An Administrative Claims Model for Profiling Hospital 30-
Day Mortality Rates for Pneumonia Patients
Dale W. Bratzler1, Sharon-Lise T. Normand2, Yun Wang3, Walter J. O’Donnell4, Mark Metersky5, Lein F.
Han6, Michael T. Rapp6,7, Harlan M. Krumholz3,8*
1Oklahoma Foundation for Medical Quality, Oklahoma City, Oklahoma, United States of America, 2Department of Health Care Policy, Harvard Medical School and
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America, 3Center for Outcomes Research and Evaluation, Yale-New
Haven Hospital, and Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of
America, 4Pulmonary and Critical Care Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America, 5Division of
Pulmonary and Critical Care Medicine, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America, 6Centers for Medicare and
Medicaid Services, Baltimore, Maryland, United States of America, 7Department of Emergency Medicine, George Washington University School of Medicine and Health
Sciences, Washington, D.C., United States of America, 8Robert Wood Johnson Clinical Scholars Program, Department of Internal Medicine, and Section of Health Policy
and Administration, School of Public Health, Yale University School of Medicine, New Haven, Connecticut, United States of America
Background: Outcome measures for patients hospitalized with pneumonia may complement process measures in
characterizing quality of care. We sought to develop and validate a hierarchical regression model using Medicare claims
data that produces hospital-level, risk-standardized 30-day mortality rates useful for public reporting for patients
hospitalized with pneumonia.
Methodology/Principal Findings: Retrospective study of fee-for-service Medicare beneficiaries age 66 years and older with
a principal discharge diagnosis of pneumonia. Candidate risk-adjustment variables included patient demographics,
administrative diagnosis codes from the index hospitalization, and all inpatient and outpatient encounters from the year
before admission. The model derivation cohort included 224,608 pneumonia cases admitted to 4,664 hospitals in 2000, and
validation cohorts included cases from each of years 1998–2003. We compared model-derived state-level standardized
mortality estimates with medical record-derived state-level standardized mortality estimates using data from the Medicare
National Pneumonia Project on 50,858 patients hospitalized from 1998–2001. The final model included 31 variables and had
an area under the Receiver Operating Characteristic curve of 0.72. In each administrative claims validation cohort, model fit
was similar to the derivation cohort. The distribution of standardized mortality rates among hospitals ranged from 13.0% to
23.7%, with 25th, 50th, and 75thpercentiles of 16.5%, 17.4%, and 18.3%, respectively. Comparing model-derived risk-
standardized state mortality rates with medical record-derived estimates, the correlation coefficient was 0.86 (Standard
Conclusions/Significance: An administrative claims-based model for profiling hospitals for pneumonia mortality performs
consistently over several years and produces hospital estimates close to those using a medical record model.
Citation: Bratzler DW, Normand S-LT, Wang Y, O’Donnell WJ, Metersky M, et al. (2011) An Administrative Claims Model for Profiling Hospital 30-Day Mortality
Rates for Pneumonia Patients. PLoS ONE 6(4): e17401. doi:10.1371/journal.pone.0017401
Editor: Rory Edward Morty, University of Giessen Lung Center, Germany
Received July 23, 2010; Accepted February 3, 2011; Published April 12, 2011
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: The analyses upon which this publication is based were performed in part under Contract Numbers 500-02-OK-03 and Subcontract #500-05-CO01,
funded by the Centers for Medicare and Medicaid Services, an agency of the U.S. Department of Health and Human Services. The content of this publication does
not necessarily reflect the views of policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or
organizations imply endorsement by the U.S. Government. The authors assume full responsibility for the accuracy and completeness of the ideas presented.
(025_HQMSS_OK0402_0407) CMS reviewed and approved the use of its data for this work and approved submission of the manuscript. The funder had no role in
study design, data collection and analysis, or manuscript preparation. Dr. Krumholz reports that he is a consultant to UnitedHealthcare, and that he is supported
by grant U01 HL105270-01 from the National Heart, Lung, and Blood Institute. Dr. Normand reports that she is funded by the Massachusetts Department of Public
Health to monitor the quality of care following cardiac surgery or percutaneous coronary intervention. The other authors report no conflicts.
Competing Interests: Dr. Metersky discloses that he is a consultant to the Centers for Medicare and Medicaid Services, and is a volunteer on the Executive
Committee of the Physician Consortium for Performance Improvement.
* E-mail: firstname.lastname@example.org
Pneumonia, the second most common cause of hospitalization of
the elderly, accounts for approximately 770,000 admissions
annually among patients 65 years of age or older in the United
States [1,2]. Hospitalization rates for pneumonia have increased by
20%from 1988–1990 to 2000–2002 forpatients aged 65 to 84 years
. The combined reporting category of pneumonia and influenza
remains the fifth leading cause of death in this age group .
Care of patients with pneumonia has been the target of quality
measurement and reporting initiatives [4,5]. Two of the largest
initiatives focused on the quality of pneumonia care are the
Centers for Medicare & Medicaid Service’s (CMS) National
Pneumonia Project and The Joint Commission ORYXH initiatives
PLoS ONE | www.plosone.org1April 2011 | Volume 6 | Issue 4 | e17401
previous research has demonstrated varying degrees of accuracy in
pneumonia coding by hospitals [27,28].
We developed an administrative claims-based model for
profiling hospitals for pneumonia mortality that is a good proxy
for results from a medical record model. Despite limitations of
currently available data, this model represents a valuable tool in
assessing the outcomes achieved by states and hospitals in caring
for patients with pneumonia, and has been endorsed by the
National Quality Forum as a measure for acute care hospital
performance . Since initial development, several minor
changes have been made to the model including expanding the
cohort to include patients with viral pneumonia (adenovirus
[480.0], respiratory syncytial virus [480.1], and parainfluenza
virus [480.2]), and excluding patients who had enrolled in the
Medicare hospice benefit before hospitalization (,1% of patients
with pneumonia) . CMS first began using the model for public
reporting in August 2008 and hospital-specific findings are now
reported on Hospital Compare .
Pneumonia study sample used for the derivation and
Model (HGLM) included in the final administrative claims model
to predict 30-day mortality.
Covariates from Hierarchical Generalized Linear
Pneumonia administrative model and medical record
Covariates included in the final medical records model
The authors thank Maria Johnson, Jennifer Mattera, Amy Rich, and
Yongfei Wang from the Yale University School of Medicine for their
contributions to this work.
Conceived and designed the experiments: DWB HMK. Analyzed the data:
DWB STN YW HMK WJO MM LFH MTR. Wrote the paper: DWB
STN YW WJO MM LFH MTR HMK.
1. Russo CA, Elixhauser A (2006) Hospitalizations in the elderly population, 2003.
Statistical Brief #6. Rockville: Agency for Healthcare Research and Quality,
Available: http://www.hcup-us.ahrq.gov/reports/statbriefs/sb6.pdf. Accessed
2011 Mar 22.
2. Fry AM, Shay DK, Holman RC, Curns AT, Anderson LJ (2005) Trends in
hospitalizations for pneumonia among persons aged 65 years or older in the
United States, 1988–2002. JAMA 294: 2712–2719.
3. Anderson RN, Smith BL (2005) Deaths: leading causes for 2002. National Vital
Statistics Reports; vol 53 no 17. Hyattsville: National Center for Health
Statistics, Available: http://www.cdc.gov/nchs/data/nvsr/nvsr53/nvsr53_17.
pdf. Accessed 2011 Mar 22.
4. Centers for Medicare & Medicaid Services website. Available: http://
Accessed 2011 Mar 22.
5. The Joint Commission website. Available: http://www.jointcommission.org/
assets/1/6/Pneumonia.pdf. Accessed 2011 Mar 22.
6. Bratzler DW, Nsa W, Houck PM (2007) Performance measures for pneumonia:
are they valuable and are process measures adequate? Curr Opin Infect Dis 20:
7. Jha AK, Li Z, Orav EJ, Epstein AM (2005) Care in U.S. hospitals–the Hospital
Quality Alliance program. N Engl J Med 353: 265–274.
8. Williams SC, Schmaltz SP, Morton DJ, Koss RG, Loeb JM (2005) Quality of
care in U.S. hospitals as reflected by standardized measures, 2002–2004.
N Engl J Med 353: 255–264.
9. Krumholz HM, Normand SL, Spertus JA, Shahian DM, Bradley EH (2007)
Measuring performance for treating heart attacks and heart failure: the case for
outcomes measurement. Health Aff 26: 75–85.
10. Werner RM, Bradlow ET (2006) Relationship between Medicare’s Hospital
Compare performance measures and mortality rates. JAMA 296: 2694–2702.
11. Krumholz HM, Wang Y, Mattera JA, Wang Y, Han LF, et al. (2006) An
administrative claims model suitable for profiling hospital performance based on
30-day mortality rates among patients with an acute myocardial infarction.
Circulation 113: 1683–1692.
12. Krumholz HM, Wang Y, Mattera JA, Wang Y, Han LF, et al. (2006) An
administrative claims model suitable for profiling hospital performance based on
30-day mortality rates among patients with heart failure. Circulation 113:
13. Krumholz HM, Brindis RG, Brush JE, Cohen DJ, Epstein AJ, et al. (2006)
Standards for statistical models used for public reporting of health outcomes: an
American Heart Association Scientific Statement from the Quality of Care and
Outcomes Research Interdisciplinary Writing Group. Circulation 113: 456–462.
14. Houck PM, Bratzler DW, Nsa W, Ma A, Bartlett JG (2004) Timing of antibiotic
administration and outcomes for Medicare patients hospitalized with commu-
nity-acquired pneumonia. Arch Intern Med 164: 637–644.
15. Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, et al. (2004) Risk adjustment
of Medicare capitation payments using the CMS-HCC model. Health Care
Financ R 25: 119–141.
16. Normand ST, Glickman ME, Gatsonis CA (1997) Statistical methods for
profiling providers of medical care: issues and applications. J Am Stat Assoc 92:
17. Shahian DM, Normand ST, Torchiana DF, Lewis SM, Pastore JO, et al. (2001)
Cardiac surgery report cards: comprehensive review and statistical critique. Ann
Thorac Surg 72: 2155–2168.
18. Goldstein H, Spiegelhalter DJ (1996) League tables and their limitations:
statistical aspects of institutional performance. J Royal Stat Soc 159: 385–444.
19. Harrell FE (2001) Regression modeling strategies with applications to linear
models, logistic regression, and survival analysis. 3rd ed. New York: Springer-
Verlag. 579 p.
20. Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, et al. (1991) A
prediction rule to identify low-risk patients with community-acquired pneumo-
nia. N Engl J Med 336: 243–250.
21. Shahian DM, Torchiana DF, Shemin RJ, Rawn JD, Normand ST (2005) The
Massachusetts cardiac surgery report card: implications of statistical methodol-
ogy. Ann Thorac Surg 80: 2106–2113.
22. Meehan TP, Fine MJ, Krumholz HM, Scinto JD, Galusha DH, et al. (1997)
Quality of care, process, and outcomes in elderly patients with pneumonia.
JAMA 278: 2080–2084.
23. Gleason PP, Meehan TP, Fine JM, Galusha DH, Fine MJ (1999) Associations
between initial antimicrobial therapy and medical outcomes for hospitalized
elderly patients with pneumonia. Arch Intern Med 159: 2562–2572.
24. National Quality Forum website. Available: http://www.qualityforum.org/
cessed 2011 Mar 22.
25. Normand SLT, Wang Y, Krumholz HM (2007) Assessing surrogacy of data
sources for institutional comparisons. Health Serv Outcomes Res Method 7:
26. Wang OJ, Wang Y, Lichtman JH, Bradley EH, Normand S-LT, Krumholz HM
(2007) America’s Best Hospitals in the treatment of acute myocardial infarction.
Arch Intern Med 167: 1345–1351.
27. Whittle J, Fine MJ, Joyce DZ, Lave JR, Young WW, et al. (1997) Community-
acquired pneumonia: can it be defined with claims data? Am J Med Qual 12:
28. Guevara RE, Butler JC, Marston BJ, Plouffe JF, File TM, Jr., et al. (1999)
Accuracy of ICD-9-CM codes in detecting community-acquired pneumococcal
pneumonia for incidence and vaccine efficacy studies. Am J Epidemiol 149:
29. National Quality Forum website. Available: http://www.qualityforum.org/
Measures_List.aspx. Accessed 2011 Mar 22.
30. QualityNet website.Available: http://www.qualitynet.org/dcs/ContentServer?cid
Accessed 2011 Mar 22.
31. U.S. Department of Health & Human Services (Hospital Compare) website.
Available: http://www.hospitalcompare.hhs.gov. Accessed 2011 Mar 22.
Claims Model for Mortality
PLoS ONE | www.plosone.org7 April 2011 | Volume 6 | Issue 4 | e17401