grade or less, some high school or technical school, high school graduate, some college or technical school graduate, college graduate, or postgraduate); annual household income (<$10,000, $10,000-$14,999, $15,000-$19,999, $20,000-$24,999, $25,000-$34,999, $35,000-$49,999, and >$50,000); and smoking status (never smoked, past smoker, less than one-half pack per day, one-half to 1 pack per day, 1-2 packs per day, or >2 packs per day).

Healthcare Utilization—Based Predictor Variables. 

Using a unique record number, survey records were matched to administrative data from 2000. Secondary utilization predictor variables identified in 2000 included 3 severity measures and 1 treatment measure. The severity measures included any asthma-specific emergency or hospitalization utilization versus nonutilization, ß-agonist overuse (>14 vs <14 canisters), and any oral corticosteroid use versus nonuse.

 The treatment measure included any inhaled corticosteroid use (>1 canisters vs nonuse).

 Dependent variables included 1 survey-based outcome measure from 2000. Also included were 3 healthcare utilization—based outcome measures from 2001.

The Mini Asthma Quality of Life Questionnaire (MiniAQLQ)

 was the survey-based outcome measure used. The MiniAQLQ includes 15 questions in 4 domains (symptoms, activity, emotions, and environment). Scores range from 1 to 7, with higher scores indicating better quality of life.

Healthcare Utilization—Based Outcome Measures. 

Using a unique record number, survey records were matched to administrative data from 2001. Healthcare utilization—based outcome measures included (1) any inhaled corticosteroid use (>1 canisters vs nonutilization); (2) medication use ratio of inhaled corticosteroids to ß-agonist medications (>0.5 vs <0.5), a measure shown to be related to better patient-reported and healthcare utilization asthma outcomes

; and (3) EHC, which comprised any asthma-specific hospitalization or ED visit (utilization vs nonutilization). For these analyses, asthma-related hospitalizations and ED visits were combined into a single variable to increase power.

 All analyses were performed using commercially available statistical software (SAS version 9.1 for Windows; SAS Institute, Cary, NC). We examined bivariate associations of race/ethnicity with each categorical outcome measure using Pearson product moment 

2 analysis; analysis of variance was also used for the MiniAQLQ scores. Multiple logistic regression models were constructed to analyze the independent effect of race/ethnicity on categorical outcomes (with significant relationships [P <.05] in bivariate analysis) after adjustment for possible confounding factors, where race/ethnicity was treated as a 4-level categorical variable (white [reference group], African American, Hispanic, Asian/Pacific Islander, and Native American/Aleutian/Eskimo). Ordinary least squares regression was used to analyze the independent effect of race/ethnicity on MiniAQLQ scores.

 Each outcome measure was regressed on race/ethnicity, adjusting for the following sequence of added covariates: (1) model 1, with no additional covariates; (2) model 2, with added demographics (age and sex); (3) model 3, with model 2 plus SES characteristics and smoking status; (4) model 4, with model 3 plus the severity markers of EHC utilization, ß-agonist overuse, and oral corticosteroid use; and (5) model 5, with model 4 plus inhaled corticosteroid use, all in 2000. In these models, age, education, income, and smoking status were analyzed as continuous measures, while EHC (use vs nonuse), ß-agonist overuse (>14 vs <14 canisters), oral corticosteroid use (>1 dispensings vs nonuse), and inhaled corticosteroid use (>1 canisters vs nonuse), all in 2000, were analyzed as dichotomous measures.

, whites constituted most of the participants (72.2%), followed by African Americans (9.3%), Hispanics (8.5%), Asian/Pacific Islanders (6.9%), and Native Americans/Aleutians/Eskimos (3.1%). The study population was also well educated (almost 40% had a college degree or higher), 67.9% were female, the mean age was 43.2 years, and less than half were current or former smokers.

 Reflecting an asthma population with good health in 2000, less than 20% of the population reported ß-agonist overuse or oral corticosteroid use in 2000, while 11.4% reported any EHC utilization (Table 1). These data are consistent with quality-of-care results reported within the 2 Kaiser Permanente study regions during the observed study periods (unpublished data, Michael Schatz, MD, MS, oral communication, 2009).

 The descriptive analysis of study outcome measures also reflects a population with moderately high MiniAQLQ scores, high asthma controller use, and low EHC utilization (Table 1). The mean (SD) MiniAQLQ score was 4.8 (1.3) in 2000, most members of the study population (81.8%) were dispensed controller medications in 2001, and most (64.4%) had medication use ratios of 0.5 or higher in 2001. The 2001 EHC utilization was 11.4%.

 Race/ethnicity was significantly associated with 2 of 4 study outcomes (self-reported MiniAQLQ scores in 2000 and EHC utilization in 2001) in unadjusted and multivariate analyses (

). African Americans (standardized b coefficient, −0.12) and Native Americans/Aleutians/Eskimos (standardized b coefficient, −0.14) reported significantly lower MiniAQLQ scores compared with whites even after adjusting for study covariates (P <.05 for both) (Table 3). African Americans were significantly more likely to have any EHC utilization (odds ratio = 5.2, 95% confidence interval, 2.6-10.3; P <.05) compared with whites (Table 4). This association remained even after adjusting for age, sex, SES, smoking status, severity measures in 2000 (EHC utilization, ß-agonist overuse, and oral corticosteroid use), and treatment measures (controller use) (Table 3). Compared with whites, Hispanics and Asian/Pacific Islanders were more likely to have had EHC utilization in unadjusted analysis; however, this association lost statistical significance after adjusting for potential confounders.

 This study found significant racial/ethnic disparities in quality of life and EHC utilization among adults with persistent asthma. Specifically, African Americans and Native Americans/Aleutians/Eskimos were more likely to report lower asthma-specific quality-of-life scores compared with whites. Moreover, African Americans were more likely to have asthma-specific EHC utilization compared with whites even after adjusting for various factors that could explain these differences. Therefore, in a large integrated healthcare delivery system with universal access to health services, substantial racial/ethnic quality-of-life and EHC utilization disparities were identified even after adjusting for a comprehensive array of demographics, SES, severity of illness, and use of controller medications. Of note, these disparities existed even though there were no reported differences in controller medication use among racial/ethnic groups.

 Higher asthma morbidity among African American populations has been reported in other studies.

 Our finding that African American patients were more likely to have asthma-specific EHC utilization may, in part, be driven by higher disease severity in the African American population. Although we adjusted for several proxy measures of asthma severity, residual confounding due to unmeasured disease severity could contribute to the observed racial/ethnic differences in EHC utilization.

 Lower asthma-specific quality-of-life scores among African Americans and Native Americans/Aleutians/Eskimos and higher EHC utilization among African Americans could be driven by differential response to asthma medications. However, no information was available in this study to evaluate patient response to medications. Future studies are needed to better understand whether minority populations respond differently than white populations to asthma medications, and if so, why. The need for this research is highlighted by the fact that obese patients may be less responsive to asthma medications

 and that obesity rates may vary by race/ethnicity.

 Uncollected survey data related to several factors may have explained differences in asthma-related outcomes among African Americans and Native Americans/Aleutians/Eskimos with persistent asthma. Such factors as medication adherence, efficacy in self-management behaviors, and patient activation (ie, skills and abilities of an individual to manage health and engage providers in shared decision making) may explain differences in asthma-related outcomes. A 2007 study

 conducted by Kaiser Permanente’s Care Management Institute found that higher levels of self-efficacy and patient activation were associated with improved outcomes among adults with chronic conditions. It is possible that minority patients having persistent asthma differed in self-efficacy and patient activation

 compared with white patients having persistent asthma, possibly explaining some of the differences seen in our study outcomes.

 Moreover, we had no information regarding specific environmental characteristics that may have contributed to overall disparities in health outcomes. For example, African Americans may have had higher EHC utilization because they were more likely to reside in neighborhoods with greater air pollution, which can exacerbate asthma. Such information might have explained disparate outcomes found in our study but were unavailable for analysis.

 This study has several potential limitations. First, treatment factors other than controller medications may have explained racial/ethnic disparities in study outcomes. Specifically, Schatz and colleagues

 found that 3 treatment factors (regular use of controller medication, regular use of long-acting ß-agonist medications, and provision of care by an asthma specialist) were associated with better asthma outcome control. However, when we included these additional treatment characteristics in multivariate models (data not shown), the overall study findings remained the same, suggesting that particular differences in these treatment measures do not explain the racial/ethnic disparities in asthma-related outcomes reported herein. Nevertheless, it is possible other unmeasured factors may explain the observed disparities. Second, limited measures of asthma control were available for analysis. Forced expiratory volume in the first second of expiration is an important measure of asthma control

 that is also related to quality of life.

 The lack of spirometry data in this study is mitigated by the fact that MiniAQLQ scores do not require spirometry testing. Third, no information was available regarding environmental factors such as air pollution and other adverse exposures that may affect asthma control. Fourth, we also had no information regarding comorbid conditions (eg, gastroesophageal reflux disease and obesity) that may have contributed to differences in study outcomes by race/ethnicity. Fifth, information was unavailable regarding nonrespondents. Sixth, this study was set in a large group-model integrated healthcare delivery system. It is possible that results are not generalizable to adults with asthma in other healthcare delivery systems. However, the Kaiser Permanente population has been shown to be representative of the demographics within regions it serves,

 which may increase the generalizability of our findings. Moreover, Kaiser Permanente is a particularly valuable setting to study racial/ethnic disparities in asthma outcome measures because SES is more homogeneous compared with public delivery systems, in which racial/ethnic differences in outcome measures may be confounded by larger SES differences.

African Americans had more asthma-specific emergency care utilization, and African Americans and Native Americans/Aleutians/Eskimos were more likely to report lower asthma-specific quality-of-life scores, than whites.

Medication Use, Emergency Hospital Care Utilization, and Quality-of-Life Outcome Disparities by Race/Ethnicity Among Adults With Asthma

To describe the relationships between persistent asthma defined by administrative versus survey data and their stability over time.

 Longitudinal survey and retrospective administrative database.

 Administrative data were used to identify patients meeting the Healthcare Effectiveness Data and Information Set (HEDIS) criteria for persistent asthma in year 1 (2006). At the end of year 2 and on 3 occasions during year 3, patients were mailed a survey to define persistent asthma based on symptoms and medication use in the prior month and exacerbations in the prior 12 months. Administrative data were also used to define medical utilization for asthma in year 3.

 Of 13,833 eligible patients, 2895 (20.9%) returned the survey; 2751 of these respondents reported physician-diagnosed asthma, of whom 2517 (91.5%) had survey-defined persistent asthma. Patients having survey-defined persistent asthma (68.0%) were more likely to requalify as having HEDIS-defined persistent asthma in year 2 than patients not having survey-defined persistent asthma (22.2%). However, 81.6% of survey respondents who did not requalify as having HEDIS-defined persistent asthma in year 2 had survey-defined persistent asthma. Patients with survey-defined persistent asthma in year 2 had significantly more medical utilization for asthma in year 3 than patients without survey-defined persistent asthma. Approximately 82% of the 799 patients completing all 4 surveys had persistent asthma on all surveys.

HEDIS-defined persistent asthma is generally consistent with survey-defined persistent asthma over a 3-year period.

Michael Schatz, MD, MS

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