The Journal of Allergy and Clinical Immunology
Volume 128, Issue 2 , Pages 273-277, August 2011

Improving asthma outcomes in large populations

  • Michael Schatz, MD, MS

      Affiliations

    • Corresponding Author InformationReprint requests: Michael Schatz, MD, MS, Department of Allergy, Kaiser Permanente Medical Center, 7060 Clairemont Mesa Blvd, San Diego, CA 92111.
    • ,
  • Robert S. Zeiger, MD, PhD

Department of Allergy, Kaiser Permanente Medical Center, San Diego, Calif

Received 1 February 2011; received in revised form 18 March 2011; accepted 24 March 2011. published online 19 April 2011.

Article Outline

This article summarizes our experience using administrative, survey, and telephone information to define asthma severity, impairment, risk, and quality of care in our large Kaiser Permanente population. Our data suggest that the 2-year Healthcare Effectiveness Data and Information Set definition of persistent asthma is a good surrogate for survey-defined persistent asthma, and thus it would be reasonable to direct asthma population management and quality-of-care assessments at patients with Healthcare Effectiveness Data and Information Set–defined persistent asthma for 2 years in a row. For population management, the numbers of short-acting β-agonist (SABA) canisters dispensed and validated tools on mail or telephone surveys have been used to assess asthma impairment. Algorithms based on pharmacy data (SABA canister and oral corticosteroid dispensings and prior emergency hospital care) have been used to assess the risk domain of asthma control. The asthma medication ratio (controllers divided by controllers plus SABAs) has been shown to be related to improved outcomes and is recommended as an asthma quality-of-care marker. It is hoped that outreach to patients and providers based on these indicators will improve asthma outcomes in patients cared for in individual practices, as well as in large health plans.

Key words: Asthma, population management, quality of care, administrative data, asthma control

Abbreviations used: ACT, Asthma Control Test, AQLQ, Mini-Asthma Quality of Life Questionnaire, ATAQ, Asthma Therapy Assessment Questionnaire, EPR3, National Asthma Education and Prevention Program Guidelines for the Management of Asthma Expert Panel Report 3, HEDIS, Healthcare Effectiveness Data and Information Set, OR, Odds ratio, SABA, Short-acting β-agonist

 

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Asthma is a common chronic medical condition that is associated with substantial individual morbidity and cost to society.1, 2 Recent surveys show that more than 40% of both adults3 and children4 in the United States report uncontrolled asthma. Explanations for this high prevalence of uncontrolled asthma include that asthmatic patients are not seeking medical attention appropriately, that they are not receiving optimal care when seen, or both. The traditional approach to improving disease outcomes has been by one-on-one physician-patient interactions, but this alone cannot correct the above 2 problems. Computerization of medical utilization and claims data (“administrative data”) has allowed 2 more recent approaches for asthma that deal with (1) population management and (2) asthma quality of care. By initiating outreach to patients with uncontrolled asthma and identifying providers whose management practices could be improved, these approaches could address the above 2 factors (patients not seeking care and inadequate quality of care) potentially related to the high prevalence of uncontrolled asthma in this country.

For population management, patients with inadequately controlled asthma are identified based on administrative data criteria, and targeted interventions by mail, telephone, or in-person visits are implemented. Information obtained from mailed surveys or by telephone can supplement the administrative data and increase the sensitivity, specificity, or both of the information obtained.

For asthma quality of care, performance measures derived from administrative data are used by managed care organizations and others to improve the performance of their providers, with the expectation that improved performance on the measures will lead to improved asthma outcomes.5

From this perspective, we would like to summarize our experience using administrative, survey, and telephone information to define asthma severity, impairment, risk, and quality of care in our large Kaiser Permanente population. We hope that we and others can use this information to improve outcomes in patients with asthma.

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Population management 

Persistent asthma 

Although patients with intermittent asthma can suffer severe exacerbations,6 most outreach efforts in population management (and quality of care assessments) are directed at patients with persistent asthma. The recent National Asthma Education and Prevention Program Guidelines for the Management of Asthma Expert Panel Report 3 (EPR3) defines persistent asthma on the basis of symptom frequency and impact, rescue therapy use, pulmonary function, and exacerbation history.6

The most widely used administrative data marker of persistent asthma is the Healthcare Effectiveness Data and Information Set (HEDIS) definition,7 which entails meeting 1 or more of the following criteria in a 12-month period: (1) 4 or more asthma medication dispensings, (2) 1 or more emergency department visits or hospitalizations with a principal diagnosis of asthma, or (3) 4 or more asthma outpatient visits with 2 or more asthma medication dispensings. After we demonstrated a relationship between the number of consecutive years of HEDIS qualification and markers of persistent asthma,8 the National Committee for Quality Assurance modified its specification of the HEDIS persistent asthma population to require meeting the above administrative data criteria for persistent asthma for both the premeasurement year and the measurement year (the year the measure is being assessed).7

Only limited data have explored the relationship of the (1 or 2 years) HEDIS administrative data definition of persistent asthma to a more clinical definition. We have recently explored this relationship in a cohort of almost 3000 patients with HEDIS-defined persistent asthma in 2006 who completed a mailed survey in 2007.9 The survey defined persistent asthma based on EPR3 guidelines,6 with 1 addition (regular controller therapy was considered indicative of persistent asthma) and 1 deletion (pulmonary function could not be assessed). We found that nearly 92% of patients with HEDIS-defined persistent asthma in 2006 had survey-defined persistent asthma in 2007. We also found that patients who requalified for having HEDIS-defined persistent asthma in 2007 were more likely (P < .0001) to report survey-persistent asthma (97%) than patients who did not requalify for having HEDIS-persistent asthma in 2007 (82%).9 These data suggest a good concordance between the HEDIS administrative data definition of persistent asthma and a more clinically defined (survey) definition, especially using the 2-year HEDIS definition.

Impairment 

EPR3 defines 2 separate domains of asthma severity and control.6 Asthma impairment is defined by EPR3 as “the frequency and intensity of symptoms and functional limitations the patient is currently experiencing or has recently experienced.” This domain of asthma control includes rescue therapy use. We have identified asthma impairment on a population basis by means of (1) development and validation of a rescue therapy scale based on the number of canisters of short-acting β-agonists (SABAs) dispensed in a 12-month period and (2) administration of the Asthma Control Test (ACT) on automated telephone calls.

SABA long-term control scale 

The 4-level scale based on canisters of SABAs dispensed over a 12-month period (0-2, 3-6, 7-12, and >12) was validated in a random sample of 2250 Kaiser Permanente patients aged 18 to 56 years with HEDIS-defined persistent asthma and a separate sample of 62,369 members aged 18 to 56 years with persistent or intermittent asthma.10 A factor analysis performed on information obtained in the former sample by surveys that included the mini-Asthma Quality of Life Questionnaire (AQLQ), the Asthma Therapy Assessment Questionnaire (ATAQ), and the Asthma Outcomes Monitoring Survey showed loading of the 4-level SABA scale on the symptom control factor.10 In addition, impairment, as measured by these validated questionnaires, was linearly related to the 4-level scale (Fig 1).10 The prospective validity of the scale was tested in the larger sample by assessing the relationship of the scale value during 1 year to emergency hospital care or oral corticosteroid dispensings the following year. A significant linear relationship was also seen between this SABA scale and the subsequent risk of exacerbations (Fig 2).10

  • View full-size image.
  • Fig 1. 

    Relationship of the 4-level SABA scale (number of canisters dispensed in a 12-month period) to mean scores on patient-reported outcome tools. AOMS, Asthma Outcomes Monitoring Survey (higher values indicate more symptoms); AQLQ, Mini-Asthma Quality of Life Questionnaire (higher values indicate better quality of life); ATAQ, Asthma Therapy Assessment Questionnaire (higher values indicate more control problems). All P < .0001.

Data are from Schatz et al.10

  • View full-size image.
  • Fig 2. 

    Relationship of the 4-level SABA scale (number of canisters dispensed in a 12-month period) to the incidence (as a percentage) of subsequent asthma exacerbations, as defined by asthma hospitalization or emergency department visit (Hosp/ED) or any oral corticosteroid dispensing (Steroids). All P < .0001.

Data are from Schatz et al.10

Telephone administration of the ACT 

The ACT was completed during automated telephone calls using speech-recognition technology by 2244 patients with physician-diagnosed asthma.11 Convergent validity was demonstrated by relationships of the ACT score to smoking (lower in smokers) and to patient-described recent course of asthma (lower in patients with a worse course of asthma). Predictive validity was demonstrated by the relationship of the ACT score to subsequent exacerbations and SABA dispensings. After adjusting for demographic characteristics, an ACT score of 15 or less was associated with a significantly increased 12-month risk of emergency hospital care (odds ratio [OR], 2.5; 95% CI, 1.3-3.7), any oral corticosteroid dispensing (OR, 2.6; 95% CI, 1.9-3.5), and more than 6 canisters of SABAs dispensed (OR, 6.8; 95% CI, 4.8-9.5) compared with patients with a score of 20 or greater.11 An ACT score of 16 to 19 was not associated with a significantly increased risk of subsequent emergency hospital care but was associated with an increased risk of subsequent oral corticosteroid dispensing (OR, 1.7; 95% CI, 1.3-2.3) and dispensing of more than 6 canisters of SABAs (OR, 3.1; 95% CI, 2.2-4.5).11

Risk 

Asthma risk is defined by the EPR3 to include the risk of future exacerbations.6 We have used both administrative data and survey data to try to identify patients at risk of experiencing future exacerbations.

Administrative data 

We have developed 2 administrative data algorithms for identifying patients at increased risk of subsequent asthma exacerbations (Table I).12, 13 The first was a 3-level scale developed by using prior emergency department visits and hospitalizations (emergency hospital care) and pharmacy data. This algorithm identified a high-risk group approximately 3 times more likely than other patients to experience a subsequent exacerbation.12 The second was a 4-level scale based only on pharmacy data that identified a high-risk group more than 6 times more likely to require emergency hospital care than patients in the lowest risk group (Table I).13 However, comparison of the predictive properties of the 2 schemes suggested that the algorithm including prior emergency hospital care provided a more robust prediction and would thus be recommended if that information was available.13 As noted above, the number of canisters of SABAs dispensed in a 12-month period is itself related to subsequent exacerbations, but the predictive properties of this SABA scale versus the other 2 algorithms have not been specifically compared.

Table I. Administrative data algorithms for identifying patients at increased risk of subsequent asthma exacerbations
Three-level scale12Four-level scale13
PredictorsPoint allocation
SABA dispensings in past year1 for ≥ 141 for 5-131 for > 13
Oral corticosteroid dispensings in past year1 for any1 for > 2
Emergency/hospital care in past year2Not used
Intensity scale levelPositive predictive value (%) in testing set by level
16.2 (0 points)4.0 (0 points)
28.1 (1 point)6.4 (1 point)
322.0 (≥2 points)12.4 (2 points)
4Not applicable26.8 (3 points)
Survey data 

In our initial studies we showed that certain levels of increased asthma impairment identified on mailed surveys by using the ATAQ asthma control tool or the AQLQ asthma-specific quality-of-life tool were significantly associated with subsequent emergency hospital care.14, 15 More recently, we have found that low scores on 3 validated questionnaire tools that reflect asthma impairment (ACT score <16, AQLQ score ≤4.7, and Asthma Impact Survey score >60) administered on mailed surveys were each related to subsequent exacerbations above and beyond a history of prior exacerbations.16 These data also showed that each of the 3 tools provided similar predictive power (OR, 1.3) and were not independent of each other in their relationships to subsequent exacerbations.16 These observations would suggest that any of the 3 tools could be used in addition to a history of prior exacerbations to improve the prediction of subsequent exacerbations, but there is no reason to use more than 1 of these tools to do so.

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Quality of care 

The most widely used asthma administrative data quality-of-care measure is the one developed by the National Committee for Quality Assurance to evaluate the performance of health plans, the HEDIS measure.7 This measure identifies patients with persistent asthma based on the administrative data criteria described above and then determines the proportion of these patients who receive at least 1 asthma controller medication dispensing the following year. Although used for approximately a decade, few studies have attempted to validate the HEDIS measure as being associated with improved subsequent outcomes. One study in children demonstrated such a relationship,17 but 3 studies in children and adults failed to show that improved performance on this measure is associated with improved asthma outcomes.18, 19, 20

The lack of a relationship of the HEDIS asthma measure to improved outcomes has led to the search for an improved asthma quality-of-care measure based on administrative data that would be associated with improved outcomes. The medication ratio measure has been defined from computerized pharmacy data as the ratio of controllers to total asthma medications (controllers plus relievers) dispensed in a 12-month period. We initially showed that patients with a medication ratio of 0.5 or greater were significantly (P < .0001) less likely (4.5%) than patients with a ratio of less than 0.5 (8.3%) to experience emergency hospital care the following year.21 We then reported that patients with a medication ratio of 0.5 or greater reported significantly better asthma quality of life and fewer asthma control problems than patients with a ratio of less than 0.5.22 Subsequently, several other studies19, 20, 23 have confirmed the relationship between a medication ratio of 0.5 or greater and a reduced likelihood of subsequent asthma exacerbations. Thus because it has been consistently associated with improved outcomes, the medication ratio might be a better administrative data measure of asthma quality of care than the HEDIS any controller measure.

The number of SABAs dispensed is even more strongly related to subsequent asthma exacerbations than the medication ratio measure.23 However, the medication ratio measure has one advantage over SABA dispensings as a quality-of-care marker. The SABA measure does not account for severity or the attempt to appropriately use controllers. A patient with severe treated disease might still require high levels of SABAs, but a high medication ratio at least reflects an attempt to use controllers to manage the illness. This would further support the use of the medication ratio measure as an administrative data asthma quality-of-care marker.

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Discussion 

The research described above shows that administrative and survey data can be used to reflect asthma severity, impairment, risk, and quality of care in large populations. A strength of these studies is that they were performed in a real-world setting. However, an important issue regarding these studies is their generalizability to other populations. This is especially relevant regarding racial, ethnic, and socioeconomic status because these disparities have well-documented effects on asthma care and outcomes at Kaiser Permanente24 and elsewhere.25, 26 Our patients are demographically similar to the overall southern California community (Steve Derose, MD, unpublished data, October 2005), and administrative data not depending on survey responses should reflect this generalizability. However, survey data might reflect a more select population. For example, respondents in our most recent survey study had higher socioeconomic characteristics (based on census block assessments) than nonrespondents,9 and a majority (54%) were white.16 We have also studied primarily younger patients (≤64 years), and recent studies suggest that patients aged 65 years and older might have worse short- and long-term asthma control than younger patients.27 Studies like ours in large populations, such as Medicaid and Medicare patients, would test the generalizability of our findings to more demographically, racially, and socioeconomically diverse populations.

Pending studies in broader populations, we believe the data summarized in this article have the following implications for large populations similar to ours. First, it would be reasonable to direct asthma population management and quality-of-care assessments at patients with HEDIS-defined persistent asthma for 2 years in a row. If 2 years of data were not available, qualification based on 1 year could be used.

Patients at high risk of exacerbations could be identified from administrative or survey data in several ways (Table II). In addition, patients with evidence of poor asthma control based on the impairment domain can also be identified in several ways (Table II). Once identified, it would be hoped that targeted interventions would improve asthma control and asthma outcomes in these patients. We have shown that regular inhaled corticosteroid therapy, regular long-acting β-agonist therapy, and specialty care are associated with improved outcomes in our population.28, 29, 30 We have also shown that inhaled corticosteroid monotherapy is more cost effective than other forms of controller monotherapy in our patients31 and that stepping up therapy (defined based on an administrative data algorithm) is associated with reduced subsequent asthma impairment based on SABA canister dispensings.32 However, we have not yet specifically shown that interventions in patients identified by the markers of high risk or uncontrolled disease described above are associated with improved outcomes in those patients. Such studies would be welcome.

Table II. Identifying patients for targeted intervention who are at high risk of asthma exacerbations or uncontrolled impairment based on administrative or survey data
Asthma exacerbations
Level 3 in 3-level algorithm (see Table I)
Level 4 in 4-level algorithm (see Table I)
>6 SABA canister dispensings in a 12-mo period
ACT score <16
ATAQ score ≥2
Asthma Impact Survey score >60
AQLQ score ≤4.7
Asthma impairment
>6 SABA canister dispensings in a 12-mo period
ACT score by telephone or survey <20
ATAQ score by survey >0

The medication ratio measure could be used to compare health plans (eg, percentage of 2-year HEDIS-defined persistent asthmatic patients with a ratio ≥0.5), such that best practices from high-performing plans could be shared with other plans. In addition, “inreach” to individual providers with higher proportions of patients with persistent asthma who have low ratios could potentially improve the provider’s prescribing habits or clinical approach to patients. As above, although it would be hoped that this provider-oriented use of the medication ratio measure would improve asthma outcomes for patients, such an effect remains to be demonstrated.

One limitation of using administrative and survey data, as described above, is that this approach does not directly consider pulmonary function, which is not routinely captured in these venues. The diagnosis of asthma in these studies is usually based on physician-diagnosed asthma (based on encounter coding or patient report) rather than documented reversible airway obstruction. However, identification of physician-diagnosed asthma has been shown to be a valid way to identify asthmatic patients in epidemiologic studies.33 Having pulmonary function data would still be desirable because prior studies have shown that FEV1 is independently associated with future asthma exacerbations, even after accounting for other significant demographic, environmental, allergic, and asthma severity risk factors.34, 35 As electronic medical records become increasingly available and integrated with other electronic data, pulmonary function data should be able to be included in future computer-based population assessments.

Although the methods described in this article were designed to be applied to large populations, there are some potential applications to individual or group practices. Pharmacy data are increasingly available to all practitioners, which could allow use of the β-agonist control scale, the ratio measure, and the pharmacy-based risk assessment by individual practitioners. Specifically, practitioners could reach out to patients in their practice whose pharmacy data suggest uncontrolled asthma (>6 β-agonist canisters dispensed in a year), undertreated asthma (medication ratio <0.5), or higher-risk asthma (level 4 in the 4-level algorithm, Table I). Individual physicians could also make use of survey data (eg, the ACT) administered by electronic media, mail, or telephone to evaluate both their patients’ current level of impairment and implications regarding future risk without requiring an in-person visit. We thus hope that the approaches described in this article could benefit patients with asthma cared for in individual practices, as well as in large health plans.

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References 

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 Disclosure of potential conflict of interest: M. Schatz has consulted for Amgen, Merck, and GlaxoSmithKline and has received research support from Aerocrine, Genentech, GlaxoSmithKline, and Merck. R. S. Zeiger declares that he has no relevant conflict of interest.

PII: S0091-6749(11)00506-9

doi:10.1016/j.jaci.2011.03.027

The Journal of Allergy and Clinical Immunology
Volume 128, Issue 2 , Pages 273-277, August 2011

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