Variables

We constructed 10 quality metrics that can be measured using data from administrative claims. These encompass outcome measures as well as process measures. The measures were selected because they are frequently used in quality measurement and are often tied to pay‐for‐performance programs (Virnig et al. 2002; Zhang, Baicker, and Newhouse 2010). The outcome measures include 30‐day readmission following an inpatient admission as well as two prevention quality indicators (PQIs) (e.g., ambulatory care sensitive hospitalizations for acute and chronic conditions) endorsed by the Agency for Health care Research and Quality (Davies et al. 2001; National Quality Measures Clearinghouse 2013). For PQI conditions, which are relatively rare within the commercial and under‐65 population, composite measures were created and designed to include enrollees admitted for any of the indicated acute and chronic conditions.

We also constructed six Healthcare Effectiveness Data and Information Set (HEDIS) process measures including (1) mammography screening within the last 2 years among women ages 42–65; (2) disease‐modifying antirheumatic drugs (DMARDs) treatment for rheumatoid arthritis; (3) use of bronchodilator within 30 days of chronic obstructive pulmonary disease (COPD) diagnosis; (4) major depression prescription treatment management and adherence; (5) annual hemoglobin A1c testing among patients with diabetes; and (6) avoidance of imaging for patients with lower back pain (National Committee for Quality Assurance [NCQA] 2012). Lastly, an indicator was constructed for the appropriate use of antibiotic prescriptions for bacterial pneumonia based on the Physician Consortium for Performance Improvement (PCPI) guidelines (American Medical Association [AMA] 2012). Continuous enrollment throughout measure‐specific periods was required to ensure completeness of recorded measures. For instance, to be included in the mammography cohort, an enrollee was required to have at least 2 years of continuous enrollment and was only eligible once within the 3‐year window. For measures based on follow‐up to a specific event, such as a readmission, diagnosis of back pain, and diagnosis of COPD, continuous enrollment was only required during the relevant follow‐up period.

Spending included actual paid amounts for services and was measured by aggregating all hospital, physician, and drug claims. We obtained Hospital Referral Regions (HRR) level risk‐adjusted spending, including adjustments for input price, age, sex, and health status, following methods described in the Institute of Medicine (IOM) report on geographic variation (IOM 2013). Our final spending measure is an HRR‐level average, calculated per beneficiary per year, with adjustments for enrollees with partial‐year enrollment.

We adjust for variation in input prices using the Hospital Wage Index for inpatient facility claims and the Geographic Practice Cost Indices for outpatient and physician claims (Melnick and Keeler 2007). Health status was evaluated based on the enrollees' prior‐year diagnostic‐cost‐group (DxCG) risk score. The DxCG risk score, commonly used by private payers as a risk‐adjustment tool, calculates enrollee health status using demographic characteristics, claims, and enrollment information as well as diagnoses (DxCG RiskSmart Stand Alone Software 2013). This system is similar to the Hierarchical Condition Categories used by Medicare (Pope et al. 2004).

For enrollees with missing data from the prior year (29.7 percent of enrollee/years), DxCG imputation was performed using the average scores among enrollees with available information adjusted based on age and sex. Earlier work for the IOM found estimates of geographic variation in spending results insensitive to inclusion of case mix measures (Harvard University 2012), suggesting case mix would not have a large effect on quality measures related to spending (e.g., readmissions). We used the HRRs to define geographic regions.