We evaluated the cost-effectiveness of one-time pharmacogenomic testing for preventing adverse drug reactions (ADRs) over a patient’s lifetime. We developed a Markov-based Monte Carlo microsimulation model to represent the ADR events in the lifetime of each patient. The base-case considered a 40-year-old patient. We measured health outcomes in life years (LYs) and quality-adjusted LYs (QALYs) and estimated costs using 2013 US$. In the base-case, one-time genetic testing had an incremental cost-effectiveness ratio (ICER) of $43 165 (95% confidence interval (CI) is ($42 769, $43 561)) per additional LY and $53 680 per additional QALY (95% CI is ($53 182, $54 179)), hence under the base-case one-time genetic testing is cost-effective. The ICER values were most sensitive to the average probability of death due to ADR, reduction in ADR rate due to genetic testing, mean ADR rate and cost of genetic testing.

 

INTRODUCTION

Medication prescription is very common in both inpatient and outpatient settings. Due to the high volume of prescribed medications, safety is of critical concern in addition to the beneficial effects of pharmacotherapy. Recent data suggest that adverse drug reactions (ADRs) are a major cause of disability and death. In addition, even when medications cause no harm they are far too often ineffective. Pharmacogenomics is a promising area that has the potential to significantly improve healthcare outcomes by tailoring pharmacotherapy to individual patients. In particular, there is evidence that ADRs, which lead to 4100 000 deaths annually in the US, can be significantly reduced via pharmacogenomics.

As a result, pharmacogenomics is rapidly gaining popularity to optimize drug delivery. As policymakers and providers attempt to prioritize high quality healthcare, they are confronted with a dearth of level I data on the use of personalized medicine, specifically, little data exists on the healthcare resource expenditures relative to possible medical benefit when pharmacogenomics testing is routinely used to help minimize ADRs. Such an analysis would be extremely valuable and necessary in setting priorities when choices must be made in the face of limited resources.

There are three main types of economic evaluations in healthcare:

• Cost effectiveness
• Cost utility
• Cost benefit analyses

The valuation of costs in all three types is made in monetary units, whereas they differ in the way the health outcomes are identified and valued. In a cost-benefit analysis, health outcomes are also measured in monetary units. In a cost-effectiveness analysis, health outcomes are measured using a single clinical effect of interest such as life-years gained, number of ADRs prevented and so on. On the other hand, in a cost-utility analysis, health outcomes are measured in single or multiple effects such as Quality Adjusted Life Year (QALY), which is a composite measure of the quantity and quality of life. Therefore, cost-utility analysis may provide a better appreciation of the overall health benefits, harms and costs of laboratory tests in the diagnostic decision making process and the induced health outcomes. While cost-utility analysis is a broader form of analysis than cost-effectiveness analysis, many authors prefer not to make a distinction between the two types due to their similarity and use the two terms interchangeably.

The purpose of this study is to determine if pharmacogenomic testing is cost effective to minimize lifetime ADRs for a given age group. While pharmacogenomics testing is becoming more popular for helping to select treatment for a particular individual, there have been few studies considering the cost-effectiveness of pharmacogenomics testing over a patient’s lifetime in relation to several prescribed medications. All cost-effectiveness studies in the literature focus on patients who already are considered for treatment for a particular disease such as certain psychiatric illnesses, smoking cessation therapy and cardiovascular diseases. On the other hand, to the best of our knowledge, this work is the first to study the cost-effectiveness of one-time genetic testing over a patient’s lifetime.

MATERIALS AND METHODS

A Markov-based Monte Carlo simulation model was built to estimate the incremental cost-effectiveness of one-time genetic testing compared with no genetic testing. The base-case scenario considered a 40-year-old patient and simulated his/her lifetime outcomes. During sensitivity analyses, patients at the age groups of 50, 65, 60 and 75 were also considered. Only direct medical costs were taken into consideration to avoid excessive confounding variables. Costs and benefits were discounted using a 3% discount rate as recommended by the cost-effectiveness panel.

Estimates include LYs, QALYs, costs (in 2013 USD) and calculated incremental cost-effectiveness ratio (ICER) as cost per LY gained and cost per QALY gained. QALYs are similar to LYs with the significant difference being QALYs consider morbidity and quality of life effects on patients. In calculating QALYs, one needs to assign a utility score between 0 (representing death) and 1 (representing perfect health state) to the current health state and then adjust LYs using that value. For example, suppose a patient with a disease assigns a utility score of 0.8 for his/her disease status. Assuming that this patient lives in that particular health state for 1 year, his/her QALYs during that time is equal to 0.8 QALYs.

Figure 1. Decision tree representing the Markov model for genetic testing problem for a target age group. This figure shows the conceptual model used in this study. In the figure, the square represents decision nodes, the circles represent chance nodes (random events), the reverse triangles represent the outcomes/end points and the node with ‘M’ label shows the Markov nodes. A patient entering the simulation model is either offered a genetic testing or not. Then, at every age, the patient may experience an ADR that leads the patient to visit emergency department (ED) or outpatient clinic (OC) with a probability estimated from the literature. Once the patient visits ED/OC due to an ADR, given certain probabilities the patient may be hospitalized or die. ADR, adverse drug reaction.

 

 

 

 

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