Impact of clinical trials on the adoption of new drugs within a university hospital

Denig, Petra; Haaijer-Ruskamp, F. M.; Wesseling, H; Versluis, A.

doi:10.1007/bf00314961

Cited by 19 publications

(12 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All four items also compose a distinctive factor "Effi cacy of the drug" revealed in factor analysis, which explains 14.9% of the variation. We could link effi cacy and safety to the fi ndings of previous research on prescribing [1,3,9,11,12]. However, the results of our study show that even when we expected socially desirable answers in favor of scientifi c-medical reasons infl uencing doctors' decisions, the answers encompassing hard scientifi c infl uences explained a far smaller proportion of the variation than did other factors (Table 4).…”

Section: Discussioncontrasting

confidence: 71%

See 1 more Smart Citation

Explanatory model of prescribing behavior in prescription of statins in family practice

Tušek-Bunc

Kersnik

Petek-Ster

et al. 2010

Wien Klin Wochenschr

View full text Add to dashboard Cite

The determinants that influence statin prescribing behavior among GPs in our study covered an array of explanatory items consistent with proposals in the literature but factors differ to some extent from proposed theoretical models. The explanatory model explained a high proportion of the variation in deciding on a particular statin. Efficacy and safety remain important factors in selection of an appropriate drug but are far from being the most or only important factors.

show abstract

Section: Discussioncontrasting

confidence: 71%

“…Among the key infl uencing factors are those that are medical-evidence based; clinical trials can lower the barriers for adoption of new drugs [12]. Doctors are often infl uenced by scientifi c conferences [13], and journal articles have been reported to be one of the most important factors infl uencing prescribing habits [14].…”

Section: Introductionmentioning

confidence: 99%

Explanatory model of prescribing behavior in prescription of statins in family practice

Tušek-Bunc

Kersnik

Petek-Ster

et al. 2010

Wien Klin Wochenschr

View full text Add to dashboard Cite

show abstract

“…Not surprisingly, newly marketed drugs and``me-too'' drugs have been identi®ed as indicators. Both registered and o -registered prescribing of newly marketed, often semi-innovative, (proprietary) products have shown to be associated with prescribers' interaction with pharmaceutical companies and participation in clinical trials [53,55,56,57,58,59,60]. The identi®cation of preadmission drug therapy as an indicator is important, because it supports the observation that prescribing in secondary health care is strongly linked with prescribing in primary health care [61].…”

Section: Discussionmentioning

confidence: 74%

Assessment of indicators for hospital drug formulary non-adherence

Fijn

Lenderink

Egberts

et al. 2001

Eur J Clin Pharmacol

View full text Add to dashboard Cite

Background: Translation of rational drug therapy into practice remains an international problem. Although pharmacotherapeutic treatment guidelines (PTGs) as managerial tools are favoured over hospital drug formularies (HDFs), the latter are still applied in most hospitals. HDF enforcement often leads to timeconsuming consultation from the perspective of both pharmacy sta and prescriber. So far, research on HDFs has only been conducted outside Europe. Moreover, this research has only been descriptive. Straightforward indicators qualitatively characterising HDF non-adherence have never been assessed. Methods: A retrospective 1:1 case-control study was conducted across three general teaching hospitals. Non-HDF requests were compared with HDF requests. Data were multivariably analysed, considering patient, prescriber, drug, and HDF characteristics as possible indicators for non-adherence. Results: HDF adherence was almost universal across characteristics. Non-adherence was characterised by newly marketed drugs, drugs that were part of patients' pre-admission drug therapy, drugs with many fellow drugs within the drug group on the market, and drugs originating from a drug group for which the HDF was highly restrictive. Contrary to common perception, nonadherence was independent of medical specialty, therapeutic area, and patient characteristics. Conclusion: This research provides an epidemiological framework for hospitals (drug and therapeutics committees) for evaluating pharmacy data on HDF nonadherence. It can be used for educational tailor-made feedback to prescribers and for drug selection when the inclusion of newly marketed drugs is considered or HDF restrictiveness for certain drug groups is reconsidered. Moreover, it demonstrates the importance of a regional approach involving secondary and primary health care to establish continuity in seamless care of drug therapy.

show abstract

“…Bunavail use was concentrated in Tennessee likely because of its preferred status in the Medicaid formulary . Some high‐dispensing sites for new products corresponded to Phase 3 clinical trial sites, a phenomenon previously described …”

Section: Discussionmentioning

confidence: 87%

Causal inference for evaluating prescription opioid abuse using trend‐in‐trend design

Dasgupta

Schwarz

Hennessy

et al. 2019

Pharmacoepidemiology and Drug

View full text Add to dashboard Cite

Purpose One response to the opioid crisis in the United States has been the development of opioid analgesics with properties intended to reduce non‐oral use. Previous evaluations of abuse in the community have relied on population averaged interrupted time series Poisson models with utilization offsets. However, competing interventions and secular trends complicate interpretation of time‐series analyses. An alternative research design, trend‐in‐trend, accounts for heterogeneity in per capita opioid dispensing and unmeasured time‐varying confounding, which provides a causal evaluation, provided that underlying assumptions are met. Methods Trend‐in‐trend can be modeled using a logistic regression framework. In logistic regression, exposure was any product‐specific outpatient dispensing by three‐digit ZIP code and calendar quarter, for 22 opioids. The outcome was any product‐specific abuse case ascertained from poison centers and drug treatment programs, covering 94% of the US population, between July 2009 and December 2016. Product‐specific odds ratios compared places without dispensing with places with any dispensing; the causal contrast represents the odds of product‐specific abuse in the community given exposure. Results Dispensing of new and low‐volume opioids varied considerably across the country, with no region showing high of all products. Of 22 opioids analyzed, the three with approved labeling as intended to deter abuse ranked near the lowest in both absolute (population‐adjusted rates: 1.7, 0.9, and 8.2 per million people per quarter, respectively) and relative measures (trend‐in‐trend ORs: 1.96, 1.79, 1.69, respectively). Conclusions Postmarketing studies of prescription opioid abuse may benefit by evolving from unadjusted surveillance rates to a causal inference approach.

show abstract

Impact of clinical trials on the adoption of new drugs within a university hospital

Cited by 19 publications

References 2 publications

Explanatory model of prescribing behavior in prescription of statins in family practice

Explanatory model of prescribing behavior in prescription of statins in family practice

Assessment of indicators for hospital drug formulary non-adherence

Causal inference for evaluating prescription opioid abuse using trend‐in‐trend design

Contact Info

Product

Resources

About