Opioids: The Painful Public Health Reality

Multinational appraisal of the epidemiological distribution of opioid fatalities: a systematic review and meta-analysis

Background

The global or multinational scientific evidence on the distribution of opioid fatality is unknown. Hence, the current study collects epidemiological characteristics to shed light on the ongoing global or multinational opioid crisis and to promote the development of public health prevention/management strategies.

Method

All documents on PRISMA standards were retrieved via electronic databases.

Results

Among the 47 articles relevant to our studies, which depict a total population size of 10,191 individuals, the prevalence of opioid fatal overdose was 15,022 (14.74%). Among the 47 articles, 14 of them reported the gender of the participants, with 22,125 (15.79%) male individuals and 7,235 (5.17%) female individuals, and the age distribution of the participants that was most affected by the overdose was as follows: 29,272 (31.13%) belonged to the 18-34-year-old age group and 25,316 (26.92%) belonged to the less than 18-year-old age group. Eighteen studies qualified for the meta-analysis of the multinational prevalence of fatal opioid overdose, depicting an overall pooled prevalence estimate of 19.66%, with 95% CIs (0.13-0.29), I2 = 99.76% determined using the random-effects model, and Q statistic of 7198.77 (p < 0.0001). The Egger test models of publication bias revealed an insubstantial level of bias (p = 0.015). The subgroup analysis of the study design (cohort or other) revealed that others have the highest prevalence estimate of 34.37, 95% CIs (0.1600-0.5901), I2 = 97.04%, and a sample size of less than 1,000 shows the highest prevalence of 34.66, 95% CIs (0.2039-0.5234), I2 = 97.82%, compared to that of more than 1,000 with a prevalence of 12.28, 95% CIs (0.0675-0.2131), I2 = 99.85%. The meta-regression analysis revealed that sample size (less-than or greater-than 1,000), (p = 0.0098; R2 = 3.83%) is significantly associated with the observed heterogeneity.

Conclusion

Research-based findings of fatal opioid overdose are grossly lacking in middle- and low-income nations. We established that there is a need for opioid fatality surveillance systems in developing nations.

Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients

Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage.

Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We’ve also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern.

Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse.

Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.

Ten-year trend of opioid and nonopioid analgesic use in the French adult population

AIMS

Analgesics are the most widely used medicines worldwide. In parallel, opioid abuse has increased and is of major concern. The accessibility of pharmacologically powerful medicines and the addictovigilance signals in France about the risk of opiates addiction call for an overview of analgesic use. The objective of this study was to investigate the use of analgesics reimbursed in France over a 10-year period through its prevalence.

METHODS

A cross-sectional study repeated yearly was conducted by using data from the French reimbursement database from 2006 to 2015. Analgesics were classified according to their pharmacological potency: prevalence of use for each category and sociodemographic characteristics of patients treated were analysed.

RESULTS

The annual prevalence of analgesic use was high and increased during the study period (59.8%, 253 976 users in 2015). In 2015, prevalence was always higher in women and increased with age, except for those older than 84 years. Peripheral analgesics were the most used (55.3%, 234 739 users). The prevalence of weak analgesic use decreased (21.3%, 90 257 users), mainly due to the definitive withdrawal of dextropropoxyphene in France in 2011, which was not offset by an increase in the consumption of other weak analgesics. For strong analgesics (1.2%, 5129 users), morphine was the most widely used, with a dramatic increase in oxycodone use, especially in the elderly.

CONCLUSION

The prevalence of analgesic use is high: approximately 31 million adults had at least 1 analgesic reimbursed in 2015. The most widely used analgesics were peripheral analgesics, far ahead of opioid analgesics.

Higher doses of naloxone are needed in the synthetic opiod era

There has been a dramatic increase of deaths due to illicit fentanyl. We examined the pharmacology of fentanyl and reviewed data on the number of repeat doses of naloxone used to treat fentanyl overdoses. Multiple sequential doses of naloxone have been required in a certain percentage of opioid overdoses due to fentanyl. In addition, fentanyl appears to differ from other opioids as having a very rapid onset with high systemic levels found in overdose victims. A rapid competition is required by naloxone to out-compete large numbers of opioid receptors occupied by fentanyl in the CNS. Taken together, we propose that higher doses of naloxone are needed to combat the new era of overdoses due to the more potent synthetic opioids such as fentanyl.