Do new prescription drugs pay for themselves?

Duggan, Mark

doi:10.1016/j.jhealeco.2004.08.001

Cited by 106 publications

(17 citation statements)

References 25 publications

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“…An appropriate instrumental variable is one that strongly predicts whether or not someone receives the treatment of interest, but that does not directly affect outcomes. 25 A common example in pharmacoepidemiology is to use prescribers’ prescribing preference as an instrument for drugs received 7 ; the idea is that which prescriber a given patient happens to see is somewhat random and likely affects which drug an individual is first prescribed (e.g., a particular provider may happen to prescribe Cox-2 inhibitors rather than nonselective nonsteroidal anti-inflammatory drugs (NSAID’s) for pain control), but (the argument is) that provider’s prescribing preference likely does not affect patients’ outcomes directly. 30,31 Another common instrumental variable used is distance, for example estimating the effect of cardiac catheterization using distance to a cardiac catheterization-providing hospital as an instrument for that procedure (under the argument that the distance does not affect outcomes directly, except through whether or not someone gets cardiac catheterization).…”

Section: Causal Inference Frameworkmentioning

confidence: 99%

Estimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions

Stuart¹,

DuGoff²,

Abrams

et al. 2013

eGEMs

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Electronic health data sets, including electronic health records (EHR) and other administrative databases, are rich data sources that have the potential to help answer important questions about the effects of clinical interventions as well as policy changes. However, analyses using such data are almost always non-experimental, leading to concerns that those who receive a particular intervention are likely different from those who do not in ways that may confound the effects of interest. This paper outlines the challenges in estimating causal effects using electronic health data and offers some solutions, with particular attention paid to propensity score methods that help ensure comparisons between similar groups. The methods are illustrated with a case study describing the design of a study using Medicare and Medicaid administrative data to estimate the effect of the Medicare Part D prescription drug program on individuals with serious mental illness.

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Section: Causal Inference Frameworkmentioning

confidence: 99%

Estimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions

Stuart¹,

DuGoff²,

Abrams

et al. 2013

eGEMs

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“…Newly introduced drugs are substantially more expensive than the older alternatives and have contributed to both rising health care costs, as well as pharmaceutical revenues (Duggan, 2005). However, the majority of new drugs developed by pharmaceutical companies are minor variations on existing medications that offer few or no benefits over existing alternatives but often produce significant adverse reactions (Light and Lexchin, 2012).…”

Section: Introductionmentioning

confidence: 99%

Gifts and influence: Conflict of interest policies and prescribing of psychotropic medications in the United States

King

Bearman

2017

Social Science & Medicine

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The pharmaceutical industry spends roughly 15 billion dollars annually on detailing – providing gifts, information, samples, trips, honoraria and other inducements – to physicians in order to encourage them to prescribe their drugs. In response, several states in the United States adopted policies that restrict detailing. Some states banned gifts from pharmaceutical companies to doctors, other states simply required physicians to disclose the gifts they receive, while most states allowed unrestricted detailing. We exploit this geographic variation to examine the relationship between gift regulation and the diffusion of four newly marketed medications. Using a dataset that captures 189 million psychotropic prescriptions written between 2005 and 2009, we find that uptake of new costly medications was significantly lower in states with marketing regulation than in areas that allowed unrestricted pharmaceutical marketing. In states with gift bans, we observed reductions in market shares ranging from 39% to 83%. Policies banning or restricting gifts were associated with the largest reductions in uptake. Disclosure policies were associated with a significantly smaller reduction in prescribing than gift bans and gift restrictions. In states that ban gift-giving, peer influence substituted for pharmaceutical detailing when a relatively beneficial drug came to market and provided a less biased channel for physicians to learn about new medications. Our work suggests that policies banning or limiting gifts from pharmaceutical representatives to doctors are likely to be more effective than disclosure policies alone.

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“…While drugs for individuals in capitated Medicaid programs are not included in the dependent variables, the percent of the Medicaid enrollees in capitated programs is included as an additional covariate to reflect changes in prescribing practices due to managed care that may have spilled over to FFS Medicaid patients (Baker 1999; Domino 2012), with a stronger “signal” from managed care expected in states/years that have a higher level of capitation. It should be noted that there is not a strong evidence base for the greater cost-effectiveness of newer psychotropic medications; their costs are higher and they have not been found to deter other types of medical or psychiatric use (Duggan 2005; Rosenheck et al 2006) to compensate for these greater costs. We therefore might not necessarily expect greater use of newer medications to be desirable to capitated plans, since the overall health care costs are not expected to decrease.…”

Section: Datamentioning

confidence: 99%

State investments in psychiatric innovation: investigating unmeasured state factors

Domino

Beadles

2014

Health Serv Outcomes Res Method

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We apply three separate panel data estimation methods to examine the diffusion of technologies at the state-level. These methods include the Hausman-Taylor random effects model, the fixed effects vector decomposition (FEVD), and generalized estimating equations (GEE). We discuss the assumptions required of each and assess the stability of our policy results across the three models for a longitudinal study of the diffusion of newer psychotropic technologies. We find a reasonable level of consistency among marginal effects for time varying independent variables between our three estimation methods but some discrepancy in the estimated measure of precision in our empirical application. We find a number of policy conclusions are quite stable across estimation methods and may be of interest to state-level mental health policy decision makers.

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Do new prescription drugs pay for themselves?

Cited by 106 publications

References 25 publications

Estimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions

Estimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions

Gifts and influence: Conflict of interest policies and prescribing of psychotropic medications in the United States

State investments in psychiatric innovation: investigating unmeasured state factors

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