Oxycodone initiation in Australia (2014-2018): Sociodemographic factors and preceding health service use.

AIMS: Oxycodone is the most commonly prescribed strong opioid in Australia. This study describes health service antecedents and sociodemographic factors associated with oxycodone initiation. METHODS: Population-based new user cohort study linking medicine dispensings, hospitalizations, emergency department visits, medical services and cancer notifications from New South Wales (NSW) for 2014-2018. New users had no dispensings of any opioid in the preceding year. We analysed health service use in the 5 days preceding initiation and proportion of people on treatment over 1 year and fitted an area-based, multivariable initiation model with sociodemographic covariates. RESULTS: Oxycodone accounted for 30% of opioid initiations. Annually, 3% of the NSW population initiated oxycodone, and 5-6% were prevalent users; the new user cohort comprised 830 963 people. Discharge from hospital (39.3%), therapeutic procedures (21.4%) and emergency department visits (19.7%) were common; a hospital admission for injury (6.0%) or a past-year history of cancer (7.2%) were less common. At 1 year after initiation, 4.6% of people were using oxycodone. In the multivariable model, new use of oxycodone increased with age and was higher for people outside major cities, for example, an incidence rate ratio of 1.43 (95% confidence interval 1.36-1.51) for inner regional areas relative to major cities; there was no evidence of variation in rates of new use by social disadvantage. CONCLUSION: About half of new oxycodone use in NSW was preceded by a recent episode of hospital care or a therapeutic procedure. Higher rates of oxycodone initiation in rural and regional areas were not explained by sociodemographic factors.

Medicine utilization studies in Australian individual-level dispensing data: A blinded, multi-center replicated analysis.

PURPOSE: Medicine dispensing data require extensive preparation when used for research and decisions during this process may lead to results that do not replicate between independent studies. We conducted an experiment to examine the impact of these decisions on results of a study measuring discontinuation, intensification, and switching in a cohort of patients initiating metformin. METHODS: Four Australian sites independently developed a HARmonized Protocol template to Enhance Reproducibility (HARPER) protocol and executed their analyses using the Australian Pharmaceutical Benefits Scheme 10% sample dataset. Each site calculated cohort size and demographics and measured treatment events including discontinuation, switch to another diabetes medicine, and intensification (addition of another diabetes medicine). Time to event and hazard ratios for associations between cohort characteristics and each event were also calculated. Concordance was assessed by measuring deviations from the calculated median of each value across the sites. RESULTS: Good agreement was found across sites for the number of initiators (median: 53 127, range: 51 848-55 273), gender (56.9% female, range: 56.8%-57.1%) and age group. Each site employed different methods for estimating days supply and used different operational definitions for the treatment events. Consequently, poor agreement was found for incidence of discontinuation (median 55%, range: 34%-67%), switching (median 3.5%, range: 1%-7%), intensification (median 8%, range: 5%-12%), time to event estimates and hazard ratios. CONCLUSIONS: Differences in analytical decisions when deriving exposure from dispensing data affect replicability. Detailed analytical protocols, such as HARPER, are critical for transparency of operational definitions and interpretations of key study parameters.

Age-Related Risk of Serious Fall Events and Opioid Analgesic Use.

Importance: Opioid analgesics may be associated with increased risk of falls, particularly among older adults. Objective: To quantify the age-related risk of serious fall events among adults prescribed opioids by opioid exposure, time from initiation, and daily dose. Design, Setting, and Participants: This population-based cohort study conducted in New South Wales, Australia, used data linking national pharmaceutical claims to national and state datasets, including information on sociodemographic characteristics, clinical characteristics, medicines use, health services utilization, and mortality (POPPY II study). It included adults (18 years or older) who initiated prescription opioid treatment, which was defined as no prior dispensing during the preceding 365 days, between January 1, 2005, and December 31, 2018. Data were analyzed from February to June 2023. Exposure: Time-dependent periods of opioid exposure were evaluated from dispensing records. Main Outcome and Measures: Serious fall events identified from emergency department, hospitalization, and mortality records. Negative binomial models were used to assess associations between time-dependent opioid exposure (overall, by time from initiation, and by dose), age, and risk of fall events. Models were adjusted for known fall risk factors, including other fall risk-increasing drugs, frailty risk, and prior serious fall events. Results: The cohort comprised 3 212 369 individuals who initiated prescription opioid treatment (1 702 332 women [53%]; median [IQR] age at initiation, 49 [32-65] years). Overall, 506 573 serious fall events were identified, including 5210 fatal falls. During exposure to opioids, the risk of serious fall events was elevated among all age groups; compared with the group aged 18 to 44 years, this risk was highest among those 85 years or older (adjusted incident rate ratio, 6.35; 95% CI, 6.20-6.51). Across all age groups, the first 28 days following opioid initiation was a time of increased serious fall risk; this risk increased with age. Among individuals aged 18 to 84 years, associations were identified between higher daily opioid doses and serious fall events. Conclusions and Relevance: The results of this cohort study suggest that prescription opioids were associated with increased risk of serious fall events among adults of all ages, with individuals 85 years or older at greatest risk. These risks should be considered when prescribing opioids, particularly for individuals with preexisting risk factors or when opioids are prescribed at higher doses. Targeted falls prevention efforts may be most effective within the first month following opioid initiation.

Prescription opioid use among people with opioid dependence and concurrent benzodiazepine and gabapentinoid exposure: An analysis of overdose and all-cause mortality.

BACKGROUND: Studies investigating mortality risk associated with use of opioid analgesics, benzodiazepines, gabapentinoids, and opioid agonist treatment (OAT) among people with opioid dependence (PWOD) are lacking. This study addresses this gap using a cohort of 37,994 PWOD initiating opioid analgesics between July 2003 and July 2018 in New South Wales, Australia. METHODS: Linked administrative records provided data on dispensings, sociodemographics, clinical characteristics, OAT, and mortality. Cox proportional hazards models assessed associations between time-varying measures of individual and concurrent medicine use and OAT with all-cause mortality, accidental opioid overdose, non-drug induced accidents, and non-drug-induced suicide. Opioid analgesic dose effects, expressed as oral morphine equivalents (OMEs) per day, were also examined. OUTCOMES: During the study period, 3167 individuals died. Compared with no use, all medicines of interest were associated with increased accidental opioid overdose risk; hazard ratios (HR) ranged from 1.33 (95 % CI: 1.05-1.68) for opioid analgesic use to 6.10 (95 % CI: 4.11-9.06) for opioid analgesic, benzodiazepine and gabapentinoid use. Benzodiazepine use was associated with increased non-drug-induced accidents and non-drug-induced suicides. For all-cause mortality, all combinations of benzodiazepines and gabapentinoids with opioid analgesics were associated with increased risk (aHRs ranged from 1.35 to 2.73). For most medicines/medicine combinations, all-cause mortality risk was reduced when in OAT compared to out of OAT. Higher opioid analgesic doses were associated with increased all-cause mortality (e.g., 90-199 mg vs 1-49 mg OME per day: HR 1.90 [95 % CI: 1.52-2.40]). INTERPRETATION: The increased mortality risk associated with benzodiazepines and gabapentinoids among PWOD appear to be reduced when engaged in OAT. A greater focus on encouraging OAT engagement, providing overdose prevention education, and access and coverage of overdose antidotes is necessary to minimise the unintended consequences of medicines use in this population.

Trends in use of medicines for opioid agonist treatment in Australia, 2013-2022.

BACKGROUND: There are limited longitudinal data on national patterns of opioid agonist treatment (OAT). This study describes 10-year trends in the sales of OAT medicines in Australia. METHODS: A descriptive and time-series analysis of methadone, sublingual (SL) buprenorphine (+/-naloxone), and long-acting injectable (LAI) buprenorphine sold in Australia between 2013 and 2022 was performed. Total units sold were converted into an estimate of the number of clients that could be treated over a 28-day period with that amount of medicine ('client-months'). RESULTS: Between January 2013 and December 2022, the estimated number of client-months on: any OAT increased by 50 % to 53,501, methadone decreased (-8.5%), SL buprenorphine increased (+78%), and LAI buprenorphine increased substantially after September 2019. In January 2013, 78 % of OAT client-months received methadone. By December 2022, 48 % received methadone, 26 % SL buprenorphine, and 26 % LAI buprenorphine. Between 2013 to 2022, OAT client-months per capita were highest in the state of New South Wales. Over the study period, greater increases in OAT were observed in very remote areas (88%) compared to major cities (53%). The number of client-months in non-community pharmacy settings remained stable from 2013 to 2019/20, before increasing markedly. The introduction of LAI buprenorphine was associated with an immediate, sustained increase of 1,636 OAT client-months, and further increases of 190 OAT client-months each month. CONCLUSION: Patterns of OAT have shifted over the last 10-years with buprenorphine (SL/LAI) now the most common OAT used in Australia. The introduction of LAI buprenorphine has expanded OAT access, particularly in non-community pharmacy settings, and in remote areas.

Five-Year Trajectories of Prescription Opioid Use.

Importance: There are known risks of using opioids for extended periods. However, less is known about the long-term trajectories of opioid use following initiation. Objective: To identify 5-year trajectories of prescription opioid use, and to examine the characteristics of each trajectory group. Design, Setting, and Participants: This population-based cohort study conducted in New South Wales, Australia, linked national pharmaceutical claims data to 10 national and state data sets to determine sociodemographic characteristics, clinical characteristics, drug use, and health services use. The cohort included adult residents (aged ≥18 years) of New South Wales who initiated a prescription opioid between July 1, 2003, and December 31, 2018. Statistical analyses were conducted from February to September 2022. Exposure: Dispensing of a prescription opioid, with no evidence of opioid dispensing in the preceding 365 days, identified from pharmaceutical claims data. Main Outcomes and Measures: The main outcome was the trajectories of monthly opioid use over 60 months from opioid initiation. Group-based trajectory modeling was used to classify these trajectories. Linked health care data sets were used to examine characteristics of individuals in different trajectory groups. Results: Among 3 474 490 individuals who initiated a prescription opioid (1 831 230 females [52.7%]; mean [SD] age, 49.7 [19.3] years), 5 trajectories of long-term opioid use were identified: very low use (75.4%), low use (16.6%), moderate decreasing to low use (2.6%), low increasing to moderate use (2.6%), and sustained use (2.8%). Compared with individuals in the very low use trajectory group, those in the sustained use trajectory group were older (age ≥65 years: 22.0% vs 58.4%); had more comorbidities, including cancer (4.1% vs 22.2%); had increased health services contact, including hospital admissions (36.9% vs 51.6%); had higher use of psychotropic (16.4% vs 42.4%) and other analgesic drugs (22.9% vs 47.3%) prior to opioid initiation, and were initiated on stronger opioids (20.0% vs 50.2%). Conclusions and relevance: Results of this cohort study suggest that most individuals commencing treatment with prescription opioids had relatively low and time-limited exposure to opioids over a 5-year period. The small proportion of individuals with sustained or increasing use was older with more comorbidities and use of psychotropic and other analgesic drugs, likely reflecting a higher prevalence of pain and treatment needs in these individuals.

Towards measuring effective coverage: critical bottlenecks in quality- and user-adjusted coverage for major depressive disorder in São Paulo metropolitan area, Brazil.

BACKGROUND: Major depressive disorder (MDD) contributes to a significant proportion of disease burden, disability, economic losses, and impact on need of treatment and health care in Brazil, but systematic information about its treatment coverage is scarce. This paper aims to estimate the gap in treatment coverage for MDD and identify key bottlenecks in obtaining adequate treatment among adult residents in the São Paulo Metropolitan area, Brazil. METHODS: A representative face-to-face household survey was conducted among 2942 respondents aged 18+ years to assess 12-month MDD, characteristics of 12-month treatment received, and bottlenecks to deliver care through the World Mental Health Composite International Diagnostic Interview. RESULTS: Among those with MDD (n = 491), 164 (33.3% [SE, 1.9]) were seen in health services, with an overall 66.7% treatment gap, and only 25.2% [SE, 4.2] received effective treatment coverage, which represents 8.5% of those in need, with a 91.5% gap in adequate care (66.4% due to lack of utilization and 25.1% due to inadequate quality and adherence). Critical service bottlenecks identified were: use of psychotropic medication (12.2 percentage points drop), use of antidepressants (6.5), adequate medication control (6.8), receiving psychotherapy (19.8). CONCLUSIONS: This is the first study demonstrating the huge treatment gaps for MDD in Brazil, considering not only overall coverage, but also identifying specific quality- and user-adjusted bottlenecks in delivering pharmacological and psychotherapeutic care. These results call for urgent combined actions focused in reducing effective treatment gaps within services utilization, as well as in reducing gaps in availability and accessibility of services, and acceptability of care for those in need.

Mortality during and after specialist alcohol and other drug treatment: Variation in rates according to principal drug of concern and treatment modality.

INTRODUCTION: For people accessing treatment for problems with drugs other than opioids, little is known about the relationship between treatment and mortality risk, nor how mortality risk varies across treatment modalities. We addressed these evidence gaps by determining mortality rates during and after treatment for people accessing a range of treatment modalities for several drugs of concern. METHODS: We conducted a cohort study using linked data on publicly funded specialist alcohol or other drug treatment service use and mortality for people receiving treatment in New South Wales between January 2012 and December 2018. We calculated and compared during-treatment and post-treatment crude mortality rates and age- and sex-standardised mortality rates, separately for each principal drug of concern and modality. RESULTS: Over the study period, 45,026 people accessed treatment for problems with alcohol, 26,407 for amphetamine-type stimulants, 23,047 for cannabinoids and 21,556 for opioids. People treated for alcohol or opioid problems had higher crude mortality rates (1.48, 1.91, 1.09 per 100 person years, respectively) than those with problems with amphetamine-type stimulants or cannabinoids (0.46, 0.30 per 100 person years, respectively). Mortality rates differed according to treatment status and modality only among people with alcohol or opioid problems. DISCUSSION AND CONCLUSIONS: The observed variation in mortality rates indicates there is scope to reduce mortality among people accessing treatment with alcohol or opioid problems. Future research on mortality among people accessing drug and alcohol treatment should account for the variation in mortality by drug of concern and treatment modality.

Agreement between self-reported illicit drug use and biological samples: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Studies often rely upon self-report and biological testing methods for measuring illicit drug use, although evidence for their agreement is limited to specific populations and self-report instruments. We aimed to examine comprehensively the evidence for agreement between self-reported and biologically measured illicit drug use among all major illicit drug classes, biological indicators, populations and settings. METHODS: We systematically searched peer-reviewed databases (Medline, Embase and PsycINFO) and grey literature. Included studies reported 2 × 2 table counts or agreement estimates comparing self-reported and biologically measured use published up to March 2022. With biological results considered to be the reference standard and use of random-effect regression models, we evaluated pooled estimates for overall agreement (primary outcome), sensitivity, specificity, false omission rates (proportion reporting no use that test positive) and false discovery rates (proportion reporting use that test negative) by drug class, potential consequences attached to self-report (i.e. work, legal or treatment impacts) and time-frame of use. Heterogeneity was assessed by inspecting forest plots. RESULTS: From 7924 studies, we extracted data from 207 eligible studies. Overall agreement ranged from good to excellent (> 0.79). False omission rates were generally low, while false discovery rates varied by setting. Specificity was generally high but sensitivity varied by drug, sample type and setting. Self-report in clinical trials and situations of no consequences was generally reliable. For urine, recent (i.e. past 1-4 days) self-report produced lower sensitivity and false discovery rates than past month. Agreement was higher in studies that informed participants biological testing would occur (diagnostic odds ratio = 2.91, 95% confidence interval = 1.25-6.78). The main source of bias was biological assessments (51% studies). CONCLUSIONS: While there are limitations associated with self-report and biological testing to measure illicit drug use, overall agreement between the two methods is high, suggesting both provide good measures of illicit drug use. Recommended methods of biological testing are more likely to provide reliable measures of recent use if there are problems with self-disclosure.

Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use.

AIMS: Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost-effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. DESIGN: A prediction model was developed for DSM-IV AD onset by age 25 years using an ensemble machine-learning algorithm known as 'Super Learner'. Shapley additive explanations (SHAP) assessed variable importance. SETTING AND PARTICIPANTS: Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. MEASUREMENTS: The primary outcome to be predicted was onset of life-time DSM-IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. FINDINGS: AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74-0.81] higher than any individual candidate risk model (0.73-0.77) and an area under the precision-recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. CONCLUSIONS: A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models.

A data-informed approach using individualised dispensing patterns to estimate medicine exposure periods and dose from pharmaceutical claims data.

Pharmaceutical claims data are often used as the primary information source to define medicine exposure periods in pharmacoepidemiological studies. However, often critical information on directions for use and the intended duration of medicine supply are not available. In the absence of this information, alternative approaches are needed to support the assignment of exposure periods. This study summarises the key methods commonly used to estimate medicine exposure periods and dose from pharmaceutical claims data; and describes a method using individualised dispensing patterns to define time-dependent estimates of medicine exposure and dose. This method extends on important features of existing methods and also accounts for recent changes in an individual's medicine use. Specifically, this method constructs medicine exposure periods and estimates the dose used by considering characteristics from an individual's prior dispensings, accounting for the time between prior dispensings and the amount supplied at prior dispensings. Guidance on the practical applications of this method is also provided. Although developed primarily for application to databases, which do not contain duration of supply or dose information, use of this method may also facilitate investigations when such information is available and there is a need to consider individualised and/or changing dosing regimens. By shifting the reliance on prescribed duration and dose to determine exposure and dose estimates, individualised dispensing information is used to estimate patterns of exposure and dose for an individual. Reflecting real-world individualised use of medicines with complex and variable dosing regimens, this method offers a pragmatic approach that can be applied to all medicine classes.

The associations between traumatic experiences and subsequent onset of a substance use disorder: Findings from the World Health Organization World Mental Health surveys.

AIM: Exposure to traumatic events (TEs) is associated with substance use disorders (SUDs). However, most studies focus on a single TE, and are limited to single countries, rather than across countries with variation in economic, social and cultural characteristics. We used cross-national data to examine associations of diverse TEs with SUD onset, and variation in associations over time. METHODS: Data come from World Mental Health surveys across 22 countries. Adults (n = 65,165) retrospectively reported exposure to 29 TEs in six categories: "exposure to organised violence"; "participation in organised violence"; "interpersonal violence"; "sexual-relationship violence"; "other life-threatening events"; and those involving loved ones ("network traumas"). Discrete-time survival analyses were used to examine associations with subsequent first SUD onset. RESULTS: Most (71.0%) reported experiencing at least one TE, with network traumas (38.8%) most common and exposure to organised violence (9.5%) least. One in five (20.3%) had been exposed to sexual-relationship violence and 26.6% to interpersonal violence. Among the TE exposed, lifetime SUD prevalence was 14.5% compared to 5.1% with no trauma exposure. Most TE categories (except organised violence) were associated with increased odds of SUD. Increased odds of SUD were also found following interpersonal violence exposure across all age ranges (ORs from 1.56 to 1.78), and sexual-relationship violence exposure during adulthood (ORs from 1.33 to 1.44), with associations persisting even after >11 years. CONCLUSION: Sexual and interpersonal violence have the most consistent associations with progression to SUD; increased risk remains for many years post-exposure. These need to be considered when working with people exposed to such traumas.

Trends in opioid analgesic utilisation among people with a history of opioid dependence.

BACKGROUND: We aimed to characterise opioid analgesic utilisation over a 16-year period among a cohort of people with a history of opioid dependence, comparing rates of use in and out of opioid agonist treatment (OAT). METHODS: Retrospective cohort study in New South Wales, Australia, including 28,891 people with documented opioid dependence initiating opioid analgesics between July 2003 and December 2018. Linked administrative records provided data on prescription dispensings, sociodemographics, clinical characteristics, and OAT. Generalised estimating equation models estimated the incidence and adjusted incidence rate ratios (IRR) comparing periods in and out of OAT for the number of opioid analgesic dispensings (overall, for strong opioids, and the most commonly dispensed opioid types) and the amount dispensed in oral morphine equivalent milligrams (OME). RESULTS: At initiation, 43.7% of the cohort were enrolled in OAT. The most commonly initiated opioid was codeine (including combinations with paracetamol; 67.8%), and 49.6% of the cohort were dispensed a psychotropic medicine in the previous 90 days. Incidence of all opioid analgesic dispensings was higher during periods out of OAT compared to in OAT (5.8 v. 2.3 dispensings per person-year; IRR 0.39, 95% CI 0.38, 0.41), with findings similar when stratified by type. Being in OAT was associated with a lower OME amount dispensed compared to out of OAT (-57.7%, 95% CI-58.8, -56.7). CONCLUSIONS: People with opioid dependence had high rates of recent psychotropic medicine utilisation and current OAT enrolment at the time of opioid analgesic initiation. OAT was associated with a significant reduction in opioid analgesic dispensing.

Patterns and correlates of patient-reported helpfulness of treatment for common mental and substance use disorders in the WHO World Mental Health Surveys.

Patient-reported helpfulness of treatment is an important indicator of quality in patient-centered care. We examined its pathways and predictors among respondents to household surveys who reported ever receiving treatment for major depression, generalized anxiety disorder, social phobia, specific phobia, post-traumatic stress disorder, bipolar disorder, or alcohol use disorder. Data came from 30 community epidemiological surveys - 17 in high-income countries (HICs) and 13 in low- and middle-income countries (LMICs) - carried out as part of the World Health Organization (WHO)'s World Mental Health (WMH) Surveys. Respondents were asked whether treatment of each disorder was ever helpful and, if so, the number of professionals seen before receiving helpful treatment. Across all surveys and diagnostic categories, 26.1% of patients (N=10,035) reported being helped by the very first professional they saw. Persisting to a second professional after a first unhelpful treatment brought the cumulative probability of receiving helpful treatment to 51.2%. If patients persisted with up through eight professionals, the cumulative probability rose to 90.6%. However, only an estimated 22.8% of patients would have persisted in seeing these many professionals after repeatedly receiving treatments they considered not helpful. Although the proportion of individuals with disorders who sought treatment was higher and they were more persistent in HICs than LMICs, proportional helpfulness among treated cases was no different between HICs and LMICs. A wide range of predictors of perceived treatment helpfulness were found, some of them consistent across diagnostic categories and others unique to specific disorders. These results provide novel information about patient evaluations of treatment across diagnoses and countries varying in income level, and suggest that a critical issue in improving the quality of care for mental disorders should be fostering persistence in professional help-seeking if earlier treatments are not helpful.

Trends in comorbid opioid and stimulant use disorders among Veterans receiving care from the Veterans Health Administration, 2005-2019.

BACKGROUND: Identifying solutions to the continued rise in overdose deaths is a public health priority. However, there is evidence of change in recent substance type associated with morbidity and mortality. To better understand the continued rise in overdose deaths, in particular those attributed to opioid and stimulant use disorders, increased knowledge of patterns of use is needed. METHODS: Retrospective cohort study of Veterans diagnosed with an opioid or stimulant use disorder between 2005 and 2019. The outcome of interest was diagnosis of substance use disorders, specifically examining combinations of opioid and stimulant use disorders among this population. RESULTS: A total of 1932,188 Veterans were diagnosed with at least one substance use disorder (SUD) during the study period, 2005 through 2019. While the annual prevalence of opioid use disorder (OUD) diagnoses increased more than 155%, OUD diagnoses absent of any other SUD diagnosis increased by an average of 6.9% (95% CI, 6.4, 7.5) per year between 2005 and 2019. Between 2011 and 2019, diagnoses of co-morbid methamphetamine use disorder (MUD) and OUD increased at a higher rate than other SUD combinations. CONCLUSIONS: The prevalence of comorbid SUD, in particular co-occurring opioid and methamphetamine use disorder, increased at a higher rate than other combinations between 2005 and 2019. These findings underscore the urgent need to offer patients a combination of evidence-based treatments for each co-morbid SUD, such MOUD and contingency management for persons with comorbid opioid and methamphetamine use disorders.

Diagnostic Accuracy of Assays Using Point-of-Care Testing or Dried Blood Spot Samples for the Determination of Hepatitis C Virus RNA: A Systematic Review.

BACKGROUND: Finger-stick point-of-care and dried blood spot (DBS) hepatitis C virus (HCV) RNA testing increases testing uptake and linkage to care. This systematic review evaluated the diagnostic accuracy of point-of-care testing and DBS to detect HCV RNA. METHODS: Bibliographic databases and conference presentations were searched for eligible studies. Meta-analysis was used to pool estimates. RESULTS: Of 359 articles identified, 43 studies were eligible and included. When comparing the Xpert HCV Viral Load Fingerstick assay to venous blood samples (7 studies with 987 samples), the sensitivity and specificity for HCV RNA detection was 99% (95% confidence interval [CI], 97%-99%) and 99% (95% CI, 94%-100%) and for HCV RNA quantification was 100% (95% CI, 93%-100%) and 100% (95% CI, 94%-100%). The proportion of invalid results following Xpert HCV Viral Load Fingerstick testing was 6% (95% CI, 3%-11%). When comparing DBS to venous blood samples (28 studies with 3988 samples) the sensitivity and specificity for HCV RNA detection was 97% (95% CI, 95%-98%) and 100% (95% CI, 98%-100%) and for HCV RNA quantification was 98% (95% CI, 96%-99%) and 100% (95% CI, 95%-100%). CONCLUSIONS: Excellent diagnostic accuracy was observed across assays for detection of HCV RNA from finger-stick and DBS samples. The proportion of invalid results following Xpert HCV Viral Load Fingerstick testing highlights the importance of operator training and quality assurance programs.

Modeling the population-level impact of opioid agonist treatment on mortality among people accessing treatment between 2001 and 2020 in New South Wales, Australia.

BACKGROUND AND AIMS: The individual-level effectiveness of opioid agonist treatment (OAT) in reducing mortality is well established, but there is less evidence on population-level benefits. We use modeling informed with linked data from the OAT program in New South Wales (NSW), Australia, to estimate the impact of OAT provision in the community and prisons on mortality and the impact of eliminating excess mortality during OAT initiation/discontinuation. DESIGN: Dynamic modeling. SETTING AND PARTICIPANTS: A cohort of 49 359 individuals who ever received OAT in NSW from 2001 to 2018. MEASUREMENTS: Receipt of OAT was represented through five stages: (i) first month on OAT, (ii) short (1-9 months) and (iii) longer (9+ months) duration on OAT, (iv) first month following OAT discontinuation and (v) rest of time following OAT discontinuation. Incarceration was represented as four strata: (i) never or not incarcerated in the past year, (ii) currently incarcerated, (iii) released from prison within the past month and (iv) released from prison 1-12 months ago. The model incorporated elevated mortality post-release from prison and OAT impact on reducing mortality and incarceration. FINDINGS: Among the cohort, mortality was 0.9 per 100 person-years, OAT coverage and retention remained high (> 50%, 1.74 years/episode). During 2001-20, we estimate that OAT provision reduced overdose and other cause mortality among the cohort by 52.8% [95% credible interval (CrI) = 49.4-56.9%] and 26.6% (95% CrI =22.1-30.5%), respectively. We estimate 1.2 deaths averted and 9.7 life-years gained per 100 person-years on OAT. Prison OAT with post-release OAT-linkage accounted for 12.4% (95% CrI = 11.5-13.5%) of all deaths averted by the OAT program, primarily through preventing deaths in the first month post-release. Preventing elevated mortality during OAT initiation and discontinuation could have averted up to 1.4% (95% CrI = 0.8-2.0%) and 3.0% (95% CrI = 2.1-5.3%) of deaths, respectively. CONCLUSION: The community and prison opioid agonist treatment program in New South Wales, Australia appears to have substantially reduced population-level overdose and all-cause mortality in the past 20 years, partially due to high retention.

Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.

BACKGROUND: Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep learning extensions of survival analysis models with Cox proportional hazards models for predicting cardiovascular disease (CVD) risk in national health administrative datasets. METHODS: Using individual person linkage of administrative datasets, we constructed a cohort of all New Zealanders aged 30-74 who interacted with public health services during 2012. After excluding people with prior CVD, we developed sex-specific deep learning and Cox proportional hazards models to estimate the risk of CVD events within 5 years. Models were compared based on the proportion of explained variance, model calibration and discrimination, and hazard ratios for predictor variables. RESULTS: First CVD events occurred in 61 927 of 2 164 872 people. Within the reference group, the largest hazard ratios estimated by the deep learning models were for tobacco use in women (2.04, 95% CI: 1.99, 2.10) and chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95% CI: 1.50, 1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk factors. Deep learning outperformed Cox proportional hazards models on the basis of proportion of explained variance (R2: 0.468 vs 0.425 in women and 0.383 vs 0.348 in men), calibration and discrimination (all P <0.0001). CONCLUSIONS: Deep learning extensions of survival analysis models can be applied to large health administrative datasets to derive interpretable CVD risk prediction equations that are more accurate than traditional Cox proportional hazards models.

Perceived helpfulness of treatment for alcohol use disorders: Findings from the World Mental Health Surveys.

AIM: We examined prevalence and factors associated with receiving perceived helpful alcohol use disorder (AUD) treatment, and persistence in help-seeking after earlier unhelpful treatment. METHODS: Data came from 27 community epidemiologic surveys of adults in 24 countries using the World Health Organization World Mental Health surveys (n = 93,843). Participants with a lifetime history of treated AUD were asked if they ever received helpful AUD treatment, and how many professionals they had talked to up to and including the first time they received helpful treatment (or how many ever, if they had not received helpful treatment). RESULTS: 11.8% of respondents with lifetime AUD reported ever obtaining treatment (n = 9378); of these, 44% reported that treatment was helpful. The probability of obtaining helpful treatment from the first professional seen was 21.8%; the conditional probability of subsequent professionals being helpful after earlier unhelpful treatment tended to decrease as more professionals were seen. The cumulative probability of receiving helpful treatment at least once increased from 21.8% after the first professional to 79.7% after the seventh professional seen, following earlier unhelpful treatment. However, the cumulative probability of persisting with up to seven professionals in the face of prior treatments being unhelpful was only 13.2%. CONCLUSION: Fewer than half of people with AUDs who sought treatment found treatment helpful; the most important factor was persistence in seeking further treatment if a previous professional had not helped. Future research should examine how to increase the likelihood that AUD treatment is found to be helpful on any given contact.

Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment.

BACKGROUND: Longer retention in opioid agonist treatment (OAT) is associated with improved treatment outcomes but 12-month retention rates are often low. Innovative approaches are needed to strengthen retention in OAT. We develop and compare traditional and deep learning-extensions of Cox regression to examine the potential for predicting time in OAT at individuals' first episode entry. METHODS: Retrospective cohort study in New South Wales, Australia including 16,576 people entering OAT for the first time between January 2006 and December 2017. We develop 12-month OAT cessation prediction models using traditional and deep learning-extensions of the Cox regression algorithm with predictors evaluated from linked administrative datasets. Proportion of explained variation, calibration, and discrimination are compared using 5 × 2 cross-validation. RESULTS: Twelve-month cessation rate was 58.4%. The largest hazard ratios for earlier cessation from the deep learning model were observed for treatment factors, including private dosing points (HR=1.54, 95% CI=1.49-1.60) and buprenorphine medication (HR=1.43, 95% CI=1.39-1.46). Diagnostic codes for homelessness (HR=1.09, 95% CI=1.04-1.13), outpatient treatment for drug use disorders (HR=1.10, 95% CI=1.06-1.15), and occupant of vehicle accident (HR=1.04, 95% CI=1.01-1.07) from past-year health service presentations were identified as significant predictors of retention. We observed no improvement in performance of the deep learning model over traditional Cox regression. CONCLUSIONS: Deep learning may be more useful in identifying novel risk factors of OAT retention from administrative data than evaluating individual-level risk. An increased focus on addressing structural issues at the population level and considering alternate models of care may be more effective at improving retention than delivering fully personalised OAT.