Methods of effectiveness research: Intro

I'm currently in the last semester of my Master's in Economics degree and am completing an independent study related to types of Economic analysis with respect to health care. Evidence-based medicine (EBM) is an "it" topic when discussing health care reform, or health care policy. While I'm not directly studying EBM this summer-- my topic is related as effectiveness research not only looks at which treatments are effective (like EBM), but also looks into quantifying and valuing the costs associated with the treatments--and attempting to make decisions related to which treatments/ procedures are worth their costs.

I'm looking at four types of analysis-- Cost of Illness studies, Cost-Benefit analysis, Cost-Effectiveness analysis, and Cost-Utility analysis. I'm also examining issues and practices that arise in the practice of these types of analysis--such as sensitivity analysis, discounting, and challenges associated with data collection and data interpretation.

Many of these topics have recently received a lot of attention from medical and academic communities--especially cost-effectiveness research. To understand why this has become so popular you have to understand where this type of research has come from. Cost-Benefit analysis is largely considered to be the gold standard form of analysis as far as Economists and other professionals are concerned. It's a very simple concept-- you subtract the benefits from the costs (in a common unit-- like dollars) and if the result is positive (ie: benefits > costs) then the decision is considered to be favorable. We do this everyday without noticing. For example, do the added calories from an extra Oreo outweigh how happy eating the extra Oreo is going to make you? If so, you will likely choose to put down the Oreo because the net benefit is going to be negative.

As far as health care is concerned, do the benefits of a twice yearly pap smear outweigh the costs associated with going to the OB-GYN for a pap? Maybe--maybe not. It depends on who you are (do you have a history of cervical cancer? how old are you? have you ever had an irregular pap?). What are the costs of going to the OB-GYN for a pap? Obviously the cost you pay to the office is a cost-- but what about what the insurance company pays? What about your lost productivity or wages at work due to the office visit? What about the emotional stress of receiving a pap twice a year? These are the types of problem you run into with Cost-Benefit analysis when you start trying to analyze these types of procedures. It's difficult to quantify costs-- but even more difficult to quantify benefits (ie: how much money is it worth to prevent cervical cancer?) For this reason, there has been a movement towards Cost-effectiveness analysis.

Cost-effectiveness analysis tries to get around the issue of having to put a dollar amount on benefits. Rather, benefits are measured in another form-- such as the number of cases of cervical cancer reduced due to an increase in screenings, or days of regular blood sugar when taking a new Diabetes medicine, or the % reduction in the size of tumor due to a new cancer treatment. Once the benefits are quantified in this form you can take the cost (which, granted, is still hard to quantify completely) and divide it by the measured benefits (ie: the number of cervical cancer cases avoided). This gives you what is known as an ICER-- incremental cost effectiveness ratio. When other ICERs are calculated (using the same units and same measures) they can be compared.

Cost-utility analysis, which is a form of cost-effectivenss analysis-- an is often just called cost-effectiveness analysis, takes the analysis one step further. Cost-utility analysis uses QALYs (quality adjusted life years) to value benefits rather than trying to directly measure an outcome.

Simply put:

As you can see, the numerator stays the same in all forms, but the denominator changes. Often times people prefer to avoid Cost-benefit analysis because of the difficulties associated with valuing benefits. Cost-effectiveness analysis and the ICER are useful, but sometimes ICERs can not be compared if the denominator isn't being measured in the same way (ie: for a Diabetes treatment: one researcher may measure days within a range of healthy blood sugar levels for a non-Diabetic person, while another researcher may measure days within a healthy blood sugar range for people are are healthy, or pre-Diabetic.) Another benefit associated with using QALY's is that QALYs take into account, not only mortality avoided, but also morbidity avoided due to a treatment. Researchers often like QALYs because of their ability to account for the increased quality of life people achieve after going through a treatment--something not accounted for when measuring a specific health outcome like days of healthy blood sugar. QALY's are, of course, not without their own problems as is described in "Cost-Utility anaysis: Use QALY's only with great caution". In this article, McGregor describes how mis-measuring (or not understanding what is being measured ) with regard to QALY's can limit how powerful the explanatory or comparative power of results of a Cost-Utility Analysis are.

So why does any of this even matter? It matters because no one is able to spend infinite dollars on health care. When there are scare resources, decisions have to be made. Using these forms of analysis we can make more informed decisions--which desperately needed by the federal and state governments as more and more of their money is going towards health care expenditures. We need to make purchasing health care more like purchasing a refrigerator. If you were going to purchase a fridge, you would research the models you were interested and determine which bells and whistles you wanted, and then research how much each model cost in one present day currency (as in 2010 dollars). From there you'd pick which one best fit your needs and your budget. We need to approach health care the same way-- determine what the costs are and which benefits we desire-- and then weigh each one in a standardized way to determine which treatments and procedures we want to (and can feasibly) support.