Using Syndromic Surveillance for All-Hazards Public Health Surveillance: Successes, Challenges, and the Future

Relationships of Self-reported Opioid Use and Misuse and Pain Severity With Probable Major Depression Among Participants With Spinal Cord Injury

OBJECTIVES

To examine the relations of pain intensity, opioid use, and opioid misuse with depressive symptom severity and probable major depression (PMD) among participants with spinal cord injuries (SCI), controlling for demographic, injury, and socioeconomic characteristics.

STUDY DESIGN

Cohort study.

SETTING

Medical University in the Southeastern United States (US).

PARTICIPANTS

Participants (N=918) were identified from 1 of 2 sources including a specialty hospital and a state-based surveillance system in the Southeastern US. Participants were a minimum of 18 years old at enrollment and had SCI with non-complete recovery. Participants were on average 57.5 years old at the time of the study and an average of 24.4 years post SCI onset.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Participants completed a self-report assessment that included frequency of prescription opioid use and misuse, based on the National Survey on Drug Use and Health (NSDUH), and the PHQ - 9 to measure depressive symptom severity and PMD.

RESULTS

Opioid use, opioid misuse, and pain intensity were related to elevated depressive symptom severity and higher odds of PMD. Non-Hispanic Blacks had fewer depressive symptoms and lower odds of PMD, as did those with higher incomes. Veterans had lower risk of PMD, whereas ambulatory participants had a higher risk of PMD. Age at SCI onset had a mixed pattern of significance, whereas years of education and years since injury were not significant.

CONCLUSIONS

The relation between pain intensity with depressive symptom severity and PMD was profound, consistent with the biopsychosocial model of pain. The greater risk of PMD and higher depressive symptom severity among those using opioids and misusing opioids raises further concern about long-term prescription opioid use. Alternative treatments are needed.

Emergency Department Syndromic Surveillance to Monitor Tick-Borne Diseases: A 6-Year Small-Area Analysis in Northeastern Italy

Tick-borne diseases (TBD) are endemic in Europe. However, surveillance is currently incomplete. Alternative strategies need to be considered. The aim of this study was to test an Emergency Department Syndromic Surveillance (EDSyS) system as a complementary data source to describe the impact of tick bites and TBD using a small-area analysis approach and to monitor the risk of TBD to target prevention. ED databases in the Local Health Authority 8 District (Veneto, Italy) were queried for tick-bite and TBD-related visits between January 2017 and December 2022. Hospitalisations were also collected. Events involving the resident population were used to calculate incidence rates. A total of 4187 ED visits for tick-bite and 143 for TBD were recorded; in addition, 62 TBD-related hospitalisations (of which 72.6% in over 50 s and 22.6% in over 65 s). ED visits peaked in spring and in autumn, followed by a 4-week lag in the increase in hospital admissions. The small-area analysis identified two areas at higher risk of bites and TBD. The use of a EDSyS system allowed two natural foci to be identified. This approach proved useful in predicting temporal and geographic risk of TBD and in identifying local endemic areas, thus enabling an effective multidisciplinary prevention strategy.

Linking coastal environmental and health observations for human wellbeing

Coastal areas have long been attractive places to live, work, and recreate and remain so even in the face of growing threats from global environmental change. At any moment, a significant portion of the human population is exposed to both positive and negative health effects associated with coastal locations. Some locations may be "hotspots" of concern for human health due to ongoing climatic and other changes, accentuating the need for better understanding of coastal environment-human health linkages. This paper describes how environmental and health data could be combined to create a coastal environmental and human health observing system. While largely based on information from the US and Europe, the concept should be relevant to almost any coastal area. If implemented, a coastal health observing system would connect a variety of human health data and environmental observations for individuals and communities, and where possible cohorts. Health data would be derived from questionnaires and other personal sources, clinical examinations, electronic health records, wearable devices, and syndromic surveillance, plus information on vulnerability and health-relevant community characteristics, and social media observations. Environmental data sources would include weather and climate, beach and coastal conditions, sentinel species, occurrences of harmful organisms and substances, seafood safety advisories, and distribution, proximity, and characteristics of health-promoting green and blue spaces. Where available, information on supporting resources could be added. Establishment of a linked network of coastal health observatories could provide powerful tools for understanding the positive and negative health effects of coastal living, lead to better health protections and enhanced wellbeing, and provide significant benefits to coastal residents, including the historically disadvantaged, as well as the military, hospitals and emergency departments, academic medical, public health, and environmental health programs, and others. Early networks could provide best practices and lessons learned to assist later entries.

Development and Validation of a Syndrome Definition for Suspected Nonfatal Unintentional/Undetermined Intent Stimulant-Involved Overdoses

OBJECTIVES

To monitor stimulant-involved overdose (SOD) trends, the Centers for Disease Control and Prevention (CDC) developed and evaluated the validity of a syndromic surveillance definition for suspected nonfatal, unintentional/undetermined intent stimulant-involved overdose (UUSOD).

METHODS

We analyzed all emergency department (ED) visits in CDC's surveillance system that met the UUSOD syndrome definition (January 2018-December 2019). We classified visits as true positive, possible, or not UUSODs after reviewing diagnosis codes and chief complaints. We first assessed whether visits were acute SODs, subsequently classifying acute SODs by intent. The percentage of true-positive UUSODs did not include intentional or possibly intentional visits. We considered all visits with UUSOD diagnosis codes to be acute SODs and reviewed them for intent. We manually reviewed and double-coded a 10% random sample of visits without UUSOD diagnosis codes using decision rules based on signs and symptoms. The overall percentage of true-positive UUSODs was a weighted average of the percentage of true-positive UUSODs based on diagnosis codes and the percentage of true-positive UUSODs determined by manually reviewing visits without codes.

RESULTS

During 2018-2019, 40 045 ED visits met the syndrome definition for UUSOD. Approximately half (n = 18 793; 46.9%) of 40 045 visits had UUSOD diagnosis codes, indicating acute SOD; of these, 98.6% (n = 18 534) were true-positive UUSODs. Of 2125 manually reviewed visits without UUSOD diagnosis codes, 32.6% (n = 693) were true-positive UUSODs, 54.2% (n = 1151) were possible UUSODs, and 13.2% (n = 281) were not UUSODs. Overall, 63.6% of visits were true-positive UUSODs, 29.3% were possible UUSODs, and 7.1% were not UUSODs.

PRACTICE IMPLICATIONS

CDC's UUSOD definition may assist in surveillance efforts with further refinement to capture data on SOD clusters and trends.

Building Infrastructure for Surveillance of Adverse and Positive Childhood Experiences: Integrated, Multimethod Approaches to Generate Data for Prevention Action

Adverse and positive childhood experiences have a profound impact on lifespan health and well-being. However, their incorporation into ongoing population-based surveillance systems has been limited. This paper outlines critical steps in building a comprehensive approach to adverse and positive childhood experiences surveillance, provides examples from the Preventing Adverse Childhood Experiences: Data to Action cooperative agreement, and describes improvements needed to optimize surveillance data for action. Components of a comprehensive approach to adverse and positive childhood experiences surveillance include revisiting definitions and measurement, including generating and using uniform definitions for adverse and positive childhood experiences across data collection efforts; conducting youth-based surveillance of adverse and positive childhood experiences; using innovative methods to gather and analyze near real-time data; leveraging available data, including from administrative sources; and integrating data on community- and societal-level risk and protective factors for adverse childhood experiences, including social and health inequities such as racism and poverty, as well as policies and conditions that create healthy environments for children and families. Comprehensive surveillance data on adverse and positive childhood experiences can inform data-driven prevention and intervention efforts, including focusing prevention programming and services to populations in greatest need. Data can be used to evaluate progress in reducing the occurrence of adverse childhood experiences and bolstering the occurrence of positive childhood experiences. Through expansion and improvement in adverse and positive childhood experiences surveillance-including at federal, state, territorial, tribal, and local levels-data-driven action can reduce children's exposure to violence and other adversities and improve lifelong health and well-being.

An evaluation of Internet searches as a marker of trends in population mental health in the US

The absence of continuous, real-time mental health assessment has made it challenging to quantify the impacts of the COVID-19 pandemic on population mental health. We examined publicly available, anonymized, aggregated data on weekly trends in Google searches related to anxiety, depression, and suicidal ideation from 2018 to 2020 in the US. We correlated these trends with (1) emergency department (ED) visits for mental health problems and suicide attempts, and (2) surveys of self-reported symptoms of anxiety, depression, and mental health care use. Search queries related to anxiety, depression, and suicidal ideation decreased sharply around March 2020, returning to pre-pandemic levels by summer 2020. Searches related to depression were correlated with the proportion of individuals reporting receiving therapy (r = 0.73), taking medication (r = 0.62) and having unmet mental healthcare needs (r = 0.57) on US Census Household Pulse Survey and modestly correlated with rates of ED visits for mental health conditions. Results were similar when considering instead searches for anxiety. Searches for suicidal ideation did not correlate with external variables. These results suggest aggregated data on Internet searches can provide timely and continuous insights into population mental health and complement other existing tools in this domain.

Infectious diseases prevention and control using an integrated health big data system in China

BACKGROUND

The Yinzhou Center for Disease Prevention and Control (CDC) in China implemented an integrated health big data platform (IHBDP) that pooled health data from healthcare providers to combat the spread of infectious diseases, such as dengue fever and pulmonary tuberculosis (TB), and to identify gaps in vaccination uptake among migrant children.

METHODS

IHBDP is composed of medical data from clinics, electronic health records, residents' annual medical checkup and immunization records, as well as administrative data, such as student registries. We programmed IHBDP to automatically scan for and detect dengue and TB carriers, as well as identify migrant children with incomplete immunization according to a comprehensive set of screening criteria developed by public health and medical experts. We compared the effectiveness of the big data screening with existing traditional screening methods.

RESULTS

IHBDP successfully identified six cases of dengue out of a pool of 3972 suspected cases, whereas the traditional method only identified four cases (which were also detected by IHBDP). For TB, IHBDP identified 288 suspected cases from a total of 43,521 university students, in which three cases were eventually confirmed to be TB carriers through subsequent follow up CT or T-SPOT.TB tests. As for immunization screenings, IHBDP identified 240 migrant children with incomplete immunization, but the traditional door-to-door screening method only identified 20 ones.

CONCLUSIONS

Our study has demonstrated the effectiveness of using IHBDP to detect both acute and chronic infectious disease patients and identify children with incomplete immunization as compared to traditional screening methods.

An Evaluation of Syndromic Surveillance-Related Practices Among Selected State and Local Health Agencies

CONTEXT

Syndromic surveillance consists of the systematic collection and use of near real-time data about health-related events for situational awareness and public health action. As syndromic surveillance programs continue to adopt new technologies and expand, it is valuable to evaluate these syndromic surveillance systems and practices to ensure that they meet public health needs.

OBJECTIVE

This assessment's aim is to provide recent information about syndromic surveillance systems and practice characteristics among a group of state and local health departments.

DESIGN/SETTING

Information was obtained between November 2017 and June 2018 through a telephone survey using an Office of Management and Budget-approved standardized data collection tool. Participants were syndromic surveillance staff from each of 31 state and local health departments participating in the National Syndromic Surveillance Program funded by the Centers for Disease Control and Prevention. Questions included jurisdictional experience, data sources and analysis systems used, syndromic system data processing characteristics, data quality verification procedures, and surveillance activities conducted with syndromic data.

MEASURES

Practice-specific information such as types of systems and data sources used for syndromic surveillance, data quality monitoring, and uses of data for public health situational awareness (eg, investigating occurrences of or trends in diseases).

RESULTS

The survey analysis revealed a wide range of experiences with syndromic surveillance. Participants reported the receipt of data daily or more frequently. Emergency department data were the primary data source; however, other data sources are being integrated into these systems. All health departments routinely monitored data quality. Syndromes of highest priority across the respondents for health events monitoring were influenza-like illness and drug-related syndromes. However, a wide variety of syndromes were reported as priorities across the health departments.

CONCLUSION

Overall, syndromic surveillance was relevantly integrated into the public health surveillance infrastructure. The near real-time nature of the data and its flexibility to monitor different types of health-related issues make it especially useful for public health practitioners. Despite these advances, syndromic surveillance capacity, locally and nationally, must continue to evolve and progress should be monitored to ensure that syndromic surveillance systems and data are optimally able to meet jurisdictional needs.

Emergency Medical Services and Syndromic Surveillance: A Comparison With Traditional Surveillance and Effects on Timeliness

OBJECTIVE

Traditional public health surveillance of nonfatal opioid overdose relies on emergency department (ED) billing data, which can be delayed substantially. We compared the timeliness of 2 new data sources for rapid drug overdose surveillance-emergency medical services (EMS) and syndromic surveillance-with ED billing data.

METHODS

We used data on nonfatal opioid overdoses in Kentucky captured in EMS, syndromic surveillance, and ED billing systems during 2018-2019. We evaluated the time-series relationships between EMS and ED billing data and syndromic surveillance and ED billing data by calculating cross-correlation functions, controlling for influences of autocorrelations. A case example demonstrates the usefulness of EMS and syndromic surveillance data to monitor rapid changes in opioid overdose encounters in Kentucky during the COVID-19 epidemic.

RESULTS

EMS and syndromic surveillance data showed moderate-to-strong correlation with ED billing data on a lag of 0 (r = 0.694; 95% CI, 0.579-0.782; t = 9.73; df = 101; P < .001; and r = 0.656; 95% CI, 0.530-0.754; t = 8.73; df = 101; P < .001; respectively) at the week-aggregated level. After the COVID-19 emergency declaration, EMS and syndromic surveillance time series had steep increases in April and May 2020, followed by declines from June through September 2020. The ED billing data were available for analysis 3 months after the end of a calendar quarter but closely followed the trends identified by the EMS and syndromic surveillance data.

CONCLUSION

Data from EMS and syndromic surveillance systems can be reliably used to monitor nonfatal opioid overdose trends in Kentucky in near-real time to inform timely public health response.

Development and Validation of a Syndrome Definition to Identify Suspected Nonfatal Heroin-Involved Overdoses Treated in Emergency Departments

CONTEXT

The Centers for Disease Control and Prevention (CDC) works closely with states and local jurisdictions that are leveraging data from syndromic surveillance systems to identify meaningful changes in overdose trends. CDC developed a suspected nonfatal heroin overdose syndrome definition for use with emergency department (ED) data to help monitor trends at the national, state, and local levels.

OBJECTIVE

This study assesses the percentage of true-positive unintentional and undetermined intent heroin-involved overdose (UUHOD) captured by this definition.

DESIGN/SETTING

CDC applied the UUHOD definition to ED data available in CDC's National Syndromic Surveillance Program (NSSP). Data were analyzed from 18 states that shared access to their syndromic data in NSSP with the CDC overdose morbidity team. Data were analyzed using queries and manual reviews to identify heroin overdose diagnosis codes and text describing chief complaint reasons for ED visits.

MEASURES

The percentage of true-positive UUHOD was calculated as the number of true-positives divided by the number of total visits captured by the syndrome definition.

RESULTS

In total, 99 617 heroin overdose visits were identified by the syndrome definition. Among 95 323 visits identified as acute heroin-involved overdoses, based on reviews of chief complaint text and diagnosis codes, 967 (1.0%) were classified as possible intentional drug overdoses. Among all 99 617 visits, 94 356 (94.7%) were classified as true-positive UUHOD; 2226 (2.2%) and 3035 (3.0%) were classified as "no" and "maybe" UUHOD, respectively.

CONCLUSION

Analysis of the CDC heroin overdose syndrome definition determined that nearly all visits were captured accurately for patients presenting to the ED for a suspected acute UUHOD. This definition will continue to be valuable for ongoing heroin overdose surveillance and epidemiologic analysis of heroin overdose patterns. CDC will evaluate possible definition refinements as new products and terms for heroin overdose emerge.

Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions

OBJECTIVE

Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.

MATERIALS AND METHODS

We searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges.

RESULTS

Finally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals.

CONCLUSIONS

EHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.

Trends in US Emergency Department Visits for Mental Health, Overdose, and Violence Outcomes Before and During the COVID-19 Pandemic

IMPORTANCE

The coronavirus disease 2019 (COVID-19) pandemic, associated mitigation measures, and social and economic impacts may affect mental health, suicidal behavior, substance use, and violence.

OBJECTIVE

To examine changes in US emergency department (ED) visits for mental health conditions (MHCs), suicide attempts (SAs), overdose (OD), and violence outcomes during the COVID-19 pandemic.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used data from the Centers for Disease Control and Prevention's National Syndromic Surveillance Program to examine national changes in ED visits for MHCs, SAs, ODs, and violence from December 30, 2018, to October 10, 2020 (before and during the COVID-19 pandemic). The National Syndromic Surveillance Program captures approximately 70% of US ED visits from more than 3500 EDs that cover 48 states and Washington, DC.

MAIN OUTCOMES AND MEASURES

Outcome measures were MHCs, SAs, all drug ODs, opioid ODs, intimate partner violence (IPV), and suspected child abuse and neglect (SCAN) ED visit counts and rates. Weekly ED visit counts and rates were computed overall and stratified by sex.

RESULTS

From December 30, 2018, to October 10, 2020, a total of 187 508 065 total ED visits (53.6% female and 46.1% male) were captured; 6 018 318 included at least 1 study outcome (visits not mutually exclusive). Total ED visit volume decreased after COVID-19 mitigation measures were implemented in the US beginning on March 16, 2020. Weekly ED visit counts for all 6 outcomes decreased between March 8 and 28, 2020 (March 8: MHCs = 42 903, SAs = 5212, all ODs = 14 543, opioid ODs = 4752, IPV = 444, and SCAN = 1090; March 28: MHCs = 17 574, SAs = 4241, all ODs = 12 399, opioid ODs = 4306, IPV = 347, and SCAN = 487). Conversely, ED visit rates increased beginning the week of March 22 to 28, 2020. When the median ED visit counts between March 15 and October 10, 2020, were compared with the same period in 2019, the 2020 counts were significantly higher for SAs (n = 4940 vs 4656, P = .02), all ODs (n = 15 604 vs 13 371, P < .001), and opioid ODs (n = 5502 vs 4168, P < .001); counts were significantly lower for IPV ED visits (n = 442 vs 484, P < .001) and SCAN ED visits (n = 884 vs 1038, P < .001). Median rates during the same period were significantly higher in 2020 compared with 2019 for all outcomes except IPV.

CONCLUSIONS AND RELEVANCE

These findings suggest that ED care seeking shifts during a pandemic, underscoring the need to integrate mental health, substance use, and violence screening and prevention services into response activities during public health crises.

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.

The intersection of genomics and big data with public health: Opportunities for precision public health

Muin Khoury and co-authors discuss anticipated contributions of genomics and other forms of large-scale data in public health.

Pulmonary Illness Related to E-Cigarette Use in Illinois and Wisconsin - Final Report

BACKGROUND

E-cigarettes are battery-operated devices that heat a liquid and deliver an aerosolized product to the user. Pulmonary illnesses related to e-cigarette use have been reported, but no large series has been described. In July 2019, the Wisconsin Department of Health Services and the Illinois Department of Public Health received reports of lung injury associated with the use of e-cigarettes (also called vaping) and launched a coordinated public health investigation.

METHODS

We defined case patients as persons who reported use of e-cigarette devices and related products in the 90 days before symptom onset and had pulmonary infiltrates on imaging and whose illnesses were not attributed to other causes. Medical record abstraction and case patient interviews were conducted with the use of standardized tools.

RESULTS

There were 98 case patients, 79% of whom were male; the median age of the patients was 21 years. The majority of patients presented with respiratory symptoms (97%), gastrointestinal symptoms (77%), and constitutional symptoms (100%). All case patients had bilateral infiltrates on chest imaging. A total of 95% of the patients were hospitalized, 26% underwent intubation and mechanical ventilation, and two deaths were reported. A total of 89% of the patients reported having used tetrahydrocannabinol products in e-cigarette devices, although a wide variety of products and devices was reported. Syndromic surveillance data from Illinois showed that the mean monthly rate of visits related to severe respiratory illness in June through August of 2019 was twice the rate that was observed in the same months in 2018.

CONCLUSIONS

Case patients presented with similar clinical characteristics. Although the definitive substance or substances contributing to injury have not been determined, this initial cluster of illnesses represents an emerging clinical syndrome or syndromes. Additional work is needed to characterize the pathophysiology and to identify the definitive causes.

Review of Emerging Infectious Disease Preparedness Measures for the State of Delaware

Emerging infectious diseases (EIDs) are a growing global concern as more of these pathogens or their associated illnesses are identified, and human migration continues to increase. The state of Delaware has an intricate system to monitor, prepare for, and take action against these diseases. To effectively prepare for an outbreak of an infectious disease, there is a high level of inter- and intra-organizational communication, evolutions from previous situations, and cooperation with the public. EID preparedness plans are constantly changing to adapt to the situations at hand, making collaboration with all stakeholders crucial for a sufficient outbreak response.

Descriptive evaluation of methods for identifying work-related emergency department injury visits

BACKGROUND

Use of worker's compensation (WC) as payer underestimates work-related (WR) injuries. We evaluated three methods to identify WR injuries: WC as payer, ICD-9-CM work-status codes E000.0/E000.1, and other ICD-9-CM external cause codes.

METHODS

We identified injury-related emergency department visits from North Carolina's syndromic surveillance system (2010-2013). Characteristics were compared by indicator. We manually reviewed 800 admission notes to confirm if the visit was WR or non-WR; WR keywords from the review were applied to all visits.

RESULTS

133 156 injury-related visits (age, 16 years or older) were identified: WC = 69%, work-status codes = 18%, other ICD-9-CM codes = 13%. Among manually reviewed visits: few visits identified by WC (0.3%) or work-status codes (2%) were non-WR, while 12% of other ICD-9-CM code identified visits were non-WR; 53%, 46%, and 31% of visits identified by WC, work-status codes, and other ICD-9-CM codes were WR, respectively.

CONCLUSIONS

Findings support use of WC and work-status codes to capture WR injuries; other ICD-9-CM codes should be used with caution or in combination with other indicators.

Chief complaint classification with recurrent neural networks

Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. In this paper, we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-words classifiers: multinomial naïve Bayes (MNB) and a support vector machine (SVM). The MNB classifier is one of only two machine learning algorithms currently being used for syndromic surveillance. All four models are trained to predict diagnostic code groups as defined by Clinical Classification Software, first to predict from discharge diagnosis, and then from chief complaint fields. The classifiers are trained on 3.6 million de-identified emergency department records from a single United States jurisdiction. We compare performance of these models primarily using the F1 score, and we measure absolute model performance to determine which conditions are the most amenable to surveillance based on chief complaint alone. Using discharge diagnoses, the LSTM classifier performs best, though all models exhibit an F1 score above 96.00. Using chief complaints, the GRU performs best (F1 = 47.38), and MNB with bigrams performs worst (F1 = 39.40). We also note that certain syndrome types are easier to detect than others. For example, chief complaints using the GRU model predicts alcohol-related disorders well (F1 = 78.91) but predicts influenza poorly (F1 = 14.80). In all instances, the RNN models outperformed the bag-of-words classifiers suggesting deep learning models could substantially improve the automatic classification of unstructured text for syndromic surveillance.

Syndromic Surveillance of Emergency Department Visits for Acute Adverse Effects of Marijuana, Tri-County Health Department, Colorado, 2016-2017

OBJECTIVES

In Colorado, legalization of recreational marijuana in 2014 increased public access to marijuana and might also have led to an increase in emergency department (ED) visits. We examined the validity of using syndromic surveillance data to detect marijuana-associated ED visits by comparing the performance of surveillance queries with physician-reviewed medical records.

METHODS

We developed queries of combinations of marijuana-specific International Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes or keywords. We applied these queries to ED visit data submitted through the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) syndromic surveillance system at 3 hospitals during 2016-2017. One physician reviewed the medical records of ED visits identified by ≥1 query and calculated the positive predictive value (PPV) of each query. We defined cases of acute adverse effects of marijuana (AAEM) as determined by the ED provider's clinical impression during the visit.

RESULTS

Of 44 942 total ED visits, ESSENCE queries detected 453 (1%) as potential AAEM cases; a review of 422 (93%) medical records identified 188 (45%) true AAEM cases. Queries using ICD-10 diagnostic codes or keywords in the triage note identified all true AAEM cases; PPV varied by hospital from 36% to 64%. Of the 188 true AAEM cases, 109 (58%) were among men and 178 (95%) reported intentional use of marijuana. Compared with noncases of AAEM, cases were significantly more likely to be among non-Colorado residents than among Colorado residents and were significantly more likely to report edible marijuana use rather than smoked marijuana use ( P < .001).

CONCLUSIONS

ICD-10 diagnostic codes and triage note keyword queries in ESSENCE, validated by medical record review, can be used to track ED visits for AAEM.

Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities

Objective:  To summarize the recent public and population health informatics literature with a focus on the synergistic “bridging” of electronic data to benefit communities and other populations.

Methods:  The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords “public health informatics” and “social determinants of health”. The “social determinants of health” search was refined to include articles that contained the keywords “public health”, “population health” or “surveillance”.

Results:  Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health.

Conclusions:  Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this “bridging” is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities.

Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses - United States, July 2016-September 2017

Introduction

From 2015 to 2016, opioid overdose deaths increased 27.7%, indicating a worsening of the opioid overdose epidemic and highlighting the importance of rapid data collection, analysis, and dissemination.

Methods

Emergency department (ED) syndromic and hospital billing data on opioid-involved overdoses during July 2016–September 2017 were examined. Temporal trends in opioid overdoses from 52 jurisdictions in 45 states were analyzed at the regional level and by demographic characteristics. To assess trends based on urban development, data from 16 states were analyzed by state and urbanization level.

Results

From July 2016 through September 2017, a total of 142,557 ED visits (15.7 per 10,000 visits) from 52 jurisdictions in 45 states were suspected opioid-involved overdoses. This rate increased on average by 5.6% per quarter. Rates increased across demographic groups and all five U.S. regions, with largest increases in the Southwest, Midwest, and West (approximately 7%–11% per quarter). In 16 states, 119,198 ED visits (26.7 per 10,000 visits) were suspected opioid-involved overdoses. Ten states (Delaware, Illinois, Indiana, Maine, Missouri, Nevada, North Carolina, Ohio, Pennsylvania, and Wisconsin) experienced significant quarterly rate increases from third quarter 2016 to third quarter 2017, and in one state (Kentucky), rates decreased significantly. The highest rate increases occurred in large central metropolitan areas.

Conclusions and Implications for Public Health Practice

With continued increases in opioid overdoses, availability of timely data are important to inform actions taken by EDs and public health practitioners. Increases in opioid overdoses varied by region and urbanization level, indicating a need for localized responses. Educating ED physicians and staff members about appropriate services for immediate care and treatment and implementing a post-overdose protocol that includes naloxone provision and linking persons into treatment could assist EDs with preventing overdose.