Objectives:Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define C

https://czresearch.com/dropbox/Quan_MedCare_2005v43p1130.pdf

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data.

/pdf/updating-and-validating-the-charlson-comorbidity-index-and-u1djuhty32.pdf

Updating and Validating the Charlson Comorbidity Index and Score for Risk Adjustment in Hospital Discharge Abstracts Using Data From 6 Countries

The mission of the Agency for Healthcare Research and Quality is “to improve the quality, safety, efficiency, and effectiveness of health care for all Americans”. The organization and selected major activities of the Agency are briefly described.

The Agency for Healthcare Research and Quality

Methods: A cross-sectional analysis was conducted on a nationally random sample of 1217103 Medicare feefor-service beneficiaries aged 65 and older living in the United States and enrolled in both Medicare Part A and Medicare Part B during 1999. Multiple logistic regression was used to analyze the influence of age, sex, and number of types of chronic conditions on the risk of incurring inpatient hospitalizations for ambulatory care sensitive conditions and hospitalizations with preventable complications among aged Medicare beneficiaries. Results: In 1999, 82% of aged Medicare beneficiaries had 1 or more chronic conditions, and 65% had multiple chronic conditions. Inpatient admissions for ambulatory care sensitive conditions and hospitalizations with preventable complications increased with the number of chronic conditions. For example, Medicare beneficiaries with 4 or more chronic conditions were 99 times more likely than a beneficiary without any chronic conditions to have an admission for an ambulatory care sensitive condition (95% confidence interval, 86-113). Per capita Medicare expenditures increased with the number of types of chronic conditions from $211 among beneficiaries without a chronic condition to $13973 among beneficiaries with 4 or more types of chronic conditions. Conclusions: The risk of an avoidable inpatient admission or a preventable complication in an inpatient setting increases dramatically with the number of chronic conditions. Better primary care, especially coordination of care, could reduce avoidable hospitalization rates, especially for individuals with multiple chronic conditions.

/pdf/prevalence-expenditures-and-complications-of-multiple-4mi1gwp4mw.pdf

Prevalence, expenditures, and complications of multiple chronic conditions in the elderly

https://research.vu.nl/ws/files/2902143/267402.pdf

How to measure comorbidity: a critical review of available methods

Objectives.This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets.Methods.The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other

https://czresearch.com/dropbox/Elixhauser_MedCare_1998v36p8.pdf

Comorbidity measures for use with administrative data.

Database . Healthcare Cost and Utilization Project (HCUP)-a family of databases including the State Inpatient Databases (SID), the Nationwide Inpatient Sample (NIS), the Kids' Inpatient Database (KID), and the outpatient databases State Ambulatory Surgery Data (SASD) and State Emergency Department Data (SEDD). Description Multistate, inpatient (SID, NIS, KID) and outpatient (SASD, SEDD) discharge records on insured and uninsured patients. Source Partnership between the Agency for Healthcare Research and Quality (AHRQ) and public and private statewide data organizations. AVAILABLE DATA: Selected data elements from inpatient and outpatient discharge records, including patient demographic, clinical, disposition and diagnostic/procedural information; hospital identification (ID); facility charges; and other facility information. DATA YEARS AVAILABLE: Varies by database: NIS 1988-2000; SID 1995-2000; KID 1997 and 2000; SASD 1995-2000; and SEDD in pilot phase. Future data years anticipated for all datasets and back years for SID and SASD. UNITS OF ANALYSIS: Patient (in states with encrypted patient identification), physician, market, and state. Research questions Quality assessment, use and cost of hospital services, medical treatment variations, use of ambulatory surgery services, diffusion of medical technology, impact of health policy changes, access to care (inference), study of rare illness or procedures, small area variations, and care of special populations. STRENGTHS: Largest collection of all-payer, uniform, state-based inpatient and ambulatory surgery administrative data. Limitations Lacks clinical detail (e.g., stage of disease, vital statistics) and laboratory and pharmacy data. Ability to track patients across time and setting varies by state. Access to data Access available to all users who sign and abide by the Data Use Agreement. Application kits available at www.ahrq.gov/data/hcup. HCUPnet, an on-line interactive query tool, allows access to data without purchase (www.ahrq. gov/data/hcup/hcupnet.htm).

The healthcare cost and utilization project: an overview.

Objective:We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomes: in-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly us

Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index.

Clostridium difficile is an anaerobic, spore-forming bacterium that is a common cause of healthcare-associated infectious colitis. Symptoms of infection range from mild diarrhea to the sometimes-fatal pseudomembranous colitis and toxic megacolon. C. difficile infections (CDI) often occur as a complication of antibiotic therapy; ampicillin, clindamycin, thirdgeneration cephalosporins (such as cefotaxime and ceftazidime), and fluoroquinolones are commonly-identified high-risk drugs. The elderly are at highest risk of developing CDI. CDI is often associated with hospitalized patients who are frequently exposed to antibiotics, making them more susceptible to the disease. A previous report in this series described a doubling of CDI hospitalizations between 2001 and 2005, significant variation by region of the country, and high severity of illness among CDI patients.

Clostridium Difficile Infections (CDI) in Hospital Stays, 2009

Cost-benefit (CBA) and cost-effectiveness analyses (CEA) are methods that enumerate the costs and consequences associated with health-related technologies, services, and programs. This article examines the trends in published CBA and CEA of personal health services from 1979 through 1990. It is based on a bibliography that was compiled to help address the immense need for information on the variation and effectiveness of medical practices, particularly as researchers expand their analysis to a study of the cost effectiveness of medical and surgical interventions, health care technologies, preventive practices, and other health programs. A systematic search was conducted for all articles under the heading "cost-benefit analysis" (which includes cost-effectiveness analysis) and "costs and cost analysis." Data sources included the MEDLARS (National Library of Medicine) database, other bibliographies in specialized areas, reference lists in key articles, and contacts with researchers in the field. All titles and abstracts were scanned to determine if the articles pertained to personal health services and if both costs and consequences were assessed. If both criteria were met, the article was included in the bibliography. This search resulted in 3,206 eligible CBA/CEA publications from 1979 through 1990. The publications were subdivided into two major categories: reports of studies and "other" publications, including reviews, descriptions of methodology, letters, and editorials. Reports of studies and "other" publications were classified into approximately 250 different topic areas. The studies were further classified by parameters, such as study type, publication vehicle, and medical function. This article describes the results of this classification and describes trends during 1979 to 1990 compared with 1966 to 1978. The classification of study reports and "other" publications into 250 topic areas is presented in Appendix A. The entire bibliography is reproduced in Appendix B. Detailed tables of findings are presented in Appendix C, and the results are illustrated graphically in Appendix D. Appendix E provides the coding scheme used in the bibliography's data base.

Anne Elixhauser

Papers

Comorbidity measures for use with administrative data.

The healthcare cost and utilization project: an overview.

Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index.

Clostridium Difficile Infections (CDI) in Hospital Stays, 2009

Health care CBA/CEA: an update on the growth and composition of the literature.