Linked data for pharmacoepidemiology research in Australia: Examining medicine use and outcomes among people with opioid dependence

Despite strong evidence for the effectiveness of a range of interventions to improve the health and wellbeing of people who are dependent on opioids, morbidity and mortality in this population remains higher than that of the general population. There is a need for innovative approaches to monitor and improve the quality use and safety of available medicines, and to better understand risk factors impacting adverse outcomes in this population. In this thesis, routinely collected administrative data on people with opioid dependence in New South Wales (NSW), Australia, were used to investigate medicine use, including Opioid Agonist Treatment (OAT), opioid analgesics, and other psychotropic medicines. Studies in this thesis examined novel methodological approaches to evaluate medicine exposure and quantify risk, both observed and predicted. This thesis used a diverse range of data sources, including controlled drug registries and pharmaceutical claims databases, linked with health service use and mortality records; and implemented a range of statistical methodologies, including generalised estimating equations, Cox proportional hazards models, and deep learning algorithms. Specifically, this thesis aimed to: (i) estimate retention in OAT and identify person, treatment, and prescriber characteristics that are associated with retention; (ii) develop, evaluate and compare models predicting OAT cessation risk at entry to treatment; (iii) examine trends in opioid analgesic utilisation during periods in and out of OAT; (iv) review methods for generating exposure periods from pharmaceutical dispensing data; and (v) evaluating the all-cause and cause-specific mortality risk associated with opioid analgesics, benzodiazepines, gabapentinoids, and OAT. The first study found retention in OAT to be affected not only by characteristics of the person and their treatment, but also of their prescriber, with longer prescribing tenure associated with increased retention of people in OAT. The results from the second study indicated time-to-event prediction models may be limited in their ability to identify individuals at high cessation risk on entry to OAT. Of the methods used in model development, machine learning approaches performed similarly to traditional statistical methods. In the third study, people with opioid dependence were found to have high rates of recent psychotropic medicine utilisation at the time of opioid analgesic initiation, and reduced opioid analgesic dispensing while engaged in OAT. The fourth study describes a novel method for generating medicine exposure periods from dispensing claims data, developed especially for application to medicines with complex and variable dosing regimens. Finally, in the fifth study, benzodiazepines and gabapentinoids appear to increase mortality risk when used in combination with opioid analgesics, although the risk may be reduced when engaged in OAT. This thesis demonstrates the utility of person-level data linkage and innovative analytical methods to generate real-world evidence about the use and outcomes of prescribed medicines among people with opioid dependence. Awareness of harms in clinical settings and evaluating outcome risk during medicine use would give clinicians the ability to understand who needs prevention and treatment services, ensuring efforts and resources are targeted towards those most at-risk. These represent important strategies for improving quality medicine use and reducing harms among people with opioid dependence

The effect of person, treatment and prescriber characteristics on retention in opioid agonist treatment:a 15-year retrospective cohort study

AbstractBackground and Aims: There is limited evidence on the relationship between retention in opioid agonist treatment for opioid dependence and characteristics of treatment prescribers. This study estimated retention in buprenorphine and methadone treatment and its relationship with person, treatment, and prescriber characteristics. Design: Retrospective longitudinal study.Setting: New South Wales, Australia.Participants: People entering the opioid agonist treatment program for the first time between August 2001 and December 2015.Measurements: Time in opioid agonist treatment (primary outcome) was modelled using a generalised estimating equation model to estimate associations with person, treatment, and prescriber characteristics. Findings: The impact of medication type on opioid agonist treatment retention reduced over time; risk of leaving treatment when on buprenorphine compared with methadone was higher among those that entered treatment earlier (e.g. 2001-2003: OR 1.59, 95% CI 1.44-1.74) and lowest among those that entered most recently (2013-2015: OR 1.24, 95% CI 1.12-1.37). In adjusted analyses, risk of leaving was reduced among people whose prescriber had longer tenure of prescribing (e.g. 3 versus 8 years: OR 0.94, 95% CI 0.93-0.95) compared with prescribers with shorter tenure. Aboriginal and Torres Strait Islander people, being of younger age, past-year psychosis disorder, and having been convicted of more criminal charges in the year prior to treatment entry were associated with increased risk of leaving treatment. Conclusion: In New South Wales, Australia, retention in buprenorphine treatment for opioid dependence, compared with methadone, has improved over time since its introduction in 2001. Opioid agonist treatment (OAT) retention is affected not only by characteristics of the person and his or her treatment, but also of the prescriber, with those of longer prescribing tenure associated with increased retention of people in OAT. <br/

The effect of entry and retention in opioid agonist treatment on contact with the criminal justice system among opioid-dependent people:a retrospective cohort study

Background: Evidence on the effectiveness of opioid agonist treatment (OAT) in reducing crime is mixed. We aimed to assess the effect of OAT on crime in terms of delaying time to first charge and reducing overall charge rates, as well as the relationship between OAT retention and overall charge rates. Methods: We did a retrospective cohort study of opioid-dependent people who entered OAT for the first time between Jan 1, 2004, and Dec 30, 2010, in New South Wales (NSW), Australia. We used three linked NSW and national administrative datasets. Data on OAT were obtained from the Pharmaceutical Drugs of Addiction System, data on charges were obtained from the Reoffending Database, and data on mortality were obtained from the National Death Index. The cohort was followed up until Dec 31, 2011. Time-dependent OAT exposure was modelled using Cox proportional hazards models (time to first charge) and Andersen-Gill intensity models (total charge-days). Retention in OAT was modelled using two features of treatment engagement, number of OAT episodes and proportion of follow-up time in OAT (presented in quartile groupings: lowest, low-mid, low-high, highest) using zero-inflated negative binomial regression (total charges). All models were adjusted for sociodemographic, criminographic, and treatment-related variables. Findings: 10 744 new OAT entrants were included in the study. 5751 (53·5%) people were charged with an offence. In adjusted analyses, OAT was associated with an initial benefit in delaying the time to first charge (hazard ratio 0·43, 95% CI 0·33–0·55) and reducing total charge-days (0·39, 95% CI 0·30–0·52); however, these protective effects reduced over time. Total charge rates were higher as the number of OAT episodes increased (incident rate ratio [IRR] 1·13, 95% CI 1·11–1·15), and when relatively lower proportions of time were spent in OAT (IRR among the lowest three quartiles ranged from 1·11 [95% CI 1·02–1·21] to 1·22 [95% CI 1·12–1·33]). Interpretation: OAT was associated with a reduction in overall charge rates and was more protective as treatment engagement increased. Maximising treatment retention is crucial to achieving long-term health and social benefits of OAT. Funding: Australian National Health and Medical Research Council, Australian Institute of Criminology, National Institute on Drug Abuse, Australian Government Department of Health, UNSW Sydney