Risk of Overdose Associated With Co-prescription of Antipsychotics and Opioids: A Population-Based Cohort Study

Patterns of filled prescriptions and the association with risk of drug-induced death. A population-based nested case-control register study

PURPOSE

Opioid analgesics (OA) and other pharmaceuticals have been associated with drug-induced deaths. However, there is a lack of knowledge regarding patterns of use of these pharmaceuticals in the population and regarding such associations. We identify and describe subgroups of people with different patterns of filled prescriptions of OA and other relevant pharmaceuticals and examine associations with drug-induced deaths. In addition, we estimate the proportion of drug-induced deaths with a filled OA prescription and OA as cause of death.

METHODS

A Norwegian population-based nested case-control register study with cases (drug-induced deaths 2010-2018, N = 2388) and population controls matched for age, gender and year of inclusion (N = 21 465). Patterns of filled prescriptions for opioid analgesics (OA), benzodiazepines and benzodiazepine-related drugs, gabapentinoids, ADHD medication and antidepressants/antipsychotics were explored by k-means cluster analysis. Associations with drug-induced deaths were estimated by conditional logistic regression adjusted for sociodemographic characteristics. Overlap of filled OA prescriptions and OA as cause of death was estimated.

RESULTS

Five clusters were identified: 'few prescriptions', 'weak OA', 'ADHD medication', 'sedative/psychiatric morbidity' and 'strong OA'. The 'strong OA' cluster had higher socioeconomic status compared to the other groupings. The risk of drug-induced death was also highest in this cluster (OR = 35.5; CI 25.6-49.3) and, for 68% (CI 64-73) of cases, filled prescriptions for OA was indicated as the underlying cause of death.

CONCLUSIONS

The cluster analysis identified a subgroup with filled prescriptions of OA and other pharmaceuticals and a higher socioeconomic status than other subgroups. This subgroup had a high risk of drug-induced death that needs to be addressed.

Non-fatal overdose risk associated with prescribing opioid agonists concurrently with other medication: Cohort study conducted using linked primary care, secondary care and mortality records

BACKGROUND AND AIMS

An apparently protective effect of opioid agonist treatment (OAT) on all-cause and cause-specific mortality risk has been widely reported. Non-fatal overdose (NFO) often precedes subsequent drug-poisoning deaths. We hypothesized that benzodiazepines, gabapentinoids, antipsychotics, antidepressants, Z-drugs or opioids increase the NFO risk when co-prescribed with OAT.

DESIGN

We conducted a cohort study using the Clinical Practice Research Datalink GOLD and Aurum databases. The cohort was linked to Hospital Episode Statistics admitted patient care data (HES-APC), neighbourhood- and practice-level Index of Multiple Deprivation quintiles and mortality records from the Office for National Statistics.

SETTING

Primary care in England.

PARTICIPANTS

We studied patients with opioid use disorder, aged 18-64 years, who were prescribed OAT (15155 methadone and 5743 buprenorphine recipients) between Jan 1, 1998, and Dec 31, 2017.

MEASUREMENTS

The main outcome examined was NFO risk during co-prescription of OAT with benzodiazepines, antipsychotics, gabapentinoids, antidepressants, Z-drugs or opioids. Overdose was defined according to International Classification of Diseases codes from the HES-APC data set. Negative binomial regression models were used to estimate weighted rate ratios (wRR) for NFO during co-prescription of OAT and benzodiazepines, antipsychotics, gabapentinoids, antidepressants, Z-drugs or opioids with periods of exclusive OAT usage.

FINDINGS

Among 20 898 patients observed over 83 856 person-years, we found an elevated overdose risk that resulted in hospital admission during co-prescription of OAT with benzodiazepines [wRR: 1.45; 95% confidence interval (CI) = 1.26-1.67], gabapentinoids (wRR = 2.22; 95% CI = 1.77-2.79), Z-drugs (wRR = 1.60; 95% CI = 1.31-1.96), antipsychotics (wRR = 1.85; 95% CI = 1.53-2.25) and opioids (wRR = 1.28; 95% CI = 1.02-1.60). The risk ratio for antidepressant co-prescriptions was below unity (wRR = 0.90; 95% CI = 0.79-1.02) but this result was not statistically significant.

CONCLUSION

Elevated risk of non-fatal overdose among opioid agonist treatment recipients is associated with concurrent use of medication prescribed for other reasons.

Patterns of suboptimal antipsychotic use and misuse in Australia: What can routinely collected data tell us?

AIMS

There are increasing concerns about harms related to suboptimal antipsychotic use. Here we describe recent population-based trends in antipsychotic use and harms in Australia and identify population groups exhibiting patterns of use likely to contribute to these harms.

METHODS

Using population-based data from the Australian Pharmaceutical Benefits Scheme (2015-2020), poisoning calls to the New South Wales (NSW) Poisons Information Centre (2015-2020) and poisoning deaths in all coronial records (2005-2018) in Australia, we measured trends in the prevalence of antipsychotic use and related deaths and poisonings. We applied latent class analyses to identify patterns of antipsychotic use that may contribute to harms.

RESULTS

Quetiapine and olanzapine had the highest prevalence of use between 2015 and 2020. Noteworthy trends included increases of 9.1% and 30.8% in quetiapine use and poisonings, while olanzapine use decreased by 4.5% but poisonings increased by 32.7%. Quetiapine and olanzapine poisonings and related deaths had the highest rates of co-ingestion of opioids, benzodiazepines and pregabalin compared to other antipsychotics. We identified six distinct population groups using antipsychotics: (i) ongoing high-dose use with sedatives (8%), (ii) ongoing use (42%), (iii) ongoing use with analgesics and sedatives (11%), (iv) long-term low-dose use (9%), (v) sporadic use (20%) and (vi) sporadic use with analgesics (10%).

CONCLUSION

Ongoing potentially suboptimal antipsychotic use and associated harms highlight the need to monitor such patterns of use, for example through prescription monitoring systems.

Predictors of persistent opioid use in non-cancer older adults: a retrospective cohort study

BACKGROUND

Long-term opioid use and associated adverse outcomes have increased dramatically in recent years. Limited research is available on long-term opioid use in older adults.

OBJECTIVE

We aimed to determine the incidence and predictors of long-term or persistent opioid use (POU) amongst opioid-naïve older adults without a cancer diagnosis.

METHODS

This was a retrospective cohort study using five national administrative healthcare databases in New Zealand. We included all opioid-naïve older adults (≥65 years) who were initiated on opioid therapy between January 2013 and June 2018. The outcome of interest was POU, defined as having continuously filled ≥1 opioid prescription within 91-180 days after the index opioid prescription. Multivariable logistic regression was used to examine the predictors of POU.

RESULTS

The final sample included 268,857 opioid-naïve older adults; of these, 5,849(2.2%) developed POU. Several predictors of POU were identified. The use of fentanyl (adjusted odds ratio (AOR) = 3.61; 95% confidence interval (CI) 2.63-4.95), slow-release opioids (AOR = 3.02; 95%CI 2.78-3.29), strong opioids (AOR = 2.03; 95%CI 1.55-2.65), Charlson Comorbidity Score ≥ 3 (AOR = 2.09; 95% CI 1.78-2.46), history of substance abuse (AOR = 1.52; 95%CI 1.35-1.72), living in most socioeconomically deprived areas (AOR = 1.40; 95%CI 1.27-1.54), and anti-epileptics (AOR = 2.07; 95%CI 1.89-2.26), non-opioid analgesics (AOR = 2.05; 95%CI 1.89-2.21), antipsychotics (AOR = 1.96; 95%CI 1.78-2.17) or antidepressants (AOR = 1.50; 95%CI 1.41-1.59) medication use were the strongest predictors of POU.

CONCLUSION

A significant proportion of patients developed POU, and several factors were associated with POU. The findings will enable healthcare providers and policymakers to target early interventions to prevent POU and related adverse events.

Signal mining and analysis for central nervous system adverse events due to taking oxycodone based on FAERS database

OBJECTIVES

Central nervous system adverse events (AEs) occur when oxycodone is used in combination with benzodiazepines, antidepressants and anticonvulsants. There have been no reports of central nervous system AEs with oxycodone alone or in combination with oxycodone. Based on USA Food and Drug Administration Adverse Event Reporting System (FAERS) data, this study aims to explore the risk signals of central nervous system AEs with oxycodone alone or in combination with benzodiazepines, antidepressants and anticonvulsants, and to provide a reference for the safe and rational use of this drug.

METHODS

Extracted AEs data from the FAERS for oxycodone alone and in combination with benzodiazepines, antidepressants, and anticonvulsants from Q1 2004 to Q2 2021. The risk signal mining analysis of AEs was performed using the proportional imbalance method and Bayesian method. Number of reports ≥3 and lower 95% CI limit of reporting odds ratio (ROR)>1; number of reports ≥3, proportional reporting ratio (PRR)≥2 and χ2≥4; lower information components (IC) lower 95% CI limit (IC025)>0; empirical Bayes geometric mean (EBGM) lower 95% CI limit (EBGM05)>2, and N>0 were defined as positive signals.

RESULTS

A total of 5 793 reports of central nervous system AEs with oxycodone alone were tapped, and 366, 622, and 740 reports of combined benzodiazepines, antidepressants, and anticonvulsants, respectively. Consumers and physicians were the main reporting population. The age distribution of oxycodone alone was mainly from 61 to 80 years old. The age distribution of oxycodone in combination with related drugs was mainly from 46 to 60 years old. The risk of AEs was greater in women than in men, and the United States was the predominant reporting country. Oxycodone alone was strongly associated with myoclonus [ROR=2.92, 95% CI 2.28 to 3.76); PRR=2.92, χ2(77.49); IC=1.52, IC025(0.65); EBGM=2.89, EBGM05(2.33)], delirium [ROR=4.69, 95% CI 4.24 to 5.21; PRR=4.66, χ2(1 052.64); IC=2.17, IC025(1.81); EBGM=4.50, EBGM05 (4.13)], mental disorder [ROR=2.95, 95% CI 2.53 to 3.44; PRR=2.94, χ2(206.93); IC=1.56, IC025(0.96); EBGM=2.95, EBGM05(2.58)], and acute central respiratory depression [ROR=2.87, 95% CI 2.68 to 3.08); PRR=2.82, χ2(971.62); IC=1.52, IC025(1.33), EBGM=2.87, EBGM05 (2.76)]. Combination of benzodiazepines was most strongly associated with mental disorder [ROR=10.08, 95% CI 9.38 to 10.78; PRR=9.90, χ2(64.06); IC=3.33, IC025 (1.65); EBGM=10.08, EBGM05(5.61)], and tremor [ROR=3.09, 95% CI 2.76 to 3.42); PRR=3.08, χ2(48.93); IC=1.63, IC025 (1.17); EBGM=3.09, EBGM05(2.34)]. Combination of antidepressants was most strongly associated with delirium [ROR=13.23, 95% CI 12.23 to 14.23; PRR=12.87, χ2(43.86); IC=3.69, IC025(1.36); EBGM=12.23, EBGM05 (5.32)] and somnolence [ROR=6.74, 95% CI 6.15 to 7.33); PRR=6.73, χ2(53.42); IC=2.75, IC025(1.52); EBGM=6.73, EBGM05(4.10)]. Combination of anticonvulsants was most strongly associated with myoclonus [ROR=17.89, 95% CI 17.46 to 18.32; PRR=17.72, χ2(971.39); IC=4.16, IC025(2.70); EBGM=17.89, EBGM05(12.46)] and delirium [ROR=4.86, 95% CI 4.45 to 5.27); PRR=4.82, χ2(69.49); IC=2.28, IC025 (1.51); EBGM=4.86, EBGM05(3.44)].

CONCLUSIONS

Based on pharmacovigilance studies of the FAERS database, clinical medication monitoring of oxycodone alone and in combination with benzodiazepines, antidepressants, and anticonvulsants should be strengthened to be alert to the occurrence of central nervous system-related AEs.

High opioid doses, naloxone, and central nervous system active medications received by Medicare-enrolled adults

BACKGROUND

A limited number of studies have analyzed prescribing among Medicare-enrolled adults at risk for opioid overdoses. The objectives of this study were to evaluate prescribing for naloxone and central nervous system (CNS) active medications and to determine the relationships of patient characteristics with exposure to these medications.

METHODS

This was a retrospective cross-sectional analysis of a Medicare-enrolled medication therapy management eligible cohort. Patients were stratified into two cohorts, individuals with a mean daily morphine milligram equivalent (MME) dose <50 and individuals with MME ≥50. Medications assessed included benzodiazepines, skeletal muscle relaxants (SMR), hypnotics, gabapentanoids, selective-serotonin reuptake inhibitors (SSRI), serotonin-norepinephrine reuptake inhibitors (SNRI), tricyclic antidepressants (TCA), antipsychotics, barbiturates, other antiepileptics, hydroxyzine, and naloxone. Chi-square with odds ratios and logistic regressions determined the relationships of medications and patient characteristics with mean daily MME ≥50. Relationship between medications and opioid dose was adjusted for age and sex.

RESULTS

There were 3452 patients with a daily MME <50 and 1116 with a daily MME ≥50. After adjusting for age and sex, patients with a daily MME ≥50 were more likely to be prescribed hypnotics (OR: 1.41, 95% CI 1.17-1.70), SNRIs (OR: 1.39, 95% CI 1.17-1.64), and naloxone (OR: 3.21, 95% CI 2.49-4.12) (p < 0.001). Nine percent of eligible patients received naloxone. Age groups of persons <85 years of age had 1.58-4.04 (p ≤ 0.004) times the odds of being prescribed a mean daily MME ≥50.

CONCLUSION

Nearly one-fourth of patients were prescribed a mean daily opioid therapy of MME ≥50. These patients were more likely to be prescribed hypnotics, SNRIs, and naloxone. Patients receiving chronic high-dose opioid therapy were more likely to be in age groups of persons <85 years. Naloxone may be underprescribed among eligible adults. Targeted medication services may ensure optimal prescribing among Medicare patients with chronic opioid therapies.

Opioid Use Disorder and Older Adults: Navigating Treatment Options

The intent of the current article is to highlight via a case vignette challenges faced when managing pain across care transitions in an older adult with multiple comorbidities, including, but not limited to, opioid use disorder (OUD). This case will highlight the role of different medications for OUD, namely buprenorphine/naloxone, methadone, and naltrexone. Furthermore, the case illustrates medication-related considerations in addition to action steps that are needed when working with older adults. [Journal of Gerontological Nursing, 48(7), 4-9.].