Impact of 30-day prescribed opioid dose trajectory on fatal overdose risk: A population-based, statewide cohort study.

BACKGROUND: Both increases and decreases in patients' prescribed daily opioid dose have been linked to increased overdose risk, but associations between 30-day dose trajectories and subsequent overdose risk have not been systematically examined. OBJECTIVE: To examine the associations between 30-day prescribed opioid dose trajectories and fatal opioid overdose risk during the subsequent 15 days. DESIGN: Statewide cohort study using linked prescription drug monitoring program and death certificate data. We constructed a multivariable Cox proportional hazards model that accounted for time-varying prescription-, prescriber-, and pharmacy-level factors. PARTICIPANTS: All patients prescribed an opioid analgesic in California from March to December, 2013 (5,326,392 patients). MAIN MEASURES: Dependent variable: fatal drug overdose involving opioids. Primary independent variable: a 16-level variable denoting all possible opioid dose trajectories using the following categories for current and 30-day previously prescribed daily dose: 0-29, 30-59, 60-89, or ≥90 milligram morphine equivalents (MME). KEY RESULTS: Relative to patients prescribed a stable daily dose of 0-29 MME, large (≥2 categories) dose increases and having a previous or current dose ≥60 MME per day were associated with significantly greater 15-day overdose risk. Patients whose dose decreased from ≥90 to 0-29 MME per day had significantly greater overdose risk compared to both patients prescribed a stable daily dose of ≥90 MME (aHR 3.56, 95%CI 2.24-5.67) and to patients prescribed a stable daily dose of 0-29 MME (aHR 7.87, 95%CI 5.49-11.28). Patients prescribed benzodiazepines also had significantly greater overdose risk; being prescribed Z-drugs, carisoprodol, or psychostimulants was not associated with overdose risk. CONCLUSIONS: Large (≥2 categories) 30-day dose increases and decreases were both associated with increased risk of fatal opioid overdose, particularly for patients taking ≥90 MME whose opioids were abruptly stopped. Results align with 2022 CDC guidelines that urge caution when reducing opioid doses for patients taking long-term opioid for chronic pain.

Association of state-level prescription drug monitoring program implementation with opioid prescribing transitions in primary care in Australia.

AIMS: This study aimed to evaluate whether voluntary and mandatory prescription drug monitoring program (PDMP) use in Victoria, Australia, had an impact on prescribing behaviour, focusing on individual patients' prescribed opioid doses and transition to prescribing of nonmonitored medications. METHODS: This was a retrospective cross-sectional study using routinely collected primary healthcare data. A 90-day moving average prescribed opioid dose in oral morphine equivalents was used to estimate opioid dosage. A Markov transition matrix was used to describe how patients prescribed medications transitioned between opioid dose groups and other nonopioid treatment options during 3 transition periods: transition between 2 control periods prior to PDMP implementation (T1 to T2); during the voluntary PDMP implementation (T2 to T3); and during mandatory PDMP implementation (T3 to T4). RESULTS: Among patients prescribed opioids in our study, we noted an increased probability of transitioning to not being prescribed opioids during the mandatory PDMP period (T3 to T4). This increase was attributed mainly to the ceasing of low-dose opioid prescribing. Membership in an opioid dose group remained relatively stable for most patients who were prescribed high opioid doses. For those who were only prescribed nonmonitored medications initially, the probability of being prescribed opioids increased during the mandatory PDMP when compared to other transition periods. CONCLUSION: The introduction of PDMP mandates appeared to have an impact on the prescribing for patients who were prescribed low-dose opioids, while its impact on individuals prescribed higher opioid doses was comparatively limited.

Prescription drug monitoring programs and prescription pain medication misuse among U.S. high school students-2019.

BACKGROUND: Prescription drug monitoring programs (PDMPs) are state-level databases that track and inform prescribing practices to reduce prescription drug diversion and misuse. To our knowledge, only three studies have examined the impact of PDMPs on opioid-related outcomes among adolescents, and none have focused on prescription pain medication misuse among adolescents. METHODS: This study leveraged data from the 2019 National Youth Risk Behavior Survey (YRBS) to explore the associations between five categories of PDMP dimensions and the prevalence of self-reported prescription pain medication misuse. Demographic factors' associations with self-reported prescription pain medication misuse were also examined. RESULTS: In 2019, none of the PDMP dimensions were associated with self-reported prescription pain medication misuse among U.S. high school students, adjusting for gender, grade, race/ethnicity, and sexual orientation. CONCLUSIONS: None of the five PDMP dimensions were associated with lower prescription pain medication misuse, however further research is needed, especially as new YRBS data become available.

National Estimates and Physician-Reported Impacts of Prescription Drug Monitoring Program Use.

BACKGROUND: Despite widespread adoption of state prescription drug monitoring programs (PDMPs), it is unclear how often PDMPs are accessed through an electronic health record system (EHR-PDMP integration), or whether efforts to make PDMPs easier to access and use have improved their utility. OBJECTIVE: To produce national-level estimates on the use of PDMPs among office-based physicians and benefits associated with their use. DESIGN: We use nationally representative survey data to produce descriptive statistics on PDMP use and associated benefits among office-based physicians in the USA. PARTICIPANTS: 1398 office-based physicians who prescribe controlled substances. MAIN MEASURES: We examined physician-reported ease and frequency of PDMP use, and how EHR-PDMP integration affects frequency and ease of use. Multivariate models were used to assess whether characteristics of PDMP use were related to physician-reported benefits such as reduced prescribing of controlled substances and perceived improvements in clinical decision-making. KEY RESULTS: In 2019, two-thirds of office-based physicians in the USA reported frequent use of their state PDMP and over three-quarters reported they were easy to use. Both frequency and ease of use were positively correlated with PDMP integration status. Respondents who frequently checked their state's PDMP were 8.7 percentage points (95% CI -.4 to 17.8) more likely to report perceived benefits and reported 2.2 (95% CI 1.54 to 2.83) more benefits. Respondents who indicated their PDMP was easy to use were 12.7 percentage points (95% CI .040 to .214) more likely to report perceived benefits and reported 0.94 (95% CI 0.26 to 1.61) more benefits. CONCLUSIONS: Our findings suggest efforts to make PDMPs easier to access and use aided physicians in making informed clinical decisions that may not be captured by reduced prescribing alone. Efforts to further increase frequency and ease of use-including advancing a standards-based approach to PDMP and EHR data interoperability-may further increase the benefit of PDMPs.

Antibiotic consumption in secondary and tertiary hospitals in Colombia: national surveillance from 2018-2020.

Objective: To assess the compliance in secondary and tertiary level hospitals with monthly reporting of antibiotic consumption to the Colombian National Public Health Surveillance System (SIVIGILA-INS), and to describe reported antibiotic consumption during 2018-2020. Methods: This study involved a secondary analysis of antibiotic consumption data reported to SIVIGILA-INS. Frequency of hospital reporting was assessed and compared against expected reports, disaggregated by intensive care units (ICU)/non-ICU wards and geographical regions. Consumption was expressed as defined daily dose (DDD) per 100 occupied beds for seven antibiotics. Results: More than 70% of hospitals reported antibiotic consumption at least once in each of the three years (79% in ICU and 71% in non-ICU wards). Of these, ICU monthly reporting was complete (12 monthly reports per year) for 59% in the period 2018-2019 but only 4% in 2020. Non-ICU reporting was complete for 52% in 2019 and for 2% in 2020. Most regions had an overall decrease in reporting in 2020. Analysis of antibiotic consumption showed an increase for piperacillin/tazobactam, ertapenem, and cefepime from 2019 to 2020. Conclusions: There were gaps in the consistency and frequency of reporting. Efforts are needed to improve compliance with monthly reporting, which declined in 2020, possibly due to the COVID-19 pandemic. Non-compliance on reporting and data quality issues should be addressed with the hospitals to enable valid interpretation of antibiotic consumption trends.

Characteristics of statewide prescription drug monitoring programs and potentially inappropriate opioid prescribing to patients with non-cancer chronic pain: A machine learning application.

Unnecessary/unsafe opioid prescribing has become a major public health concern in the U.S. Statewide prescription drug monitoring programs (PDMPs) with varying characteristics have been implemented to improve safe prescribing practice. Yet, no studies have comprehensively evaluated the effectiveness of PDMP characteristics in reducing opioid-related potentially inappropriate prescribing (PIP) practices. The objective of the study is to apply machine learning methods to evaluate PDMP effectiveness by examining how different PDMP characteristics are associated with opioid-related PIPs for non-cancer chronic pain (NCCP) treatment. This was a retrospective observational study that included 802,926 adult patients who were diagnosed NCCP, obtained opioid prescriptions, and were continuously enrolled in plans of a major U.S. insurer for over a year. Four outcomes of opioid-related PIP practices, including dosage ≥50 MME/day and ≥90 MME/day, days supply ≥7 days, and benzodiazepine-opioid co-prescription were examined. Machine learning models were applied, including logistic regression, least absolute shrinkage and selection operation regression, classification and regression trees, random forests, and gradient boost modeling (GBM). The SHapley Additive exPlanations (SHAP) method was applied to interpret model results. The results show that among 1,886,146 NCCP opioid-related claims, 22.8% had an opioid dosage ≥50 MME/day and 8.9% ≥90 MME/day, 70.3% had days supply ≥7 days, and 10.3% were when benzodiazepine was filled ≤7 days ago. GBM had superior model performance. We identified the most salient PDMP characteristics that predict opioid-related PIPs (e.g., broader access to patient prescription history, monitoring Schedule IV controlled substances), which could be informative to the states considering the redesign of PDMPs.

Substance use disorder approaches in US primary care clinics with national reputations as workforce innovators.

BACKGROUND: Over the last decade, primary care clinics in the United States have responded both to national policies encouraging clinics to support substance use disorders (SUD) service expansion and to regulations aiming to curb the opioid epidemic. OBJECTIVE: To characterize approaches to SUD service expansion in primary care clinics with national reputations as workforce innovators. METHODS: Comparative case studies were conducted to characterize different approaches among 12 primary care clinics purposively and iteratively recruited from a national registry of workforce innovators. Observational field notes and qualitative interviews from site visits were coded and analysed to identify and characterize clinic attributes. RESULTS: Codes describing clinic SUD expansion approaches emerged from our analysis. Clinics were characterized as: avoidant (n = 3), contemplative (n = 5) and responsive (n = 4). Avoidant clinics were resistant to planning SUD service expansion; had no or few on-site behavioural health staff; and lacked on-site medication treatment (previously termed medication-assisted therapy) waivered providers. Contemplative clinics were planning or had partially implemented SUD services; members expressed uncertainties about expansion; had co-located behavioural healthcare providers, but no on-site medication treatment waivered and prescribing providers. Responsive clinics had fully implemented SUD; members used non-judgmental language about SUD services; had both co-located SUD behavioural health staff trained in SUD service provision and waivered medication treatment physicians and/or a coordinated referral pathway. CONCLUSIONS: Efforts to support SUD service expansion should tailor implementation supports based on specific clinic training and capacity building needs. Future work should inform the adaption of evidence-based practices that are responsive to resource constraints to optimize SUD treatment access.

Primary care clinics in the US have been encouraged to expand addiction services to increase treatment access and respond to the opioid epidemic. This study uses structured observations and depth interviews to assess and compare how primary care clinics with innovative workforces have responded to the growing need for substance use disorder treatment. Each of the clinics studied represents a 'case.' We systematically compared cases to understand how and if addiction services were expanded. Twelve clinic 'cases' were coded and characterized based on a continuum of receptivity ranging from avoidant (i.e., resistant), contemplative (i.e., organization members plan to implement change) and responsive (i.e. expansions implemented). Our analysis characterized three clinics as avoidant to expanding addiction services reporting no plans to respond to calls to expand addiction services. Five clinics were characterized as contemplative, meaning they recognized the need but still had reservations and concerns about the expansion. Four clinics were characterized as responsive to addiction service expansion and had several organizational-wide strategies to assess, intervene and treat patients with addictions. Despite national and state-based policies to entice clinics to expand addiction services there was a diversity of approaches observed in clinics. Avoidant and contemplative clinics may need implementation support to build capacity for this type of delivery expansion.

Outpatient prescription patterns of COVID-19 drugs in the metropolitan area of Mexico City.

BACKGROUND: We aimed to describe the use of drugs with apparent efficacy in ambulatory patients with confirmed COVID-19 and the relationship of Google Trends searches with prescriptions and the total number of COVID-19 cases in Mexico City. METHODS: Between March 2020 and February 2021, we surveyed 350 patients confirmed to have COVID-19 across 3 hospitals in Mexico City for their ambulatory prescriptions. We analysed the correlation between prescription patterns of 4 drugs with apparent efficacy against COVID-19, Google Trends searches for these drugs, and the overall number of confirmed COVID-19 cases in Mexico City. RESULTS: We included 350 patients, of whom 59% were women with a median age of 38 years (interquartile range, 29-51), and 72% had a bachelor's degree or higher. There were ambulatory medical prescriptions in 172 (49%) patients, and self-prescriptions were reported in 99 (28%) patients. The prescription rate was high for hydroxychloroquine/azithromycin (19%) and dexamethasone (25%). There was a decrease in the prescription of hydroxychloroquine (P < 0.001) and a strong positive correlation between hydroxychloroquine (r = 0.66; 95% confidence interval, 0.11-0.90; P = 0.02) prescription and online searches for hydroxychloroquine. There was a strong positive correlation between online searches for azithromycin, dexamethasone, ivermectin, and vitamin D and the number of confirmed COVID-19 cases. CONCLUSIONS: During the COVID-19 pandemic, there was a high proportion of prescriptions for hydroxychloroquine/azithromycin and dexamethasone despite their unproven efficacy. Analysis of Google Trends showed a strong correlation between the overall number of confirmed COVID-19 cases and searches for such drugs, suggesting a higher rate of prescriptions. Analysis of online searches could thus help to actively survey public health behaviours in the future.

[Use of antibiotics in Paediatric Primary Health Care before and during the COVID-19 pandemic]. / Consumo de antibióticos en Pediatría de Atención Primaria antes y durante la pandemia de COVID-19.

Introduction Consumption of antibiotics is high in Spain, primarily in children. Excessive use of then contributes to the development of antimicrobial resistance. The aim of our study is to analyse the evolution of antibiotic consumption at the Primary Health Care in the paediatric population of Asturias, Spain, from 2014 to 2021, and to evaluate the impact of COVID-19 pandemic on it.Methods Retrospective and observational study using data about antibacterial agents for systemic use dispensed for official prescriptions to children under 14 years in Primary Care. Antibiotic consumption is expressed as defined daily dose (DDD) per 1000 inhabitants per day (DID).Results The antibiotic consumption rate dropped from 13.9 DID in 2014 to 4.0 in 2021 (ß = -1,42, p=0,002), with and inflection point in 2019. From 2019 to 2020 antibiotic use dropped by 47.1%. Antibiotic consumption remained very low from April 2020 to September 2021, and then moderately increased from October 2021. Prevalence of antibiotic use dropped from 39.9% in 2014 to 17.5% in 2021 (ß = -3,64, p=0,006). Relative consumption of amoxicillin/clavulanic acid decreased, while those of amoxiciline and third-generation cephalosporins increased.Conclusions Paediatric antibiotic consumption collapsed in Asturias in 2020, coinciding with COVID-19 pandemic. Monitoring of antimicrobial usage indicators will allow to check if these changes are sustained over time.

[Antibiotic consumption in primary care in the adult population of Asturias during 2014-2020 period]. / Consumo de antibióticos en atención primaria en población adulta de Asturias durante el periodo 2014-2020.

Trend study of the consumption of systemic antibiotics in the adult population in of Primary Care of the Health Service of the Principality of Asturias (SESPA) during the period 2014̶2020. Retrospective observational study. SESPA, Primary Care. Population from the Individual Health Card database. Data were collected on the prescription of antibiotics, carried out in the family medicine consultations, dispensed in the pharmacy offices with charge of SESPA. Antibiotic use and consumption variables were analyzed using linear regression models. Prevalence of antibiotic use (population percentage); consumption rate of systemic antibiotics (DTD), relative consumption of narrow-spectrum antibiotics (percentage DDD). The average prevalence of the use of antibiotics for the 2014̶2019 period was 32.2% and 23.9% in 2020. The rate of consumption of systemic antibiotics decreased from 21.4 DTD in 2014 to 12.7 DTD in 2020. The consumption of narrow-spectrum antibiotics remained stable (19.4% DDD in 2014 and 19.3% DDD in 2020) (CI95: -0.10, 0.26). In the period from March to December 2020, the consumption of antibiotics decreased by 28.6% compared to the same period in 2019. In 2014̶2020, the consumption of antibiotics decreased, especially since the COVID-19 pandemic, with stabilization of the consumption of narrow-spectrum antibiotics compared to the total. There is variability in consumption by therapeutic subgroups.

Using Prescription Drug Monitoring Program Data to Assess Likelihood of Incident Long-Term Opioid Use: a Statewide Cohort Study.

BACKGROUND: Limiting the incidence of opioid-naïve patients who transition to long-term opioid use (i.e., continual use for > 90 days) is a key strategy for reducing opioid-related harms. OBJECTIVE: To identify variables constructed from data routinely collected by prescription drug monitoring programs that are associated with opioid-naïve patients' likelihood of transitioning to long-term use after an initial opioid prescription. DESIGN: Statewide cohort study using prescription drug monitoring program data PARTICIPANTS: All opioid-naïve patients in California (no opioid prescriptions within the prior 2 years) age ≥ 12 years prescribed an initial oral opioid analgesic from 2010 to 2017. METHODS AND MAIN MEASURES: Multiple logistic regression models using variables constructed from prescription drug monitoring program data through the day of each patient's initial opioid prescription, and, alternatively, data available up to 30 and 60 days after the initial prescription were constructed to identify probability of transition to long-term use. Model fit was determined by the area under the receiver operating characteristic curve (C-statistic). KEY RESULTS: Among 30,569,125 episodes of patients receiving new opioid prescriptions, 1,809,750 (5.9%) resulted in long-term use. Variables with the highest adjusted odds ratios included concurrent benzodiazepine use, ≥ 2 unique prescribers, and receipt of non-pill, non-liquid formulations. C-statistics for the day 0, day 30, and day 60 models were 0.81, 0.88, and 0.94, respectively. Models assessing opioid dose using the number of pills prescribed had greater discriminative capacity than those using milligram morphine equivalents. CONCLUSIONS: Data routinely collected by prescription drug monitoring programs can be used to identify patients who are likely to develop long-term use. Guidelines for new opioid prescriptions based on pill counts may be simpler and more clinically useful than guidelines based on days' supply or milligram morphine equivalents.

Impacts of prescription drug monitoring program policy changes and county opioid safety coalitions on prescribing and overdose outcomes in California, 2015-2018.

In 2015, California received funding to implement the Prescription Drug Overdose Prevention Initiative, a 4-year program to reduce deaths involving prescription opioids by 1) leveraging improvements to California's prescription drug monitoring program (PDMP) (i.e., mandatory PDMP registration for prescribers and pharmacists), and 2) supporting county opioid safety coalitions. We used statewide data from 2011 to 2018 to evaluate the Initiative's impact on opioid prescribing and overdose rates. Prescribing data were obtained from California's PDMP; fatal and non-fatal overdose data were obtained from the California Department of Public Health. Outcomes were monthly opioid prescribing rates and opioid overdose rates, modeled using generalized linear mixed models. Exposures were mandatory PDMP registration, presence of county coalitions, and Initiative support for county coalitions. Mandatory PDMP registration was associated with a 25% decrease (95%CI, 0.71-0.79) in opioid prescribing rates after 24 months. Having a county coalition was associated with a 2% decrease (95%CI, 0.96-0.99) in the opioid prescribing rate; receiving Initiative support was associated with an additional 2% decrease (95%CI, 0.97-0.98). Mandatory PDMP registration and county coalitions were associated with a 35% decrease (95%CI, 0.43-0.97) and a 21% decrease (95% CI, 0.70-0.90), respectively in prescription opioid overdose deaths. Both interventions were also associated with significantly fewer deaths involving any opioid but had no significant association with non-fatal overdose rates. Findings add to the knowledge available to guide policy to prevent high-risk prescribing and opioid overdoses. While further study is needed, coalitions and mandatory PDMP registration may be important components in such efforts.

Opioid Prescribing After Implementation of Single Click Access to a State Prescription Drug Monitoring Program Database in a Health System's Electronic Health Record.

OBJECTIVES: To determine the effect of one-click integration of a state's prescription drug monitoring program (PDMP) on the number of PDMP searches and opioid prescriptions, stratified by specialty. METHODS: Our large health system worked with the state department of public health to integrate the PDMP with the electronic health record (EHR), which enabled providers to query the data with a single click inside the EHR environment. We evaluated Schedule II or III opioid prescriptions reported to the Massachusetts PDMP 6 months before (November 15, 2017-May 15, 2018) and 6 months after (May 16, 2018, to November 16, 2018) integration. Search counts, prescriptions, patients, morphine milligram equivalents, as well as prescriber specialty were compared. RESULTS: There were 3,185 unique prescribers with a record of a Schedule II and/or III opioid prescription in both study periods that met inclusion criteria. After integration, the number of PDMP searches increased from 208,684 in the pre-integration phase to 298,478 searches in the post-integration phase (+43.0%). The number of opioid prescriptions dispensed decreased by 4.8%, the number of patients receiving a prescription decreased by 5.1%, and the mean morphine milligram equivalents (MMEs) per prescriber decreased by 5.4%. There were some notable specialty-specific differences in these measures. CONCLUSIONS: Integration of the PDMP into the EHR markedly increased the number of searches but was associated with modest decreases in opioids prescribed and patients receiving a prescription. Single click EHR integration of the PDMP, if implemented broadly, may be a way for states to significantly increase PDMP utilization.

Factors Related to Prescription Drug Monitoring Program Queries for Veterans Receiving Long-Term Opioid Therapy.

OBJECTIVE: State prescription drug monitoring programs (PDMPs) identify controlled medications dispensed across providers and systems. Department of Veterans Affairs (VA) policy requires electronic health record documentation of PDMP queries at least annually for VA patients receiving controlled medications; however, queries are not uniformly conducted. We examined factors associated with PDMP queries for veterans receiving long-term opioid therapy. METHODS: Veterans with a VA provider who received long-term opioid therapy between August 2015 and August 2016 within a four-state region were identified; 9,879 were due for a PDMP query between August 2016 and February 2017. Likelihood of veterans' PDMP queries during this follow-up period was modeled as a function of patient, provider, and facility characteristics of interest in mixed-effects modified Poisson models estimating relative risk and 95% confidence intervals. Multivariable models controlled for potential confounders identified through the use of directed acyclic graphs. RESULTS: PDMP queries were documented for 62.1% of veterans that were due for a PDMP query. Veterans were more likely to be queried if they were Hispanic or if they received methadone, had average daily milligram morphine equivalents >20, or received urine drug screening during the studied period. Veterans were less likely to be queried if they had a rural address, mail order medication, or cancer diagnosis. Likelihood of PDMP queries was also lower for veterans whose opioid-prescribing provider was an oncologist or working in a low-complexity facility. CONCLUSIONS: Adherence to PDMP query policy within the VA varied by patient, clinician, and facility factors. Mechanisms to standardize the conduct of PDMP queries may be needed.

Health Care Provider Utilization of Prescription Monitoring Programs: A Systematic Review and Meta-Analysis.

OBJECTIVE: To synthesize the literature on the proportion of health care providers who access and use prescription monitoring program data in their practice, as well as associated barriers to the use of such data. DESIGN: We performed a systematic review using a standard systematic review method with meta-analysis and qualitative meta-summary. We included full-published peer-reviewed reports of study data, as well as theses and dissertations. METHODS: We identified relevant quantitative and qualitative studies. We synthesized outcomes related to prescription monitoring program data use (i.e., ever used, frequency of use). We pooled the proportion of health care providers who had ever used prescription monitoring program data by using random effects models, and we used meta-summary methodology to identify prescription monitoring program use barriers. RESULTS: Fifty-three studies were included in our review, all from the United States. Of these, 46 reported on prescription monitoring program use and 32 reported on barriers. The pooled proportion of health care providers who had ever used prescription monitoring program data was 0.57 (95% confidence interval: 0.48-0.66). Common barriers to prescription monitoring program data use included time constraints and administrative burdens, low perceived value of prescription monitoring program data, and problems with prescription monitoring program system usability. CONCLUSIONS: Our study found that health care providers underutilize prescription monitoring program data and that many barriers exist to prescription monitoring program data use.

Over-prescription of short-acting beta agonists in the treatment of asthma.

BACKGROUND: Despite a clear guideline for asthma medication, excessive use of short-acting ß2-agonists (SABAs) is common in clinical practice. Previous research has shown that excessive use of SABAs is associated with poor asthma control. OBJECTIVE: This study examines current use of asthma medication in primary care and whether excessive use of SABAs is associated with exacerbations. METHODS: The study design was a retrospective analysis using information from electronical medical records from patients aged 18 and older of five Julius Health Centers located in Utrecht, the Netherlands, in the period of 1 July 2018 through 1 July 2019. Excessive SABA use was defined as ≥400 inhalations per year. An exacerbation was defined as an acute worsening of asthma symptoms with the need for systemic corticosteroids. RESULTS: A total of 1161 patients were included in the study. Of the patients using SABAs (n = 766), 193 (25%) overused SABAs. Among the patients with inappropriate SABA use (n = 193), 19% had an exacerbation compared with 7% of the appropriate SABA users. For patients using asthma medication the odds of having an exacerbation were 2.9 times higher if they used an inappropriate number of SABAs than if SABAs were used appropriately (odds ratio, 2.897; 95% confidence interval, 1.87-4.48). CONCLUSIONS: This study shows that overuse of SABAs is still common and that it is associated with asthma exacerbations. It highlights that clinicians need to be aware of inappropriate SABA use as it is a sign of and can even contribute to poor asthma control.

Opioid Use Among Individuals With Spinal Cord Injury: Prevalence Estimates Based on State Prescription Drug Monitoring Program Data.

OBJECTIVE: To identify the prevalence of opioid use in individuals with chronic spinal cord injury (SCI) living in South Carolina. DESIGN: Cohort study. SETTING: Data from 2 statewide population-based databases, an SCI Registry and the state prescription drug monitoring program, were linked and analyzed. PARTICIPANTS: The study included individuals (N=503) with chronic (>1y) SCI who were injured between 2013 and 2014 in South Carolina and who survived at least 3 years postinjury. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Filled opioid prescriptions over a 2-year period (months 13-36 after injury). The main outcomes were total number of days with an opioid prescription over the 2-year period, length of coverage period [(final day of prescription coverage+the days supplied)-first day of prescription coverage], average daily morphine milligram equivalents (MME) over the coverage period, and concurrent days covered by an opioid and a prescription for benzodiazepines, sedatives, or hypnotics. RESULTS: A total of 53.5% of the cohort (269 individuals) filled at least 1 opioid prescription during their second or third year after SCI. In total, there were 3386 opioid fills during the 2-year study. On average, the total number of opioid prescription days was 293±367. The average coverage period was 389±290 days, and the average daily MME during the coverage period was 41±70 MME. Of those who filled an opioid prescription, 23% had high-risk fills (>50 MME), and 38% had concurrent prescriptions for benzodiazepines, sedatives, or hypnotics. CONCLUSIONS: The prevalence of opioid use was high among individuals with chronic SCI, exceeding rates observed in the general population. Also concerning were the rates of high-risk fills, based on average daily MME and concurrent benzodiazepine, sedative, or hypnotic prescriptions. These findings, taken together with those of earlier studies, should be used by providers to assess and monitor opioid use, decrease concurrent high-risk medication use, and attenuate the risk of adverse outcomes.

Review of the Current State of Urine Drug Testing in Chronic Pain: Still Effective as a Clinical Tool and Curbing Abuse, or an Arcane Test?

PURPOSE OF REVIEW: Therapeutic use, misuse, abuse, and diversion of controlled substances in managing chronic non-cancer pain remain a major concern for physicians, the government, payers, and patients. The challenge remains finding effective diagnostic tools that can be clinically validated to eliminate or substantially reduce the abuse of controlled prescription drugs, while still assuring the proper treatment of those patients in pain. Urine drug testing still remains an important means of adherence monitoring, but questions arise as to its relevance and effectiveness. This review examines the role of UDT, determines its utility in current clinical practice, and investigates its relevance in current chronic pain management. RECENT FINDINGS: A review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Literature was searched from year 2000 to present examining the relevance and role of UDT in monitoring chronic opioid therapy along with reliability and accuracy, appropriate use, overuse, misuse, and abuse. There are only a limited number of reviews and investigations on UDT, despite the fact that clinicians who prescribe controlled medications for chronic states commonly are expected to utilize UDT. Therefore, despite highly prevalent use, there is a limited publication base from which to draw in this present study. Regardless of experience or training background, physicians and healthcare providers can much more adequately assess opioid therapy with the aid of UDT, which often requires confirmatory testing by a laboratory for clinical and therapeutic prescribing decisions. It has become a strongly recommended aspect of pain care with controlled substances locally, regionally, and nationally. Incorporating UDT for all patients in whom chronic opioid therapy is undertaken is consistent with state and national guidelines and best practice strategies. Practice standards vary as to the frequency of UDT locally, regionally, and nationally, however.

Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose.

Background: Prescription Drug Monitoring Programs (PDMPs) collect controlled substance prescriptions dispensed within a state. Many PDMP programs perform targeted outreach (i.e., "unsolicited reporting") for patients who exceed numerical thresholds, however, the degree to which patients at highest risk of fatal opioid overdose are identified has not been compared with one another or with a predictive model. Methods: A retrospective analysis was performed using statewide PDMP data for Maryland residents aged 18 to 80 years with an opioid fill between April to June 2015. The outcome was opioid-related overdose death in 2015 or 2016. A multivariable logistic regression model and three PDMP thresholds were evaluated: (1) multiple provider episodes; (2) high daily average morphine milligram equivalents (MME); and (3) overlapping opioid and benzodiazepine prescriptions. Results: The validation cohort consisted of 170,433 individuals and 244 deaths. The predictive model captured more individuals who died (46.3% of total deaths) and had a higher death rate (7.12 per 1000) when the risk score cutoff (0.0030) was selected for a comparable size of high-risk individuals (n = 15,881) than those meeting the overlapping opioid/benzodiazepine prescriptions (n = 17,440; 33.2% of total deaths; 4.64 deaths per 1000) and high MME (n = 14,675; 24.6% of total deaths; 4.09 deaths per 1000) thresholds. Conclusions: The predictive model identified more individuals at risk of fatal opioid overdose as compared with PDMP thresholds commonly used for unsolicited reporting. PDMP programs could improve their targeting of unsolicited reports to reach more individuals at risk of overdose by using predictive models instead of simple threshold-based approaches.

Do prescription drug monitoring programs encourage prescription - or illicit - opioid abuse?

BACKGROUND: Prescription drug monitoring programs (PDMPs) are tools that states can use to fight prescription opioid misuse within their jurisdiction. However, because PDMPs make prescription opioids more difficult to access, these programs may have the unintended consequence of increasing deaths related to illicit opioids. Methods: This study uses fixed effects models to estimate how PDMP regulatory strength is associated with both prescription opioid- and heroin-related deaths between 1999 and 2016. PDMP regulatory strength is measured by creating a score using multiple correspondence analysis (MCA). Additional models replace the MCA score with a binary indicator for the presence of one particular regulation requiring physicians to access the system before writing opioid prescriptions. Results: Results show that continuous measures of PDMP strength are not generally associated with prescription opioid- or heroin-related death rates. Yet, one model does show that PDMP scores are positively associated with the heroin-related death rate. The models using the binary mandatory access variable show a strong positive association with both prescription opioid and heroin deaths. Conclusions: This study supports the theory that more stringent state PDMPs are associated with higher rates of heroin-related deaths, potentially due to decreases in prescription opioid availability.