Estimating the prevalence of problem drug use from drug-related mortality data

Prevalence of opioid dependence in Scotland 2015-2020: A multi-parameter estimation of prevalence (MPEP) study

BACKGROUND AND AIMS

Drug-related deaths in Scotland more than doubled between 2011 and 2020. To inform policymakers and understand drivers of this increase, we estimated the number of people with opioid dependence aged 15-64 from 2014/15 to 2019/20.

DESIGN

We fitted a Bayesian multi-parameter estimation of prevalence (MPEP) model, using adverse event rates to estimate prevalence of opioid dependence jointly from Opioid Agonist Therapy (OAT), opioid-related mortality and hospital admissions data. Estimates are stratified by age group, sex and year.

SETTING

Scotland, 2014/15 to 2019/20.

PARTICIPANTS

People with opioid dependence and potential to benefit from OAT, whether ever treated or not. Using data from the Scottish Public Health Drug Linkage Programme, we identified a baseline cohort of individuals who had received OAT within the last 5 years, and all opioid-related deaths and hospital admissions (whether among or outside of this cohort).

MEASUREMENTS

Rates of each adverse event type and (unobserved) prevalence were jointly modelled.

FINDINGS

The estimated number and prevalence of people with opioid dependence in Scotland in 2019/20 was 47 100 (95% Credible Interval [CrI] 45 700 to 48 600) and 1.32% (95% CrI 1.28% to 1.37%). Of these, 61% received OAT during 2019/20. Prevalence in Greater Glasgow and Clyde was estimated as 1.77% (95% CrI 1.69% to 1.85%). There was weak evidence that overall prevalence fell slightly from 2014/15 (change -0.07%, 95% CrI -0.14% to 0.00%). The population of people with opioid dependence is ageing, with the estimated number of people aged 15-34 reducing by 5100 (95% CrI 3800 to 6400) and number aged 50-64 increasing by 2800 (95% CrI 2100 to 3500) between 2014/15 and 2019/20.

CONCLUSIONS

The prevalence of opioid dependence in Scotland remained high but was relatively stable, with only weak evidence of a small reduction, between 2014/15 and 2019/20. Increased numbers of opioid-related deaths can be attributed to increased risk among people with opioid dependence, rather than increasing prevalence.

Estimation of the prevalence of opioid misuse in New York State counties, 2007-2018: a bayesian spatiotemporal abundance model approach

An important challenge to addressing the opioid overdose crisis is the lack of information on the size of the population of people who misuse opioids (PWMO) in local areas. This estimate is needed for better resource allocation, estimation of treatment and overdose outcome rates using appropriate denominators (ie, the population at risk), and proper evaluation of intervention effects. In this study, we used a bayesian hierarchical spatiotemporal integrated abundance model that integrates multiple types of county-level surveillance outcome data, state-level information on opioid misuse, and covariates to estimate the latent (hidden) numbers of PWMO and latent prevalence of opioid misuse across New York State counties (2007-2018). The model assumes that each opioid-related outcome reflects a partial count of the number of PWMO, and it leverages these multiple sources of data to circumvent limitations of parameter estimation associated with other types of abundance models. Model estimates showed a reduction in the prevalence of PWMO during the study period, with important spatial and temporal variability. The model also provided county-level estimates of rates of treatment and opioid overdose using the numbers of PWMO as denominators. This modeling approach can identify the sizes of hidden populations to guide public health efforts in confronting the opioid overdose crisis across local areas. This article is part of a Special Collection on Mental Health.

Massachusetts Prevalence of Opioid Use Disorder Estimation Revisited: Comparing a Bayesian Approach to Standard Capture-Recapture Methods

BACKGROUND

The National Survey on Drug Use and Health (NSDUH) estimated the prevalence of opioid use disorder (OUD) among the civilian, noninstitutionalized people aged 12 years or older in Massachusetts as 1.2% between 2015 and 2017. Accurate estimation of the prevalence of OUD is critical to the success of treatment and resource planning. Various indirect estimation approaches have been used but are subject to data availability and infrastructure-related issues.

METHODS

We used 2015 data from the Massachusetts Public Health Data Warehouse (PHD) to compare the results of two approaches to estimating OUD prevalence in the Massachusetts population. First, we used a seven-dataset capture-recapture analysis under log-linear model parameterization, controlling for the source dependence and effects of age, sex, and county through stratification. Second, we applied a benchmark-multiplier method in a Bayesian framework by linking health care claims data to death certificate data assuming an extrapolation of death rates from observed untreated OUD to unobserved OUD.

RESULTS

Our estimates for OUD prevalence among Massachusetts residents (aged 18-64 years) were 4.62% (95% CI = 4.59%, 4.64%) in the capture-recapture approach and 4.29% (95% CrI = 3.49%, 5.32%) in the Bayesian model. Both estimates were approximately four times higher than NSDUH estimates.

CONCLUSION

The synthesis of our findings suggests that the disease surveillance system misses a large portion of the population with OUD. Our study also suggests that concurrent use of multiple methods improves the justification and facilitates the triangulation and interpretation of the resulting estimates.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04111939.

Prevalence of opioid dependence in New South Wales, Australia, 2014-16: Indirect estimation from multiple data sources using a Bayesian approach

AIMS

To estimate the prevalence of, and number of unobserved people with opioid dependence by sex and age group in New South Wales (NSW), Australia.

DESIGN

We applied a Bayesian statistical modelling approach to opioid agonist treatment records linked to adverse event rate data. We estimated prevalence from three types of adverse event separately: opioid mortality, opioid-poisoning hospitalizations and opioid-related charges. We extended the model and produced prevalence estimates from a 'multi-source' model based on all three types of adverse event data.

SETTING, PARTICIPANTS AND MEASUREMENTS

This study was conducted in NSW, Australia, 2014-16 using data from the Opioid Agonist Treatment and Safety (OATS) study, which included all people who had received treatment for opioid dependence in NSW. Aggregate data were obtained on numbers of adverse events in NSW. Rates of each adverse event type within the OATS cohort were modelled. Population data were provided by State and Commonwealth agencies.

FINDINGS

Prevalence of opioid dependence among those aged 15-64 years in 2016 was estimated to be 0.96% (95% credible interval [CrI] = 0.82%, 1.12%) from the mortality model, 0.75% (95% CrI = 0.70%, 0.83%) from hospitalizations, 0.95% (95% CrI = 0.90%, 0.99%) from charges and 0.92% (95% CrI = 0.88%, 0.96%) from the multi-source model. Of the estimated 46 460 (95% CrI = 44 680, 48 410) people with opioid dependence in 2016 from the multi-source model, approximately one-third (16 750, 95% CrI = 14 960, 18 690) had no record of opioid agonist treatment within the last 4 years. From the multi-source model, prevalence in 2016 was estimated to be 1.24% (95% CrI = 1.18%, 1.31%) in men aged 15-44, 1.22% (95% CrI = 1.14%, 1.31%) in men 45-64, 0.63% (95% CrI = 0.59%, 0.68%) in women aged 15-44 and 0.56% (95% CrI = 0.50%, 0.63%) in women aged 45-64.

CONCLUSIONS

A Bayesian statistical approach to estimate prevalence from multiple adverse event types simultaneously calculates that the estimated prevalence of opioid dependence in NSW, Australia in 2016 was 0.92%, higher than previous estimates.

The prevalence of opioid use disorder in Kentucky's counties: A two-year multi-sample capture-recapture analysis

BACKGROUND

Kentucky has one of the highest opioid overdose mortality rates in the United States. Accurate estimates of people with opioid use disorder (OUD) are critical to plan for the scope of interventions required to reduce overdose and opioid misuse. Commonly used household surveys are known to underestimate OUD at the state-level and do not provide county-level estimates.

METHODS

We performed a multi-sample capture-recapture analysis to estimate OUD prevalence in Kentucky in 2018 and 2019. We utilized four statewide datasets that were linked at the individual level: 1) Registry of Vital Statistics, 2) Emergency Medical Services (EMS), 3) Kentucky's Prescription Drug Monitoring Program (PDMP), and 4) Kentucky Medicaid. We included persons aged 18-64 years who resided in Kentucky between 2018 and 2019. We identified individuals with administrative data consistent with OUD in each of the datasets, including a fatal opioid-involved overdose (Vital Statistics), EMS runs for suspected opioid overdose, receipt of buprenorphine for OUD treatment (PDMP), or Medicaid claims for OUD. Observed and estimated counts of OUD cases and prevalence of OUD among the adult population in Kentucky.

RESULTS

The estimated statewide OUD prevalence was 5.5 % and 5.9 % for 2018 and 2019, respectively, ranging from 1.3 % to 17.7 % across Kentucky counties. As expected, counties with the highest OUD rates were Appalachian counties (eastern area) of the state.

CONCLUSIONS

Our analysis reveals a substantially larger proportion of KY residents have OUD than previously estimated. Our approach offers a model for states needing county-level estimates of OUD.

Characterizing Help-Seeking Searches for Substance Use Treatment From Google Trends and Assessing Their Use for Infoveillance: Longitudinal Descriptive and Validation Statistical Analysis

BACKGROUND

There is no recognized gold standard method for estimating the number of individuals with substance use disorders (SUDs) seeking help within a given geographical area. This presents a challenge to policy makers in the effective deployment of resources for the treatment of SUDs. Internet search queries related to help seeking for SUDs using Google Trends may represent a low-cost, real-time, and data-driven infoveillance tool to address this shortfall in information.

OBJECTIVE

This paper assesses the feasibility of using search query data related to help seeking for SUDs as an indicator of unmet treatment needs, demand for treatment, and predictor of the health harms related to unmet treatment needs. We explore a continuum of hypotheses to account for different outcomes that might be expected to occur depending on the demand for treatment relative to the system capacity and the timing of help seeking in relation to trajectories of substance use and behavior change.

METHODS

We used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries from Google Trends by US state for cocaine, methamphetamine, opioids, cannabis, and alcohol from 2010 to 2020. To validate the value of these data for surveillance purposes, we then used negative binomial regression models to investigate the relationship between SUD help-seeking searches and state-level outcomes across the continuum of care (including lack of care). We started by looking at associations with self-reported treatment need using data from the National Survey on Drug Use and Health, a national survey of the US general population. Next, we explored associations with treatment admission rates from the Treatment Episode Data Set, a national data system on SUD treatment facilities. Finally, we studied associations with state-level rates of people experiencing and dying from an opioid overdose, using data from the Agency for Healthcare Research and Quality and the CDC WONDER database.

RESULTS

Statistically significant differences in help-seeking searches were observed over time between 2010 and 2020 (based on P<.05 for the corresponding Wald tests). We were able to identify outlier states for each drug over time (eg, West Virginia for both opioids and methamphetamine), indicating significantly higher help-seeking behaviors compared to national trends. Results from our validation analyses across different outcomes showed positive, statistically significant associations for the models relating to treatment need for alcohol use, treatment admissions for opioid and methamphetamine use, emergency department visits related to opioid use, and opioid overdose mortality data (based on regression coefficients having P≤.05).

CONCLUSIONS

This study demonstrates the clear potential for using internet search queries from Google Trends as an infoveillance tool to predict the demand for substance use treatment spatially and temporally, especially for opioid use disorders.

Estimating the number of livebirths to Hepatitis C seropositive women in England in 2013 and 2018 using Bayesian modelling

BACKGROUND

The UK National Screening Committee currently recommends against antenatal screening for Hepatitis C virus (HCV) infection in England due to lack of HCV prevalence data and treatment licensed for use in pregnancy. We aimed to produce regional and national estimates of the number and proportion of livebirths to HCV seropositive women in England in 2013 and 2018.

METHODS

A logistic regression model fitted in the Bayesian framework estimated probabilities of HCV seropositivity among 24,599 mothers delivering in the North Thames area of England in 2012 adjusted by maternal age and region of birth. These probabilities were applied to the underlying population structures of women delivering livebirths in England in 2013 and 2018 to estimate the number of livebirths to HCV seropositive women in these years nationally and by region. The Bayesian approach allowed the uncertainty associated with all estimates to be properly quantified.

RESULTS

Nationally, the estimated number of livebirths to women seropositive for HCV for England was 464 (95% credible interval [CI] 300-692) in 2013 and 481 (95%CI 310-716) in 2018, or 70.0 (95%CI 45.0-104.1) per 100,000 and 76.9 (95%CI 49.5-114.4) per 100,000 in these years respectively. Regions with the highest estimated number of livebirths to HCV seropositive women in 2013 and 2018 included London with 118.5 and 124.4 and the South East with 67.0 and 74.0 per 100,000 livebirths.

CONCLUSION

Few previous studies have investigated HCV among pregnant women in England. These findings complement and supplement existing research by providing national and regional estimates for the number of livebirths to HCV seropositive women in England. Bayesian modelling allows future national and regional estimates to be produced and the associated uncertainty to be properly quantified.

Opioid Use Disorder Among Ohio's Medicaid Population: Prevalence Estimates From 19 Counties Using a Multiplier Method

The decades-long overdose epidemic in the United States is driven by opioid misuse. Overdoses commonly, although not exclusively, occur in individuals with opioid use disorder (OUD). To allocate adequate resources and develop appropriately scaled public health responses, accurate estimation of the prevalence of OUD is needed. Indirect methods (e.g., a multiplier method) of estimating prevalence of problematic substance-use behavior circumvent some limitations of household surveys and use of administrative data. We used a multiplier method to estimate OUD prevalence among the adult Medicaid population (ages 18-64 years) in 19 Ohio counties that are highly affected by overdose. We used Medicaid claims data and the US National Vital Statistics System overdose death data, which were linked at the person level. A statistical model leveraged opioid-related death rate information from a group with known OUD to estimate prevalence among a group with unknown OUD status given recorded opioid-related deaths in that group. We estimated that 13.6% of the total study population had OUD in 2019. Men (16.7%) had a higher prevalence of OUD than women (11.4%), and persons aged 35-54 had the highest prevalence (16.7%). Our approach to prevalence estimation has important implications for OUD surveillance and treatment in the United States.

Association of Bupropion, Naltrexone, and Opioid Agonist Treatment With Stimulant-Related Admissions Among People With Opioid Use Disorder: A Case-Crossover Analysis

Background: Stimulant use has substantially increased among people with opioid use disorder (OUD) and is associated with worse treatment outcomes. This study's objective was to compare risk of stimulant-related emergency department (ED) and hospital admissions associated with exposure to bupropion, OUD medication (buprenorphine, naltrexone, and methadone), and selective serotonin reuptake inhibitors (SSRIs; active comparator) relative to days without active prescriptions for medication. Methods: This recurrent-event, case-crossover study used insurance claims from 51,084 individuals with OUD enrolled in the IBM MarketScan (2006-2016) Databases who had at least 1 stimulant-related ED or hospital admission. Conditional logistic regression models estimated the risk of admissions between days without active prescriptions and days with prescriptions for bupropion, OUD medication, and SSRIs. Secondary analyses were conducted by stimulant subtype (cocaine; amphetamine) and event subtype (falls, injuries, or poisonings; psychotic events). Results: Compared to days without active prescriptions, days with bupropion treatment were associated with decreased odds of stimulant-related ED or hospital admissions (odds ratio [OR] = 0.77; 95% confidence interval [CI], 0.72-0.82) Among OUD medications, we observed strong protective associations with decreased admissions for buprenorphine (OR = 0.67; 95% CI, 0.64-0.71), naltrexone (OR = 0.65; 95% CI, 0.60-0.70), and methadone (OR = 0.59; 95% CI, 0.51-0.67). The SSRI active comparator group was associated with a small protective association with decreased admissions (OR = 0.90; 95% CI, 0.86-0.93). These effects were sustained in secondary analyses stratifying by stimulant and event subtype. Conclusions: Bupropion and OUD medication, including both naltrexone and opioid agonists, are associated with fewer stimulant-related ED or hospital admissions in patients with OUD. Bupropion may show promise as adjunctive therapy targeting stimulant-specific poisoning risk.

Addressing delayed case reporting in infectious disease forecast modeling

Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden.

In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.

The public health community and policymakers are interested in using models to predict future disease rates using information about disease rates in the past. However, our data about the recent past are less reliable than older data, due to a time lag between someone getting sick and their subsequent diagnosis being officially reported. In this paper, we describe strategies to correct reported disease rates from the recent past to account for disease diagnoses that haven’t yet been reported. Using more accurate information about the recent past, we can do a better job predicting what will happen in the future.

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.

What is the prevalence of and trend in opioid use disorder in the United States from 2010 to 2019? Using multiplier approaches to estimate prevalence for an unknown population size

Opioid-related overdose deaths have increased since 2010 in the U.S., but information on trends in opioid use disorder (OUD) prevalence is limited due to unreliable data. Multiplier methods are a classical epidemiological technique to estimate prevalence when direct estimation is infeasible or unreliable. We used two different multiplier approaches to estimate OUD prevalence from 2010 to 2019. First, we estimated OUD in National Survey on Drug Use and Health (NSDUH), and based on existing capture-recapture studies, multiplied prevalence by 4.5x. Second, we estimated the probability of drug poisoning death among people with OUD (Meta-analysis indicates 0.52/100,000), and divided the number of drug poisoning deaths in the US by this probability. Estimates were weighted to account for increase in drug-related mortality in recent years due to fentanyl. Estimated OUD prevalence was lowest when estimated in NSDUH with no multiplier, and highest when estimated from vital statistics data without adjustment. Consistent findings emerged with two methods: NSDUH data with multiplier correction, and vital statistics data with multiplier and adjustment. From these two methods, OUD prevalence increased from 2010 to 2014; then stabilized and slightly declined annually (survey data with multiplier, highest prevalence of 4.0% in 2015; death data with a multiplier and correction, highest prevalence of 2.35% in 2016). The number of US adolescent and adult individuals with OUD in 2019 was estimated between 6.7-7.6 million. When multipliers and corrections are used, OUD may have stabilized or slightly declined after 2015. Nevertheless, it remains highly prevalent, affecting 6-7 million US adolescents and adults.

Invited Commentary: Drug Checking for Novel Insights Into the Unregulated Drug Supply

Tobias et al. (Am J Epidemiol. 2022;191 (2):241–247) present a novel analysis of time trends in fentanyl concentrations in the unregulated drug supply in British Columbia, Canada. The preexisting knowledge about unregulated drugs had come from law-enforcement seizures and postmortem toxicology. As both of these data sources are subject to selection bias, large-scale drug-checking programs are poised to be a crucial component of the public health response to the unrelenting increase in overdose in North America. As programs expand, we offer 2 guiding principles. First, the primary purpose of these programs is to deliver timely results to people who use drugs to mitigate health risks. Second, innovation is needed to go beyond criminal justice paradigms in laboratory analysis for a more nuanced understanding of health concerns. We provide examples of the role adulterants play in our understanding of drug harms. We also describe the applications and limitations of common laboratory assays, with implications for epidemiologic surveillance. While the research and direct service teams in British Columbia have taken groundbreaking steps, there is still a need to establish best practices for communicating results to sample donors in an approachable yet nonalarmist tone.

Using Economic Evaluation to Inform Responses to the Opioid Epidemic in the United States: Challenges and Suggestions for Future Research

Background: Several aspects of the opioid epidemic and of public health care organization in the United States (US) make the conduct of economic evaluation and the design of policies to respond to this crisis particularly challenging. Objectives: This commentary offers suggestions for how economic evaluation may address and overcome four key features of the opioid epidemic: 1) its magnitude and geographical distribution, 2) its intersection with multiple epidemics, 3) its rapidly changing dynamics, 4) its multi-sectoral causes and consequences. Results: We first offer pragmatic suggestions to address the difficulties in delivering a coordinated response given the fragmented nature of health care in the US. In view of the broad suite of responses required to address opioid use disorder and its associated comorbidities, we highlight the need for economic evaluations which consider interventions throughout the continuum of care (i.e. primary, secondary and tertiary levels of prevention). We examine how the use of predictive modelling alongside economic evaluation might be adopted to address the rapidly evolving situation affecting distinct populations and geographic areas and encourage investments in epidemic preparedness. Finally, we propose methods to capture the interdependence of various sectors of government affected by the opioid crisis in economic evaluations to ensure optimal levels of investment towards a comprehensive response. Conclusions: The opioid epidemic in the US represents an unprecedented public health challenge, but sound epidemiological modelling and economic analysis can help to guide use of limited resources committed to addressing it in ways that can have greatest impact in limiting its adverse consequences.

Heroin use cannot be measured adequately with a general population survey

BACKGROUND

Globally, heroin and other opioids account for more than half of deaths and years-of-life-lost due to drug use and comprise one of the four major markets for illegal drugs. Having sound estimates of the number of problematic heroin users is fundamental to formulating sound health and criminal justice policies. Researchers and policymakers rely heavily upon general population surveys (GPS), such as the US National Survey on Drug Use and Health (NSDUH), to estimate heroin use, without confronting their limitations. GPS-based estimates are also ubiquitous for cocaine and methamphetamine, so insights pertaining to GPS for estimating heroin use are also relevant for those drug markets.

ANALYSIS

Four sources of potential errors in NSDUH are assessed: selective non-response, small sample size, sampling frame omissions and under-reporting. An alternative estimate drawing on a variety of sources including a survey of adult male arrestees is presented and explained. Other approaches to prevalence estimation are discussed.

FINDINGS

Under-reporting and selective non-response in NSDUH are likely to lead to substantial underestimation. Small sample size leads to imprecise estimates and erratic year-to-year fluctuations. The alternative estimate provides credible evidence that NSDUH underestimates the number of frequent heroin users by at least three-quarters and perhaps much more.

IMPLICATIONS

GPS, even those as strong as NSDUH, are doomed by their nature to estimate poorly a rare and stigmatized behavior concentrated in a hard-to-track population. Although many European nations avoid reliance upon these surveys, many others follow the US model. Better estimation requires models that draw upon a variety of data sources, including GPS, to provide credible estimates. Recent methodological developments in selected countries can provide guidance. Journals should require researchers to critically assess the soundness of GPS estimates for any stigmatized drug-related behaviors with low prevalence rates.

Role of community drug and alcohol services in physical healthcare for people who use illicit opioids: a qualitative study of clinical staff in the UK

OBJECTIVES

To understand how clinicians working in addiction services perceive their responsibilities for physical healthcare of clients who use opioids, and how physical healthcare could be improved for this group.

DESIGN

Qualitative study comprising semistructured interviews.

PARTICIPANTS

16 clinicians, including nurses and nurse practitioners, nurse consultants, addiction psychiatrists, specialist general practitioners and psychiatry specialty registrars.

SETTING

Community-based drug and alcohol treatment services in the UK, with services including outpatient opioid agonist therapy.

RESULTS

We identified three overarching themes. First, clients have unmet physical health needs that are often first identified in community drug and alcohol services. Participants reported attempts to improve their clients' access to healthcare by liaising directly with health services and undertaking other forms of health advocacy, but report limited success, with many referrals ending in non-attendance. Second, most participants saw their role as supporting access to mainstream health services rather than providing physical healthcare directly, though sometimes reported frustration at being unable to provide certain treatments such as antibiotics for a respiratory infection. A minority of participants felt that people who use illicit opioids would be best served by an integrated 'one-stop-shop' model, but felt this model is currently unlikely to receive funding. Third, participants felt isolated from other health services, in part due to commissioning arrangements in which funding is provided through local government rather than the National Health Service.

CONCLUSIONS

Clinicians participating in this study serve a patient group with unmet physical health needs, but lack the resources to respond effectively to these needs.

Opioids in the United Kingdom: safety and surveillance during COVID-19

PURPOSE OF REVIEW

Opioid use is prevalent in the United Kingdom and prior to the COVID-19 pandemic it had been recognized that the safety of opioids was an important issue to be monitored by the UK medicines regulatory agency. With the emergence of COVID-19, this requirement has been even greater. This review was undertaken to determine the impact of the pandemic on safety and surveillance of opioids in the United Kingdom.

RECENT FINDINGS

During the COVID-19 pandemic, the surveillance of opioids in the United Kingdom continued, although primary research was often conducted with data prior to the pandemic. Of those studies that were conducted while the pandemic was ongoing, access to opioids (or opioid substitution therapy) and the subsequent effect on patient safety was the main theme.

SUMMARY

In the United Kingdom, changes in accessibility to the healthcare system and how healthcare providers operated during the COVID-19 pandemic may have had unintended consequences on use and safety of opioids, due to the shift in focus to preventing COVID-19 from overwhelming the healthcare system. The findings from this review support the need to continue surveillance in the United Kingdom, including the impact of the COVID-19 pandemic on opioid utilization and safety.

Healthcare use by people who use illicit opioids (HUPIO): development of a cohort based on electronic primary care records in England

Background: People who use illicit opioids such as heroin have substantial health needs, but there are few longitudinal studies of general health and healthcare in this population. Most research to date has focused on a narrow set of outcomes, including overdoses and HIV or hepatitis infections. We developed and validated a cohort using UK primary care electronic health records (Clinical Practice Research Datalink GOLD and AURUM databases) to facilitate research into healthcare use by people who use illicit opioid use (HUPIO). Methods: Participants are patients in England with primary care records indicating a history of illicit opioid use. We identified codes including prescriptions of opioid agonist therapies (methadone and buprenorphine) and clinical observations such as 'heroin dependence'. We constructed a cohort of patients with at least one of these codes and aged 18-64 at cohort entry, with follow-up between January 1997 and March 2020. We validated the cohort by comparing patient characteristics and mortality rates to other cohorts of people who use illicit opioids, with different recruitment methods. Results: Up to March 2020, the HUPIO cohort included 138,761 patients with a history of illicit opioid use. Demographic characteristics and all-cause mortality were similar to existing cohorts: 69% were male; the median age at index for patients in CPRD AURUM (the database with more included participants) was 35.3 (interquartile range 29.1-42.6); the average age of new cohort entrants increased over time; 76% had records indicating current tobacco smoking; patients disproportionately lived in deprived neighbourhoods; and all-cause mortality risk was 6.6 (95% CI 6.5-6.7) times the general population of England. Conclusions: Primary care data offer new opportunities to study holistic health outcomes and healthcare of this population. The large sample enables investigation of rare outcomes, whilst the availability of linkage to external datasets allows investigation of hospital use, cancer treatment, and mortality.

Healthcare use by people who use illicit opioids (HUPIO): development of a cohort based on electronic primary care records in England

Background: People who use illicit opioids such as heroin have substantial health needs, but there are few longitudinal studies of general health and healthcare in this population. Most research to date has focused on a narrow set of outcomes, including overdoses and HIV or hepatitis infections. We developed and validated a cohort using UK primary care electronic health records (Clinical Practice Research Datalink GOLD and AURUM databases) to facilitate research into healthcare use by people who use illicit opioid use (HUPIO). Methods: Participants are patients in England with primary care records indicating a history of illicit opioid use. We identified codes including prescriptions of opioid agonist therapies (methadone and buprenorphine) and clinical observations such as 'heroin dependence'. We constructed a cohort of patients with at least one of these codes and aged 18-64 at cohort entry, with follow-up between January 1997 and March 2020. We validated the cohort by comparing patient characteristics and mortality rates to other cohorts of people who use illicit opioids, with different recruitment methods. Results: Up to March 2020, the HUPIO cohort included 138,761 patients with a history of illicit opioid use. Demographic characteristics and all-cause mortality were similar to existing cohorts: 69% were male; the median age at index for patients in CPRD AURUM (the database with more included participants) was 35.3 (IQR 29.1-42.6); the average age of new cohort entrants increased over time; 76% had records indicating current tobacco smoking; patients disproportionately lived in deprived neighbourhoods; and all-cause mortality risk was 5.4 (95% CI 5.3-5.5) times the general population of England. Conclusions: Primary care data offer new opportunities to study holistic health outcomes and healthcare of this population. The large sample enables investigation of rare outcomes, whilst the availability of linkage to external datasets allows investigation of hospital use, cancer treatment, and mortality.