Pharmaco-economics_An Article by Sahjesh Soni_Part 2

BRIEF HISTORY REGARDING PHARMACOECONOMICS

One of the first times that the term Pharmacoeconomics was used in a public forum was in 1986, at a meeting of Pharmacists in Toronto, Canada, when Ray Townsend, from the Upjohn Company, used the term in a presentation. Ray and a few others had been performing studies using the term Pharmacoeconomics within the pharmaceutical industry since the early eighties. Today, Pharmacoeconomics research is a flourishing industry with many practitioners, a large research and applications agenda, several journals and flourishing professional societies including the International Society for Pharmacoeconomics and Outcomes Research           

                                                                                             

Pharmaceutical models:

The main pharmaceutical models are:

1) Decision tree model:

A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules.

 

Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. 

 

Pros: Intuitive, very versatile and flexible, easy to depict clinical pathways graphically.

Cons: data requirements easily expand beyond available information , does not allow direct accounting of time spent in various states of health

 

2) Markov models:

They are often used to model the probabilities of different states and the rates of transitions among them. Markov models can also be used to recognize patterns, make predictions and to learn the statistics of sequential data.

 

Markov models can be expressed in equations or in graphical models. Graphic Markov models typically use circles (each containing states) and directional arrows to indicate possible transitional changes between them. The directional arrows are labelled with the rate or the variable one for the rate. 

 

Pros: All costs and outcomes easily calculated for every time interval, parameter uncertainly and sensitivity analysis easy to incorporate.

Cons: Assumes that transitions history does not matter, state transition probabilities are fixed over time.

3) Discrete event simulation models:

Depicts patient health state progression over multiple time cycles with variable health state transition probabilities

 

Pros: Costs and outcomes calculated at each time interval more realistic then fixed transition probability models.

Cons: Can be difficult to compute results and sensitivity analyses .Has greater data requirements

 

 

4) Monte Carlo simulation models:

Depicts patient health state progression through computer simulation of multiple individuals with random pathways based on model probabilities

Pros: Extremely flexible and realistic

Cons: Requires the most data, most computationally difficult.

 

Techniques used in Pharmacoeconomics:

The Pharmacoeconomic evaluation techniques are:

(A)Cost Minimization Analysis (CMA)

According to World health organization Cost minimization analysis is a method of calculating drug costs to project the least costly drug or therapeutic modality. Cost minimization analysis is most commonly done to compare two drugs that are supposedly equivalent in dose and therapeutic effects. Once this equivalency in outcome is confirmed, the costs can be identified, measured, and compared in monetary units (dollars). The rationale behind this analysis is that when a new drug is licensed for marketing is similar to an old drug in its therapeutic benefits and side effects etc then the price should arguably be same as that of old drug. But in reality it is not as simple as it sounds to be because of the fact that sound trial-based information of both drugs are needed for coming to such a conclusion which is always not the case. And for this very reason the critics of cost-minimization analysis have argued against using cost minimization analysis as a useful tool of Pharmacoeconomics studies.

For example, if drugs A and B are antiulcer agents and have been documented as equivalent in efficacy and incidence of ADRs, then the costs of using these drugs could be compared using CMA. These costs should extend beyond a comparison of drug acquisition costs and include costs of drug preparation (pharmacist and technician time), administration (nursing time), and storage. When appropriate, other costs to be valued can include the cost of physician visits, number of hospital days, and pharmacokinetic consultations.

(B)Cost-effectiveness analysis (CEA)

This is a way of summarizing the health benefits and resources used by competing healthcare programs so that policymakers can choose among them. CEA involves comparing programs or treatment alternatives with different safety and efficacy profiles. Cost is measured in dollars, and outcomes are measured in terms of obtaining a specific therapeutic outcome. These outcomes are often expressed in physical units, natural units, or nondollar units (e.g., lives saved, cases cured, life expectancy, or drop in blood pressure. CEA can be calculated by using ACER and ICER.

 The ACER (Average Cost-Effectiveness Ratio) can be summarized as follows:

This allows the costs and outcomes to be reduced to a single value to allow for comparison. Using this ratio, the clinician would choose the alternative with the least cost per outcome gained. The most cost-effective alternative is not always the least costly alternative for obtaining a specific therapeutic objective. 

The ICER (Incremental cost-effectiveness ratio) can be summarized as follows:

This formula yields the additional cost required to obtain the additional effect gained by switching from drug A to drug B.

CEA is particularly useful in balancing cost with patient outcome, determining which treatment alternatives represent the best health outcome per dollar spent, and deciding when it is appropriate to measure outcome in terms of obtaining a specific therapeutic objective. 

(C) Cost-utility analysis (CUA):

This is a method for comparing treatment alternatives that integrates patient preferences. CUA can compare cost, quality, and the quantity of patient-years. Cost is measured in dollars, and therapeutic outcome is measured in patient-weighted utilities rather than in physical units. Often the utility measurement used is a quality-adjusted life year (QALY) gained. QALY is a common measure of health status used in CUA, combining morbidity and mortality data.

Results of CUA are also expressed in a ratio, a cost-utility ratio (C:U ratio). Most often this ratio is translated as the cost per QALY gained or some other health-state utility measurement. The preferred treatment alternative is that with the lowest cost per QALY (or other health-status utility).

CUA is the most appropriate method to use when comparing programs and treatment alternatives that are life extending with serious side effects (e.g., cancer chemotherapy), those which produce reductions in morbidity rather than mortality (e.g., medical treatment of arthritis). CUA can be limited in scope of application from a hospital.

Applications of Pharmacoeconomics:

Healthcare practitioners, regardless of practice setting, can benefit from applying the principles and methods of Pharmacoeconomics to their daily practice settings.  Today's practitioners increasingly are required to justify the value of the products and services they provide. Applied Pharmacoeconomics can provide the means or tools for this valuation.

One of the primary applications of pharmacoeconomics in clinical practice today is to aid clinical and policy decision making. Through the appropriate application of pharmacoeconomics, practitioners and administrators can make better, more informed decisions regarding the products and services they provide. Complete pharmacotherapy decisions should contain assessments of three basic outcome areas whenever appropriate: economic, clinical, and humanistic outcomes (ECHO). Traditionally, most drug therapy decisions were based solely on the clinical outcomes (e.g., safety and efficacy) associated with a treatment alternative. Over the past 20 years, it has become quite popular also to include an assessment of the economic outcomes associated with a treatment alternative.

Pharmacoeconomic data can be a powerful tool to support various clinical decisions, ranging from the level of the patient to the level of an entire healthcare system.  shows various decisions that can be supported using pharmacoeconomics, including effective formulary management, individual patient treatment, medication policy, and resource allocation.

Need of Pharmacoeconomics:

1.     Rising health expenditures have led to the necessity to find the optimal therapy at the lowest price. Pharmacoeconomics is an innovative method that aims to decrease health expenditures, whilst optimising healthcare results.

2.     Pharmaceutical expenditures, which constitute a large part of healthcare expenditures, have been increasing much faster than total healthcare expenditures.

3.     Numerous drug alternatives and empowered consumers also fuel the need for economic evaluations of pharmaceutical products.

4.     The increasing cost of healthcare products and services has become a great concern for patients, healthcare professionals, insurers, politicians and the public.

5.     This increasing concern has prompted demand for the use of economic evaluations of alternative healthcare outcomes. This escalation in healthcare spending is due to increased life expectancy, increased technology, increased expectations, increased standards of living and an increased demand in healthcare quality and services.

6.     Healthcare resources are not easily accessible and affordable to many patients, therefore pharmacoeconomic evaluations play an important role in the allocation of these resources.