ObjectiveTo describe the impact of civil unrest on the mental health of a community in near real-time using syndromic surveillance.IntroductionAs part of a wide-spread community discussion on the presence of monuments to Confederate Civil War figures, the Charlottesville city council voted to remove a statue of General Robert E. Lee.1 Multiple rallies were then held to protest the statue’s removal. A Ku Klux Klan (KKK) rally on July 8, 2017 (MMWR Week 27) and a Unite the Right rally on August 12, 2017 (MMWR Week 32) held in Charlottesville both resulted in violence and media attention.2,3 The violence associated with the Unite the Right rally included fatalities connected to motor vehicle and helicopter crashes.Syndromic surveillance has been used to study the impact of terrorism on a community’s mental health4 while more traditional data sources have looked at the impact of racially-charged civil unrest.5 Syndromic surveillance, however, has not previously been used to document the effect of racially-charged violence on the health of a community.MethodsThe Virginia Department of Health (VDH) analyzed syndromic surveillance data from three emergency departments (EDs) in the Charlottesville area (defined to include Charlottesville city and Albemarle county), regardless of patient residence following the Unite the Right rally. Visits to these EDs between January 1 and September 2, 2017 were analyzed using the Enhanced Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) and Microsoft SQL 2012. Encounters were identified as acute anxiety-related visits based on an International Classification of Diseases, Tenth Revision (ICD-10) discharge diagnosis beginning with ’F41’. Analyses were conducted using the ESSENCE algorithm EWMA 1.2 and SAS 9.3.ResultsThe greatest number of visits with a primary diagnosis of anxiety in 2017 (N=20) was observed in MMWR week 34 (August 20-26). This represented a statistically significant increase over baseline with a p-value of 0.01.By race, a significant increase over baseline in visits with a primary diagnosis of anxiety was observed among blacks or African Americans. The largest volume of visits was observed in MMWR week 33 with a total of 8 identified visits or 1.8% of total ED visit volume. The increase in visits for anxiety observed in weeks 33-35 was 2.2 times greater among blacks or African Americans than it was among whites, p = 0.016, 95% CI [1.14, 4.16].ConclusionsPrevious work done in Virginia to identify ED visits related to anxiety included only chief complaint criteria in the syndrome definition. Due to a change in how one ED in the Charlottesville area reported data during the study period, this syndrome definition could not be applied. In order to remove any potential data artifacts, only those visits with an initial diagnosis of anxiety were included in the analysis. The resulting syndrome definition likely underestimated the occurrence of anxiety in the Charlottesville area, both because it lacked chief complaint information and because syndromic surveillance does not include data on visits to mental health providers outside of EDs. This analysis presents a trend over time rather than a true measure of the prevalence of anxiety.This analysis, while conservative in its inclusion criteria, still identified an increase in visits for anxiety, particularly among blacks or African Americans. In today’s political environment of race-related civil unrest, a way to measure the burden of mental illness occurring in the community can be invaluable for public health response. In Charlottesville, the identification of a community-wide need for mental health support prompted many local providers to offer their services to those in need pro-bono.6References1 Suarez, C. (2017, February 6). Charlottesville City Council votes to remove statue from Lee Park. The Daily Progress. Retrieved from http://bit.ly/2wYOHhv2 Spencer, H., & Stevens, M. (2017, July 8). 23 Arrested and Tear Gas Deployed After a K.K.K. Rally in Virginia. The New York Times. Retrieved from http://nyti.ms/2tCiBGU3 Hanna, J., Hartung, K., Sayers, D., & Almasy, S. (2017, August 13). Virginia governor to white nationalists: ‘Go home … shame on you’. CNN. Retrieved from http://cnn.it/2vvAGHt4 Vandentorren, S., Paty, A. C., Baffert, E., Chansard, P., Caserio-Schönemann, C. (2016, February). Syndromic surveillance during the Paris terrorist attacks. The Lancet (387(10021), 846-847. doi:10.1016/S0140-6736(16)00507-95 Yimgang, D. P., Wang, Y., Paik, G., Hager, E. R., & Black, M. M. Civil Unrest in the Context of Chronic Community Violence: Impact on Maternal Depressive Symptoms. American Journal of Public Health 107(9), 1455-1462. doi:10.2105/AJPH.2017.3038766 DeLuca, P. (2017, August 19). Downtown Charlottesville Library Offers Free Counseling. NBC29.com. Retrieved from http://bit.ly/2yIzHbl

Objective: Provide justification for the collection and reporting of urgent care (UC) data for public health syndromic surveillance.Introduction: While UC does not have a standard definition, it can generally be described as the delivery of ambulatory medical care outside of a hospital emergency department (ED) on a walk-in basis, without a scheduled appointment, available at extended hours, and providing an array of services comparable to typical primary care offices.1 UC facilities represent a growing sector of the United States healthcare industry, doubling in size between 2008 and 2011.1 The Urgent Care Association of America (UCAOA) estimates that UC facilities had 160 million patient encounters in 2013.2 This compares to 130.4 million patient encounters in EDs in 2013, as reported by the National Hospital Ambulatory Medical Care Survey.3 Public Health (PH) is actively working to broaden syndromic surveillance to include urgent care data as more individuals use these services.4 PH needs justification when reaching out to healthcare partners to get buy-in for collecting and reporting UC data.Description: The International Society for Disease Surveillance (ISDS) Community of Practice (CoP) platform was used to host a webinar introducing the topic of urgent care participation in syndromic surveillance. This webinar provided a valuable opportunity to obtain insight from jurisdictions pursuing and using UC data. A workgroup was formed to create documentation justifying the collection and reporting of UC data. Using this forum, the workgroup brought together partners from various jurisdictions working with UC data to participate in a literature review of SCOPUS, PubMed, and the Online Journal of Public Health Informatics publications and to share their experiences. These two main sources of information – previous literature and jurisdictional experience – were combined and condensed to provide tangible justifications for the collection and use of UC data.While the workgroup found little in the literature to justify the collection of UC data as a part of syndromic surveillance, the shared experiences of the CoP jurisdictions working to onboard UC facilities provided valuable insight. From this collaborative response, three main reasons to collect UC data were identified.1) Healthcare reform is directing patients away from EDs and toward UC facilities. UC represents reduced cost and more efficient patient processing, thus easing the burden on both patient and healthcare system (according to a 2016 American Academy of Pediatrics article entitled “Urgent Care and Emergency Department Visits in the Pediatric Medicaid Population”). If syndromic surveillance does not adapt to include UC data, the potential exists to lose significant patient populations, which may lead to decreased situational awareness.2) According to the Centers for Medicare and Medicaid Services Stage 3 guidance, Meaningful Use (MU) will change the relationship between eligible professionals (EPs) and syndromic surveillance by restricting EPs to those who practice in a UC facility. This approach to EP participation simplifies the syndromic surveillance MU objective, thereby making it easier for PH jurisdictions to onboard UC facilities.3) Patients with certain conditions that are acute but non-emergent may report more frequently to an UC facility than to an ED. Broadening syndromic surveillance to include UC facilities may increase reporting of “rare event” encounters, which will lower the relative standard error for statistical calculation. Surveillance efforts for conditions like influenza-like illness and Zika virus may improve substantially with a larger data pool.How the Moderator Intends to Engage the Audience in Discussions on the Topic: The moderator will begin discussion with a brief presentation from the literature review and jurisdictional experience, highlighting three justifications for collecting and reporting UC data. The audience will be divided into 3 groups to discuss and validate 3 additional topics: creation of syndromic surveillance talking points to share with UC facility management, creation of jurisdictional UC facility listings, and UC onboarding best practices. Feedback from the 3 groups will be shared with the whole group, followed by a brief summary of the discussion and recommendations for next steps.

ObjectiveTo describe the differences in patient populations between those who seek care for heat exposure during the work week and those who seek care during the weekend.IntroductionAs global temperatures increase, so too does interest in the effect of climate change on the population’s health. 2016 represented the hottest year on record globally and well above the 20th century average in Virginia.1,2 With large-scale climate change comes an increase in severe weather patterns, including heat waves.3 Heat waves can have immense health impacts on a community, including heat stroke, heat exhaustion, and dehydration.Previous analyses of emergency department (ED) data indicate that certain populations – specifically males and rural residents – are more at risk for heat-related illness.4,5 None of these studies, however, looked for temporal relationships between the population seeking care and the day of the week. Syndromic surveillance data can be used to further describe those communities affected by heat exposure as well as identify any temporal patterns in visits.MethodsThe Virginia Department of Health (VDH) receives data from 148 EDs and urgent care centers (UCCs) as part of its syndromic surveillance program. During regular surveillance of a heat wave, it was observed that males made up a larger proportion of heat-related visits during the week than they did over the weekend. Data received on visits between January 1, 2015 and July 31, 2017 were used for a retrospective, cross-sectional analysis of demographic risk factors for heat-related illness. During this time frame, 6,739 visits were identified using the September 2016 Council for State and Territorial Epidemiologists (CSTE) syndrome definition for heat-related illness.6The effect of various demographics and visit factors on weekday heat exposure was measured using chi-squared tests. The variables in question included sex, race, ethnicity, rural vs. urban residence, and age group. Odds ratios, 95% confidence intervals, and p-values were reported for these analyses. Analyses were conducted using SAS 9.3 with a significance level of 0.05.ResultsOf the total 6,739 visits identified for heat-related illness, 4,782 (71.0%) occurred during the work week and 1,957 (29.0%) occurred on the weekend. The odds of seeking care for heat-related illness on a weekday were 1.84 times higher for males than for females, p < 0.001, 95% CI [1.65, 2.06]. Blacks or African Americans were more likely to seek care than whites during the work week with an odds ratio of 1.38, p < 0.001. 95% CI [1.20, 1.57]. Adults aged 18-64 years were more likely to seek care during the work week than both children aged 0-17 years (OR = 1.61, p < 0.001, 95% CI [1.37, 1.89]) and adults aged 65 years or older (OR = 1.36, p < 0.001, 95% CI [1.17, 1.58]). No significant relationship between ethnicity or rural vs. urban residence and work week visits for heat-related illness was observed.ConclusionsThe patient population that seeks care for heat-related illness differs between the work week and the weekend. These data suggest the presence of potential mediators or confounders that make males, blacks or African Americans, and adults aged 18-64 more likely to suffer from heat-related illness during the week. Collecting data on patients’ health behaviors, risk factors, and occupation could further elucidate this relationship. Syndromic surveillance, however, does not include the level of detail needed to investigate anything beyond basic demographics.With an increase in the intensity and frequency of heat waves on the horizon, the issue of heat-related illness is one of growing public health concern. Syndromic surveillance data can be used to describe patterns in the patient population most at risk. Public health action is then needed to protect these communities while further research explores the relationships in greater depth.References1 Nuccitelli, D. (2017, July 31). 2017 is so far the second-hottest year on record thanks to global warming. The Guardian. Retrieved from http://bit.ly/2vkPZpg2 Boyer, J. (2017, January 18). 2016 was the planet’s warmest year in modern records, but it wasn’t for Richmond or even Va. Richmond Times-Dispatch. Retrieved from http://bit.ly/2jptCKg3 Duffy, P. B. (2012, January 21). Increasing prevalence of extreme summer temperatures in the U.S. Climate Change, 111(2), 487-495. https://doi.org/10.1007/s10584-012-0396-64 Hess, J. J., Saha, S., & Luber, G. (2014 November). Summertime Acute Heat Illness in U.S. Emergency Departments from 2006 through 2010: Analysis of a Nationally Representatitve Sample. Environmental Health Perspectives 122(11), 1209. http://dx.doi.org.proxy.library.vcu.edu/10.1289/ehp.13067965 Sanchez, C. A., Thomas, K. E., Malilay, J., & Annest, J. L. (2010, January). Nonfatal natural and environmental injuries treated in emergency departments, United States, 2001-2004. Family & Community Health 33(1), 3-10. doi:10.1097/FCH.0b013e3181c4e2fa6 Berisha, V., Braun, C. R., Cameron, L., Hoppe, B., Lane, K., Mamou, F., Menager, H., Roach, M., White, J. R., Wurster, J. (2016, September). Heat-Related Illness Syndrome Query: A Guidance Document for Implementing Heat-Related Illness Syndromic Surveillance in Public Health Practice. Retrieved from http://bit.ly/2w884aJ

ObjectiveTo develop and evaluate syndrome definitions for the identificationof acute unintentional drug overdose events including opioid, heroin,and unspecified substances among emergency department (ED) visitsin Virginia.IntroductionNationally, deaths due to opioid overdose have continuallyincreased for the past 15 years1. Deaths specifically related to heroinincreased more than four-fold between 2002 and 20142. Hospitalinpatient discharge data provide information on non-fatal overdoses,but include a significant lag in reporting time3. Syndromic ED visitdata provide near real-time identification of public health issues andcan be leveraged to inform public health actions on the emergingthreat of drug overdose.MethodsVirginia Department of Health (VDH) developed two syndromedefinitions in 2014 to capture acute unintentional drug overdoseevents among syndromic ED visit data. Syndrome 1 captured visitsfor overdose, whether or not a specific substance was mentioned.Syndrome 2 captured only visits for heroin overdose. Definitionswere based on free-text terms found within the chief complaintand standardized text or International Classification of Diseases(ICD) codes within the diagnosis field. In 2016, both definitionswere revised to identify additional inclusion and exclusion criteriaaccording to CDC guidance documentation and syndrome definitionsused by other state jurisdictions.Microsoft SQL was used to modify both definitions based on thenewly identified chief complaint and diagnosis criteria. Record leveldata were analyzed for their adherence to established criteria using aniterative evaluation process.The scope of Syndrome 1 (2016) was narrowed from the 2014version by excluding visits for non-opioid substances, heroin, andnon-acute indicators. It included chief complaint and diagnosisterms related to opioids, unspecified substance overdose, narcotics,and Narcan or naloxone, and excluded terms related to suicide,alcohol overdose alone, withdrawal, detoxification, rehab, addiction,constipation, chronic pain, and any specified non-opioid drug ormedication. Syndrome 2 (2016) included chief complaint or diagnosisterms mentioning heroin overdose and excluded suicide, withdrawal,detoxification, rehab, and addiction. Visits with mention of suicide,rehab, or addiction were identified during the evaluation process,resulting in the exclusion of these terms in the revised query.From January 1, 2015 to July 31, 2016, the number of visitscaptured by the revised syndrome definitions was compared to thenumber captured by the 2014 definitions. Correlation coefficientswere calculated using SAS 9.3.ResultsThe revised Syndrome 1 found 4296 fewer ED visits(29% decrease) for acute unintentional drug overdose betweenJanuary 1, 2015 and July 31, 2016 compared to the 2014 definition.Despite the drop in volume, the monthly trends were similar forthe 2014 and 2016 definitions (correlation coefficient = 0.95,p < 0.001). For the same time period, the revised Syndrome 2 definitionreturned 108 fewer visits (6% decrease) for acute unintentional heroinoverdose. The monthly trends were also similar for the 2014 and 2016definitions (correlation coefficient = 0.98, p < 0.001).ConclusionsBoth revised syndrome definitions improved specificity incapturing overdose visits as Syndrome 1 (2016) identified 29% fewervisits and Syndrome 2 (2016) identified 6% fewer visits found to beunrelated to the desired overdose criteria.When developing the revised syndrome definitions, VDH decidedto exclude non-acute drug-related visits. Terms such as addiction,detoxification, rehab, withdrawal, chronic pain, and constipation wereindicative of habitual drug use or abuse instead of acute overdose andwere thus excluded. In narrowing the scope of Syndrome 1, VDHalso identified and excluded visits for specified drug and medicationoverdose. Together, these expanded exclusion criteria resulted ingreater specificity with both updated syndromes.These revised syndrome definitions enable VDH to better trackopioid and heroin overdose trends in near real-time and overextended time periods which can be used to inform public healthactions. Limitations include the inconsistency of diagnosis codingamong syndromic data submitters, which may lead to geographicunderrepresentation of unintentional drug overdose visits based onthe location of health care systems. VDH will continue to evaluate andrefine these overdose syndrome definitions as this emerging healthissue evolves.