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An Online Survey for Pharmacoepidemiological Investigation (Survey of Non-Medical Use of Prescription Drugs Program): Validation Study.

ABSTRACT: BACKGROUND:In rapidly changing fields such as the study of drug use, the need for accurate and timely data is paramount to properly inform policy and intervention decisions. Trends in drug use can change rapidly by month, and using study designs with flexible modules could present advantages. Timely data from online panels can inform proactive interventions against emerging trends, leading to a faster public response. However, threats to validity from using online panels must be addressed to create accurate estimates. OBJECTIVE:The objective of this study was to demonstrate a comprehensive methodological approach that optimizes a nonprobability, online opt-in sample to provide timely, accurate national estimates on prevalence of drug use. METHODS:The Survey of Non-Medical Use of Prescription Drugs Program from the Researched Abuse, Diversion and Addiction Related Surveillance (RADARS) System is an online, cross-sectional survey on drug use in the United States, and several best practices were implemented. To optimize final estimates, two best practices were investigated in detail: exclusion of respondents showing careless or improbable responding patterns and calibration of weights. The approach in this work was to cumulatively implement each method, which improved key estimates during the third quarter 2018 survey launch. Cutoffs for five exclusion criteria were tested. Using a series of benchmarks, average relative bias and changes in bias were calculated for 33 different weighting variable combinations. RESULTS:There were 148,274 invitations sent to panelists, with 40,021 who initiated the survey (26.99%). After eligibility assessment, 20.23% (29,998/148,274) of the completed questionnaires were available for analysis. A total of 0.52% (157/29,998) of respondents were excluded based on careless or improbable responses; however, these exclusions had larger impacts on lower volume drugs. Number of exclusions applied were negatively correlated to total dispensing volume by drug (Spearman ?=-.88, P<.001). A weighting scheme including three demographic and two health characteristics reduced average relative bias by 31.2%. After weighting, estimates of drug use decreased, reflecting a weighted sample that had healthier benchmarks than the unweighted sample. CONCLUSIONS:Our study illustrates a new approach to using nonprobability online panels to achieve national prevalence estimates for drug abuse. We were able to overcome challenges with using nonprobability internet samples, including misclassification due to improbable responses. Final drug use and health estimates demonstrated concurrent validity to national probability-based drug use and health surveys. Inclusion of multiple best practices cumulatively improved the estimates generated. This method can bridge the information gap when there is a need for prompt, accurate national data.

SUBMITTER: Black JC

PROVIDER: S-EPMC6914238 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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An Online Survey for Pharmacoepidemiological Investigation (Survey of Non-Medical Use of Prescription Drugs Program): Validation Study.

Black Joshua Curtis JC Rockhill Karilynn K Forber Alyssa A Amioka Elise E May K Patrick KP Haynes Colleen M CM Dasgupta Nabarun N Dart Richard C RC

Journal of medical Internet research 20191025 10

<h4>Background</h4>In rapidly changing fields such as the study of drug use, the need for accurate and timely data is paramount to properly inform policy and intervention decisions. Trends in drug use can change rapidly by month, and using study designs with flexible modules could present advantages. Timely data from online panels can inform proactive interventions against emerging trends, leading to a faster public response. However, threats to validity from using online panels must be addresse ...[more]

PMID: 31654568

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Project description:BackgroundCommunity pharmacists are well-positioned to identify patients engaged in non-medical prescription opioid use (NMPOU) through Prescription Drug Monitoring Program (PDMP) databases. Integrating patient-reported outcomes with PDMP data may improve the interpretability of PDMP information to support clinical decision-making.ObjectiveThis study linked patient-reported clinical measures of substance use with PDMP data to examine relationships between average daily opioid dose in morphine milligram equivalents (MME) and visits to multiple pharmacies/prescribers with self-reported NMPOU.MethodsData from a cross-sectional health assessment given to patients aged ≥18 years filling opioid prescriptions were linked to PDMP records. NMPOU in the past three months was assessed on a continuous scale (range 0-39) using an adapted version of the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). PDMP measures included average daily MME and number of distinct pharmacies/prescribers visited in the past 180 days. Univariable and multivariable zero-inflated negative binomial models estimated associations between PDMP measures and any NMPOU and severity of use.ResultsThe sample included 1421 participants. In multivariable models adjusted for sociodemographic, mental health, and physical health characteristics, any NMPOU was associated with higher average daily MME (adjusted OR = 1.22, 95% CI = 1.05-1.39) and number of distinct prescribers visited (adjusted OR = 1.15, 95% CI = 1.01-1.30). Higher average daily MME (adjusted mean ratio (MR) = 1.12, 95% CI = 1.08-1.15), number of distinct pharmacies visited (adjusted MR = 1.11, 95% CI = 1.04-1.18), and number of distinct prescribers visited (adjusted MR = 1.07, 95% CI = 1.02-1.11) were associated with increased NMPOU severity.ConclusionsWe observed significant, positive associations between average daily MME and visits to multiple pharmacies/prescribers with any NMPOU and severity of use. This study demonstrates self-report clinical measures of substance use can be cross-walked to PDMP data and translated into clinically interpretable information.

Project description:BackgroundPrescription Drug Monitoring Programs (PDMPs) are electronic databases that track controlled substance prescriptions in a state. They are underused tools in preventing opioid abuse. Most PDMP education research measures changes in knowledge or confidence rather than behavior.ObjectiveTo evaluate the impact of online case-based training on healthcare provider use of the Maryland (USA) PDMP.MethodsWe used e-mail distribution lists to recruit providers to complete a brief educational module. Using a pre-training and post-training survey in the module, we measured self-reported PDMP use patterns and perceived PDMP value in specific clinical situations and compared pre- and post-training responses. Within the module, we presented three fictional pain cases and asked participants how they would manage each, both before, and then after presenting prescription drug history simulating a PDMP report. We measured changes in the fictional case treatment plans before and after seeing prescription history. Finally, we measured and compared how often each participant accessed the Maryland PDMP database before and after completing the educational module. We used multivariate logistic regression to measure the effect of the intervention on actual PDMP use frequency.ResultsOne hundred and fifty participants enrolled and completed the training module, and we successfully retrieved real-world PDMP use data of 137 of them. Participants' decisions to prescribe opioids changed significantly after reviewing PDMP data in each of the fictional cases provided in the module. In the months following the training, the rate of PDMP use increased by a median of four use-cases per month among providers in practice for less than 20 years (p = 0.039) and two use-cases per month among infrequent opioid prescribers (p = 0.014).ConclusionA brief online case-based educational intervention was associated with a significant increase in the rate of PDMP use among infrequent opioid prescribers and those in practice less than 20 years.

Project description:While the number of studies of the non-medical use of prescription drugs to augment cognitive functions is growing steadily, psychological factors that can potentially help explain variance in such pharmaceutical cognitive enhancement (CE) behavior are often neglected in research. This study investigates the association between the Big Five personality traits and a retrospective (prior CE-drug use) as well as a prospective (willingness to use CE drugs) measure of taking prescription drugs with the purpose of augmenting one's cognitive functions (e.g., concentration, memory, or vigilance) without medical necessity. We use data from a large representative survey of German employees (N = 6454, response rate = 29.8%). The Five Factor Model (FFM) of Personality was measured with a short version of the Big Five Personality Traits Inventory (BFI-S), which includes: openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. Together with this, demographic variables such as gender, age, education, and income were used as potential confounders in multiple logistic regression models. Our results show a 2.96% lifetime prevalence of CE-drug use and a 10.45% willingness to (re)use such drugs in the future. We found that less conscientious and more neurotic respondents have a higher probability of prior CE-drug use and a greater willingness to use CE drugs in the future. No significant effects were found for openness, extraversion, or agreeableness. Prior CE-drug use was strongly associated with a greater willingness to take such drugs in the future. This study shows that specific personality traits are not only associated with prior enhancement behavior, but also affect the willingness to (re)use such drugs. It helps increase understanding of the risk factors of CE-drug use, which is a health-related behavior that can entail severe side-effects for consumers. The knowledge gathered can thus help improve interventions aimed at minimizing health problems.

Project description:ImportanceBetween 1997 and 2017, the United States saw increases in nonmedical prescription opioid use and its consequences, as well as changes in marijuana policies. Ecological-level research hypothesized that medical marijuana legalization may reduce prescription opioid use by allowing medical marijuana as an alternative.ObjectivesTo investigate the association of state-level medical marijuana law enactment with individual-level nonmedical prescription opioid use and prescription opioid use disorder among prescription opioid users and to determine whether these outcomes varied by age and racial/ethnic groups.Design, setting, and participantsThis cross-sectional study used restricted data on 627 000 individuals aged 12 years and older from the 2004 to 2014 National Survey on Drug Use and Health, a population-based survey representative of the civilian population of the United States. Analyses were completed from March 2018 to May 2018.ExposuresTime-varying indicator of state-level medical marijuana law enactment (0 = never law enactment, 1 = before law enactment, and 2 = after law enactment).Main outcomes and measuresPast-year nonmedical prescription opioid use and prescription opioid use disorder among prescription opioid users. Odds ratios of nonmedical prescription opioid use and prescription opioid use disorder comparing the period before and after law enactment were presented overall, by age and racial/ethnic group, and adjusted for individual- and state-level confounders.ResultsThe study sample included 627 000 participants (51.51% female; 9.88% aged 12-17 years, 13.30% aged 18-25 years, 14.30% aged 26-34 years, 25.02% aged 35-49 years, and 37.50% aged ≥50 years; the racial/ethnic distribution was 66.97% non-Hispanic white, 11.83% non-Hispanic black, 14.47% Hispanic, and 6.73% other). Screening and interview response rates were 82% to 91% and 71% to 77%, respectively. Overall, there were small changes in nonmedical prescription opioid use prevalence after medical marijuana law enactment (4.32% to 4.86%; adjusted odds ratio, 1.13; 95% CI, 1.06-1.20). Prescription opioid use disorder prevalence among prescription opioid users decreased slightly after law enactment, but the change was not statistically significant (15.41% to 14.76%; adjusted odds ratio, 0.95; 95% CI, 0.81-1.11). Outcomes were similar when stratified by age and race/ethnicity.Conclusions and relevanceThis study found little evidence of an association between medical marijuana law enactment and nonmedical prescription opioid use or prescription opioid use disorder among prescription opioid users. Further research should disentangle the potential mechanisms through which medical marijuana laws may reduce opioid-related harm.

Project description:Study objectiveTo examine national trends in prescription antiobesity drug use in the United States.DesignData analysis.Data sourceThe IMS Health Vector One National and Total Patient Tracker and Encuity Research Treatment Answers databases, the Source Healthcare Analytics Source Lx database, and IMS LifeLink database.Measurements and main resultsNational drug use estimates from 1991-2011 were extracted from the IMS Health Vector One National database, and patient characteristics from 2008-2011 were extracted from the Vector One Total Patient Tracker and Encuity Research Treatment Answers databases. The Source Healthcare Analytics Source Lx database was used to examine duration of antiobesity drug use from 2002-2011, with a sensitivity analysis performed using the IMS LifeLink database. In 2011, approximately 2.74 million patients used antiobesity drugs, predominantly phentermine (2.43 million patients). The use of prescription orlistat and sibutramine was relatively uncommon. Eighty-five percent of antiobesity drug users were female, 62% were aged 17-44 years, and 4.5% had a body mass index of ≤ 24.9 kg/m(2) . Duration of use was generally short and most patients only had one episode of antiobesity drug use during the observation period. The longest episode of use was 30 days or less in 47-58% of patients. Approximately one quarter of the patients used antiobesity drugs for longer than 90 days, including phentermine and other amphetamine congeners whose labels recommend short-term use, not exceeding "a few weeks." Only 1.3-4.2% of antiobesity drug users used them for longer than 1 year. Concomitant use of two or more prescription weight-loss drugs was generally uncommon, although phentermine was dispensed during 13-16% of benzphetamine, diethylpropion, or phendimetrazine episodes of use.ConclusionPhentermine dominated the prescription weight-loss market. Despite the indication of short-term use for amphetamine congeners, duration of use was similar to other antiobesity drugs. Nevertheless, the reasons for and implications of the limited duration of use observed with all prescription antiobesity drugs deserve further investigation.

Project description:BackgroundAustralia has had a framework for legal medicinal cannabis since 2016, yet prior online surveys in 2016 and 2018 indicated that most consumers continued to use illicit medical cannabis products. Regulatory data indicate an increase in the prescription of medicinal cannabis since 2019, and this survey examines consumer experiences of prescribed and illicit medical cannabis (MC) use in Australia.MethodsA cross-sectional anonymous online survey was administered September 2020 to January 2021. Recruitment via social media, professional and consumer forums, and medical practices. Participant eligibility: ≥ 18 years; used a cannabis product for self-identified medical reason(s) in the past year, and resident in Australia. Outcome measures included consumer characteristics, conditions treated, source and patterns of MC use, and perspectives on accessing MC.ResultsOf the 1600 participants (mean age 46.4 ± 14.3 years, 53% male), 62.4% (n = 999) reported using only illicit and 37.6% (n = 601) used prescribed MC in the past year. MC was used on a median of 28 (IQR: 12, 28) of the past 28 days and cost $AUD 74 ± 72 weekly (median = $40, IQR: $7, $100). Prescribed participants were more likely to treat pain conditions than those using illicit MC (52% v 40%, OR = 1.7, 1.3-2.1) and less likely to treat sleep conditions (6% v 11%, OR = 0.5, 0.3-0.8), with mental health conditions also a common indication in both groups (26%, 31%). Prescribed MC was consumed predominately by oral routes (72%), whereas illicit MC was most commonly smoked (41%). Prescribed MC was 'mainly THC' (26%), 'equal THC/CBD' (40%), 'mainly CBD' (31%) and 'uncertain' (3%), while 34% of those using illicit MC were 'uncertain' of the cannabinoid profile. Cost and difficulties finding medical practitioners to prescribe remain significant barriers to accessing prescribed MC, and few (10.8%) described the existing model for accessing prescribed MC as 'straightforward or easy'.ConclusionsThere has been a notable shift from illicit to prescribed MC by many consumers compared to prior surveys. Consumers using prescribed MC reported a range of advantages compared to illicit MC, including safer routes of administration, and greater certainty regarding access and composition of products.

Dataset Information

An Online Survey for Pharmacoepidemiological Investigation (Survey of Non-Medical Use of Prescription Drugs Program): Validation Study.

Publications

An Online Survey for Pharmacoepidemiological Investigation (Survey of Non-Medical Use of Prescription Drugs Program): Validation Study.

Similar Datasets

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