In Search of the Perfect Comorbidity Measure for Use With Administrative Claims Data

Department of Family Medicine, University of Washington, Seattle, Washington 98195-4982, USA.
Medical Care (Impact Factor: 3.23). 09/2006; 44(8):745-53. DOI: 10.1097/01.mlr.0000223475.70440.07
Source: PubMed


Numerous measures of comorbidity have been developed for health services research with administrative claims.
We sought to compare the performance of 4 claims-based comorbidity measures.
We undertook a retrospective cohort study of 5777 Medicare beneficiaries ages 66 and older with stage III colon cancer reported to the Surveillance, Epidemiology, and End Results Program between January 1, 1992 and December 31, 1996.
Comorbidity measures included Elixhauser's set of 30 condition indicators, Klabunde's outpatient and inpatient indices weighted for colorectal cancer patients, Diagnostic Cost Groups, and the Adjusted Clinical Group (ACG) System. Outcomes included receipt of adjuvant chemotherapy and 2 year noncancer mortality.
For all measures, greater comorbidity significantly predicted lower receipt of chemotherapy and higher noncancer death. Nested logistic regression modeling suggests that using more claims sources to measure comorbidity generally improves the prediction of chemotherapy receipt and noncancer death, but depends on the measure type and outcome studied. All 4 comorbidity measures significantly improved the fit of baseline regression models for both chemotherapy receipt (baseline c-statistic 0.776; ranging from 0.779 after adding ACGs and Klabunde to 0.789 after Elixhauser) and noncancer death (baseline c-statistic 0.687; ranging from 0.717 after adding ACGs to 0.744 after Elixhauser).
Although some comorbidity measures demonstrate minor advantages over others, each is fairly robust in predicting both chemotherapy receipt and noncancer death. Investigators should choose among these measures based on their availability, comfort with the methodology, and outcomes of interest.

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    • "Patient co-morbidity was estimated using the adjusted clinical group (ACG) method [14]. Each patient was assigned to any number of 12 collapsed aggregate diagnosis groups (CADGs), defined by constellations of conditions of similar severity and chronicity based on ICD versions 9 and 10 (ICD-9 and ICD-10) and on OHIP coding using a 2-year review period [15]. We identified whether patients were admitted to hospital at the time of a closed reduction by the presence of a DAD or an SDS entry associated chronologically with the service date of the index event OHIP elbow relocation fee code. "
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    ABSTRACT: Introduction: Simple elbow dislocations treated by closed reduction are thought to result in a satisfactory return of function in most patients. Little, however, is known about how many patients ultimately proceed to subsequent surgical treatment due to the low patient numbers and significant loss to follow-up in the current literature. The purpose of this study was to establish the rate of and risk factors for subsequent surgical treatment after closed reduction of a simple elbow dislocation at a population level. Patients and methods: All patients aged 16 years or older who underwent closed reduction of a simple elbow dislocation between 1994 and 2010 were identified using a population database. Subsequent procedures performed for joint contractures, instability or arthritis were recorded. Outcomes were modelled as a function of age, sex, income quintile, co-morbidity, urban/rural status, physician speciality performing the initial reduction and whether orthopaedic consultation and/or post-reduction radiograph was performed within 28 days of the injury, in a time-to-event analysis. Results: We identified 4878 elbow dislocations with a minimum 2-year follow-up: stabilisation surgery was performed in 112 (2.3%) at a median time of 1 month, contracture release in 59 (1.2%) at median 9 months and arthroplasty in seven (0.1%) at median 25 months. Admission to hospital for the initial reduction was associated with an increased risk of undergoing stabilisation (hazard ratio (HR), 2.50; 95% confidence interval (CI), 1.67-3.74) and contracture release (HR, 1.93; CI, 1.08-3.44). Multiple reduction attempts increased the risk of requiring contracture release (HR, 3.71; CI, 1.22-11.29). Survival analysis demonstrated that all subsequent procedures had taken place by 4-5 years. Conclusion: Few patients with simple elbow dislocations develop complications requiring surgery, but those that do most commonly undergo soft-tissue stabilisation or contracture release within 4 years of the injury. Contrary to current thinking, surgery for instability is performed more often than joint contracture release, albeit with slightly different time patterns.
    Injury 03/2015; 46(6). DOI:10.1016/j.injury.2015.02.009 · 2.14 Impact Factor
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    • "The number of ADGs was calculated from administrative data for each year the child was between the ages 12 and 17; this excludes routine visits (for immunizations, for example) before the age of 12 [25]. This index is one of several claims-based measures similarly correlated with mortality [54]. The mean morbidity score of all cohort members was 9.69. "
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    ABSTRACT: Background Well-organized administrative data with large numbers of cases (building on linked files from several government departments) and a population registry facilitate new studies of population health and child development. Analyses of family relationships and a number of outcomes--educational achievement, health, teen pregnancy, and receipt of income assistance--are relatively easy to conduct using several birth cohorts. Looking both at means/proportions and at sibling correlations enriches our study of opportunity and well-being in late adolescence. With observational research possibly exaggerating the causal effects of risk factors, sibling comparisons involving individuals sharing both many family characteristics and many genes help deal with such criticisms. Methods This paper uses a rich dataset from one Canadian province (Manitoba) covering a wide range of geographical areas (cities to rural regions). Influences on opportunity and well-being are analyzed looking at both means/proportions and sibling correlations. We measure a variety of outcomes that may reflect different causal influences. A creative application of linear programming advances the use of data on residential location. Results Predicting educational achievement using available variables was much easier than predicting adolescent health status (R-square of .200 versus R-square of .043). Low levels of educational achievement, high levels of teenage pregnancy, and high sibling correlations outside Winnipeg and within Winnipeg’s lower income areas highlight inequalities across socioeconomic and geographic backgrounds. Stratifying our analyses by different variables, such as income quintiles, reveals differences in means and correlations within outcomes and across groups. Particular events--changes in mother’s marital status and in place of residence--were associated with less favorable outcomes in late adolescence. Conclusion Our findings suggest a paradox: Canadian developmental outcomes through late adolescence appear quite similar to those in the United States, even though intergenerational mobility in Canada is closer to mobility in the Nordic countries than to that in the United States.
    BMC Public Health 05/2014; 14(1):506. DOI:10.1186/1471-2458-14-506 · 2.26 Impact Factor
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    • "Furthermore the prevalence and prognosis for some of the 19 medical conditions have changed radically since the origin, and an update of the index is relevant. Other comorbidity indices exist; however the results from most of these are comparable [7, 34, 35]. "
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    ABSTRACT: Background. Comorbidity is an important prognostic factor for survival in different cancers; however, neither the prevalence nor the impact of comorbidity has been investigated in bone sarcoma. Methods. All adult bone sarcoma patients from western Denmark treated at the Aarhus Sarcoma Centre in the period from 1979 to 2008 were identified through a validated population-based database. Charlson Comorbidity Index scores were computed, using discharge diagnoses from the Danish National Patient Registry. Survival was assessed as overall and disease-specific mortality. The impact of comorbidity was examined as rates according to the level of comorbidity as well as uni- and multivariately using proportional hazard models. Results. A total of 453 patients were identified. The overall prevalence of comorbidity was 19%. The prevalence increased with age and over the study period. In patients with Ewing/osteosarcoma, comorbidity was not associated with an increased overall or disease-specific mortality. However, patients with bone sarcomas other than Ewing/osteosarcoma had increased overall mortality. Independent prognostic factors for disease-specific survival were age, tumor size, stage at diagnosis, soft tissue involvement, grade, and surgery. Conclusion. The prevalence of comorbidity in bone sarcoma patients is low. Comorbidity impaired survival in patients with non-Ewing/nonosteosarcoma, histology. This emphasizes the importance of not only treating the sarcoma but also comorbidity.
    Sarcoma 02/2014; 2014:690316. DOI:10.1155/2014/690316
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