Examining NSDUH's Assessment of Fentanyl Use: A Comparison of Trends in Fentanyl Use and Fentanyl Overdose Deaths from 2015-2020.

OBJECTIVE: The National Survey on Drug Use and Health (NSDUH), as the primary source of epidemiological substance use data in the US, could illuminate trends in fentanyl use behaviors contributing to the opioid overdose crisis. We hypothesized that the trend in NSDUH prevalence of lifetime fentanyl injection would match the direction and magnitude of the trend in synthetic opioid overdose deaths. METHOD: Using logistic regression, we modeled the 2015-2020 trend in synthetic opioid overdose deaths as a proportion of all deaths. We modeled contemporary trends from cross-sectional NSDUH data for (1) lifetime fentanyl injection, (2) past year prescription fentanyl (PF) misuse, (3) prescription tramadol misuse (the other synthetic opioid counted alongside fentanyl in the overdose deaths category), and (4) combined prescription fentanyl or tramadol misuse. Average annual NSDUH weighted sample size was 272,519,038 (51.5% female, 48.5% male). RESULTS: Synthetic opioid overdose deaths increased from 2015-2020 (OR 3.39, meaning the odds of a death being from synthetic opioid overdose in 2020 were 3.39 times the odds of death from that cause in 2015, 95% CI: 3.34, 3.44). None of the substance use trends significantly increased. CONCLUSION: Per NSDUH data, the prevalence of fentanyl misuse did not significantly increase in tandem with synthetic opioid overdose deaths from 2015 to 2020. Scrutiny of NSDUH's approach to assessing fentanyl misuse casts doubt on the utility of NSDUH fentanyl data collection. We acknowledge recent changes to the survey and recommend two further changes to optimize a vital source of data on behaviors related to the opioid overdose crisis.

Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review.

BACKGROUND: Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention. METHODS: This analysis followed systematic review procedures, registered with PROSPERO. We conducted a search across major databases including PubMed, Embase, Web of Science, and Scopus, up to December 2023, using keywords related to NLP, LLM, and infectious diseases. We also employed the QUADAS-2 tool for evaluating the quality and robustness of the included studies. RESULTS: Our review identified 15 studies with diverse applications of NLP in infectious disease management. Notable examples include GPT-4's application in detecting urinary tract infections and BERTweet's use in Lyme Disease surveillance through social media analysis. These models demonstrated effective disease monitoring and public health tracking capabilities. However, the effectiveness varied across studies. For instance, while some NLP tools showed high accuracy in pneumonia detection and high sensitivity in identifying invasive mold diseases from medical reports, others fell short in areas like bloodstream infection management. CONCLUSION: This review highlights the yet-to-be-fully-realized promise of NLP and LLMs in infectious disease management. It calls for more exploration to fully harness AI's capabilities, particularly in the areas of diagnosis, surveillance, predicting disease courses, and tracking epidemiological trends.

Development and Evaluation of Surveillance System for Identifying Jail-Associated COVID-19 Cases in Minnesota, USA, 2022.

Confinement facilities are high-risk settings for the spread of infectious disease, necessitating timely surveillance to inform public health action. To identify jail-associated COVID-19 cases from electronic laboratory reports maintained in the Minnesota Electronic Disease Surveillance System (MEDSS), Minnesota, USA, the Minnesota Department of Health developed a surveillance system that used keyword and address matching (KAM). The KAM system used a SAS program (SAS Institute Inc., https://www.sas.com) and an automated program within MEDSS to identify confinement keywords and addresses. To evaluate KAM, we matched jail booking data from the Minnesota Statewide Supervision System by full name and birthdate to the MEDSS records of adults with COVID-19 for 2022. The KAM system identified 2,212 cases in persons detained in jail; sensitivity was 92.40% and specificity was 99.95%. The success of KAM demonstrates its potential to be applied to other diseases and congregate-living settings for real-time surveillance without added reporting burden.

Erratum: Health in Germany: Establishment of a population-based health panel.

[This corrects the article on p. 2,21 in vol. 9, PMID: 38282983.].

Assessing the performance of the integrated disease surveillance and response systems: a systematic review of global evidence.

OBJECTIVES: Public health surveillance systems are critical for detecting and responding to health threats. This review aims to analyze international literature on the performance of these systems in terms of core, support, and attributes of surveillance system. STUDY DESIGN: Systematic review. METHODS: Following the preregistered protocol (PROSPERO: CRD42022366051), a systematic search was conducted on PubMed/MEDLINE, CINHAL, CABI, Web of Science, and Google Scholar for articles evaluating Public Health Surveillance System performance from inception to July 21, 2023. Various study designs were included, and quality assessment was performed. Thematic analysis categorized findings into key surveillance system functions. RESULTS: Nine studies from different countries assessed core and supportive functions, as well as surveillance attributes. Performance varied among countries, with some excelling overall and others showing poor performance in specific areas. Many countries' surveillance systems had inadequate performance in key measures in terms of the core and supportive functions, as well as the attributes of the surveillance system. CONCLUSION: This review shows significant variations in the performance of public health surveillance systems across countries. Further research is needed to understand underperformance reasons and inform global policymaking for strengthening surveillance systems.

Simultaneous detection and characterization of common respiratory pathogens in wastewater through genomic sequencing.

Genomic surveillance of SARS-CoV-2 has given insight into the evolution and epidemiology of the virus and its variant lineages during the COVID-19 pandemic. Expanding this approach to include a range of respiratory pathogens can better inform public health preparedness for potential outbreaks and epidemics. Here, we simultaneously sequenced 38 pathogens including influenza viruses, coronaviruses and bocaviruses, to examine the abundance and seasonality of respiratory pathogens in urban wastewater. We deployed a targeted bait capture method and short-read sequencing (Illumina Respiratory Virus Oligos Panel; RVOP) on composite wastewater samples from 8 wastewater treatment plants (WWTPs) and one associated hospital site. By combining seasonal sampling with whole genome sequencing, we were able to concurrently detect and characterise a range of common respiratory pathogens, including SARS-CoV-2, adenovirus and parainfluenza virus. We demonstrated that 38 respiratory pathogens can be detected at low abundances year-round, that hospital pathogen diversity is higher in winter vs. summer sampling events, and that significantly more viruses are detected in raw influent compared to treated effluent samples. Finally, we compared detection sensitivity of RT-qPCR vs. next generation sequencing for SARS-CoV-2, enteroviruses, influenza A/B, and respiratory syncytial viruses. We conclude that both should be used in combination; RT-qPCR allowed accurate quantification, whilst genomic sequencing detected pathogens at lower abundance. We demonstrate the valuable role of wastewater genomic surveillance and its contribution to the field of wastewater-based epidemiology, gaining rapid understanding of the seasonal presence and persistence for common respiratory pathogens. By simultaneously monitoring seasonal trends and early warning signs of many viruses circulating in communities, public health agencies can implement targeted prevention and rapid response plans.

Wastewater surveillance for COVID-19 in shelters: A creative strategy for a complex setting.

People experiencing homelessness experience disproportionate rates of morbidity and mortality from coronavirus disease 2019 (COVID-19) compared to the general population and shelters for people experiencing homelessness are a major contributing factor to these negative outcomes. As a result of their unique structure, population and physical space, these settings pose several challenges to the prevention of COVID-19 infection that are not adequately addressed by conventional non-pharmaceutical public health interventions. Wastewater surveillance for COVID-19 is a viable strategy for health protection in shelters due to its ability to meet these unique challenges. Its passive nature does not depend on individual health-seeking behaviours, and it can provide useful epidemiological information early on in an outbreak setting. In this commentary, the authors examine a recent application of wastewater surveillance of COVID-19 in a men's shelter in Toronto. Further applications of wastewater surveillance for other infectious diseases of concern in shelters are proposed, and the need for the development of ethical frameworks governing the use of this technology is discussed.

Genomic epidemiology of SARS-CoV-2 δ sublineages of the second wave of 2021 in Antioquia, Colombia / Epidemiología genómica de los sublinajes δ del virus SARS-CoV-2 de la segunda ola de COVID en Antioquia en el 2021.

Introduction. During the development of the SARS-CoV-2 pandemic in Antioquia, we experienced epidemiological peaks related to the α, É£, ß, ƛ, and δ variants. δ had the highest incidence and prevalence. This lineage is of concern due to its clinical manifestations and epidemiological characteristics. A total of 253 δ sublineages have been reported in the PANGOLIN database. The sublineage identification through genomic analysis has made it possible to trace their evolution and propagation. Objective. To characterize the genetic diversity of the different SARS-CoV-2 δ sublineages in Antioquia and to describe its prevalence. Materials and methods. We collected sociodemographic information from 2,675 samples, and obtained 1,115 genomes from the GISAID database between July 12th, 2021, and January 18th, 2022. From the analyzed genomes, 515 were selected because of their high coverage values (>90%) to perform phylogenetic analysis and to infer allele frequencies of mutations of interest. Results. We characterized 24 sublineages. The most prevalent was AY.25. Mutations of interest as L452R, P681R, and P681H were identified in this sublineage, comprising a frequency close to 0.99. Conclusions. This study identified that the AY.25 sublineage has a transmission advantage compared to the other δ sublineages. This attribute may be related to the presence of the L452R and P681R mutations associated in other studies with higher evasion of the immune system and less efficacy of drugs against SARS-CoV-2.

Introducción. Durante el desarrollo de la pandemia por SARS-CoV-2 en Antioquia se presentaron picos epidemiológicos relacionados con las variantes α, É£, ß, ƛ y δ, donde δ tuvo la mayor incidencia y prevalencia. Este linaje se considera una variante de preocupación dadas las manifestaciones clínicas que desencadena y sus características epidemiológicas. Se han informado 253 sublinajes δ en la base de datos PANGOLIN. La identificación de estos sublinajes mediante análisis genómico ha permitido rastrear su evolución y propagación. Objetivo. Caracterizar la diversidad genética de los diferentes sublinajes δ de SARSCoV-2 en Antioquia y determinar su prevalencia. Materiales y métodos. Se recopiló información sociodemográfica de 2.675 muestras y de 1.115 genomas del repositorio GISAID entre el 12 de julio de 2021 y el 18 de enero de 2022. Se seleccionaron 501 por su alto porcentaje de cobertura (>90 %) para realizar análisis filogenéticos e inferencia de frecuencias alélicas de mutaciones de interés. Resultados. Se caracterizaron 24 sublinajes donde el más prevalente fue AY.25. En este sublinaje se identificaron mutaciones de interés como L452R, P681R y P681H, que comprendían una frecuencia cercana a 0,99. Conclusiones. Este estudio permitió identificar que el sublinaje AY.25 tiene una ventaja de transmisión en comparación con los otros sublinajes δ. Esto puede estar relacionado con la presencia de las mutaciones L452R y P681R que en otros estudios se han visto asociadas con una mayor transmisibilidad, evasión del sistema inmunitario y menor eficacia de los medicamentos contra SARS-CoV-2.

[First record of Aedes albopictus in Galicia, obtained by citizen science through Mosquito Alert]. / Primera cita de Aedes albopictus para Galicia, obtenida mediante ciencia ciudadana por Mosquito Alert.

The first detection of the tiger mosquito, Aedes (Stegomyia) albopictus (Skuse, 1894), in the autonomous community of Galicia (Spain) is reported. The finding has been possible thanks to the collaboration between citizens, the citizen science application Mosquito Alert and the Rede Galega de Vixilancia de Vectores (ReGaViVec). At the beginning of August 2023, a same person submitted through the app several reports consistent with the tiger mosquito in the municipality of Moaña, in Pontevedra. The ReGaViVec entomological team confirmed the species and conducted vector surveillance in the area by placing traps (11 ovitraps and 3 BG-Sentinel 2 with BG-Lure attractant) with a weekly collection frequency. This finding represents the most northwestern detection of the tiger mosquito in the Iberian Peninsula and shows the crucial role of citizen science in vector surveillance.

Monitoring progress towards the elimination of hepatitis C as a public health threat in Norway: a modelling study among people who inject drugs and immigrants.

BACKGROUND: The global incidence target for the elimination of hepatitis C among people who inject drugs (PWID) is <2/100. In Norway, the hepatitis C epidemic is concentrated in PWID. Immigrants are the second most important risk group for chronic infection. We modelled the incidence of hepatitis C among active PWID, and the prevalence of chronic infection among active PWID, ex-PWID and immigrants in Norway until 2022. METHODS: We built a stochastic compartmental model, which was informed using data from national data sources, literature, and expert opinion. We report median values with 95% credible intervals (CrI). RESULTS: The model estimated 30 (95% Crl: 13-52) new infections among active PWID in 2022, or 0.37/100 (95% Crl: 0.17-0.65), down from a peak of 726 (95% Crl: 506-1,067) in 2000. Across all groups, the model estimated 3,202 (95% Crl: 1,273-6,601) chronically infected persons in 2022. Results were robust in sensitivity analyses. CONCLUSIONS: Norway provides an example of the feasibility of hepatitis C elimination in a setting with a concentrated epidemic, high coverage of harm reduction services and no treatment restrictions. Continued momentum is needed to further reduce the transmission and burden of hepatitis C in Norway.

Community testing practices for autism within the autism and developmental disabilities monitoring network.

BACKGROUND: No data exist at the population level on what tests are used to aid in the diagnosis of autism spectrum disorder in community practice. OBJECTIVES: To describe autism spectrum disorder testing practices to inform autism spectrum disorder identification efforts. METHODS: Data are from the Autism and Developmental Disabilities Monitoring Network, a multi-site surveillance system reporting prevalence estimates and characteristics of 8-year-old children with autism spectrum disorder. Percentages of children with autism spectrum disorder who received any autism spectrum disorder test or a 'gold standard' test were calculated by site, sex, race, median household income, and intellectual ability status. Risk ratios were calculated to compare group differences. RESULTS: Of 5058 8-year-old children with autism spectrum disorder across 11 sites, 3236 (64.0%) had a record of any autism spectrum disorder test and 2136 (42.2%) had a 'gold standard' ADOS or ADI-R test. Overall, 115 children (2.3%) had both the ADOS and ADI-R in their records. Differences persisted across race, median household income, and intellectual ability status. Asian/Pacific Islander children had the highest percent receiving any ASD test (71.8%; other groups range: 57.4-66.0%) and White children had the highest percent receiving 'gold standard' tests (46.4%; other groups range: 35.6-43.2%). Children in low-income neighbourhoods had a lower percent of any test (62.5%) and 'gold standard' tests (39.4%) compared to medium (70.2% and 47.5%, respectively) and high (69.6% and 46.8%, respectively) income neighbourhoods. Children with intellectual disability had a lower percent of any ASD test (81.7%) and 'gold standard' tests (52.6%) compared to children without intellectual disability (84.0% and 57.6%, respectively). CONCLUSIONS: Autism spectrum disorder testing practices vary widely by site and differ by race and presence of co-occurring intellectual disability, suggesting opportunities to standardise and/or improve autism spectrum disorder identification practices.

Crimean-Congo Hemorrhagic Fever Virus Seroprevalence in Human and Livestock Populations, Northern Tanzania.

We conducted a cross-sectional study of Crimean-Congo hemorrhagic fever virus (CCHFV) in northern Tanzania. CCHFV seroprevalence in humans and ruminant livestock was high, as were spatial heterogeneity levels. CCHFV could represent an unrecognized human health risk in this region and should be included as a differential diagnosis for febrile illness.

PFOA and testis cancer in the Veneto Region (Italy).

The largest documented episode of human contamination by PFOA in the world (approximately 150,000 actual residents on 1 January 2020) has occurred in Italy's Veneto Region. In this large, mostly flat plain area, a cluster of testicular cancers has also been observed. Preliminary data are reported, and the most relevant and recent recommendations regarding the health surveillance of exposed individuals are emphasized.

COVID-19 outbreaks surveillance through text mining applied to electronic health records.

BACKGROUND: The COVID-19 pandemic has caused significant disruptions to everyday life and has had social, political, and financial consequences that will persist for years. Several initiatives with intensive use of technology were quickly developed in this scenario. However, technologies that enhance epidemiological surveillance in contexts with low testing capacity and healthcare resources are scarce. Therefore, this study aims to address this gap by developing a data science model that uses routinely generated healthcare encounter records to detect possible new outbreaks early in real-time. METHODS: We defined an epidemiological indicator that is a proxy for suspected cases of COVID-19 using the health records of Emergency Care Unit (ECU) patients and text mining techniques. The open-field dataset comprises 2,760,862 medical records from nine ECUs, where each record has information about the patient's age, reported symptoms, and the time and date of admission. We also used a dataset where 1,026,804 cases of COVID-19 were officially confirmed. The records range from January 2020 to May 2022. Sample cross-correlation between two finite stochastic time series was used to evaluate the models. RESULTS: For patients with age 18 years, we find time-lag () = 72 days and cross-correlation () ~ 0.82, = 25 days and ~ 0.93, and = 17 days and ~ 0.88 for the first, second, and third waves, respectively. CONCLUSIONS: In conclusion, the developed model can aid in the early detection of signs of possible new COVID-19 outbreaks, weeks before traditional surveillance systems, thereby anticipating in initiating preventive and control actions in public health with a higher likelihood of success.

A scoping review of Islamic pilgrimage to Mecca: Mapping the health concerns and proposed solutions.

OBJECTIVES: To map the current evidence about the health concerns and the potential solutions related to the Islamic pilgrimage to Mecca. DESIGN: A scoping review was applied. Papers published in English between 2012 and 2023 were included but non-human research and sources without any related data were excluded. Data charting and extraction were used to map the current evidence. RESULTS: The total of 36 papers were included with the total number of pilgrims of 17,075,887. The majority of studies were published in the Asia Pacific region (36.11%) as original articles (88.89%). The health concerns were grouped into five main aspects. There were 7603 deaths recorded or about 44 incidences of deaths per 100,000 pilgrims during the pilgrimage. There were recorded 11,018; 6178; 3393; and 17,810 cases for communicable diseases; non-communicable diseases; injuries and trauma; and health services (i.e., cardiac catheterization) and vaccination, respectively. CONCLUSION: Relating to the five health concerns, this study identified the top seven issues in each category (i.e., hypertension, influenza vaccination), except for the death record. Moreover, there were three solutions (for general health, non- and communicable-diseases) presented. Stakeholders could use this evidence to improve healthcare quality particularly related to the annual Islamic pilgrimage to Mecca.

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.

BACKGROUND: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS: Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty.

Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.

Data quality review of the Brazilian nosocomial infections surveillance system.

BACKGROUND: Surveillance of healthcare-associated infections (HAIs) is an essential component of hospital infection prevention and control systems. We aimed to assess the quality of the data compiled by the Brazilian HAI Surveillance System from pediatric (PICUs) and neonatal intensive care units (NICUs), between 2012 and 2021. METHODS: Data Quality Review, including adherence, completeness, internal consistency, consistency over time, and consistency of population trend, were computed at both national and state levels based on quality metrics from World Health Organization Toolkit. Incidence rates (or incidence density) of ventilator-associated pneumonia (VAP) and central line-associated bloodstream infection (CLABSI) were obtained from the Brazilian National Nosocomial Infections Surveillance (NNIS) system. Data on sepsis-related mortality, spanning the period from 2012 to 2021, were extracted from the Brazilian National Health Service database (DATASUS). Additionally, correlations between sepsis-related mortality and incidence rates of VAP or CLABSI were calculated. RESULTS: Throughout the majority of the study period, adherence to VAP reporting remained below 75%, exhibiting a positive trend post-2016. Widespread outliers, as well as inconsistencies over time and in population trends, were evident across all 27 states. Only four states maintained consistent adherence levels above 75% for more than 8 years regarding HAI incidence rates. Notably, CLABSI in NICUs boasted the highest reporting adherence among all HAIs, with 148 periods out of 270 (54.8%) exhibiting reporting adherence surpassing 75%. Three states achieved commendable metrics for CLABSI in PICUs, while five states demonstrated favorable results for CLABSI in NICUs. CONCLUSIONS: While adherence to HAI report is improving among Brazilian states, an important room for improvement in the Brazilian NNIS exists. Additional efforts should be made by the Brazilian government to improve the reliability of HAI data, which could serve as valuable guidance for hospital infection prevention and control policies.

A multicentre case series of analytically confirmed gamma-hydroxybutyrate intoxications in Western Australian emergency departments: Pre-hospital circumstances, co-detections and clinical outcomes.

INTRODUCTION: Gamma-hydroxybutyrate (GHB) use is associated with high risk of accidental overdose. This study examined the pre-hospital circumstances, demographic characteristics and clinical outcomes of analytically confirmed GHB emergency department (ED) presentations in Western Australia (WA). METHODS: This case series was conducted across three WA EDs involved in the Emerging Drugs Network of Australia, from April 2020 to July 2022. Patient demographics, pre-hospital drug exposure circumstances and ED presentation and outcome characteristics were collected from ambulance and hospital medical records of GHB-confirmed cases. RESULTS: GHB was detected in 45 ED presentations. The median age was 34 years and 53.3% (n = 24) were female. Most patients arrived at the ED by ambulance (n = 37, 85.7%) and required immediate emergency care (Australasian Triage Score 1 or 2 = 97.8%). One-third of patients were admitted to intensive care (n = 14, 31.1%). Methylamphetamine was co-detected in 37 (82.2%) GHB-confirmed cases. Reduced conscious state was indicated by first recorded Glasgow Coma Scale of ≤8 (n = 29, 64.4%) and observations of patients becoming, or being found, 'unresponsive' and 'unconscious' in various pre-hospital settings (n = 28, 62.2%). 'Agitated' and/or 'erratic' mental state and behavioural observations were recorded in 20 (44.4%) cases. DISCUSSION AND CONCLUSIONS: Analytically verified data from ED presentations with acute toxicity provides an objective information source on drug use trends and emerging public health threats. In our study, patients presenting to WA EDs with GHB intoxication were acutely unwell, often requiring intensive care treatment. The unexpectedly high proportion of female GHB intoxications and methylamphetamine co-ingestion warrants further exploration.