[4,5]. Both are characterized by routine measurement of performance on process measures of care given at the time of admission (antibiotic timing and selection, oxygenation assess- ment, and blood cultures) and before discharge (smoking cessation counseling, and provision of influenza and pneumococcal polysaccharide vaccines). Although measuring processes of care for pneumonia has accelerated the pace of quality improvement [6–8], there is an ongoing need to develop new measures of pneumonia quality that focus on patient outcomes, care transitions, and efficiency of care. Process measures can provide important information about the quality of care and guide improvement efforts; however, these measures necessarily focus on a small spectrum of the overall care provided to hospitalized patients. Outcome measures that reflect the patient’s subsequent health status, such as satisfaction with care, functional status, morbidity, mortality, or parameters such as length of stay or costs of care, are generally regarded as the indicators that are most meaningful to patients and/or payers [6,9,10]. CMS has begun focusing resources of the development of new measures of patient outcomes, in part because of provisions in Section 5001 of the Deficit Reduction Act (DRA) of 2005 (Public Law 109–171) that require the Secretary of Health and Human Services to begin reporting hospital quality measures of process, structure, outcome, patients’ perspective on care, efficiency, and costs of care. This report describes the 30-day risk-standardized mortality model based on Medicare claims for patients discharged with a diagnosis of pneumonia developed for public reporting. This effort is an extension of an ongoing initiative by CMS to develop outcomes measures for common conditions that are based on available national data. In the first phase of the CMS initiative, risk-standardized measures of 30-day mortality for acute myocar- dial infarction [11] and heart failure [12] were developed using administrative data and were demonstrated to have properties suitable for public reporting [13]. CMS reports annually the measure results for all 3 conditions for the nation’s acute care hospitals. Methods Patient Sample The target population was patients aged 66 years and older who were hospitalized in non-governmental, acute care hospitals with a discharge diagnosis of pneumonia and who were enrolled in fee- for-service Medicare coverage in the 12 months before and including the time of their index hospitalization. A patient was excluded if there was incomplete administrative data for the period 12 months before the index admission date; if the patient was not enrolled in Medicare for the entire year before admission or if the patient was enrolled in a managed care (Medicare+Choice) plan during the previous year; if pneumonia was listed as a secondary diagnosis (to eliminate pneumonia resulting from complications of hospitalization); and if the patient was discharged alive and not against medical advice within the first day of admission because of concerns about the accuracy of the pneumonia diagnosis. For patients with more than 1 hospitalization, only 1 admission was randomly selected. We did this to ensure the independence of the observations. Thus our sample included all patients having a pneumonia diagnosis during a particular year. For patients who were transferred from one institution to another, we linked the hospitalizations into an episode of care. The hospitalization was considered to have begun on admission to the initial hospital. A patient needed to be admitted as an inpatient at both the transferring and receiving hospital, and was required to Claims Model for Mortality have a pneumonia discharge diagnosis at both hospitals. To exclude admissions of patients discharged and subsequently readmitted, transfers must have occurred within 1 day or less, counting from the discharge date of the initial hospitalization. For the purposes of risk-adjustment, comorbid (‘‘pre-existing’’) condi- tions were identified from the initial (index) admission only to avoid designating index admission-related complications as pre- existing conditions in the receiving hospital. Comorbid conditions that may have represented complications of the index admission were not included in the risk-adjustment model. For the purposes of calculation of risk-stratified mortality rates, the initial hospital was designated as the responsible institution for the patient’s episode of care. Derivation and Validation Cohorts We created the derivation cohort using the 2000 Medicare Provider Analysis and Review (MEDPAR) files, by selecting hospitalizations for pneumonia (International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes 481, 482.XX, 483.X, 485, 486, and 487.0. Cases were stratified by hospital and then 50% of the patients from each hospital were randomly sampled in order to include cases from all hospitals where possible (Figure 1). We evaluated the performance of the administrative claims model in validation cohorts using the other half of the 2000 MEDPAR data, and all claims data for each of years 1998, 1999, 2001, 2002, and 2003 (Figure 1). For each year we developed the study sample using the same approach as that employed for the derivation cohort. To compare the results of the statistical models developed from Medicare claims with those from models using data collected from medical records, we identified a sample of patients for whom we had both administrative claims and detailed chart abstracted medical record data. The medical record data were obtained from the CMS National Pneumonia Project, in which patient charts from hospitalizations for pneumonia are routinely abstracted [14]. Details of chart selection and abstraction in the National Pneumonia Project have been previously published [14]. Medical records for 2 large pneumonia cohorts of up to 850 randomly selected patients per state during each of 2 time periods between July 1998 through March 1999 or July 2000 and June 2001 were available for this study. Because the sample of abstracted medical records was not sufficient for hospital-level comparisons of the 2 models, comparisons of model performance were made at the state level. Outcome The patient outcome was all-cause 30-day mortality from the time of admission. We obtained mortality information from the Medicare enrollment files by linking unique patient identifiers. Because our focus was on the hospital, our primary outcome was hospital-specific, risk-standardized mortality rates (RSMRs), defined as the hospital-predicted rate divided by the hospital- expected rate and then multiplied by the observed national rate. Model Derivation: Patient Predictors of Mortality We used the ICD-9-CM codes to identify candidate variables for the Medicare claims model. The MEDPAR claims have data on each hospitalization for fee-for-service Medicare enrollees and include demographic information, principal and secondary diagnosis codes, and procedure codes. Diagnosis codes for comorbidities were also collected from physician and hospital outpatient files. Because there are more than 15,000 ICD-9-CM codes, we used the Hierarchical Condition Categories (HCC) to PLoS ONE | www.plosone.org 2 April 2011 | Volume 6 | Issue 4 | e17401