Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data.
By Sebastian Schneeweiss, J. Seeger, M. Maclure, Philip S. Wang, J. Avorn, Robert J. Glynn. 2001
731 citations recorded by the source corpus.
Subfields: Epidemiology, Genetics, Health Information Management
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Epidemiology
In Genetics
In Health Information Management
Citations in the corpus
- Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (2018, methodology): “For example, EHR data have been widely used in various healthcare research tasks such as risk prediction [15, 16, 33], retrospective epidemiologic studies [17, 26, 29] and phenotyping [12, 31].”
- The anticholinergic risk scale and anticholinergic adverse effects in older persons. (2008, methodology): “The total number of medications prescribed has been used as a surrogate for medical comorbidity.(19)”
- Drug-drug interactions among elderly patients hospitalized for drug toxicity. (2003, neutral): “(%) 500 (55) 23 910 (55) 686 (65) 34 121 (66) 272 (52) 13 538 (52)”
- A review of uses of health care utilization databases for epidemiologic research on therapeutics. (2005, methodology): “The Deyo claims data application [59] of the Charlson comorbidity score [60] that performs well in elderly patients [61] was recorded correctly in more than 40% of patients and misclassified by only one point in an additional 35% of patients when compared with ambulatory medical records [53].”
- A combined comorbidity score predicted mortality in elderly patients better than existing scores (2011, methodology): “Although differences in c-statistics among the 3 comorbidity measures appear small, it has been demonstrated that even slight improvements in the c-statistic for such indices can translate into measurable reductions in confounding bias [2].”
- Risk Prediction Models for Hospital Readmission: A Systematic Review (2011, background): “80 indicates good discriminative ability.(9,10) If the c statistic was not reported, we abstracted other operational statistics such as sensitivity, specificity, and predictive values for representative risk score cutoffs when available.”
- Predicting mortality and healthcare utilization with a single question. (2001, background): “Traditionally, administratively derived predictors have been used for such purposes, however, their limitations have led to the development of alternatives (Romano et al. 1993; Iezzoni et al. 1996; Iezzoni 1999; Schneeweiss and Maclure 2000; Schneeweiss et al. 2001, 2003).”