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International Journal of Health Economics and Management

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18.03.2019 | Research article

Who cares about a label? The effect of pediatric labeling changes on prescription drug utilization

verfasst von: Christopher Ody, Matt Schmitt

Erschienen in: International Journal of Health Economics and Management | Ausgabe 3-4/2019

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Abstract

Off-label drug use is common, particularly in pediatric populations. In response, legislation requires and/or provides financial incentives for drug manufacturers to perform pediatric clinical trials. Using New Hampshire’s all-payer claims database, we examine the impact of subsequent changes to drug labeling on pediatric drug utilization. To separate changes in utilization induced by labeling changes from other temporal factors, we estimate difference-in-differences models that compare utilization trends for pediatric patients to those of adults. We estimate that establishing safety and efficacy increases a drug’s market share by (a statistically significant) 2.8 percentage points, whereas failure to do so decreases a drug’s market share by (a statistically insignificant) 0.9 percentage points. We then interpret these estimates within the context of public and market incentives to conduct pediatric clinical trials.

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We use the terminology “indication” to describe the specific disease and patient population for which a drug is intended to be used. FDA drug approvals are often for relatively specific populations, and may therefore exclude certain age ranges and/or sexes, pregnant women, women who are breastfeeding, and patients with certain comorbidities. Any alterations in the use of a drug from its FDA-approved use constitute off-label use.

There is an ongoing debate about the appropriateness of widespread off-label drug use. For recent research comparing the efficacy of FDA-approved and off-label therapies, see, e.g., Ladanie et al. (2018).

See, e.g., Addressing the Barriers to Pediatric Drug Development: Workshop Summary, Institute of Medicine 2008.

Instead of examining utilization, an alternative approach to studying the value of pediatric testing is to explore whether pediatric clinical trials affect the frequency with which adverse outcomes are reported in children. However, as the reporting of adverse outcomes is relatively uncommon and children often represent a small patient population, available adverse outcomes data may be inadequate for that purpose (see the “Appendix” for more details on the availability of adverse event data for our specific sample of drugs). Instead, this study relies upon revealed preferences, which should incorporate, for example, changes in physician prescribing behavior resulting from anticipated changes in the likelihood of adverse events.

For a more complete discussion of the relevant legislation and its historical precedents, see, e.g., Field et al. (2012).

The data is publicly available online at the FDA’s website. (https://www.accessdata.fda.gov/scripts/sda/sdNavigation.cfm?sd=labelingdatabase).

For a small number of drugs, the labeling change specifies both positive and negative changes. For instance, the drug may have been established to be safe and effective for pediatric patients aged 12–17 but ineffective for younger patients.

For the handful of drugs with multiple labeling changes during the sample period, we identify any pediatric ages that were affected by only one labeling change during the period, excluding ages potentially affected by multiple changes.

In the final sample, there are 9 drugs whose labeling changes were attributed to BPCA, 12 to PREA, and 12 to both. Drugs under PREA enter the sample since the FDA often approves drugs for adult use while allowing manufacturers additional time to complete pediatric trials (GAO 2011). Drugs can fall under both PREA and BPCA when manufacturers performed clinical trials beyond the requirements of PREA, such as testing indications relevant to pediatric populations but which are not approved in adults (GAO 2011).

To explore whether pediatric labeling changes result in market expansion/contraction—as opposed to only shifting shares—we have also estimated models with market size as the dependent variable. The estimated effects of pediatric labeling changes on market size are not statistically significant either for positive labeling changes or negative labeling changes.

An implicit assumption of this approach is that pediatric labeling changes do not affect adult utilization. This assumption would be violated if, for instance, safety concerns from pediatric clinical trials spill over to adults (see, e.g., Busch and Barry 2009). To support the assumption that adults are largely unaffected in our sample, we have estimated models that compare adult market shares before and after pediatric labeling changes. We estimate small and statistically insignificant effects. We have also estimated our difference-in-differences models excluding negative labeling changes affecting adolescents, as such changes may be more likely to spill over to adults. The estimates are essentially unchanged.

Despite this limitation, we have also estimated models including one additional lead year ($k=-3$) and one additional lag year ($k=3$). There is no substantial difference in the results.

The results are similar using an alternative approach that drops drugs for which 75% or more of a drug’s pediatric and/or adult observations have a share of less than 1%. With that alternative approach, the coefficient on positive labeling changes is 0.122 (with a p value of 0.025) and the coefficient on negative labeling changes is $-0.005$ (with a p value of 0.898).

We do not examine negative labeling changes in an analogous way because (i) the sample size is half as large and (ii) it is less clear how to split the sample. For example, one could imagine different effects for drugs that were the first in the market to be tested, those where other drugs in the market also have negative pediatric results, those where at least one other drug has positive pediatric results, etc.

One benefit of this approach is that multiple labeling changes among competing drugs are straightforwardly incorporated into the model. If one drug in a market experiences a labeling change, the market share of competing drugs will change in turn via the logit formula [Eq. (4)].

Similar arguments have recently been raised about Orphan Drug Incentives: e.g., “there are a number of examples of drugs approved solely for orphan indications that generate sales in excess of $1 billion annually... [calling] into question the underlying premises of the incentives: that there is no viable commercial market to treat rare diseases.” Health Affairs Health Policy Brief: Pricing Orphan Drugs, July 2017. For academic research examining the impact of the Orphan Drug Act on innovation, see, e.g., Yin (2008) and Yin (2009).

An implicit assumption of this approach is that positive labeling changes do not expand the market. In models with market size as the dependent variable, we do not find clear evidence of changes in market size following labeling changes.

According to the American Community Survey between 2008 and 2013, 0.277% of the US population under the age of 18 was privately insured in New Hampshire, yielding a multiplier of about $1/0.00277=361$.

The prices we measure are gross of (unobserved) rebates from manufacturers to payers, and thus are likely greater than what manufacturers actually receive.

The estimates in Li et al. (2007) are in 2005 dollars (minimum $5.3 million; maximum $49.6 million; average $20.2 million), which we convert to 2013 dollars using the CPI.

This expression only includes additional spending in the adult population. Incorporating pediatric spending into the calculation is not straightforward because labeling changes could either increase or decrease pediatric spending, depending on the prices of the drugs being substituted to/away from. To simplify, we assume no impact on pediatric spending.

Consistent with the uptick in pediatric clinical trials post-BPCA, adding a term reflecting pediatric exclusivity to the calculations in “Manufacturer incentives to conduct pediatric trials” section [a term like Eq. (5)] indicates that the financial benefits associated with extended exclusivity often outweigh the costs of pediatric clinical trials.

In cases where a drug is covered by multiple patents, there is some uncertainty about what the appropriate date of patent expiration is for the purpose of the calculation. We rely on industry reports that state the expected timing of generic entry.

Results from the literature often differ by the sample of drugs, number of generic entrants, and time since generic entry. For instance, Frank and Salkever (1997) find that generic prices are 50% of the brand’s price at launch with 4 generic entrants and 30% with 12 or more generic entrants. Berndt and Aitken (2011) find that the average generic price relative to the brand price at the time of generic entry is 50% 1 year after generic entry and 23% 2 years after generic entry. We chose $\lambda =0.3$ to be close to the middle of these estimates from the literature.

As with the pediatric population, this scaling is calculated using the American Community Survey. According to that data, 0.337% of US adults are privately insured in New Hampshire, yielding a multiplier of about $1/0.00337=296$.

For simplicity, we generate a common estimate for both positive and negative labeling changes by imposing that they have opposite-signed effects of the same magnitude and then re-estimating Eq. (1). The resulting estimate indicates a 2.0 percentage point effect of labeling changes (statistically significant at the 1% level).

For this analysis, we restrict the calculations to the 21 drugs in the final sample whose labeling changes the FDA attributed at least partially to BPCA.

https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/default.htm.

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Titel: Who cares about a label? The effect of pediatric labeling changes on prescription drug utilization
verfasst von: Christopher Ody
Matt Schmitt
Publikationsdatum: 18.03.2019
Verlag: Springer US
Erschienen in: International Journal of Health Economics and Management / Ausgabe 3-4/2019
Print ISSN: 2199-9023
Elektronische ISSN: 2199-9031
DOI: https://doi.org/10.1007/s10754-019-09265-y

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Who cares about a label? The effect of pediatric labeling changes on prescription drug utilization

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