as well as age, Medicaid dual eligibility, and low-income supplement eligibility from Centers for Medicare & Medicaid Services (CMS) databases. These categorical variables contributed 22 dichotomous variables to the models.
Five additional measures assessed provision of guideline-recommended clinical care, following specifications of the Health Plan Employer Data and Information System (HEDIS). These were constructed from a 20 percent sample of fee-for-service Medicare claims for 2009, and included breast cancer screening and recommended testing for cardiac patients and diabetics. These measures were adjusted only for the patient’s sex.
The key predictors of interest were defined at the county level. Rurality was represented by the 2003 version of the Rural-Urban Continuum Code (RUCC) with levels from 0 (central cities of largest metropolitan areas) to 9 (least densely populated rural areas), with odd numbers representing areas not adjacent to a more urban area.1 The Health Professional Shortage Area (HPSA) designation is used to adjust Medicare physician fees at the ZIP code level.2 For this analysis, the committee coded this information by ZIP code into a five-category county-level variable that captures the percentage of the county’s population in HPSA ZIP codes: none (0 percent); between 0 and 20 percent; from 20 to 80 percent; over 80 but not 100 percent; or 100 percent (full-county HPSA). Both the physician practice geographical adjustment factor (GAF) and the physician work geographical practice cost index (GPCI) were used in separate models. The GAF drives geographical variation in total payments to practices for a given service mix, while the work GPCI is the component of the GAF specifically addressing the cost of physician labor; thus the two measures encompass the role of physicians both as operators of businesses and as workers. In most models including GAF or GPCI, the current CMS value for 2010 (excluding frontier floors) is used, on the assumption that this is best related (among current measures) to the historical experience of geographical adjustments potentially affecting the counties. The proposed Institute of Medicine (IOM) factors are used only in calculating the differences representing the potential effects on payment of shifting to the IOM committee’s method.
For each of 16 measures, 1 3 models were fitted. A baseline model included only the individual-level adjuster variables as regressors. Four models each added a single county-level variable (RUCC, HPSA, GAF, or GPCI) to the model, to assess their distinct associations with the measures. Four additional models combined a descriptive geographical variable (RUCC or HPSA) with a payment factor (GAF or GPCI) to assess whether one acted as a mediator for the other. Finally, four models entered the difference between proposed IOM and current CMS factors (either GAF or GPCI, with or without additional control for RUCC) to assess the relative impact of the change in method on payment in higher- and lower-performing areas.
All models were specified as multilevel random-effects linear models, reflecting geographical clustering of quality variations. We compared models with a single level of clustering (county) and two levels (county nested within state). For most models there was significant evidence (deviance >3.84) in favor of the latter model, which accounts for clustering at larger as well as smaller scales. We therefore used this specification for all models. Significance of fixed effects was assessed with Wald tests using the robust Huber variance estimator. Across all 143 CAHPS models (13 model specifications for 11 measures), case-mix effects for the two health variables