Asthma Population Management: Development and Validation of a Practical 3-Level Risk Stratification Scheme

Published Online: January 01, 2004
Michael Schatz, MD, MS; Randy Nakahiro, PharmD; Christine H. Jones, RN, MBA;

Objective: To define and validate a practical risk stratification scheme based on administrative data for use in identifying patients at high, medium, and low risk of requiring emergency hospital care for asthma.

Study Design: Retrospective cohort.

Patients and Methods: Predictors in 1999 were evaluated in relation to 2000 asthma emergency hospital care (any asthma hospitalization or emergency department visit) in a training set (n = 8789, 2000 emergency hospital care = 5.5%) and a testing set (n = 6104, 2000 emergency hospital care = 7.9%). Logistic regression was used to assign risk points in the training set, and positive and negative predictive values, sensitivities, and specificities were calculated in the training and testing sets.

Results: High risk was defined as asthma emergency hospital care in the previous year or use of >14 β-agonist canisters and oral corticosteroid use; medium risk was defined as no emergency hospital care but use of either >14 β-agonist canisters or oral corticosteroids; and low risk was defined as none of the above. For the high-risk groups in the training and testing sets, positive predictive values were 12.9% and 22.0%, sensitivities were 24.8% and 25.4%, specificities were 90.3% and 92.0%, and negative predictive values were 95.4% and 93.2%, respectively. The medium-risk groups identified another 32.6% of patients in the training set and 28.3% in the testing set requiring subsequent asthma emergency hospital care.

Conclusion: This simple risk stratification scheme is useful for identifying patients from administrative data who are at increased risk of experiencing emergency hospital care for asthma.

(Am J Manag Care. 2004;10:25-32)

    The prevalence of asthma has increased substantially during the past several decades; it is now estimated that nearly 1 in 10 Americans have or will have asthma in their lifetime.1 Not only is asthma increasingly common, but it causes substantial morbidity (hospitalizations, unscheduled physician and emergency department [ED] visits, absence from school and work, and reduced quality of life) and has very large associated direct and indirect costs.2 

    The prevalence and impact of asthma, combined with the availability of effective controller therapy, make it an appropriate disease for population management. Important processes in asthma population management include identification of asthmatic patients within a defined population, risk stratification, targeted intervention, and outcome measurement. The purpose of risk stratification is to define asthmatic patients who are most likely to experience morbidity and resource utilization and for whom targeted intervention would probably reduce these risks. For example, it is estimated that 80% of expenses for asthma are incurred by the 20% of patients with the most severe disease.2 

    Several studies3-6 have investigated risk factors for asthma hospitalizations or ED visits using administrative data in large populations in a way that could potentially be translated into risk stratification schemes in managed care organizations. However, the results of these studies have frequently been too complicated to use for practical risk stratification applications. In addition, although they potentially identify high-risk patients, these studies do not define a medium-risk group, which is useful in population management to identify patients who are not at high risk but who need more targeted intervention than low-risk patients. The purpose of this study is to define and validate a relatively simple and practical risk stratification scheme for use in identifying high-, medium-, and low-risk asthmatic patients in a large defined population using administrative data. 



    This study was approved by the institutional review board of Southern California Kaiser-Permanente. Data for this study were derived from the Southern California Kaiser-Permanente Asthma Case Identification Database, based on linkage of computer data from a hospital discharge database, a diagnosis and procedures database, a membership database, and a prescription database. Patients are identified as having asthma if they meet 1 of the following criteria: 

    1. Any discharge diagnosis (principal or other diagnosis) of asthma in the hospitalization database (International Classification of Diseases, Ninth Revision, code 493.xx). 

    2. Two or more asthma-related medication dispensings (excluding oral corticosteroids, inhaled ipratropium bromide, and inhaled combination albuterol sulfate/ipratropium bromide) in 1 year in the prescription database, including β-agonists (excluding oral terbutaline sulfate), inhaled            corticosteroids, other inhaled anti-inflammatory drugs, and oral leukotriene modifiers. 

    3. Any ED or regular clinic asthma-related visit in the diagnosis and procedures database. 

    This algorithm is based on evidence that identifying "physician-diagnosed asthma" is a valid way to identify asthmatic patents in epidemiologic studies.7 Physician diagnosed asthma is captured explicitly by criteria 1 and 3 and implicitly by criterion 2. Contamination of the population by patients with primarily chronic obstructive pulmonary disease (COPD) vs asthma is reduced by excluding patients without a diagnosis of asthma whose only respiratory medication is ipratropium or combination albuterol/ipratropium. In addition, the age group of this study (5-56 years) should reduce the prevalence of patients with COPD misclassified as having asthma (see next paragraph). 

    The training set consisted of all San Diego (Calif) asthmatic patients aged 5 to 56 years identified in the Asthma Case Identification Database who were continuously enrolled during 1999 and 2000 and had a Kaiser prescription benefit. The testing set consisted of all Fontana (Calif) asthmatic patients aged 5 to 56 years identified in the Asthma Case Identification Database who were continuously enrolled during 1999 and 2000. Only patients with a pharmacy benefit were included in the training set, but all patients were included in the testing set. The Kaiser pharmacy data used in the development of the risk stratification process would be of uncertain validity in patients who may obtain their prescriptions elsewhere. Although approximately 90% of patients have a pharmacy benefit, it was believed that the prediction rule would be best developed in a population in which all patients have a pharmacy benefit. In contrast, it was hoped that the resulting risk stratification scheme would be useful in the total population. Thus, the testing set included patients with and without a pharmacy benefit. The age group of 5 to 56 years was chosen for this study to conform to the National Committee for Quality Assurance Health Plan Employer Data and Information Set population.8 In addition, the proportion of patients in the 1999-2000 Asthma Case Identification Database cohort who were diagnosed as having coexistent COPD (International Classification of Diseases, Ninth Revision, codes 490.xx, 491.xx, 492.0, 492.8, 496, and 506.4) was 6.6% in the age group 5 to 56 years compared with 50.0% in patients older than 56 years. 

Outcomes and Predictors 

    The outcome being modeled in the training set and predicted in the training and testing sets was any 2000 asthma hospitalization (primary diagnosis of asthma, International Classification of Diseases, Ninth Revision, code 493.xx) or ED visit for asthma. The available predictors were the following in 1999: (1) any asthma hospitalization or ED visit; (2) number of shortacting β-agonist canisters dispensed; (3) number of oral corticosteroid dispensings; (4) number of different prescribers (as a measure of continuity of care); (5) number of routine visits for asthma; (6) number of inhaled anti-inflammatory (corticosteroids, cromolyn sodium, and nedocromil sodium) canisters dispensed; (7) ratio of the number of inhaled anti-inflammatory canisters to the number of total inhaler (anti-inflammatory plus short-acting ©¬-agonist) canisters dispensed, since higher ratios have been suggested as a quality measure of adequate controller therapy9 and lower ratios have been shown to be a risk factor for subsequent emergency hospital utilization5,10,11; (8) sex; and (9) subsidized (by Medicaid or Medicare) vs commercial insurance. Univariate relationships between predictors and outcomes variables were assessed using χ2 analysis. 


    Using the training set, preliminary modeling was performed for continuous predictors by means of logistic regression using a forward stepwise selection algorithm to determine the most predictive cutoff point. Predictors in the preliminary models included any 1999 asthma hospitalization or ED visit (the strongest predictor in all models) and the candidate predictor cutoff points in the various categories. Preliminary models were performed in the total group and in children (aged 5-17 years) and adults (aged 18-56 years) separately. 

    The final models were constructed by means of logistic regression, using the binary predictors and the dichotomized continuous predictors, based on the results of the preliminary models. Since the ideal product of this risk stratification process was believed to be a high-risk group of approximately 10% of the population, a medium-risk group of approximately 30% of the population, and a low-risk group of approximately 60% of the population, potential risk factors with incidences >40% were excluded. All analyses were performed using statistical software (SAS Version 8.2 for Windows; SAS Institute Inc, Cary, NC). 

Risk Stratification and Prediction Validation 

    Based on the contents and odds ratios of the final model, points were assigned to the final independent risk factors, and high-, medium-, and low-risk status was defined in the training set. The predictive accuracy of these risk groups was evaluated by determining the incidence of any 2000 asthma hospitalization or ED visit in the various risk groups in the training and testing sets. In addition, for the high-risk groups, sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated in comparison with non-high-risk patients in the training and testing sets. 

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