Introducing the JME Special Issue on Cost-Effectiveness Analyses

Cost-effectiveness analysis (CEA) has always been the corner-stone of most economic evaluations by virtue of its wide applications and value recognition by many decision-makers. In recent years, with the increasing trend in the use of health technology assessment (HTA) by jurisdictions all over the world, CEA has gained an even more significant role in the health economic arena. On the technical side, thanks to the continued effort of researchers around the world, we have also seen important advancements in the design and methodology of CEA studies, thus makng CEA a more reliable tool for assessment.

Health authorities of many countries have a set of guidelines to govern the conduct and result interpretation of CEA. Some jurisdictions have established very clearcut thresholds for distinguishing the technologies that are deemed cost-effective from those that are not. Well-known examples include the UK National Institute for Health and Clinical Excellence (NICE) and the Australian Pharmacy Benefits Advisory Committee (PBAC). To increase the fairness of the game, it is therefore important to have a set of guidelines in place before one embarks on performing economic analysis. More importantly, the guidelines have to be reviewed and revised on a regular basis because a health technology may have been proven to be cost-effective at one particular time point, but it does not mean that it will stay cost-effective forever. In some other countries, the health authority has no specific thesholds in CEA. Instead, cases are considered on an individual basis which would give the decision-makers more flexibility in their policy development. In fact, this approach seems to be gaining popularity in many parts of the world, especially in some newer economies such as some Asian countries including Korea and Taiwan.

Many people have the erroneous perception that favourable CEA data would automatically guarantee formulary listing, reimbursement, or favourable pricing. For years, this has been recognized as misleading, because numerous examples have proven that acceptance does not necessarily follow an ascending order of the incremental cost-effectiveness ratios (ICER) obtained from CEA. There are a few reasons to it, but the most common ones seem to be:

1. Priority setting in formulary decision is an inter-play of a number of factors apart from ICER which may include availability of alternative treatments, immediate budget impact, equity, political pressure, etc.

2. The numerical value of ICER can be up- or down-regulated through price adjustment by the manufacturer, depending on the urgency to gain access to the market while the effectiveness of the treatment will remain unchanged.

One must, therefore, always bear this in mind when interpretating the data. In other words, a favourable ICER value does not necessarily mean a health technology is clinically more effective than the comparator.

Nevertheless, CEA remains the most important element in a HTA process. While the traditional retrospective data analysis approach is still being used where a data source is available, the more dynamic and inclusive approach of modelling has been increasingly adopted in research studies. The reasons for this increasing trend are mainly 2-fold. First, the retrospective analysis clearly suffers from the disadvantage that more than often data captured does not truly reflect the reality, due to the use of therapies that are not covered by insurance schemes or outside the traditional practice of westen medicine. With the increasing use of food supplements and traditional oriental therapies such as acupuncture by patients receiving care under a western healthcare system, the data from insurance companies or hospital records is no longer adequate to perform a CEA with a reasonable degree of reliability and generalizability. Second, the use of modelling to project the economic impact of new health technologies has been proven to be of value over the years in providing information to the decision-makers with regards to the level of future savings. This has led to the development of newer modelling approaches including discrete-event simulation (DES) and micro-simulation models. This newer generation of models is usually built with greater complexities and, therefore, data requirement is also more stringent. However, because of this, they can invariably increase the dynamics and applicability of the models as well as the reliability of the data generated.

In this special issue of Journal of Medical Economics (JME), I am particularly happy to introduce to our readers a few high-quality articles on CEA from different parts of the world. The adopted methods of these studies have practically included all the most commonly used models – decision analytic, different types of Markov, and DES models1–6. I am sure readers will find the contents of this special issue exceptionally interesting. From our perspective as editorial members of JME, this is a reflection on the advancement of our journal’s status as we are receiving an increasing number of good articles from all over the world.

As Editor-in Chief of the journal, I look forward to bringing more high-quality articles to our readers in the future. I should also thank all the authors who have been contributing over the years and the editorial team for their efficient and dedicated work in bringing JME to where we are.

Transparency

Declaration of funding

The author received no payment in preparation of this manuscript.

Declaration of financial/other relationships

The author declares no conflicts of interest.