Dataset Information

Mapping prescription drug monitoring program data to self-report measures of non-medical prescription opioid use in community pharmacy settings.

ABSTRACT:

Background

Community 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.

Objective

This 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.

Methods

Data 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.

Results

The 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.

Conclusions

We 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.

SUBMITTER: Charron E

PROVIDER: S-EPMC10523937 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Mapping prescription drug monitoring program data to self-report measures of non-medical prescription opioid use in community pharmacy settings.

Charron Elizabeth E Brooks Jennifer H JH Peterson Keegan T KT Akinwolere Olusegun G OG Winhusen T T Cochran Gerald G

Research in social & administrative pharmacy : RSAP 20230423 8

<h4>Background</h4>Community 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.<h4>Objective</h4>This study linked patient-reported clinical measures of substance use with PDMP data to examine relationships between average daily opioid d ...[more]

PMID: 37142474

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Project description:BackgroundAs the opioid epidemic continues to have a major negative impact across the US, community pharmacies have come under scrutiny from legal systems attempting to hold them accountable for their role in over dispensing and lack of patient intervention. While the most available tool for monitoring patients' opioid use is Prescription Drug Monitoring Programs (PDMP), these do not provide pharmacists with actionable information and decision support. Our study addresses this gap through three objectives: [1] incorporate validated opioid risk metric thresholds into a PDMP platform to create the Opioid Risk Reduction Clinical Decision Support (ORRCDS) tool; [2] assess ORRCDS' ability to reduce patient opioid risk; [3] assess ORRCDS' sustainability and viability for broader dissemination in community pharmacy.MethodsFor objective 1, our team is partnering with leadership from the largest US PDMP organization and a top-five pharmacy chain to implement ORRCDS into the pharmacy chain's workflow following the Guideline Implementation with Decision Support (GUIDES) framework. For objective 2, our team will conduct a type-1 implementation mixed methods study using a 2-arm parallel group clustered randomized design. We anticipate enrolling ~ 6,600 patients with moderate and high opioid use risk during the 6-month enrollment phase across 80 pharmacies. This sample size will provide 96.3% power to detect a 5% or greater difference in responder rate between the intervention and control arm. Responders are patients with moderate-risk at baseline who reduce to low-risk or those with high-risk at baseline who reduce to moderate or low-risk at 180 days post last intervention. To accomplish objective 3, we will use the Consolidated Framework for Implementation Research (CFIR) to develop and execute cross-sectional qualitative interviews with pharmacists (n = 15), pharmacy leaders (n = 15), and PDMP leaders (n = 15) regarding long term adoption and sustainability of the ORRCDS tool.ConclusionsA PDMP tool that addresses moderate- and high-risk opioid use is not available in community pharmacy. This study will implement ORRCDS in a large retail pharmacy chain that will include additional screening and guidance to pharmacy staff to address risky opioid medication use. Our results will make critical advancements for protecting patient health and addressing the opioid epidemic.

Project description:Study objectiveWe assess whether an automated prescription drug monitoring program intervention in emergency department (ED) settings is associated with reductions in opioid prescribing and quantities.MethodsWe performed a retrospective cohort study of ED visits by Medicaid beneficiaries. We assessed the staggered implementation (pre-post) of automated prescription drug monitoring program queries at 86 EDs in Washington State from January 1, 2013, to September 30, 2015. The outcomes included any opioid prescribed within 1 day of the index ED visit and total dispensed morphine milligram equivalents. The exposure was the automated prescription drug monitoring program query intervention. We assessed program effects stratified by previous high-risk opioid use. We performed multiple sensitivity analyses, including restriction to pain-related visits, restriction to visits with a confirmed prescription drug monitoring program query, and assessment of 6 specific opioid high-risk indicators.ResultsThe study included 1,187,237 qualifying ED visits (898,162 preintervention; 289,075 postintervention). Compared with the preintervention period, automated prescription drug monitoring program queries were not significantly associated with reductions in the proportion of visits with opioid prescribing (5.8 per 1,000 encounters; 95% confidence interval [CI] -0.11 to 11.8) or the amount of prescribed morphine milligram equivalents (difference 2.66; 95% CI -0.15 to 5.48). There was no evidence of selective reduction in patients with previous high-risk opioid use (1.2 per 1,000 encounters, 95% CI -9.5 to 12.0; morphine milligram equivalents 1.22, 95% CI -3.39 to 5.82). The lack of a selective reduction in high-risk patients was robust to all sensitivity analyses.ConclusionAn automated prescription drug monitoring program query intervention was not associated with reductions in ED opioid prescribing or quantities, even in patients with previous high-risk opioid use.

Project description:Objective This study was aimed to estimate the percentage of women taking opioids post-cesarean who could be detected in a state prescription monitoring program (PMP) and characteristics of those not able to be detected. Study Design This observational cohort study included all women with an Illinois address who delivered via cesarean section and used opioids within 24 hours prior to discharge at a tertiary care hospital between August 21, 2017 and March 1, 2018. The Illinois PMP was queried for presence of an opioid prescription filled within the first 3 months postpartum. Sociodemographic and clinical factors associated with an undetectable PMP record were evaluated in bivariable and multivariable logistic regression analyses. Results A total of 517 women underwent a cesarean delivery during the study period, of whom 344 (66.5%) met inclusion criteria. Of these women, 169 (49%) did not have a detectable PMP record of filling any outpatient postpartum prescription opioid. On bivariable and multivariable logistic regression analysis, year of delivery (2018 vs. 2017) was significantly associated with a higher incidence of detectable postpartum prescription opioid record in the PMP with increasing relative risk of detectable records in the second year of analyses ( n = 110/244 [45%] in 2017 vs. n = 59/100 [59%] in 2018, adjusted risk ratio [aRR] = 1.32, 95% confidence interval [CI]: 1.06-1.64, p = 0.013). No other sociodemographic or clinical characteristics was significantly associated. Conclusion Nearly half of women who underwent a cesarean section and who were administered opioids 24 hours prior to discharge did not have a detectable postpartum opioid prescription in the PMP. While identification of prescription filling improved with time, many of women were not detectable in the PMP system. These data call into question the accuracy of PMPs in identifying prescription opioid filling patterns in the postpartum setting.

Project description:BackgroundMandates for prescriber use of prescription drug monitoring programs (PDMPs), databases tracking controlled substance prescriptions, are associated with reduced opioid analgesic (OA) prescribing but may contribute to care discontinuity and chronic opioid therapy (COT) cycling, or multiple initiations and terminations.ObjectiveTo estimate risks of COT cycling in New York City (NYC) due to the New York State (NYS) PDMP mandate, compared to risks in neighboring New Jersey (NJ) counties.DesignWe estimated cycling risk using Prentice, Williams, and Peterson gap-time models adjusted for age, sex, OA dose, payment type, and county population density, using a life-table difference-in-differences design. Failure time was duration between cycles. In a subgroup analysis, we estimated risk among patients receiving high-dose prescriptions. Sensitivity analyses tested robustness to cycle volume considering only first cycles using Cox proportional hazard models.ParticipantsThe cohort included 7604 patients dispensed 12,695 prescriptions.InterventionsThe exposure was the August 2013 enactment of the NYS PDMP prescriber use mandate.Main measuresWe used monthly, patient-level data on OA prescriptions dispensed in NYC and NJ between August 2011 and July 2015. We defined COT as three sequential months of prescriptions, permitting 1-month gaps. We defined recurrence as re-initiation of COT after at least 2 months without prescriptions. The exposure was enactment of the PDMP mandate in NYC; NJ was unexposed.Key resultsEnactment of the NYS PDMP mandate was associated with an adjusted hazard ratio (HR) for cycling of 1.01 (95% CI, 0.94-1.08) in NYC. For high-dose prescriptions, the risk was 1.16 (95% CI, 1.01-1.34). Sensitivity analyses estimated an overall risk of 1.01 (95% CI, 0.94-1.11) and high-dose risk of 1.09 (95% CI, 0.91-1.31).ConclusionsThe PDMP mandate had no overall effect on COT cycling in NYC but increased cycling risk among patients receiving high-dose opioid prescriptions by 16%, highlighting care discontinuity.

Dataset Information

Mapping prescription drug monitoring program data to self-report measures of non-medical prescription opioid use in community pharmacy settings.

Background

Objective

Methods

Results

Conclusions

Publications

Mapping prescription drug monitoring program data to self-report measures of non-medical prescription opioid use in community pharmacy settings.

Similar Datasets

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