Comparison of Five Methods to Estimate the Parameters for the Three-Parameter Lindley Distribution with Application to Life Data.

We have studied one of the most common distributions, namely, Lindley distribution, which is an important continuous mixed distribution with great ability to represent different systems. We studied this distribution with three parameters because of its high flexibility in modelling life data. The parameters were estimated by five different methods, namely, maximum likelihood estimation, ordinary least squares, weighted least squares, maximum product of spacing, and Cramér-von Mises. Simulation experiments were performed with different sample sizes and different parameter values. The different methods were compared on the generated data by mean square error and mean absolute error. In addition, we compared the methods for real data, which represent COVID-19 data in Iraq/Anbar Province.

Mathematical epidemiologic and simulation modelling of first wave COVID-19 in Malaysia.

Since the first coronavirus disease 2019 (COVID-19) outbreak appeared in Wuhan, mainland China on December 31, 2019, the geographical spread of the epidemic was swift. Malaysia is one of the countries that were hit substantially by the outbreak, particularly in the second wave. This study aims to simulate the infectious trend and trajectory of COVID-19 to understand the severity of the disease and determine the approximate number of days required for the trend to decline. The number of confirmed positive infectious cases [as reported by Ministry of Health, Malaysia (MOH)] were used from January 25, 2020 to March 31, 2020. This study simulated the infectious count for the same duration to assess the predictive capability of the Susceptible-Infectious-Recovered (SIR) model. The same model was used to project the simulation trajectory of confirmed positive infectious cases for 80 days from the beginning of the outbreak and extended the trajectory for another 30 days to obtain an overall picture of the severity of the disease in Malaysia. The transmission rate, ß also been utilized to predict the cumulative number of infectious individuals. Using the SIR model, the simulated infectious cases count obtained was not far from the actual count. The simulated trend was able to mimic the actual count and capture the actual spikes approximately. The infectious trajectory simulation for 80 days and the extended trajectory for 110 days depicts that the inclining trend has peaked and ended and will decline towards late April 2020. Furthermore, the predicted cumulative number of infectious individuals tallies with the preparations undertaken by the MOH. The simulation indicates the severity of COVID-19 disease in Malaysia, suggesting a peak of infectiousness in mid-March 2020 and a probable decline in late April 2020. Overall, the study findings indicate that outbreak control measures such as the Movement Control Order (MCO), social distancing and increased hygienic awareness is needed to control the transmission of the outbreak in Malaysia.

Cutting Through the Noise: Predictors of Successful Online Message Retransmission in the First 8 Months of the COVID-19 Pandemic.

In this paper, we investigate how message construction, style, content, and the textual content of embedded images impacted message retransmission over the course of the first 8 months of the coronavirus disease 2019 (COVID-19) pandemic in the United States. We analyzed a census of public communications (n = 372,466) from 704 public health agencies, state and local emergency management agencies, and elected officials posted on Twitter between January 1 and August 31, 2020, measuring message retransmission via the number of retweets (ie, a message passed on by others), an important indicator of engagement and reach. To assess content, we extended a lexicon developed from the early months of the pandemic to identify key concepts within messages, employing it to analyze both the textual content of messages themselves as well as text included within embedded images (n = 233,877), which was extracted via optical character recognition. Finally, we modelled the message retransmission process using a negative binomial regression, which allowed us to quantify the extent to which particular message features amplify or suppress retransmission, net of controls related to timing and properties of the sending account. In addition to identifying other predictors of retransmission, we show that the impact of images is strongly driven by content, with textual information in messages and embedded images operating in similar ways. We offer potential recommendations for crafting and deploying social media messages that can "cut through the noise" of an infodemic.

Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey.

COVID-19 pandemic has become a concern of every nation, and it is crucial to apply an estimation model with a favorably-high accuracy to provide an accurate perspective of the situation. In this study, three explicit mathematical prediction models were applied to forecast the COVID-19 outbreak in Iran and Turkey. These models include a recursive-based method, Boltzmann Function-based model and Beesham's prediction model. These models were exploited to analyze the confirmed and death cases of the first 106 and 87 days of the COVID-19 outbreak in Iran and Turkey, respectively. This application indicates that the three models fail to predict the first 10 to 20 days of data, depending on the prediction model. On the other hand, the results obtained for the rest of the data demonstrate that the three prediction models achieve high values for the determination coefficient, whereas they yielded to different average absolute relative errors. Based on the comparison, the recursive-based model performs the best, while it estimated the COVID-19 outbreak in Iran better than that of in Turkey. Impacts of applying or relaxing control measurements like curfew in Turkey and reopening the low-risk businesses in Iran were investigated through the recursive-based model. Finally, the results demonstrate the merit of the recursive-based model in analyzing various scenarios, which may provide suitable information for health politicians and public health decision-makers.

COVID-19 prevention and treatment information on the internet: a systematic analysis and quality assessment.

OBJECTIVE: To evaluate the quality of information regarding the prevention and treatment of COVID-19 available to the general public from all countries. DESIGN: Systematic analysis using the 'Ensuring Quality Information for Patients' (EQIP) Tool (score 0-36), Journal of American Medical Association (JAMA) benchmark (score 0-4) and the DISCERN Tool (score 16-80) to analyse websites containing information targeted at the general public. DATA SOURCES: Twelve popular search terms, including 'Coronavirus', 'COVID-19 19', 'Wuhan virus', 'How to treat coronavirus' and 'COVID-19 19 Prevention' were identified by 'Google AdWords' and 'Google Trends'. Unique links from the first 10 pages for each search term were identified and evaluated on its quality of information. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: All websites written in the English language, and provides information on prevention or treatment of COVID-19 intended for the general public were considered eligible. Any websites intended for professionals, or specific isolated populations, such as students from one particular school, were excluded, as well as websites with only video content, marketing content, daily caseload update or news dashboard pages with no health information. RESULTS: Of the 1275 identified websites, 321 (25%) were eligible for analysis. The overall EQIP, JAMA and DISCERN scores were 17.8, 2.7 and 38.0, respectively. Websites originated from 34 countries, with the majority from the USA (55%). News Services (50%) and Government/Health Departments (27%) were the most common sources of information and their information quality varied significantly. Majority of websites discuss prevention alone despite popular search trends of COVID-19 treatment. Websites discussing both prevention and treatment (n=73, 23%) score significantly higher across all tools (p<0.001). CONCLUSION: This comprehensive assessment of online COVID-19 information using EQIP, JAMA and DISCERN Tools indicate that most websites were inadequate. This necessitates improvements in online resources to facilitate public health measures during the pandemic.

Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study.

BACKGROUND: The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of "sustained decline" varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R0 and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. OBJECTIVE: This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. METHODS: Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. RESULTS: The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. CONCLUSIONS: Reopening the United States comes with three certainties: (1) the "social" end of the pandemic and reopening are going to occur before the "medical" end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.

An epidemiological modelling approach for COVID-19 via data assimilation.

The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We analyze and discuss infection rates in the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. We compare and discuss model results which conducts updates as new observations become available. A hybrid data assimilation approach is applied to make results robust to initial conditions and measurement errors in the data. We use the model to conduct inference on infection numbers as well as parameters such as the disease transmissibility rate or the rate of recovery. The parameterisation of the model is parsimonious and extendable, allowing for the incorporation of additional data and parameters of interest. This allows for scalability and the extension of the model to other locations or the adaption of novel data sources.

Multiple Epidemic Wave Model of the COVID-19 Pandemic: Modeling Study.

BACKGROUND: Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution. OBJECTIVE: The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries. METHODS: We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance. RESULTS: We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model's results, an index value was assigned to each country, quantifying in an objective manner the country's response to the pandemic. CONCLUSIONS: Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.

Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns.

BACKGROUND: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. OBJECTIVE: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. METHODS: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. RESULTS: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. CONCLUSIONS: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.

Reducing expectations for antibiotics in primary care: a randomised experiment to test the response to fear-based messages about antimicrobial resistance.

BACKGROUND: To reduce inappropriate antibiotic use, public health campaigns often provide fear-based information about antimicrobial resistance (AMR). Meta-analyses have found that fear-based campaigns in other contexts are likely to be ineffective unless respondents feel confident they can carry out the recommended behaviour ('self-efficacy'). This study aimed to test the likely impact of fear-based messages, with and without empowering self-efficacy elements, on patient consultations/antibiotic requests for influenza-like illnesses, using a randomised design. METHODS: We hypothesised that fear-based messages containing empowering information about self-management without antibiotics would be more effective than fear alone, particularly in a pre-specified subgroup with low AMR awareness. Four thousand respondents from an online panel, representative of UK adults, were randomised to receive three different messages about antibiotic use and AMR, designed to induce fear about AMR to varying degrees. Two messages (one 'strong-fear', one 'mild-fear') also contained empowering information regarding influenza-like symptoms being easily self-managed without antibiotics. The main outcome measures were self-reported effect of information on likelihood of visiting a doctor and requesting antibiotics, for influenza-like illness, analysed separately according to whether or not the AMR information was 'very/somewhat new' to respondents, pre-specified based on a previous (non-randomised) survey. RESULTS: The 'fear-only' message was 'very/somewhat new' to 285/1000 (28.5%) respondents, 'mild-fear-plus-empowerment' to 336/1500 (22.4%), and 'strong-fear-plus-empowerment' to 388/1500 (25.9%) (p = 0.002). Of those for whom the respective information was 'very/somewhat new', only those given the 'strong-fear-plus-empowerment' message said they would be less likely to request antibiotics if they visited a doctor for an influenza-like illness (p < 0.0001; 182/388 (46.9%) 'much less likely'/'less likely', versus 116/336 (34.5%) with 'mild-fear-plus-empowerment' versus 85/285 (29.8%) with 'fear-alone'). Those for whom the respective information was not 'very/somewhat new' said they would be less likely to request antibiotics for influenza-like illness (p < 0.0001) across all messages (interaction p < 0.0001 versus 'very/somewhat new' subgroup). The three messages had analogous self-reported effects on likelihood of visiting a doctor and in subgroups defined by believing antibiotics would 'definitely/probably' help an influenza-like illness. Results were reproduced in an independent randomised survey (additional 4000 adults). CONCLUSIONS: Fear could be effective in public campaigns to reduce inappropriate antibiotic use, but should be combined with messages empowering patients to self-manage symptoms effectively without antibiotics.

Using linked electronic health records to report healthcare-associated infections.

BACKGROUND: Reporting of strategic healthcare-associated infections (HCAIs) to Public Health England is mandatory for all acute hospital trusts in England, via a web-based HCAI Data Capture System (HCAI-DCS). AIM: Investigate the feasibility of automating the current, manual, HCAI reporting using linked electronic health records (linked-EHR), and assess its level of accuracy. METHODS: All data previously submitted through the HCAI-DCS by the Oxford University Hospitals infection control (IC) team for methicillin-resistant and methicillin-susceptible Staphylococcus aureus (MRSA, MSSA), Clostridium difficile, and Escherichia coli, through March 2017 were downloaded and compared to outputs created from linked-EHR, with detailed comparisons between 2013-2017. FINDINGS: Total MRSA, MSSA, E. coli and C. difficile cases entered by the IC team vs linked-EHR were 428 vs 432, 795 vs 816, 2454 vs 2450 and 3365 vs 3393 respectively. From 2013-2017, most discrepancies (32/37 (86%)) were likely due to IC recording errors. Patient and specimen identifiers were completed for >98% of cases by both methods, with very high agreement (>97%). Fields relating to the patient at the time the specimen was taken were complete to a similarly high level (>99% IC, >97% linked-EHR), and agreement was fairly good (>80%) except for the main and treatment specialties (57% and 54% respectively) and the patient category (55%). Optional, organism-specific data-fields were less complete, by both methods. Where comparisons were possible, agreement was reasonably high (mostly 70-90%). CONCLUSION: Basic factual information, such as demographic data, is almost-certainly better automated, and many other data fields can potentially be populated successfully from linked-EHR. Manual data collection is time-consuming and inefficient; automated electronic data collection would leave healthcare professionals free to focus on clinical rather than administrative work.

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks.

BACKGROUND: Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks. METHODS: A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation. RESULTS: For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques. CONCLUSIONS: The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.

Locating People Diagnosed With HIV for Public Health Action: Utility of HIV Case Surveillance and Other Data Sources.

INTRODUCTION: Human immunodeficiency virus (HIV) case surveillance and other health care databases are increasingly being used for public health action, which has the potential to optimize the health outcomes of people living with HIV (PLWH). However, often PLWH cannot be located based on the contact information available in these data sources. We assessed the accuracy of contact information for PLWH in HIV case surveillance and additional data sources and whether time since diagnosis was associated with accurate contact information in HIV case surveillance and successful contact. MATERIALS AND METHODS: The Case Surveillance-Based Sampling (CSBS) project was a pilot HIV surveillance system that selected a random population-based sample of people diagnosed with HIV from HIV case surveillance registries in 5 state and metropolitan areas. From November 2012 through June 2014, CSBS staff members attempted to locate and interview 1800 sampled people and used 22 data sources to search for contact information. RESULTS: Among 1063 contacted PLWH, HIV case surveillance data provided accurate telephone number, address, or HIV care facility information for 239 (22%), 412 (39%), and 827 (78%) sampled people, respectively. CSBS staff members used additional data sources, such as support services and commercial people-search databases, to locate and contact PLWH with insufficient contact information in HIV case surveillance. PLWH diagnosed <1 year ago were more likely to have accurate contact information in HIV case surveillance than were PLWH diagnosed ≥1 year ago ( P = .002), and the benefit from using additional data sources was greater for PLWH with more longstanding HIV infection ( P < .001). PRACTICE IMPLICATIONS: When HIV case surveillance cannot provide accurate contact information, health departments can prioritize searching additional data sources, especially for people with more longstanding HIV infection.

Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016.

INTRODUCTION: The use of syndromic surveillance has expanded from its initial purpose of bioterrorism detection. We present 6 use cases from New York City that demonstrate the value of syndromic surveillance for public health response and decision making across a broad range of health outcomes: synthetic cannabinoid drug use, heat-related illness, suspected meningococcal disease, medical needs after severe weather, asthma exacerbation after a building collapse, and Ebola-like illness in travelers returning from West Africa. MATERIALS AND METHODS: The New York City syndromic surveillance system receives data on patient visits from all emergency departments (EDs) in the city. The data are used to assign syndrome categories based on the chief complaint and discharge diagnosis, and analytic methods are used to monitor geographic and temporal trends and detect clusters. RESULTS: For all 6 use cases, syndromic surveillance using ED data provided actionable information. Syndromic surveillance helped detect a rise in synthetic cannabinoid-related ED visits, prompting a public health investigation and action. Surveillance of heat-related illness indicated increasing health effects of severe weather and led to more urgent public health messaging. Surveillance of meningitis-related ED visits helped identify unreported cases of culture-negative meningococcal disease. Syndromic surveillance also proved useful for assessing a surge of methadone-related ED visits after Superstorm Sandy, provided reassurance of no localized increases in asthma after a building collapse, and augmented traditional disease reporting during the West African Ebola outbreak. PRACTICE IMPLICATIONS: Sharing syndromic surveillance use cases can foster new ideas and build capacity for public health preparedness and response.

Harnessing Syndromic Surveillance Emergency Department Data to Monitor Health Impacts During the 2015 Special Olympics World Games.

INTRODUCTION: Mass gatherings that attract a large international presence may cause or amplify point-source outbreaks of emerging infectious disease. The Los Angeles County Department of Public Health customized its syndromic surveillance system to detect increased syndrome-specific utilization of emergency departments (EDs) and other medical encounters coincident to the 2015 Special Olympics World Games. MATERIALS AND METHODS: We queried live databases containing data on ED visits, California Poison Control System calls, and Los Angeles County coroner-investigated deaths for increases in daily counts from July 19 to August 6, 2015. We chose syndrome categories based on the potential for disease outbreaks common to international travel and dormitory settings, morbidity amplified by high temperatures, and bioterrorism threats inherent to mass gatherings. We performed line-list reviews and trend analyses of total, syndrome-specific, and region-specific daily counts, using cumulative sum-based signals. We also piloted a novel strategy of requesting that ED registrars proactively tag Special Olympics attendees in chief complaint data fields. RESULTS: The syndromic surveillance system showed that the 2015 Special Olympics did not generate large-scale acute morbidities leading to detectable stress on local EDs. We recruited 10 hospitals for proactive patient tagging, from which 16 Special Olympics attendees were detected; these patients reported various symptoms, such as injury, vomiting, and syncope. PRACTICE IMPLICATIONS: As an enhancement to traditional syndromic surveillance, proactive patient tagging can illuminate potential epidemiologic links among patients in challenging syndromic surveillance applications, such as mass gatherings. Syndromic surveillance has the potential to enhance ED patient polling and reporting of exposure, symptom, and other epidemiologic case definition criteria to public health agencies in near-real time.

Developing Syndromic Surveillance to Monitor and Respond to Adverse Health Events Related to Psychoactive Substance Use: Methods and Applications.

INTRODUCTION: Recent increases in drug overdose deaths, both in New York City and nationally, highlight the need for timely data on psychoactive drug-related morbidity. We developed drug syndrome definitions for syndromic surveillance to monitor drug-related emergency department (ED) visits in real time. MATERIALS AND METHODS: We used 2012 archived syndromic surveillance data from New York City hospitals to develop definitions for psychoactive drug-related syndromes. The dataset contained ED visit-level information that included patients' chief complaints, dates of visits, ZIP codes of residence, discharge diagnoses, and dispositions. After manually reviewing chief complaints, we developed a classification scheme comprising 3 categories (overdose, drug mention, and drug abuse/misuse), which we used to define 25 psychoactive drug syndromes. From July 2013 through December 2015, the New York City Department of Health and Mental Hygiene performed daily syndromic surveillance of psychoactive drug-related ED visits using the 25 syndrome definitions. RESULTS: Syndromic surveillance triggered 4 public health investigations, supported 8 other public health investigations that had been triggered by other mechanisms, and resulted in the identification of 5 psychoactive drug-related outbreaks. Syndromic surveillance also identified a substantial increase in synthetic cannabinoid-related visits (from an average of 3 per week in January 2014 to >300 per week in July 2015) and an increase in heroin overdose visits (from 80 to 171 in the first 3 quarters of 2012 and 2014, respectively) in a single neighborhood. PRACTICE IMPLICATIONS: Syndromic surveillance using these novel definitions enabled monitoring of trends in psychoactive drug-related morbidity, initiation and support of public health investigations, and targeting of interventions. Health departments can refine these definitions for their jurisdictions using the described methods and integrate them into existing syndromic surveillance systems.

Validation of a Syndromic Case Definition for Detecting Emergency Department Visits Potentially Related to Marijuana.

OBJECTIVES: Reliable methods are needed to monitor the public health impact of changing laws and perceptions about marijuana. Structured and free-text emergency department (ED) visit data offer an opportunity to monitor the impact of these changes in near-real time. Our objectives were to (1) generate and validate a syndromic case definition for ED visits potentially related to marijuana and (2) describe a method for doing so that was less resource intensive than traditional methods. METHODS: We developed a syndromic case definition for ED visits potentially related to marijuana, applied it to BioSense 2.0 data from 15 hospitals in the Denver, Colorado, metropolitan area for the period September through October 2015, and manually reviewed each case to determine true positives and false positives. We used the number of visits identified by and the positive predictive value (PPV) for each search term and field to refine the definition for the second round of validation on data from February through March 2016. RESULTS: Of 126 646 ED visits during the first period, terms in 524 ED visit records matched ≥1 search term in the initial case definition (PPV, 92.7%). Of 140 932 ED visits during the second period, terms in 698 ED visit records matched ≥1 search term in the revised case definition (PPV, 95.7%). After another revision, the final case definition contained 6 keywords for marijuana or derivatives and 5 diagnosis codes for cannabis use, abuse, dependence, poisoning, and lung disease. CONCLUSIONS: Our syndromic case definition and validation method for ED visits potentially related to marijuana could be used by other public health jurisdictions to monitor local trends and for other emerging concerns.