To identify physician practices providing primary care, we. used the 2007 statewide physician directory of the Massachusetts

Transcription

1 Technical Appendix Study Data and Methods Primary care practices To identify physician practices providing primary care, we used the 2007 statewide physician directory of the Massachusetts Health Quality Partners, which identifies the practice site of each physician providing primary care to at least 1 patient enrolled in any of the 5 largest commercial health plans in Massachusetts.(1) This directory, which serves as the basis for statewide quality reporting initiatives, includes over 90% of the primary care physicians in the state. The directory is updated annually via direct contact with physician groups. Because data on solo practitioners were incomplete, we defined a practice to include at least 2 physicians providing care at a single address. We classified practices as community health centers (CHCs) if they were so identified by the Massachusetts League of Community Health Centers. Sociodemographic data To describe the catchment area served by each practice, we employed an approach known as geographic retrofitting. This technique uses the addresses of each practice s patients (rather than the address of the practice site itself) to estimate the sociodemographic composition of each practice s catchment

2 area.(2) To do this, we obtained the residential ZIP codes of all 1,009,932 patients in Massachusetts who during 2005 were (a) years old, (b) continuously enrolled for 12 months in a managed care product offered by a participating health plan, and (c) recorded by the enrolling health plan as having a primary care physician in the Massachusetts Health Quality Partners physician directory. For 87% of the patients, we matched residential 9-digit ZIP codes to year 2000 United States Census data at the block group level. Remaining patients were matched to Census data using their 5-digit ZIP codes. In supplementary analyses, exclusion of patients matched on 5-digit ZIP codes rather than 9-digit zip codes did not substantively alter our results. We identified 17 Census variables describing patient sociodemographic characteristics likely to be associated with performance on measures of primary care quality(3-6): prevalence of racial and ethnic groups (non-hispanic white, black, Asian, Hispanic), income category (<100% and <200% of the federal poverty line), receipt of public assistance, spoken language (English, Spanish, other language), education (less than high school), employment (unemployment and laborforce participation), occupation, citizenship, and country of birth. To address colinearity among closely related Census variables (e.g., between non-u.s. citizenship and spoken language other than

3 English), we performed a principal components analysis including all Massachusetts Census block groups. After applying this technique, which enables selective elimination of some variables, seven Census variables remained: black race, Hispanic ethnicity, non-u.s. citizenship, education less than high school, receipt of public assistance, poverty (income <200% of the federal poverty line), and unemployment. We calculated the overall prevalence of each Census variable in the geographically retrofitted catchment area served by each practice. The median number of patients included in these sociodemographic case-mix calculations was 1,326 per practice (range 56-18,396). Sociodemographic composites To identify practice-level composites using these 7 Census variables, we performed a second principal components analysis based on the geographically retrofitted catchment areas. This analysis yielded 2 composites: racial/ethnic vulnerability (black race, Hispanic ethnicity, and non-u.s. citizenship) and economic vulnerability (less than high school education, receipt of public assistance, poverty, and unemployment). We calculated practice scores on each composite by taking the mean of its constituent Census variables, standardizing these

4 variables means and variances. Census variables within each composite were highly correlated (Cronbach alpha >0.8). The distribution of practices composite vulnerability scores was skewed, with little variation among practices in the bottom 75%. For each composite vulnerability score, we therefore divided practices catchment areas into 3 vulnerability categories: low (<75 th percentile of prevalence), medium (75 th -90 th percentile), and high (>90 th percentile). In supplementary analyses, alternative classification schemes (e.g., dividing at the 70 th and 85 th percentiles) did not substantively alter our findings. The range of variation in practice sociodemographic composition may be artificially narrow in our study because the sociodemographic composites were based on commercial health plan enrollees and did not include patients with Medicaid or those who lacked health insurance. To the extent that the inability to include these patient groups introduces bias, our results may underestimate payment differences between practices of different sociodemographic composition. Performance data The 5 participating health plans each reported performance data aggregated at the physician level for each Healthcare Effectiveness Data and Information Set (HEDIS) quality measure

5 used in the analysis, with each measure having a denominator (the number of the physician s patients eligible for inclusion in the measure) and a numerator (the number of eligible patients who received the care specified by the measure). These performance data are verified by an external auditor.(7) To represent practice-level performance on each quality measure, we separately summed the numerators and denominators across all the physicians within a practice. Study patients were each required by their health plans to identify a primary care physician, and performance was attributed to this physician. Data describing physician training were not available to us. Physicians with many training backgrounds could conceivably be chosen by patients as their primary care physicians. Simulation of performance-based payments The design specifications of pay-for-performance programs, which implicitly rely on composite measures of performance, can have important effects on the distribution of payments.(8) Therefore we chose not to base our simulation on a hypothetical pay-for-performance program. Instead, we used the design specifications of the pay-for-performance component of the Medicare Care Management Performance demonstration, adapting these specifications for application to the available

6 performance data. We used these specifications because they (a) were designed for use in a current demonstration with broad potential policy implications, (b) apply to small-to-mediumsized primary care practices like the majority of those in our sample, and (c) use a well-described, publicly-available algorithm to calculate payment amounts as shown in Appendix Exhibit 1.(9-10) Briefly, these specifications tie higher payments to higher achieved performance, relative to national performance percentiles. To simulate performance-based payments to Massachusetts primary care practices, we adapted the Medicare demonstration specifications in 3 ways. First, because no performance data were available on measures of care for congestive heart failure or coronary artery disease, payments based on care for these 2 conditions could not be simulated. Second, we used a subset of the preventive and diabetes care measures proposed in the full Medicare demonstration design and added a cervical cancer screening measure that was not part of the Medicare program. The subset included 2 measures of preventive care (screening for breast cancer and colorectal cancer) and 4 measures of diabetes care (hemoglobin A1c testing, eye exams, cholesterol testing, nephropathy monitoring) that were available to evaluate the study practices. Measure definitions are available in Appendix Exhibit 2. Third, because performance in Massachusetts

7 substantially exceeds national performance, we used Massachusetts-specific performance percentiles (rather than national performance percentiles) to calculate performance-based payments. Our intent was not to evaluate the Medicare demonstration design, but to use it as a basis for a plausible program that Massachusetts commercial health plans could apply using the statewide performance database available to them. Analysis Our main goal in analyzing performance data was to describe the likely distribution of performance-based payments rather than to infer causal relationships between sociodemographic profiles and performance. Therefore we did not adjust for potential confounders of the relationship between sociodemographic profiles and performance (with the exception of regression-based adjustment for the health plan contributing each observation--to account for possible heterogeneity in reporting between plans). We required that all practices have at least 15 denominator observations on each performance measure contributing to the simulation (the minimum number provided in the Medicare demonstration design examples).(9) We calculated the mean performance score on each measure among study practices in each sociodemographic category.

8 To calculate performance-based payments, we followed the Medicare demonstration design, with the adjustments detailed above. The overall statistical distribution of practice payments was not a normal distribution--payment amounts did not follow a bell curve. We therefore used nonparametric Wilcoxon rank-sum tests (statistical tests suitable for data that are not normally distributed) to compare payments between categories of practices. Using the simulated per-patient payments, we also calculated overall per-practice and per-physician payments for a hypothetical median practice in each sociodemographic category (using the median number of patients eligible for each performance measure and the median number of physicians). P values below 0.05 were considered statistically significant. All statistical analyses were performed with SAS software, version (SAS Institute, Inc., Cary, North Carolina).

9 Technical Appendix Notes 1. Friedberg MW, Coltin KL, Safran DG, Dresser M, Zaslavsky AM, Schneider EC. Associations between structural capabilities of primary care practices and performance on selected quality measures. Ann Intern Med Oct 6;151(7): Mullan F, Phillips RL, Jr., Kinman EL. Geographic retrofitting: a method of community definition in community-oriented primary care practices. Fam Med Jun;36(6): Institute of Medicine. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, D.C.: National Academies Press; Schneider EC, Zaslavsky AM, Epstein AM. Racial disparities in the quality of care for enrollees in medicare managed care. JAMA Mar 13;287(10): Fiscella K, Franks P. Influence of patient education on profiles of physician practices. Ann Intern Med Nov 16;131(10): Zaslavsky AM, Hochheimer JN, Schneider EC, Cleary PD, Seidman JJ, McGlynn EA, et al. Impact of sociodemographic case mix on the HEDIS measures of health plan quality. Med Care Oct;38(10):

10 7. Massachusetts Health Quality Partners website [internet] [cited 2010 Apr 21]; Available from 8. Reeves D, Campbell SM, Adams J, Shekelle PG, Kontopantelis E, Roland MO. Combining multiple indicators of clinical quality: an evaluation of different analytic approaches. Med Care Jun;45(6): Centers for Medicare and Medicaid Services. Medicare care management performance demonstration: demonstration summary [Internet] [cited 2010 Apr 21]. Available from 9_Design.pdf. 10. Wilkin JC, Wrightston CW, Knutson D, Yoshino EG, Taylor AS, Moroz KE. Medicare care management performance demonstration design report [Internet]. Columbia, Maryland: Actuarial Research Corporation; 2007 Jan [cited 2010 Apr 21]. Available from 9_DesignReport.pdf.

11 Appendix Exhibit 1. Performance-based payments in the Medicare Care Management Performance demonstration. Step 1. For each practice, calculate performance on each HEDIS measure in 4 clinical areas: Preventive care (5 measures) Diabetes care (8 measures) Congestive heart failure (8 measures) Coronary artery disease (7 measures) Step 2. For each measure, assign points to each practice based on national 2006 HEDIS Medicare performance percentiles as follows: >75 th percentile: 5 points th percentile: 4 points 50 th 62.5 th percentile: 3 points 37.5 th 50 th percentile: 2 points 25 th 37.5 th percentile: 1 point <25 th percentile: 0 points Step 3. Within each clinical area, calculate a composite score using the ratio of earned points to maximum possible points. e.g., if a practice earns a total of 20 points on the 5 measures of preventive care (and had performance data* on all 5 measures), then the composite score is 20/25 = 0.8 Step 4. Determine the annual payment per patient triggering a HEDIS measure in each clinical area, based on the composite score for the clinical area: >0.9: : <0.3: where =$25 per patient for preventive care and =$70 other clinical areas. Source: Centers for Medicare and Medicaid Services (27), Wilkin (28). Notes: *It is possible for a practice to have performance data on fewer measures and still enter the payment calculation. When this occurs, the maximum number of total points is reduced in step 3.

12 Appendix Exhibit 2. Performance measure definitions. Measure name Breast Cancer Screening Cervical Cancer Screening Colorectal Cancer Screening Comprehensive Diabetes Care: HbA1c Testing Comprehensive Diabetes Care: Eye Exams Comprehensive Diabetes Care: Cholesterol Screening Comprehensive Diabetes Care: Monitoring Diabetic Nephropathy Definition Percentage of women aged years who had at least one mammogram in the measurement year or year prior to the measurement year. Percentage of women aged years who had at least one Pap test in the measurement year or 2 years prior to the measurement year. Percentage of adults 51 to 80 years of age who had 1 or more of the following: fecal occult blood test during the measurement year, flexible sigmoidoscopy during the past 5 years, double contrast barium enema or air contrast barium enema during the past 5 years, or colonoscopy during past 10 years. Percentage of patients aged years with diabetes (type 1 and type 2) who had a hemoglobin A1c test during the measurement year. Percentage of patients aged years with diabetes (type 1 and type 2) who had a retinal or dilated eye exam by an eye care professional in the measurement year or a negative retinal exam (no evidence of retinopathy) by an eye care professional in the year prior to the measurement year. Percentage of patients aged years with diabetes (type 1 and type 2) who had a low-density lipoprotein cholesterol test during the measurement year. Percentage of patients aged years with diabetes (type 1 and type 2) who either received nephropathy screening or had evidence of nephropathy as documented through administrative data during the measurement year. Source: National Committee for Quality Assurance (NCQA). HEDIS Health plan employer data & information set. Vol. 2, Technical specifications. Washington (DC): National Committee for Quality Assurance (NCQA); 2006.

13 Appendix Exhibit 3. Distribution of simulated performance-based payments, with sociodemographic characteristic detail. Preventive care payment per eligible patient, $ Diabetes care payment per eligible patient, $ Prevalence in practice P for P for Median (IQR) Median (IQR) catchment area* difference difference Black race Low (0-4%) 15 (0-20) 39 (0-58) Medium (4-8%) 15 (0-20) (31-58) 0.10 High (8-52%) 9 (0-20) (0-47) 0.02 Hispanic ethnicity Low (1-5%) 15 (9-20) 39 (0-58) Medium (5-8%) 11 (0-20) (0-54) 0.37 High (8-52%) 0 (0-17) (0-49) 0.20 Non-U.S. citizenship Low (1-7%) 15 (0-20) 39 (0-54) Medium (7-10%) 15 (0-19) (0-58) 0.67 High (10-20%) 9 (0-17) (0-54) 0.51 Education less than high school Low (4-14%) 15 (9-22) 39 (0-58) Medium (14-17%) 13 (0-19) (0-51) 0.09 High (17-37%) 9 (0-15) < (0-62) 0.64 Receiving public assistance Low (1-2%) 15 (9-22) 43 (23-58) Medium (2-3%) 11 (0-19) (0-49) 0.06 High (3-10%) 9 (0-17) (0-54) 0.05 Poverty Low (7-20%) 15 (8-20) 39 (0-58) Medium (20-23%) 13 (0-19) (0-54) 0.83 High (24-45%) 9 (0-17) (0-43) 0.04 Unemployment Low (2-4%) 15 (0-22) 39 (0-58) Medium (4-5%) 13 (0-19) (0-54) 0.51 High (5-9%) 9 (0-17) (0-51) 0.09 Non-CHC 15 (0-20) 39 (0-54) CHC 9 (0-11) < (0-54) 0.32 Source: Authors simulation, based on analysis of patient addresses, performance data from 5 Massachusetts health plans, and the pay-for-performance design of the Medicare Care Management Performance demonstration. Abbreviation: IQR, inter-quartile range; CHC, community health center. *Prevalence categories as follows: Low ( 75 th percentile of practices), Medium (75-90 th percentile), High (>90 th percentile). Wilcoxon rank-sum test (vs. top row category for each variable). Poverty denotes income less than 200% of the Federal Poverty Line.