Dataset Information

Opioid Prescription Patterns and Risk Factors Associated With Opioid Use in the Netherlands.

ABSTRACT:

Importance

An increase in opioid prescription has been observed in the Netherlands. It is vital to understand this increase and to identify risk factors for opioid prescription to ensure that health interventions remain appropriately targeted.

Objectives

To determine the prevalence of opioid prescriptions and adverse events associated with opioids, and to identify risk factors associated with opioid prescription in the Dutch population.

Design, setting, and participants

This cohort study used national statistics from the Netherlands from January 1, 2013, to December 31, 2017, including the full Dutch population of 16?779?575 people in 2013 and 17?081?507 people in 2017. Data from the Dutch Health Monitor surveys of 2012 and 2016 were also included. Databases were anonymized prior to analysis. All analyses were performed between December 2018 and February 2019.

Exposure

Opioid prescription.

Main outcomes and measures

The main outcomes were the dynamics of opioid prescriptions, hospital admissions for opioid overdose, and opioid overdose mortalities. The secondary outcome was risk factors associated with opioid prescription.

Results

In 2013, 814?211 individuals (4.9% of the total population) received an opioid prescription. In 2017, 1?027?019 individuals (6.0% of the total population) received at least 1 opioid prescription (mean [SD] age, 59.3 [18.5] years; 613?203 [59.7%] women). The rate of hospital admissions for opioid overdose was 9.2 per 100?000 inhabitants in 2013 and 13.1 per 100?000 inhabitants in 2017 (relative risk, 1.43 [95% CI, 1.34-1.52]). Similarly, an increased risk of opioid overdose death was observed, from 0.83 per 100?000 inhabitants in 2013 to 1.2 per 100?000 inhabitants in 2017 (relative risk, 1.49 [95% CI, 1.20-1.85]). Based on data from the 2012 Dutch Health Monitor survey, risk factors associated with opioid prescription included being older than 65 years (odds ratio [OR], 4.20 [95% CI, 3.98-4.43]), having only a primary school education (OR, 3.62 [95% CI, 3.46-3.77]), being widowed (OR, 3.30 [95% CI, 3.13-3.49]), reporting always feeling symptoms of depression (OR, 3.77 [95% CI, 3.41-4.18]), and reporting poor or very poor physical health (OR, 10.40 [95% CI, 10.01-10.81]). Self-reported back pain (OR, 4.34 [95% CI, 4.23-4.46]) and rheumatoid arthritis or fibromyalgia (OR, 3.77 [95% CI, 3.65-3.90]) were also associated with opioid prescription. However, unemployment (OR, 1.05 [95% CI, 0.96-1.13]) was not associated with opioid prescription, and alcohol use disorder (OR, 0.76 [95% CI, 0.73-0.80]) was negatively associated with opioid prescription.

Conclusions and relevance

This study found that opioid prescriptions have increased in the Netherlands. Although the risk of adverse events is still relatively low, there is an urgent need to review pain management to prevent a further increase in opioid prescription.

SUBMITTER: Bedene A

PROVIDER: S-EPMC6716286 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Opioid Prescription Patterns and Risk Factors Associated With Opioid Use in the Netherlands.

Bedene Ajda A Lijfering Willem M WM Niesters Marieke M van Velzen Monique M Rosendaal Frits R FR Bouvy Marcel L ML Dahan Albert A van Dorp Eveline L A ELA

JAMA network open 20190802 8

<h4>Importance</h4>An increase in opioid prescription has been observed in the Netherlands. It is vital to understand this increase and to identify risk factors for opioid prescription to ensure that health interventions remain appropriately targeted.<h4>Objectives</h4>To determine the prevalence of opioid prescriptions and adverse events associated with opioids, and to identify risk factors associated with opioid prescription in the Dutch population.<h4>Design, setting, and participants</h4>Thi ...[more]

PMID: 31461149

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Project description:ImportanceThe opioid epidemic continues to be a public health crisis in the US.ObjectiveTo assess the patient factors and early time-varying prescription-related factors associated with opioid-related fatal or nonfatal overdose.Design, setting, and participantsThis cohort study evaluated opioid-naive adult patients in Oregon using data from the Oregon Comprehensive Opioid Risk Registry, which links all payer claims data to other health data sets in the state of Oregon. The observational, population-based sample filled a first (index) opioid prescription in 2015 and was followed up until December 31, 2018. Data analyses were performed from March 1, 2020, to June 15, 2021.ExposuresOverdose after the index opioid prescription.Main outcomes and measuresThe outcome was an overdose event. The sample was followed up to identify fatal or nonfatal opioid overdoses. Patient and prescription characteristics were identified. Prescription characteristics in the first 6 months after the index prescription were modeled as cumulative, time-dependent measures that were updated monthly through the sixth month of follow-up. A time-dependent Cox proportional hazards regression model was used to assess patient and prescription characteristics that were associated with an increased risk for overdose events.ResultsThe cohort comprised 236 921 patients (133 839 women [56.5%]), of whom 667 (0.3%) experienced opioid overdose. Risk of overdose was highest among individuals 75 years or older (adjusted hazard ratio [aHR], 3.22; 95% CI, 1.94-5.36) compared with those aged 35 to 44 years; men (aHR, 1.29; 95% CI, 1.10-1.51); those who were dually eligible for Medicaid and Medicare Advantage (aHR, 4.37; 95% CI, 3.09-6.18), had Medicaid (aHR, 3.77; 95% CI, 2.97-4.80), or had Medicare Advantage (aHR, 2.18; 95% CI, 1.44-3.31) compared with those with commercial insurance; those with comorbid substance use disorder (aHR, 2.74; 95% CI, 2.15-3.50), with depression (aHR, 1.26; 95% CI, 1.03-1.55), or with 1 to 2 comorbidities (aHR, 1.32; 95% CI, 1.08-1.62) or 3 or more comorbidities (aHR, 1.90; 95% CI, 1.42-2.53) compared with none. Patients were at an increased overdose risk if they filled oxycodone (aHR, 1.70; 95% CI, 1.04-2.77) or tramadol (aHR, 2.80; 95% CI, 1.34-5.84) compared with codeine; used benzodiazepines (aHR, 1.06; 95% CI, 1.01-1.11); used concurrent opioids and benzodiazepines (aHR, 2.11; 95% CI, 1.70-2.62); or filled opioids from 3 or more pharmacies over 6 months (aHR, 1.38; 95% CI, 1.09-1.75).Conclusions and relevanceThis cohort study used a comprehensive data set to identify patient and prescription-related risk factors that were associated with opioid overdose. These findings may guide opioid counseling and monitoring, the development of clinical decision-making tools, and opioid prevention and treatment resources for individuals who are at greatest risk for opioid overdose.

Project description:Importance:Nonmedical prescription opioid use is a pressing public health issue in the United States. Transgender youth, including adolescent girls and young women who were assigned male at birth and currently identify as women, female, transgender women, or another diverse gender identity along the transfeminine gender spectrum, are more likely than their cisgender peers to report illicit substance use and meet diagnostic criteria for substance use disorders. However, relatively little is known about the experiences of these populations in the current era of opioid addiction and misuse. Objective:To report the prevalence of and risk factors associated with lifetime nonmedical prescription opioid use in a high-risk community sample of transgender adolescent girls and young women who are sexually active. Design, Setting, and Participants:This cross-sectional study used 2012 to 2015 baseline data from Project LifeSkills, a randomized clinical trial of a behavioral intervention to reduce the risk of HIV acquisition and transmission among a diverse sample of transgender adolescent girls and young women recruited from Boston, Massachusetts, and Chicago, Illinois. A total of 297 transgender girls and women aged 16 to 29 years who were sexually active were included in this analysis. Data were analyzed from June 2019 to August 2019. Exposures:Transgender woman identification. Main Outcomes and Measures:Self-reported lifetime nonmedical prescription opioid use. Results:Among 297 transgender adolescent girls and young women (mean [SD] age, 23.4 [3.5] years), 145 (48.8%) identified as non-Hispanic/Latinx black, 76 (25.6%) identified as non-Hispanic/Latinx white, 37 (12.5%) identified as Hispanic/Latinx, 7 (2.4%) identified as non-Hispanic/Latinx Asian, and 32 (10.8%) identified as multiracial or other race/ethnicity. Thirty-five participants (11.8%) reported lifetime nonmedical prescription opioid use. Young transgender women who smoked cigarettes monthly or less (adjusted odds ratio, 3.92; 95% CI, 1.10-13.89) and who smoked daily (adjusted odds ratio, 5.69; 95% CI, 1.87-17.33) had greater odds of nonmedical prescription opioid use compared with those who did not smoke. Additionally, participants who identified as a sexual orientation other than heterosexual, gay, lesbian, or bisexual had significantly greater odds of lifetime nonmedical prescription opioid use compared with those who identified as heterosexual (adjusted odds ratio, 3.69; 95% CI, 1.07-12.72). Conclusions and Relevance:These findings suggest that transgender adolescent girls and young women have similar prevalence of lifetime nonmedical prescription opioid use compared with the US general population prevalence of 12.5%. These findings may serve as a call-to-action for public health surveillance studies and evidence-based interventions to be comprehensively tailored to examine and respond to specific trends of substance use, particularly opioid use disorder, among transgender populations.

Project description:PurposeTo determine the rate and risk factors for new persistent opioid use after ophthalmic surgery in the United States.DesignRetrospective claims-based cohort analysis.ParticipantsOpioid-naive patients 13 years of age and older who underwent incisional ophthalmic surgery between January 1, 2012, and June 30, 2017, and were included in Optum's de-identified Clinformatics Data Mart database.MethodsNew persistent opioid use was defined as filling an opioid prescription in the 90-day and the 91- to 180-day periods after the surgical procedure. The outcome variable was an initial perioperative opioid prescription fill. Rates of new persistent opioid use were calculated, and multivariate logistic regression models were used to identify variables increasing the risk of new persistent use and refill of an opioid prescription after the initial perioperative prescription in first 30 days.Main outcome measuresNew persistent opioid use and refill.ResultsA total of 327 379 opioid-naive patients (mean age, 67 years [standard deviation, 16 years]; 178 067 women [54.4%]) who underwent ophthalmic surgery were examined. Among these patients, 14 841 (4.5%) had an initial perioperative opioid fill. The rate of new persistent opioid use was 3.4% (498 of 14 841 patients) compared with 0.6% (1833 of 312 538 patients) in patients who did not have an initial perioperative opioid fill. After adjusting for patient characteristics, initial perioperative opioid fill was associated independently with increased odds of new persistent use (adjusted odds ratio [OR], 6.21; 95% confidence interval [CI], 5.57-6.91; P < 0.001). Among patients who had filled an initial perioperative prescription, a prescription size of 150 morphine milligram equivalents or more was associated with an increased odds of refill (adjusted OR, 1.87; 95% CI, 1.58-2.22; P < 0.001).ConclusionsExposure to opioids in the perioperative period is associated with new persistent use in patients who were previously opioid-naive. This suggests that exposure to opioids is an independent risk factor for persistent use in patients undergoing incisional ophthalmic surgery. Surgeons should be aware of those risks to identify at-risk patients given the current national opioid crisis and to minimize prescribing opioids when possible.

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.

Dataset Information

Opioid Prescription Patterns and Risk Factors Associated With Opioid Use in the Netherlands.

Importance

Objectives

Design, setting, and participants

Exposure

Main outcomes and measures

Results

Conclusions and relevance

Publications

Opioid Prescription Patterns and Risk Factors Associated With Opioid Use in the Netherlands.

Similar Datasets

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