Quantifying morbidity associated with the abuse and misuse of opioid analgesics: A comparison of two approaches

Smith, Meredith Y.; Schneider, Michael; Wentz, Alicia; Hughes, Alice; Haddox, J. David; Dart, Richard C.

doi:10.1080/15563650600956170

Cited by 18 publications

(14 citation statements)

References 12 publications

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“…Each filling of a drug prescription in a given quarter is counted as a single URDD, regardless of the number of prescriptions received during that time. The use of URDD to calculate rates has been described elsewhere 12 , 13 . Drug diversion and intentional exposure rates per population and per URDD were calculated for extended-release amphetamines and extended-release oral methylphenidate using case counts from the Drug Diversion and Poison Center Programs, respectively.…”

Section: Methodsmentioning

confidence: 99%

Surveillance of Diversion and Nonmedical Use of Extended-Release Prescription Amphetamine and Oral Methylphenidate in the United States

Sembower¹,

Ertischek²,

Chloe

et al. 2013

Journal of Addictive Diseases

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This article examines rates of nonmedical use and diversion of extended-release amphetamine and extended-release oral methylphenidate in the United States. Prescription dispensing data were sourced from retail pharmacies. Nonmedical use data were collected from the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS) System Drug Diversion Program and Poison Center Program. Drug diversion trends nearly overlapped for extended-release amphetamine and extended-release oral methylphenidate. Calls to poison centers were generally similar; however, calls regarding extended-release amphetamine trended slightly lower than those for extended-release oral methylphenidate. Data suggest similar diversion and poison center call rates for extended-release amphetamine and extended-release oral methylphenidate.

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Section: Methodsmentioning

confidence: 99%

Surveillance of Diversion and Nonmedical Use of Extended-Release Prescription Amphetamine and Oral Methylphenidate in the United States

Sembower¹,

Ertischek²,

Chloe

et al. 2013

Journal of Addictive Diseases

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“…Rates per 100,000 persons provide a measure of the overall public health significance. The use of URDDs to calculate rates has been described elsewhere [56]. Briefly, URDDs are a person–time denominator representing a proprietary estimate of the number of unique individuals being dispensed a prescription for the medication, in a given time and place, based on pharmacy payment information.…”

Section: Methodsmentioning

confidence: 99%

“…URDDs are also used instead of the number of prescriptions dispensed since the drugs are in different Schedules as dictated by the Controlled Substances Act and therefore subject to different rules regarding refills and length of prescriptions that may be written, in addition to being subject to specific law and regulations pertaining to their use in the management of opioid dependence in the United States [57–59]. URDD is a reasonable proxy for the medical availability of an opioid analgesic within a community, and rates per 1,000 URDD provide an indirect measure of exposure based on a product's medical availability [56]. Rate ratios are presented, which are ratios of the URDD rate for methadone divided by the URDD rate for buprenorphine.…”

Section: Methodsmentioning

confidence: 99%

Post-marketing Surveillance of Methadone and Buprenorphine in the United States

et al. 2010

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“…Rates have been traditionally generated as an "abuse ratio": for a given location, numerator counts of product-specific abuse cases are divided either by the general population to standardize across location, or by drug availability to standardize across drugs. [44][45][46] We first tested the assumption of a linear relationship between dispensing and abuse inherent to utilization-adjusted abuse ratios (Appendix Section S4). Trend-in-trend 47 allowed us to simultaneously account for dispensing variation and time-varying confounding, without making the aforementioned assumption of linearity.…”

Section: Potential Confoundersmentioning

confidence: 99%

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

Dasgupta

Schwarz

Hennessy

et al. 2019

Pharmacoepidemiology and Drug

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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.

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Quantifying morbidity associated with the abuse and misuse of opioid analgesics: A comparison of two approaches

Cited by 18 publications

References 12 publications

Surveillance of Diversion and Nonmedical Use of Extended-Release Prescription Amphetamine and Oral Methylphenidate in the United States

Surveillance of Diversion and Nonmedical Use of Extended-Release Prescription Amphetamine and Oral Methylphenidate in the United States

Post-marketing Surveillance of Methadone and Buprenorphine in the United States

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

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