Article

The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims.

Department of Medicine, the University of Chicago, Illinois, USA.
Medical Care (Impact Factor: 2.94). 12/1999; 37(11):1128-39. DOI: 10.1097/00005650-199911000-00005
Source: PubMed

ABSTRACT The Charlson Score is a particularly popular form of comorbidity adjustment in claims data analysis. However, the effects of certain implementation decisions have not been empirically examined.
To determine the effects of alternative data sources and lookback periods on the performance of Charlson scores in the prediction of mortality following hospitalization.
A representative sample of 1,387 elderly patients hospitalized in 1993, drawn from the Medicare Current Beneficiary Survey (MCBS). Three years of linked Medicare claims and survey instruments were available for all patients, as was 2-year mortality follow-up.
Nested Cox regression and comparisons of areas under the Receiver Operating Characteristic (ROC) curve were used to evaluate ability to predict mortality.
Compared with a 1-year lookback involving solely inpatient claims, statistically and empirically significant improvements in the prediction of mortality are obtained by incorporating alternative sources of data (particularly 2 years of inpatient data and 1 year of outpatient and auxiliary claims), but only if indices derived from distinct sources of data are entered into the regression distinctly. The area under the ROC curve for 1-year mortality predication increases from 0.702 to 0.741 (P = 0.002). Furthermore, these improvements in explanatory power obtained whether one also controls for Charlson scores based on self-reported health history and/or secondary diagnoses from the claim for the index hospitalization itself. Finally, claims-based comorbidity adjustment performs comparably to survey-derived adjustment, with areas under the ROC curve of 0.702 and 0.704, respectively.
The widespread practice of comorbidity adjustment in pre-existing administrative data sources can be improved by taking more complete advantage of existing administrative data sources.

Full-text

Available from: James X Zhang, Jun 02, 2015
0 Followers
 · 
91 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To conduct a systematic review of studies reporting on the development or validation of comorbidity indices using administrative health data and compare their ability to predict outcomes related to comorbidity (ie, construct validity).
    Journal of Clinical Epidemiology 10/2014; 68(1). DOI:10.1016/j.jclinepi.2014.09.010 · 5.48 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The Charlson score is a commonly used measure of comorbidity; however, there is little empirical research into the optimal implementation when studying cancer surgery outcomes using administrative data. We compared four alternative Charlson score implementations, including and excluding metastatic cancer and varying the look-back periods. Nine years of linked administrative data were used to identify patients undergoing surgery for cancer of the colon, rectum, or lung in New South Wales, Australia. Four binary outcomes of 30- and 365-day mortality, length of stay greater than 21 days, and emergency readmission within 28 days were compared between groups of similar hospitals. Hospital risk adjustment models were compared for alternative Charlson score implementations. Excluding metastatic cancer from the Charlson score improved model performance for short-term outcomes, but there was no implementation that was consistently optimal. Incorporating a look-back period reduced the number of patients for analysis but did not improve hospital risk adjustment. Charlson scores for hospital risk adjustment of short-term outcomes of cancer surgery should be calculated excluding metastatic cancer as a separate comorbidity. We found no clear best performing implementation and found no benefit in incorporating any look-back period. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of Clinical Epidemiology 12/2014; 68(4). DOI:10.1016/j.jclinepi.2014.12.002 · 5.48 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Objectives Ambulatory care sensitive conditions (ACSCs) are acute care diagnoses that could potentially be prevented through improved primary care. This study investigated how payments and charges for these ACSC visits differ by three hospital-based settings (outpatient, emergency department [ED], and inpatient) and examined differences in payments and charges by their physician and facility components.Methods This was a secondary analysis of data (2005 through 2010) from the Medical Expenditure Panel Survey. Multiple linear regression models were used to assess differences in the mean-adjusted payments and charges for ACSC visits by clinical setting and further divided payments and charges into physician and facility components.ResultsOf all ACSC visits from 2005 through 2010, 41% were outpatient visits, 36% were ED visits, and 23% were hospital admissions. After adjusting for patient demographics and comorbid conditions, charges for an inpatient ACSC visit were four times higher ($11,414 vs. $2,563) and payments were five times higher ($4,325 vs. $859) when compared to an ED visit. By comparison, charges for an ACSC ED visit were two times higher ($2,563 vs. $1,084) and payments 2.5 times higher ($859 vs. $341) relative to an ACSC visit managed in an outpatient hospital-based clinic. Across all clinical settings, hospital facility fees account for 77% to 94% of the charge differences and 81% to 93% of the payment differences.Conclusions For hospital-based ACSC visits, inpatient hospitalizations are by far the most expensive. Finding ways to expand outpatient resources and improve the health management of the chronically ill may avoid conditions that lead to more expensive hospital-based encounters. Across all hospital-based settings, facility fees are the major contributor of expense.
    Academic Emergency Medicine 01/2015; 22(2). DOI:10.1111/acem.12579 · 2.20 Impact Factor