Issues in Cost Effectiveness in Health Care

Cost-effectiveness analysis (CEA) is becoming increasingly popular as society moves toward rationalizing health costs. This review describes the applications and limitations of the technique. Conceptually simple though frequently complicated in application, CEA compares the cost of a procedure with its effectiveness, thus helping an administrator to judge whether the procedure is worth its cost. CEA also permits comparison of various interventions that result in a similar health outcome. A major benefit of CEA is that it forces decision makers to confront the tradeoffs implicit in all decisions regarding alternative approaches. Limitations of the CEA philosophy and technique also have to be understood if it is to be employed effectively; it is not an assessment of cost savings, nor is it a decision-making technique because it does not incorporate value judgments. A number of potential applications to dentistry are described.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65700/1/j.1752-7325.1989.tb02085.x.pd

A Cost-Utility Analysis Of Alternative Strategies In Screening For Breast Cancer

Screening for early detection is a primary way to control breast cancer. The choice of effective screening policies involves substantial uncertainty and difficult tradeoffs among medical costs and the duration and quality of life. In this paper, we study the choice of the age at which screening should begin and the frequency of screening tests. These have been issues of important debate in the health policy community. We address these questions using the framework of costutility analysis, as we consider it important to evaluate the outcomes of preventive care in terms of morbidity and quality of life, in addition to survival. After a brief review of background information on breast cancer screening, we introduce cost-utility analysis and its relation with Bayesian decision-making. We then discuss modelling and prior specifications, and carry out a cost-utility evaluation of the currently recommended policy. Finally we compare those to the the Bayes-optimal decision for various values o..

A review of health care models for coronary heart disease interventions

This article reviews models for the treatment of coronary heart disease (CHD). Whereas most of the models described were developed to assess the cost effectiveness of different treatment strategies, other models have also been used to extrapolate clinical trials, for capacity and resource planning, or to predict the future population with heart disease. In this paper we investigate the use of modelling techniques in relation to different types of health intervention, and we discuss the assumptions and limitations of these approaches. Many of the models reviewed in this paper use decision tree models for acute or short term interventions, and Markov or state transition models for chronic or long term interventions. Discrete event simulation has, however, been used for more complex whole system models, and for modelling resource-constrained interventions and operational planning. Nearly all of the studies in our review used cohort-based models rather than population based models, and therefore few models could estimate the likely total costs and benefits for a population group. Most studies used de novo purpose built models consisting of only a small number of health states. Models of the whole disease system were less common. The model descriptions were often incomplete. We recommend that the reporting of model structure, assumptions and input parameters is more explicit, to reduce the risk of biased reporting and ensure greater confidence in the model results