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Editors Selection IGR 13-1

Pharmacoeconomics: Changing Therapies Costs More

Comment by Rupert Bourne on:

74248 Changing Initial Glaucoma Medical Therapy Increases Healthcare Resource Utilization, Trese MGJ; Lewis AW; Blachley TS et al., Journal of Ocular Pharmacology and Therapeutics, 2017; 33: 591-597

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Large claims databases offer the opportunity to generate large datasets of information on practice patterns. This paper involves a claims based model to evaluate how frequently 15,000 USA-based patients with newly diagnosed Open Angle Glaucoma (OAG) or Ocular Hypertension (OHT) started on either a topical beta-blocker (BB) or prostaglandin analogue (PGA) underwent a change in their index therapy within 12 months of starting the medication, and the costs incurred to the healthcare provider (between 2001-2012). To be included, the enrollee had to have been continuously enrolled in the plan for 3 years before the patient's index prescription (what the authors called the 'look back period') of either a topical BB (19% of enrollees) or PGA (81%). The eligible enrollees were then divided into 2 groups based upon their index prescription (topical BB or topical PGA). The outcome measure within these 2 groups was a change in the index therapy (addition of another class of ocular hypotensive, a change in medication, cessation of all therapy, or glaucoma surgery) within 12 months after starting either of these medications. Multivariable logistic regression analysis was used to explore whether the odds of continuing initial treatment were associated with age, sex, race/ ethnicity, education level, household income, or region of residence. A higher proportion of those started on BBs (39%) had a change in therapy within 12 months of initiating therapy compared to the PGA group (29%). Those requiring a change in therapy were understandably seen more often and accrued more in median charges than those who did not require a change in their initial therapy. Older patients and those with higher income were less likely to have their initial therapy changed. Latinos and those of African-American race/ethnicity were associated with a higher likelihood of a medication change. Clearly changing medications and more frequent office visits are major contributors to the annual cost of treatment for glaucoma patients, yet the absence of clinical information in this study makes any attempt to explain the difference in findings between these two treatment groups entirely speculative. It is also impossible to really draw conclusions regarding the reasons why lower income groups and certain racial groups were at higher risk of a medication change as the reason for the change is unknown. As we know there could be a myriad of reasons ranging from practice policy, adverse events, advent of evidence that favoured PGAs, and use of generics, all of which may have changed considerably over an 11 year time period. Or indeed a 'trial-and-error approach' which the authors conclude to be the reason why a substantial proportion of patients require a change in therapy within a year. To my mind, this is a good example of how analysis of 'big data' can at first glance look attractive with the lure of thousands of patient datasets, yet the absence of key variables, in this case, clinical information, makes it very difficult to derive meaningful outcomes.

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