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Title Variations of physician group profiling indicators for asthma care Brief title Variations of physician group profiling Authors * ‡, §I-Chan Huang , PhD, Gregory B. Diette , MD, MHS, † †Francesca Dominici , PhD, Constantine Frangakis , PhD, *, ‡, §Albert W. Wu , MD, MPH Affiliation * Department of Health Policy and Management, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, Maryland † Department of Biostatistics, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, Maryland ‡ Department of Epidemiology, Bloomberg School of Public Health, The Johns Hopkins ore, Maryland § Department of Medicine, School of Medicine, The Johns Hopkins University, Baltimore, Maryland Corresponding author Albert W Wu, MD, MPH. Department of Health Policy and Management, Bloomberg School of Public Health The Johns Hopkins University, Baltimore, Maryland 624 North Broadway Baltimore, MD 21205-1901 Phone: (410) 955-6567 E-mail: awu@jhsph.edu Suggested publication category: case study Prior publication: This manuscript has NOT been published previously (either in whole or in part) NOR be submitted elsewhere in either identical or similar form thConference Presentation: Presented in part in the 20 Annual Research Meeting of AcademyHealth. June 27-29, 2003. Nashville, Tennessee, USA. Source of funding: This study was founded by the Pacific Business Group on Health (PBGH) Word count: 3 ...

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Title Variations of physician group profiling indicators for asthma care Brief title Variations of physician group profiling Authors I-Chan Huang *, PhD, Gregory B. Diette ,§, MD, MHS, Francesca Dominici , PhD, Constantine Frangakis , PhD, Albert W. Wu *, ,§, MD, MPH Affiliation * Department of Health Policy and Management, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, Maryland Department of Biostatistics, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, Maryland Department of Epidemiology, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, Maryland § Department of Medicine, School of Medicine, The Johns Hopkins University, Baltimore, Maryland Corresponding author Albert W Wu, MD, MPH. Department of Health Policy and Management, Bloomberg School of Public Health The Johns Hopkins University, Baltimore, Maryland 624 North Broadway Baltimore, MD 21205-1901 Phone: (410) 955-6567 E-mail: awu@jhsph.edu Suggested publication category: case study Prior publication: This manuscript has NOT been published previously (either in whole or in part) NOR be submitted elsewhere in either identical or similar form Conference Presentation: Presented in part in the 20th Annual Research Meeting of AcademyHealth. June 27-29, 2003. Nashville, Tennessee, USA. Source of funding: This study was founded by the Pacific Business Group on Health (PBGH) Word count: 3,638 excluding acknowledge, references, tables and figures 1

Title Variations of physician group profiling indicators for asthma care Abstract Background Patient outcomes and consistency of care with guidelines are used as indicators to profile provider performance in asthma care. However, little is know about how much of the variation in profiling measures can be attributed to provider effects, or how reliable are the resulting profiles. Objectives To determine how much of the variation in provider profiles can be attributed to provider effects, and how reliable are the indicators for asthma care profiling. Study design Cross-sectional study using data from a mailed patient survey. Variations attributable to provider effects are presented using the intraclass correlation coefficient (ICC), estimated using Bayesian hierarchical modeling. The reliability of profiling results was determined using ICC and sample size per physician group. Participants and settings Patients with asthma were selected randomly to be surveyed from each of 20 California physician groups between July 1998 and February 1999. A total of 2,515 patients responded. 2

Main outcome measures Indicators for physician group profiling included (1) NAEPP guideline-based processes of care, including accessibility of asthma care, self-management knowledge about asthma care, use of inhaled bronchodilators, and use of inhaled steroids, and (2) patient outcomes, including satisfaction with asthma care, improvement in health status, emergency visits, and hospitalizations attributable to asthma. Results The variations attributable to provider effects were small (less than 10%) for both process and outcome indicators. For process indicators, self-management knowledge about asthma care had the largest ICC (9.8%), where use of inhaled bronchodilators had the smallest ICC (3.1%). For outcome indicators, satisfaction with asthma care had the largest ICC (9.5%) and hospitalization had the smallest ICC (1.4%). Despite relatively small ICCs, large sample size per physician group yielded acceptable reliability (>0.8) of profiling results, except for use of the inhaled bronchodilators (0.77) and hospitalization (0.60). Conclusions The selected indicators for profiling asthma care at the level of physician group were generally reliable, although the ICCs for those indicators were very small. Collecting sufficient case numbers per providers is a key way to achieve the acceptable profiling results. Key words: asthma; intraclass correlation coefficient; reliability; profiling. 3

Title Variations of physician group profiling indicators for asthma care Introduction Asthma is a common disease characterized by inflammation of airways and reversible obstruction to airflow. In 1996, an estimated 14.6 million persons in the United States reported having asthma. 1 To bridge the gap between current knowledge and practice and to improve patient outcomes, the National Asthma Education and Prevention Program (NAEPP) Expert Panel, supported by the National Heart, Lung, and Blood Institute (NHLBI), published Guidelines for the Diagnosis and Management of Asthma in 1991. 32 The guidelines, which were revised in 1997, emphasize the importance of patient education and appropriateness of medication use. Currently, many performance and quality oversight organizations (e.g. FACCT, NCQA, PBGH) assess performance of asthma care by physicians, physician groups, and health plans using NAEPP-based guidelines. 4-6 The expectation is that provider profiling can increase accountability of providers to improve quality of care, serve as a tool to control health care costs, and guide consumers to high quality providers. 7 Despite increasing use of profiling to assess provider performance, some important methodological challenges can affect the usefulness of the results. One of these concerns is how much variation in indicators is across providers. The amount of variation in provider profiles that can be attributed to provider effects after 4

adjusting for patient case-mix can be estimated using the intraclass correlation coefficient (ICC). 8-11 As the size of ICC increases, quality of care measures are more similar for patients within the same provider, but less similar to those of other providers. The ICC also reflects the clustering effect of patients nested within providers. Performance indicators with larger ICC suggest that performance measures based on patient observations are not regarded as independent within each provider. The larger ICCs, however, result in sample size calculation based on standard methods not being powerful enough to determine differences in profiling results. To achieve the sufficient power for profiling comparisons, standard sample size estimates need to be inflated by using the inflation factor (IF) (or so-called design effect). 10;12;13 The inflation factor represents the ratio of estimated variances in difference between specialty groups, with and without adjustment for the clustering effect. Importantly, the ICC and number of patients for each provider (or panel size) together determine the reliability of profiling results. 9;10 Provider profiling will be useful only if provider level variation is important relative to other potential sources of variation (i.e. larger ICC) and reliability is larger. If the variation attributable to provider effects and reliability of profiling results are small, then it is not worthwhile to investing in and disseminating of provider profiles. To date few studies have examined the sizes of ICCs and reliability of profiling indicators. Based on previous studies, the range of estimated variations attributable to 5

provider effects varies, depending on type of disease and indicator. In general, the attributable variations to provider effects were very small (usually < 10%). 9;14-17 Hofer et al. who assessed variations of physician profiles for type 2 diabetes found that the overall variance in physician visits and hospitalization rates attributable to differences in physician practice were only 4% and 1%, respectively. 9 Krein and colleagues suggested that the ICCs of process and intermediate outcome indicators for diabetic care ranged from 0-9%. 16 Sixma et al. showed that the ICC of patient satisfaction with GPs was about 5%-10%. 14 A study of Orav and colleagues that assessed ICCs of process of care score demonstrated a wide range of variations due to providers, from 3% (minimum) for cancer screening to 24% (maximum) for management of digoxin.18 A review by Campbell and others suggested that the ICCs of process indicators were larger than outcomes indicators at the level of individual practice. 19 Even fewer studies have demonstrated the reliability of profiling indicators. Hofer and colleagues suggested that the reliability of physician profiles for type 2 diabetes for physician visits and hospitalization rates were only 0.41 and 0.17 respectively. 9 A study by Solomon and colleagues that evaluated reliability of performance indicators using the CAHPS survey suggested that the reliability at the level of individual physician ranged from 0.14 to 0.81, and the reliability at level of medical group ranged from 0.15 to 0.81. 71 In this study, we will address three areas underrepresented in the provider profiling literature. We used consistency with asthma guidelines and patient outcomes as 6

performance indicators of asthma care. Physician group was used as the unit for profiling. We evaluated (1) how much of the variance of physician group profiling for asthma care is attributed to provider effects? (2) How large is the design effect for physician group profiling? (3) How reliable are physician group profiling of process and outcome indicators? If the variation attributable to provider effects and reliability of profiling results are small, then current profiling practices may need reexamination. Methods Study setting This study was conducted in conjunction with 20 California physician groups that participated in the 1998 Asthma Outcomes Survey (AOS). The AOS was initiated by the Pacific Business Group on Health (PGBH)--a health care purchasing coalition in California, and HealthNet--a California-based health plan, to evaluate, improve and report on the quality of asthma care at physician group level. 20 Experts have suggested the importance of using physician group or medical group as the unit of profiling, instead of health plan. 21 Although health plans may set quality of care policy, most clinical decisions are made by physician groups, which may more directly affect patient outcomes. 7

Sample selection and data collection Details on sample selection and data collection have been described. 20 Briefly, the 20 participating physician groups were instructed to use administrative materials to identify all managed care patients with at least one asthma-related encounter in the outpatient, emergency or inpatient settings (identified by ICD-9 code 493.xx) between January 1, 1997 and December 31, 1997. Patients had to be continuously enrolled in the physician group for that calendar year. From eligible patients, the study randomly selected a sample of 650 patients from each physician group. If a physician group had fewer than 650 eligible patients, then all eligible patients were sampled. Patient data were collected by self-administered mailed survey. The instrument was developed largely based on the Health Surve yfor Asthma Patients developed at the Johns Hopkins Health Services Research & Development Center (HSRDC) for the Outcomes Management System (OMS) Consortium Asthma Project of the Managed Health Care Association (MHCA). 22-24 The instrument asked about patient characteristics, general health, asthma symptoms, effect of asthma on functioning, asthma medications and treatment, self-management knowledge and activities, access to care, and patient satisfaction. The survey was fielded by PBGH and HealthNet between July 1998 and February 1999 using identical methodology. A total of 2,515 responses were obtained for a response rate of 32.2%. 8

Performance indicator Processes of care and patient outcomes were used as asthma care performance indicators for publicly reported physician group comparisons. Process of care was assessed by consistency of care with the NAEPP asthma guidelines, including accessibility of asthma care, self-management knowledge, use of inhaled steroids, and use of inhaled bronchodilators. Access to asthma care measures accessibility of clinicians or nurse by phone, to make an appointment to see doctors, and to get asthma medications. Self-management knowledge measures ability to manage asthma flares, to appropriately adjust asthma medication, and to correctly identify asthma triggers. For asthma medication use, the NAEPP asthma guidelines advocate inhaled corticosteroids as the most consistently effective long-term control medication for anti-inflammatory. The NAEPP guidelines refer inhaled bronchodilators (or ß-2 agonists) as rescue medications for treatment on an as needed basis3. Evidence showed that overuse of ß-2 agonists and underuse of inhaled corticosteroids increases the likelihood of hospitalizations, emergency room visits, and death. 25-29 In the survey, patients were rated how many puffs of inhaled bronchodilators and inhaled steroids were used every day. We dichotomized responses for inhaled bronchodilator use into <=8 puffs as no overuse and >8 puffs as overuse, and inhlaed steroid use into <=4 puffs as underuse and >4 puffs as no underuse.23 9

Outcome measures included satisfaction with asthma care during the past week, improvement in health status during the past week, emergency room visits attributable to asthma during the past year, and hospitalizations attributable to asthma during the past year. We dichotomized responses on patient satisfaction into greater satisfaction (excellent/very good) vs. lses satisfaction (good/poor/fair); improvement in health status into greater improvement (much better/somewhat better)v s. less improvement (about the same/somewhat worse/much worse); and emergency room visit and hospitalization into no visit vs. visits >= 1 timesa nd no hospitalization vs. hospitalizations >= 1 times. Risk adjustment Characteristics of patients and physician groups are potential confounders that may influence physician group performance. However, for profiling, we would like to adjust for the effect of exogenous factors (mainly patient characteristics on which the physician groups have no influence, such as patients age, sex, education, and baseline severity) rather than endogenous factors (mainly physician group characteristics on which providers can influence, such as physician mix, number of supplementary staff, etc). 30 Adjusting for endogenous factors may mask the true performance of physician groups because these factors can influence the quality of care. Candidate risk-adjustment variables were collected from the patient survey. Those variables included patient age, sex, education level, type of health insurance, severity, number of asthma-related comorbidity, and health status (the SF-36 physical component 01

score (PCS) and SF-36 mental component score (MCS). Asthma-related comorbidities included rhinitis, sinusitis, chronic bronchitis, heartburn (gastroesophageal reflux), emphysema, and congestive heart failure. The study measured asthma severity using responses to several questions to approximate the NAEPPs four severity strata (mild-intermittent, mild-persistent, moderate-persistent, and severe-persistent). 3 Statistical analysis Chi-square and t-tests were used to identify bivariate relationships between performance indicators and candidate risk-adjustment variables. We selected risk-adjustment variables that were statistically significant (p<0.05) for inclusion in multivariate risk-adjustment models. We include all asthmatics to calculate the ICCs of profiling indicators. However, based on recommendations of the NAEPP guidelines, we only included asthma patients who have moderate-persistent and severe-persistent severity for the inhaled steroid use indicator. 3 We used Bayesian hierarchical modeling to quantify variations of performance indicators across 20 physician groups that are truly attributed to provider effects. A two-level hierarchical model (level 1 for patients and level 2 for physician groups) was developed to adjust for the clustering effect of patients nested within a specific physician group. The major advantage of hierarchical models is that they allow us to assess provider performance by quantifying random intercepts of logistic regressions at patient level. 31;32 More importantly, hierarchical model can appropriately partition variations of performance measures across physician groups into between-physician group variability 11