#PharmEconFriday: Cost-of-Illness (COI)

COII recently had the opportunity to work with a couple amazing economists on a little project investigating whether the methods used in a study to calculate the cost-of-illness (COI) had any sort of impact on what type of journal would publish the results.1  We found some evidence that researchers choosing to use more complex methods happen to publish in very high impact journals, despite the majority of COI studies published used simpler methods.  Today’s post will provide a little background on COI and why we felt the need to look into this topic (besides the fact that it served as a great learning experience for me as I work through this PhD program).

What is COI?

Cost-of-Illness research describes the work economists do to describe and estimate the costs that typically come with specific diseases thanks to the methodological framework published by Dr. Dorothy Rice almost 50 years ago.2  On the surface, this may sound rather simple and self-explanatory, but once you start digging into measuring costs from different data sets you will soon realize that determining a good estimate for COI can be quite challenging.  Dr. Ebere Onukwugha, here at the University of Maryland, has published two interesting systematic reviews of COI studies and does a fantastic job describing the different methods observed in this type of research (Her work on this database of COI literature served as the foundation for our secondary analysis).3,4

Different Ways to Calculate COI

Sum All

The first way researchers could report a COI is through identifying all patients with a specific diagnosis (ie: all Type II diabetics) in a population and simply add all of their direct medical and non-medical costs for the year.  Using this method, it is pretty likely that we will overestimate the true COI because we include costs that may be completely unrelated (Example: a diabetic patient with HIV will have significant costs related to HIV that clouds our calculation for diabetes).  While this method sits on the simple end of the method spectrum, it may be appropriate if really want to know all related and unrelated costs that are impacting your organization.  It is also conceptually easy to understand for the reader.

Sum Diagnosis Specific

This approach was the most frequent method found by Dr. Onukwugha’s team and simply takes the sum all method a little further by only adding up the costs directly related to the disease of interest.4  So for our diabetic patient with HIV, we would only focus on the claims for diabetic medications, supplies, or visits where diabetes was the chief complaint.  This method gets us a bit closer to the likely true value of COI for a disease and I’m guessing researchers like this one because it is pretty straightforward and easy to explain to the public.  However, this approach doesn’t account for other variables that may throw a wrench into our calculation (socioeconomic characteristics, geography, disease severity, etc.).


Matched studies are those where you find a group of patients with the disease of interest along with a group of patients that are very similar (age, race, education level, etc.) and compare costs for both groups.  This incremental approach started helping you adjust for those patient-level differences.


Are you ready to nerd out?  Then step right up to multivariate regression analysis where statistics junkies get their fix by developing models that estimate the incremental cost for the disease while adjusting for those darn patient-level variables.  Onukwugha’s team also found a handful of new approaches to determine an incremental or average cost of disease that they lumped into “Other Incremental” and “Other Average” groups, but I’ll let you read her articles if you want to know more about those.3,4


Given all the challenges with finding the right methodological approach to estimating a COI, there have been a few researchers call to an end of the COI citing lack of usefulness and the wide variability that can be found when two research teams calculate the COI for the same disease using very different methods.5–7  While I do enjoy watching economists duke it out over research, the general public probably doesn’t understand (or care) whether or not journals publish COI studies.  However, if you have a disease that isn’t getting a lot of attention or research around treatment you may want an economist to demonstrate why treating your disease can be good from a financial standpoint (since payers and elected officials have budgets to consider).

Final Thoughts

Our paper wasn’t advocating for more or less COI research, but hoped to add to the discussion by showing where different methods are published (as researchers do care about things like journal impact factor or prestige).  Also, as educators, it is important that we help explain research in a way that a broad audience can understand (a major reason why I do this blog) to hopefully improve decision-making and advance care for patients.



  1. Mattingly II TJ, Mullins CD, Onukwugha E. Publication of Cost-of-Illness Studies: Does Methodological Complexity Matter? Pharmacoeconomics. 2016;Online ahe:10-13. doi:10.1007/s40273-016-0438-4.
  2. Rice DP. Estimating the cost of illness. Am J Public Health. 1967;57(3):424-440. doi:10.2105/AJPH.57.3.424.
  3. Akobundu E, Ju J, Blatt L, Mullins CD. Cost-of-illness studies : a review of current methods. Pharmacoeconomics. 2006;24(9):869-890.
  4. Onukwugha E, McRae J, Kravetz A, Varga S, Khairnar R, Mullins CD. Cost-of-Illness Studies: An Updated Review of Current Methods. Pharmacoeconomics. 2015;34(1):43-58. doi:10.1007/s40273-015-0325-4.
  5. Bloom BS, de Pouvourville N, Straus WL. Cost of Illness of Alzheimer’s Disease: How Useful Are Current Estimates? Gerontologist. 2003;43(2):158-164. doi:10.1093/geront/43.2.158.
  6. Bloom BS, Bruno DJ, Maman DY, Jayadevappa R. Usefulness of US cost-of-illness studies in healthcare decision making. Pharmacoeconomics. 2001;19(2):207-213. doi:10.2165/00019053-200119020-00007.
  7. Currie G, Kerfoot KD, Donaldson C, Macarthur C. Are cost of injury studies useful? Inj Prev. 2000;6(3):175-176. doi:10.1136/ip.6.3.175.
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Joey Mattingly, PharmD, MBA is an assistant professor at the University of Maryland School of Pharmacy located in Baltimore, Maryland. Joey has managed retail and long-term care pharmacy operations in Kentucky, Illinois and Indiana. Leading Over The Counter is a blog of Joey's views and opinions on the topics of pharmacy leadership and management and do not represent the University of Maryland, Baltimore. Joey can be followed on Twitter @joeymattingly.

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