@article {938, title = {Long-term costs and resource use in elderly participants with congestive heart failure in the Cardiovascular Health Study.}, journal = {Am Heart J}, volume = {153}, year = {2007}, month = {2007 Feb}, pages = {245-52}, abstract = {

BACKGROUND: Although heart failure (HF) afflicts nearly 5 million Americans, the long-term cost of HF care has not been described previously. In a prospective, longitudinal cohort of community-dwelling elderly from 4 regions, we examined the long-term costs and resource use of elderly patients with HF.

METHODS: We linked 4860 elderly participants in the National Heart, Lung, and Blood Institute Cardiovascular Health Study to Medicare part A and part B claims from 1992 to 2003. Costs were calculated from Medicare payments and discounted at 3\% annually. We applied nonparametric estimators to calculate mean costs and resource use per patient for a 10-year period. To describe the relationship between patient characteristics and long-term costs, we constructed censoring-adjusted regression models.

RESULTS: There were 343 participants (84.8\% white; 50.1\% men; mean age, 78.2 years) with prevalent HF and 4517 participants without HF at study entry. Mean follow-up was 6.7 years (median, 6.4 years). The 10-year survival rates were 33\% and 63\% for the prevalent HF and nonprevalent HF groups (P < .001), respectively. The mean 10-year medical costs were significantly higher for the prevalent HF cohort (54,704 dollars vs 41 dollars,780, P < .001). The higher costs associated with HF were also reflected in greater resource use with more hospitalizations (P < .05) and more intensive care unit days (P < .05). Participants with HF had more physician visits (P < .05), with most of these encounters involving noncardiology physicians. However, in multivariate models, prevalent HF was not an independent predictor of higher costs.

CONCLUSION: Patients with HF consume substantially more health care resources than their elderly peers, and these higher costs persist through 10 years of follow-up. Many of these costs may be related to other comorbid conditions.

}, keywords = {Aged, Costs and Cost Analysis, Female, Health Resources, Heart Failure, Humans, Male, Medicare, Prospective Studies, Time Factors}, issn = {1097-6744}, doi = {10.1016/j.ahj.2006.11.010}, author = {Liao, Lawrence and Anstrom, Kevin J and Gottdiener, John S and Pappas, Paul A and Whellan, David J and Kitzman, Dalane W and Aurigemma, Gerard P and Mark, Daniel B and Schulman, Kevin A and Jollis, James G} } @article {1281, title = {Comparing costs associated with risk stratification rules for t-year survival.}, journal = {Biostatistics}, volume = {12}, year = {2011}, month = {2011 Oct}, pages = {597-609}, abstract = {

Accurate risk prediction is an important step in developing optimal strategies for disease prevention and treatment. Based on the predicted risks, patients can be stratified to different risk categories where each category corresponds to a particular clinical intervention. Incorrect or suboptimal interventions are likely to result in unnecessary financial and medical consequences. It is thus essential to account for the costs associated with the clinical interventions when developing and evaluating risk stratification (RS) rules for clinical use. In this article, we propose to quantify the value of an RS rule based on the total expected cost attributed to incorrect assignment of risk groups due to the rule. We have established the relationship between cost parameters and optimal threshold values used in the stratification rule that minimizes the total expected cost over the entire population of interest. Statistical inference procedures are developed for evaluating and comparing given RS rules and examined through simulation studies. The proposed procedures are illustrated with an example from the Cardiovascular Health Study.

}, keywords = {Biostatistics, Coronary Disease, Cost-Benefit Analysis, Costs and Cost Analysis, Humans, Models, Statistical, Risk Assessment, Risk Factors, Time Factors}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxr001}, author = {Cai, Tianxi and Tian, Lu and Lloyd-Jones, Donald M} }