By Dr. Anushree Priyadarshini, Change Team, ARCH
Cost-effectiveness or cost-utility analysis is an essential part of many reimbursement processes. Health technology assessment bodies – such as NICE in the UK – require this kind of information as part of a submission for a new drug or medical device or connected health technology, and it is in this context that analyses of this kind are usually conducted.
The impact of interventions on a populations’ health is paramount. Yet wide-ranging differences are observed in population health outcomes despite health systems having similar expenditure per capita. Although partly the reason for this could be variation in non-health system factors, such as the level of education of the population. But majorly it is because some systems dedicate resources to expensive interventions with little effects on population health, while low cost interventions with potentially greater benefits are not fully applied.
Cost-effectiveness analysis (CEA) is an instrument decision-makers use to assess and potentially improve the performance of their health systems. It suggests which interventions provide the highest “value for money” and helps them choose the interventions and programmes which maximize health for the available resources1.
CEA requires information on:
- The extent to which current and potential interventions improve population health, i.e., effectiveness
- The resources required to implement the interventions, i.e., costs
While cost effectiveness analysis helps us understand the ever-increasing costs of health care and making sure that we achieve good value in health care, there are limitations to the approach that must be observed and highlighted when employing the technique. Cost-effectiveness analysis involves an assessment of both cost and effectiveness. It is only as valid as its underlying measures of effectiveness and cost. If 1 or more randomized clinical trial results are used to conduct the analysis, it will only be as good as the data in the trial. Then, the time horizon of a cost-effectiveness analysis may extend beyond the data that are available; in such a case modelling of the outcome is needed as against direct measurement. Cost-effectiveness is, by nature, incremental. Therefore added costs need to be compared with a control group. Selection of the appropriate control group is a challenge itself. Moreover, the control group should represent the current standard of care, assuming that this standard is, itself, reasonably cost-effective 2.
Outlining the utility and the limits to the approach, the idea is to suggest that cost-effectiveness analysis should not be seen as something that is beneficial or not, but rather as a systematic approach that can help us understand the worth of new therapy and thus help inform both medical decision making and public policy.
 Weintraub, W. S., & Cohen, D. J. (2009). The limits of cost-effectiveness analysis. Circulation: Cardiovascular Quality and Outcomes, 2(1), 55-58.