Resource Use Trajectories for Aged Medicare Beneficiaries with Complex Coronary Conditions
ABSTRACT OBJECTIVE: To use coronary revascularization choice to illustrate the application of a method simulating a treatment's effect on subsequent resource use. DATA SOURCES: Medicare inpatient and outpatient claims from 2002 to 2008 for patients receiving multivessel revascularization for symptomatic coronary disease in 2003-2004. STUDY DESIGN: This retrospective cohort study of 102,877 beneficiaries assessed survival, days in institutional settings, and Medicare payments for up to 6 years following receipt of percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). METHODS: A three-part estimator designed to provide robust estimates of a treatment's effect in the setting of mortality and censored follow-up was used. The estimator decomposes the treatment effect into effects attributable to survival differences versus treatment-related intensity of resource use. PRINCIPAL FINDINGS: After adjustment, on average CABG recipients survived 23 days longer, spent an 11 additional days in institutional settings, and had cumulative Medicare payments that were $12,834 higher than PCI recipients. The majority of the differences in institutional days and payments were due to intensity rather than survival effects. CONCLUSIONS: In this example, the survival benefit from CABG was modest and the resource implications were substantial, although further adjustments for treatment selection are needed.
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ABSTRACT: This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets. The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death. A comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. The comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.Medical Care 02/1998; 36(1):8-27. DOI:10.1097/00005650-199801000-00004 · 2.94 Impact Factor
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ABSTRACT: Many analyses of healthcare costs involve use of data with varying periods of observation and right censoring of cases before death or at the end of the episode of illness. The prominence of observations with no expenditure for some short periods of observation and the extreme skewness typical of these data raise concerns about the robustness of estimators based on inverse probability weighting (IPW) with the survival from censoring probabilities. These estimators also cannot distinguish between the effects of covariates on survival and intensity of utilization, which jointly determine costs. In this paper, we propose a new estimator that extends the class of two-part models to deal with random right censoring and for continuous death and censoring times. Our model also addresses issues about the time to death in these analyses and separates the survival effects from the intensity effects. Using simulations, we compare our proposed estimator to the inverse probability estimator, which shows bias when censoring is large and covariates affect survival. We find our estimator to be unbiased and also more efficient for these designs. We apply our method and compare it with the IPW method using data from the Medicare-SEER files on prostate cancer.Health Economics 09/2010; 19(9):1010-28. DOI:10.1002/hec.1640 · 2.14 Impact Factor
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ABSTRACT: The major expansion of federal comparative effectiveness research launched in 2009 held the potential to supply the information needed to help slow health spending growth while improving the outcomes of care. However, when Congress passed the Patient Protection and Affordable Care Act one year later, it limited the role of cost analysis in the work sponsored by the Patient-Centered Outcomes Research Institute. Despite this restriction, cost-effectiveness analysis meets important needs and is likely to play a larger role in the future. Under the terms of the Affordable Care Act, the institute can avoid commissioning cost-effectiveness analyses and still provide information bearing on the use and costs of health care interventions. This information will enable others to investigate the comparative value of these interventions. We argue that doing so is necessary to decision makers who are attempting to raise the quality of care while reining in health spending.Health Affairs 10/2010; 29(10):1805-11. DOI:10.1377/hlthaff.2010.0647 · 4.64 Impact Factor