A regionally tailored epidemiological forecast and monitoring program to guide a healthcare system in the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, analytics and predictive models built on regional data provided timely, accurate monitoring of epidemiological behavior, informing critical planning and decision-making for health system leaders. At Atrium Health, a large, integrated healthcare system in the southeastern United States, a team of statisticians and physicians created a comprehensive forecast and monitoring program that leveraged an array of statistical methods. METHODS: The program utilized the following methodological approaches: (i) exploratory graphics, including time plots of epidemiological metrics with smoothers; (ii) infection prevalence forecasting using a Bayesian epidemiological model with time-varying infection rate; (iii) doubling and halving times computed using changepoints in local linear trend; (iv) death monitoring using combination forecasting with an ensemble of models; (v) effective reproduction number estimation with a Bayesian approach; (vi) COVID-19 patients hospital census monitored via time series models; and (vii) quantified forecast performance. RESULTS: A consolidated forecast and monitoring report was produced weekly and proved to be an effective, vital source of information and guidance as the healthcare system navigated the inherent uncertainty of the pandemic. Forecasts provided accurate and precise information that informed critical decisions on resource planning, bed capacity and staffing management, and infection prevention strategies. CONCLUSIONS: In this paper, we have presented the framework used in our epidemiological forecast and monitoring program at Atrium Health, as well as provided recommendations for implementation by other healthcare systems and institutions to facilitate use in future pandemics.

Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance During Arbaeenia Mass Gatherings in Iraq.

BACKGROUND: Large gatherings often involve extended and intimate contact among individuals, creating environments conducive to the spread of infectious diseases. Despite this, there is limited research utilizing outbreak detection algorithms to analyze real syndrome data from such events. This study sought to address this gap by examining the implementation and efficacy of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq. METHODS: For the study, 10 data collectors conducted field data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10 healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in Iraq. Various outbreak detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported syndromes. RESULTS: During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10 health clinics along a route in Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited syndromes, with influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent syndromes included food poisoning (21.2%, n=1048), heatstroke (17.7%, n=875), febrile rash (9.0%, n=446), and gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM) algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA) algorithms for detecting small shifts. CONCLUSION: Effective public health surveillance systems are crucial during mass gatherings to swiftly identify and address emerging health risks. Utilizing advanced algorithms and real-time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the protection of public health during these gatherings.

Seasonality of respiratory, enteric, and urinary viruses revealed by wastewater genomic surveillance.

Wastewater surveillance can reveal population-level infectious disease burden and emergent public health threats can be reliably assessed through wastewater surveillance. While molecular methods for wastewater monitoring of microorganisms have traditionally relied on PCR-based approaches, next-generation sequencing (NGS) can provide deeper insights via genomic analyses of multiple diverse pathogens. We conducted a year-long sequencing surveillance of 1,408 composite wastewater samples collected from 12 neighborhood-level access points in the greater Tempe area, Arizona, USA, and show that variation in wastewater viruses is driven by seasonal time and location. The temporal dynamics of viruses in wastewater were influenced cyclically, with the most dissimilarity between samples 23 weeks apart (i.e., winter vs summer, spring vs fall). We identified diverse urinary and enteric viruses including polyomaviruses, astroviruses, and noroviruses, and showed that their genotypes/subtypes shifted across seasons. We show that while wastewater data of certain respiratory viruses like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strongly correlate with clinical case rates, laboratory-reported case incidences were discordant with surges of high viral load in wastewater for other viruses like human coronavirus 229E. These results demonstrate the utility of wastewater sequencing for informing decision-making in public health.IMPORTANCEWastewater surveillance can provide insights into the spread of pathogens in communities. Advances in next-generation sequencing (NGS) methodologies allow for more precise detection of viruses in wastewater. Long-term wastewater surveillance of viruses is an important tool for public health preparedness. This system can act as a public health observatory that gives real-time early warning for infectious disease outbreaks and improved response times.

Unifying Public Health Surveillance: A Scoping Review of Global Use of the Youth Risk Behavior Survey.

PURPOSE: The Youth Risk Behavior Survey (YRBS) is a well-established surveillance tool designed to document the health risk behaviors of youth. However, there is limited insight into the use of the survey outside of the United States. The aim of this scoping review was to assess the global presence and utilization of the YRBS. METHODS: A structured electronic search of all publication years (through February 2020) was conducted to identify articles in PubMed and EBSCOhost. The search identified 128 articles that used the YRBS beyond the United States. RESULTS: More than one-third of all countries, territories, and dependencies were represented in the articles, with the greatest use among upper-middle and high-income economies and those in the East Asia and Pacific geographic region. Priority health-risk behaviors identified were alcohol and other drug use (51%), tobacco use (48%), and unintentional and intentional injuries (44%). The articles predominantly suggested that the survey data be used to influence programs, policies, and practices (57%). DISCUSSION: The development and proliferation of surveillance systems has allowed for important contributions to public health. Extensive use of the YRBS is notable; however, greater efforts are needed to support more systematic and collaborative approaches for evaluating youth behaviors around the world.

Innovations in public health surveillance: An overview of novel use of data and analytic methods.

Innovative data sources and methods for public health surveillance (PHS) have evolved rapidly over the past 10 years, suggesting the need for a closer look at the scientific maturity, feasibility, and utility of use in real-world situations. This article provides an overview of recent innovations in PHS, including data from social media, internet search engines, the Internet of Things (IoT), wastewater surveillance, participatory surveillance, artificial intelligence (AI), and nowcasting. Examples identified suggest that novel data sources and analytic methods have the potential to strengthen PHS by improving disease estimates, promoting early warning for disease outbreaks, and generating additional and/or more timely information for public health action. For example, wastewater surveillance has re-emerged as a practical tool for early detection of the coronavirus disease 2019 (COVID-19) and other pathogens, and AI is increasingly used to process large amounts of digital data. Challenges to implementing novel methods include lack of scientific maturity, limited examples of implementation in real-world public health settings, privacy and security risks, and health equity implications. Improving data governance, developing clear policies for the use of AI technologies, and public health workforce development are important next steps towards advancing the use of innovation in PHS.

Data Equity as a Building Block for Health Equity: Improving Surveillance Data for People With Disabilities, With Substance Use Disorder, or Experiencing Homelessness, United States.

OBJECTIVES: People with disabilities, people experiencing homelessness, and people who have substance use disorders face unique health challenges. Gaps in public health surveillance data limit the identification of public health needs of these groups and data-driven action. This study aimed to identify current practices, challenges, and opportunities for collecting and reporting COVID-19 surveillance data for these populations. METHODS: We used a rapid qualitative assessment to explore COVID-19 surveillance capacities. From July through October 2021, we virtually interviewed key informants from the Centers for Disease Control and Prevention, state and local health departments, and health care providers across the United States. We thematically analyzed and contextualized interview notes, peer-reviewed articles, and participant documents using a literature review. RESULTS: We identified themes centered on foundational structural and systems issues that hinder actionable surveillance data for these and other populations that are disproportionately affected by multiple health conditions. Qualitative data analysis of 61 interviews elucidated 4 primary challenges: definitions and policies, resources, data systems, and articulation of the purpose of data collection to these groups. Participants noted the use of multisector partnerships, automated data collection and integration, and data scorecards to circumvent challenges. CONCLUSIONS: This study highlights the need for multisector, systematic improvements in surveillance data collection and reporting to advance health equity. Improvements must be buttressed with adequate investment in data infrastructure and promoted through clear communication of how data are used to protect health.

Demographic characteristics, perinatal smoking patterns, and risk for neonatal health complications among pregnant smokers in the United States who begin using electronic cigarettes during pregnancy: A descriptive study using population-based surveillance data.

INTRODUCTION: Health agencies have called for research evaluating e-cigarette (EC) use in supporting prenatal smoking cessation. This study aimed to describe (a) characteristics of smokers who begin using ECs during pregnancy, (b) how frequently smokers reduce or eliminate pre- and post-natal combustible cigarette (CC) use, and (c) risk for neonatal health complications among smokers who initiate ECs during pregnancy. METHODS: Pregnant women using CCs exclusively pre-pregnancy, who participated in a U.S. surveillance study, were classified by their reported late-pregnancy smoking behavior as CC-exclusive users, EC initiators, or quitters. EC initiators were further subclassified as dual users (used both ECs and CCs) or EC replacers (used ECs exclusively). RESULTS: Of 29,505 pregnant smokers, 1.5% reported using ECs during the last 3 pregnancy months. Among them, 29.7% became EC-exclusive users. EC initiators were disproportionately non-Hispanic White. Relative to quitters, EC initiators had lower income, were less likely to be married, have intended pregnancies, receive first-trimester prenatal care, and participate in a federal assistance program. Compared to CC-exclusive users, EC initiators overall, and dual users specifically, were more likely to reduce pre- and post-natal CC usage relative to pre-pregnancy levels. EC initiators' risk for neonatal health complications fell between quitters and CC-exclusive users, though differences were not statistically significant. CONCLUSIONS: Although EC initiators reduced CC use more than CC-exclusive users, only 29.7% reported complete CC cessation, and there was insufficient evidence of reduction in neonatal health complications relative to CC-exclusive users. Currently, ECs should not be considered a viable gestational smoking cessation strategy. IMPLICATIONS: Health agencies have identified a critical need for research evaluating the use of e-cigarettes in supporting prenatal smoking cessation. Using the US Pregnancy Risk Assessment Monitoring System surveillance study data, we provide real-world evidence that prenatal e-cigarette initiation as a smoking cessation tool is used infrequently among pregnant combustible cigarettes smokers. Most using e-cigarettes in the last three months of pregnancy also used combustible cigarettes.

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis.

BACKGROUND: Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. METHODS: Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. RESULTS: Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. CONCLUSIONS: The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.

Sexual violence against children and adolescents in Paraná State: geospatial analysis and main socioeconomic indicators.

OBJECTIVE: Child sexual violence is a multidimensional problem of many contemporary societies, affecting people of all sexes, social stratum and age groups. Offenses involving children and adolescents are more serious, given their total or partial dependence on parents and caregivers. Information on child sexual violence in Brazil is found in raw form and without detail. The objective was to compare the information with social and economic data in the state of Paraná. METHODS: The authors conducted a retrospective study of secondary data from the Notifiable Diseases Information System (SINAN) on cases of sexual violence involving victims aged 0 to 19 years. Results are presented according to notification characteristics. The authors applied exploratory spatial data analysis to assess spatial autocorrelations and investigated relationships by the ordinary least squares regression model. RESULTS: Between 2017 and 2021, there were 13,403 reports of child sexual violence in Paraná State, Brazil. Most victims (82.8%) were female and aged between 10 and 14 years. The majority of sexual violence cases (67.8%) occurred in the home environment. The highest rates on a population basis were observed in the North Central and Greater Curitiba regions, mainly in cities with higher population density and with higher rates of other types of violence. CONCLUSION: The results provide data that can promote a broader understanding of the distribution of sexual violence and the state and associated variations. It is expected to improve the provision of care for victims of child sexual violence and assist in strategic planning to prevent future offenses.

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.

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 the Prospective Register of Systematic Reviews. 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 Quality Assessment of Diagnostic Accuracy Studies-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. CONCLUSIONS: 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.

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.

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.

Strengthening event-based surveillance (EBS): a case study from Afghanistan.

The sustained instability in Afghanistan, along with ongoing disease outbreaks and the impact of the COVID-19 pandemic, has significantly affected the country.During the COVID-19 pandemic, the country's detection and response capacities faced challenges. Case identification was done in all health facilities from primary to tertiary levels but neglected cases at the community level, resulting in undetected and uncontrolled transmission from communities. This emphasizes a missed opportunity for early detection that Event-Based Surveillance (EBS) could have facilitated.Therefore, Afghanistan planned to strengthen the EBS component of the national public health surveillance system to enhance the capacity for the rapid detection and response to infectious disease outbreaks, including COVID-19 and other emerging diseases. This effort was undertaken to promptly mitigate the impact of such outbreaks.We conducted a landscape assessment of Afghanistan's public health surveillance system to identify the best way to enhance EBS, and then we crafted an implementation work plan. The work plan included the following steps: establishing an EBS multisectoral coordination and working group, identifying EBS information sources, prioritizing public health events of importance, defining signals, establishing reporting mechanisms, and developing standard operating procedures and training guides.EBS is currently being piloted in seven provinces in Afghanistan. The lessons learned from the pilot phase will support its overall expansion throughout the country.

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.

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.

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

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

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.

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.