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: Estimation of the average total cost for treating patients with a particular disease is often complicated by the fact that the survival times are censored on some study subjects and their subsequent costs are unknown. The naive sample average of the observed costs from all study subjects or from the uncensored cases only can be severely biased, and the standard survival analysis techniques are not applicable. To minimize the bias induced by censoring, we partition the entire time period of interest into a number of small intervals and estimate the average total cost either by the sum of the Kaplan-Meier estimator for the probability of dying in each interval multiplied by the sample mean of the total costs from the observed deaths in that interval or by the sum of the Kaplan-Meier estimator for the probability of being alive at the start of each interval multiplied by an appropriate estimator for the average cost over the interval conditional on surviving to the start of the interval. The resultant estimators are consistent if censoring occurs solely at the boundaries of the intervals. In addition, the estimators are asymptotically normal with easily estimated variances. Extensive numerical studies show that the asymptotic approximations are adequate for practical use and the biases of the proposed estimators are small even when censoring may occur in the interiors of the intervals. An ovarian cancer study is provided.Biometrics 07/1997; 53(2):419-34. · 1.52 Impact Factor
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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. · 2.94 Impact Factor
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ABSTRACT: Most medical cost-effectiveness analyses include future costs only for related illnesses, but this approach is controversial. This paper demonstrates that cost-effectiveness analysis is consistent with lifetime utility maximization only if it includes all future medical and non-medical expenditures. Estimates of the magnitude of these future costs suggest that they may substantially alter both the absolute and relative cost-effectiveness of medical interventions, particularly when an intervention increases length of life more than quality of life. In older populations, current methods overstate the cost-effectiveness of interventions which extend life compared to interventions which improve the quality of life.Journal of Health Economics 03/1997; 16(1):33-64. · 2.25 Impact Factor