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From January 21 through February 23, 2020, public health agencies detected 14 U.S. cases of coronavirus disease 2019 (COVID-19), all related to travel from China (1,2). The first nontravel-related U.S. case was confirmed on February 26 in a California resident who had become ill on February 13 (3). Two days later, on February 28, a second nontravel-related case was confirmed in the state of Washington (4,5). Examination of four lines of evidence provides insight into the timing of introduction and early transmission of SARS-CoV-2, the virus that causes COVID-19, into the United States before the detection of these two cases. First, syndromic surveillance based on emergency department records from counties affected early by the pandemic did not show an increase in visits for COVID-19-like illness before February 28. Second, retrospective SARS-CoV-2 testing of approximately 11,000 respiratory specimens from several U.S. locations beginning January 1 identified no positive results before February 20. Third, analysis of viral RNA sequences from early cases suggested that a single lineage of virus imported directly or indirectly from China began circulating in the United States between January 18 and February 9, followed by several SARS-CoV-2 importations from Europe. Finally, the occurrence of three cases, one in a California resident who died on February 6, a second in another resident of the same county who died February 17, and a third in an unidentified passenger or crew member aboard a Pacific cruise ship that left San Francisco on February 11, confirms cryptic circulation of the virus by early February. These data indicate that sustained, community transmission had begun before detection of the first two nontravel-related U.S. cases, likely resulting from the importation of a single lineage of virus from China in late January or early February, followed by several importations from Europe. The widespread emergence of COVID-19 throughout the United States after February highlights the importance of robust public health systems to respond rapidly to emerging infectious threats.

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.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.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.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.

Objectives Widespread global transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19), continues. Many questions remain about asymptomatic or atypical infections and transmission dynamics. We used comprehensive contact tracing of the first 2 confirmed patients in Illinois with COVID-19 and serologic SARS-CoV-2 antibody testing to determine whether contacts had evidence of undetected COVID-19. Methods Contacts were eligible for serologic follow-up if previously tested for COVID-19 during an initial investigation or had greater-risk exposures. Contacts completed a standardized questionnaire during the initial investigation. We classified exposure risk as high, medium, or low based on interactions with 2 index patients and use of personal protective equipment (PPE). Serologic testing used a SARS-CoV-2 spike enzyme-linked immunosorbent assay on serum specimens collected from participants approximately 6 weeks after initial exposure to either index patient. The 2 index patients provided serum specimens throughout their illness. We collected data on demographic, exposure, and epidemiologic characteristics. Results Of 347 contacts, 110 were eligible for serologic follow-up; 59 (17% of all contacts) enrolled. Of these, 53 (90%) were health care personnel and 6 (10%) were community contacts. Seventeen (29%) reported high-risk exposures, 15 (25%) medium-risk, and 27 (46%) low-risk. No participant had evidence of SARS-CoV-2 antibodies. The 2 index patients had antibodies detected at dilutions >1:6400 within 4 weeks after symptom onset. Conclusions In serologic follow-up of the first 2 known patients in Illinois with COVID-19, we found no secondary transmission among tested contacts. Lack of seroconversion among these contacts adds to our understanding of conditions (ie, use of PPE) under which SARS-CoV-2 infections might not result in transmission and demonstrates that SARS-CoV-2 antibody testing is a useful tool to verify epidemiologic findings.

Background Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first detected in China in December, 2019. In January, 2020, state, local, and federal public health agencies investigated the first case of COVID-19 in Illinois, USA. Methods Patients with confirmed COVID-19 were defined as those with a positive SARS-CoV-2 test. Contacts were people with exposure to a patient with COVID-19 on or after the patient's symptom onset date. Contacts underwent active symptom monitoring for 14 days following their last exposure. Contacts who developed fever, cough, or shortness of breath became persons under investigation and were tested for SARS-CoV-2. A convenience sample of 32 asymptomatic health-care personnel contacts were also tested. Findings Patient 1-a woman in her 60s-returned from China in mid-January, 2020. One week later, she was hospitalised with pneumonia and tested positive for SARS-CoV-2. Her husband (Patient 2) did not travel but had frequent close contact with his wife. He was admitted 8 days later and tested positive for SARS-CoV-2. Overall, 372 contacts of both cases were identified; 347 underwent active symptom monitoring, including 152 community contacts and 195 health-care personnel. Of monitored contacts, 43 became persons under investigation, in addition to Patient 2. These 43 persons under investigation and all 32 asymptomatic health-care personnel tested negative for SARS-CoV-2. Interpretation Person-to-person transmission of SARS-CoV-2 occurred between two people with prolonged, unprotected exposure while Patient 1 was symptomatic. Despite active symptom monitoring and testing of symptomatic and some asymptomatic contacts, no further transmission was detected. Funding None.

Coronavirus disease 2019 (COVID-19), the respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in Wuhan, China and has since become pandemic. In response to the first cases identified in the United States, close contacts of confirmed COVID-19 cases were investigated to enable early identification and isolation of additional cases and to learn more about risk factors for transmission. Close contacts of nine early travel-related cases in the United States were identified and monitored daily for development of symptoms (active monitoring). Selected close contacts (including those with exposures categorized as higher risk) were targeted for collection of additional exposure information and respiratory samples. Respiratory samples were tested for SARS-CoV-2 by real-time reverse transcription polymerase chain reaction at the Centers for Disease Control and Prevention. Four hundred four close contacts were actively monitored in the jurisdictions that managed the travel-related cases. Three hundred thirty-eight of the 404 close contacts provided at least basic exposure information, of whom 159 close contacts had ≥1 set of respiratory samples collected and tested. Across all actively monitored close contacts, two additional symptomatic COVID-19 cases (i.e., secondary cases) were identified; both secondary cases were in spouses of travel-associated case patients. When considering only household members, all of whom had ≥1 respiratory sample tested for SARS-CoV-2, the secondary attack rate (i.e., the number of secondary cases as a proportion of total close contacts) was 13% (95% CI: 4-38%). The results from these contact tracing investigations suggest that household members, especially significant others, of COVID-19 cases are at highest risk of becoming infected. The importance of personal protective equipment for healthcare workers is also underlined. Isolation of persons with COVID-19, in combination with quarantine of exposed close contacts and practice of everyday preventive behaviors, is important to mitigate spread of COVID-19.

Vaping Syndromic Surveillance Increases in EVALI were identified in 2019. Using the National Syndromic Surveillance Program, which includes about 70% of U.S. EDs, researchers found a gradual increa...

Objective To determine whether emergency department (ED) visits were captured in syndromic surveillance for coagulopathy cases associated with an outbreak linked to synthetic cannabinoid (SC) use and to quantify the number of ED visits and reasons for repeat visits. Introduction In March 2018, the Illinois Department of Public Health (IDPH) was informed of a cluster of coagulopathy cases linked to SC use. By June 30, 2018, 172 cases were reported, including five deaths, where 74% were male and the mean age was 35.3 years (range: 18–65 years). All cases presented to an emergency department (ED) at least once for this illness. Ninety-four cases provided clinical specimens and all tested positive for brodifacoum, a long-acting anticoagulant used in rodenticide. Cases were reported to the health department by the Illinois Poison Control Center and direct reporting from hospitals. REDCap was the primary database for tracking cases and collecting demographic information, risk factor data and healthcare facility utilization, including number of ED visits. Syndromic surveillance was utilized to monitor ED visits related to the cluster, assist with case finding and provide situational awareness of the burden on the EDs and geographic spread. In this study, we retrospectively used syndromic surveillance along with the data in REDCap to quantify the number of ED visits per coagulopathy case, understand the reasons for repeat visits, and determine whether visits were captured in syndromic surveillance. Methods Illinois hospital ED data submitted to the National Syndromic Surveillance Platform instance of ESSENCE (ESSENCE), was compared to data present in our primary REDCap database. A subset of the cases, males 18-44 years of age (n=105; 61% of cases), were included in this analysis. Illinois ESSENCE data in males aged 18-44 years from March 10, 2018–June 30, 2018 were matched to cases in the REDCap database by age, zip code, initial visit date, facility, and reason for visit including: chief complaint, discharge diagnosis, and triage note. If the initial visit was found, the matching criteria and medical record number were used to search for additional related visits. The number of visits in ESSENCE and reasons for visits were totaled for each patient. Reasons for repeat visits were categorized into four categories: continued gross bleeding or symptoms associated with coagulopathy, medical evaluation or follow-up, laboratory work and prescription refill. Repeat visits may fall into more than one category. The number and dates of ED visits captured in ESSENCE per case were compared to that reported in REDCap. An epidemic curve was constructed to display the number of ED visits and type (i.e. primary visit or repeat visit) captured by REDCap only, ESSENCE only or both by visit date. Results Of the 105 cases in REDCap, 89 (85%) were matched to at least one ED visit in ESSENCE from March 10, 2018–June 30, 2018. The mean number of ESSENCE ED visits per case was 1.9 visits and the median was one visit (range: 1–11 visits). The main chief complaints for the primary visit included hematuria (n=31), abdominal pain (n=20), back pain/flank pain (n=13), K2 (n=11), bleeding from multiple sites (n=8), vomiting blood (n=7), and urinary tract infection or kidney stones (n=7). Of the 89 cases matched to a visit in ESSENCE, 84 (94%) cases, representing 142 (79%) of ED visits, were captured by syndrome definitions that were being utilized to monitor the cluster. Forty-three cases (48%) had at least two visits in ESSENCE. The reasons for return visits captured in ESSENCE (n=84) were continued gross bleeding or symptoms associated with coagulopathy (n=53), medical evaluation or follow-up (n=14), laboratory work (n=13), prescription refill (n=7) or unknown (n=2). Of the 105 cases, the number of ED visits reported in REDCap matched the number of visits found in ESSENCE for 49 cases (47%). For 24 cases (23%), ESSENCE identified more visits than REDCap and for 16 cases (15%), REDCap had more ED visits reported than captured in ESSENCE. Sixteen cases (15%) in REDCap were not found in ESSENCE. All of the unmatched visits were due to ESSENCE data quality, including a lack of reporting hospital admissions, lack of submitting data to ESSENCE, and missing data including: date of birth, medical record number, and triage notes. Conclusions Syndromic surveillance was a useful tool in describing the burden of ED visits for patients in the Illinois coagulopathy outbreak linked to SC use. ESSENCE data helped to quantify the number of ED visits per patient and identify patients that re-presented for the same illness. The most common reason for repeat ED visits was continued symptoms, which may be attributed to misdiagnosis at the initial healthcare visit. ED visits that were not picked up by ESSENCE were a result of data quality issues from select facilities that were not reporting hospitalizations or key information such as date of birth, medical record number or triage notes. Engagement with healthcare facilities to provide this information will improve the data quality of syndromic surveillance.

OBJECTIVETo determine the source of a healthcare-associated outbreak ofPantoea agglomeransbloodstream infections.DESIGNEpidemiologic investigation of the outbreak.SETTINGOncology clinic (clinic A).METHODSCases were defined asPantoeaisolation from blood or catheter tip cultures of clinic A patients during July 2012–May 2013. Clinic A medical charts and laboratory records were reviewed; infection prevention practices and the facility’s water system were evaluated. Environmental samples were collected for culture. Clinical and environmentalP. agglomeransisolates were compared using pulsed-field gel electrophoresis.RESULTSTwelve cases were identified; median (range) age was 65 (41–78) years. All patients had malignant tumors and had received infusions at clinic A. Deficiencies in parenteral medication preparation and handling were identified (eg, placing infusates near sinks with potential for splash-back contamination). Facility inspection revealed substantial dead-end water piping and inadequate chlorine residual in tap water from multiple sinks, including the pharmacy clean room sink.P. agglomeranswas isolated from composite surface swabs of 7 sinks and an ice machine; the pharmacy clean room sink isolate was indistinguishable by pulsed-field gel electrophoresis from 7 of 9 available patient isolates.CONCLUSIONSExposure of locally prepared infusates to a contaminated pharmacy sink caused the outbreak. Improvements in parenteral medication preparation, including moving chemotherapy preparation offsite, along with terminal sink cleaning and water system remediation ended the outbreak. Greater awareness of recommended medication preparation and handling practices as well as further efforts to better define the contribution of contaminated sinks and plumbing deficiencies to healthcare-associated infections are needed.Infect Control Hosp Epidemiol2017;38:314–319

Objective To create chronic disease categories for emergency department (ED) chief complaint data and evaluate the categories for validity against ED data with discharge diagnoses and hospital discharge data. Introduction Syndromic Surveillance (SS), traditionally applied to infectious diseases, is more recently being adapted to chronic disease prevention. Its usefulness rests on the large number of diverse individuals visiting emergency rooms with the possibility of real-time monitoring of acute health effects, including effects from environmental events and its potential ability to examine more long-term health effects and trends of chronic diseases on a local level [1-3]. Methods Emergency department chief complaint (CC) data captured by the Cook County Department of Public Health local instance of ESSENCE from Jan 1, 2006 – Dec 31, 2013 was utilized to generate chronic disease categories for: CVD, AMI, ACS, angina, stroke, diabetes, hypertension, asthma, and COPD based on disease symptoms, natural language processing for free text chief complaints, and associated terms present in EMR system menus. A standard category was created for each chronic disease category based on discharge diagnoses (ICD-9 code), and their associated terms. The ICD-9 based categories were applied to the discharge diagnosis field within the ED data. The chief complaint based chronic disease category definitions were compared to the standard classification by determining the sensitivity, specificity, positive predictive value, and negative predictive value. The standard chronic disease categories created with ICD-9 codes for the chronic disease category validation were also applied to Illinois hospital discharge data for Cook County from Jan 1, 2006 – Dec 31, 2013. This data was compared to the chief complaint categories from the ED data for the same time period by visual analysis through time series and strength of correlation by Pearson correlation coefficient analysis. ESSENCE version 1.17 was utilized for the free-text query development and SAS 9.4 was utilized to perform the analyses. Results For the validation analysis, 1,366,525 (24.76%) ED visits of individuals 40 years and older and 867,509 (15.72%) ED visits of individuals less than 18 years of age with a valid chief complaint and discharge diagnosis were included. Validation results are presented in Table 1. Specificity was generally high for most of the categories, with the narrow definitions having a higher specificity (Narrow AMI = 0.9996, Broad AMI = 0.9119). However, the loss in sensitivity is substantial in moving from the broader definition to the narrow definition (Broad AMI = 0.5444, Narrow AMI = 0.1040). The positive predictive values had a wide range from 0.0128 for the Broad ACS category to 0.7199 for the Narrow Asthma definition. The negative predictive values were high for all chronic disease categories ranging from 0.9501 for the Narrow CVD category to 0.9996 for Angina. The Pearson correlation coefficients are presented in Table 2. Graphs showing the comparisons of the chief complaint based ED data to the hospitalization data by chronic disease category definition are presented in Figure 1. Pearson correlations ranged from 0.9323 for Narrow Asthma to 0.1992 for Hypertension. Conclusions Based on the high specificity and correlation coefficients in comparison to hospital discharge data, emergency department chief complaint data captured with syndromic surveillance could be utilized to examine chronic disease categories: asthma, COPD, CVD, AMI, ACS, stroke, and diabetes at a local, state or national level. References 1. Bassil, K.L., et al., Temporal and spatial variation of heat-related illness using 911 medical dispatch data. Environ Res, 2009. 109 (5): p. 600-6. 2. Mathes, R.W., K. Ito, and T. Matte, Assessing syndromic surveillance of cardiovascular outcomes from emergency department chief complaint data in New York City. PLoS One, 2011. 6 (2): p. e14677. 3. Zanobetti, A. and J. Schwartz, Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health, 2006. 60 (10): p. 890-5.

Objective To utilize ED chief complaint data obtained from syndromic surveillance to quantify the effect of the Illinois smoking ban on acute myocardial infarction (AMI), acute coronary syndrome (ACS), stroke, and chronic obstructive pulmonary disease (COPD) related ED visits in adults in Cook County, IL. Introduction Tobacco use is the leading global cause of preventable death, killing more than five million people per year [1]. In addition, exposure to secondhand smoke is estimated to kill an additional 600,000 people globally each year [1]. In 1986, the US Surgeon General’s Report declared secondhand smoke to be a cause of lung cancer in healthy nonsmokers [2]. The first law restricting smoking in public places was enacted in 1973 in Arizona that followed the 1972 Surgeon General’s Report providing awareness of the negative health effects associated with the exposure to air pollution from tobacco smoke [3]. Smoke-free laws were slowly enacted after this time point with most occurring after the year 2000 [4]. In July 2007, the Smoke Free Illinois Act (SB0500, Public Act 095-0017) was passed in IL [5]. The ban went into effect on Jan 1, 2008 and Illinois joined 22 other states in prohibiting smoking in virtually all public places and workplaces including offices, theaters, museums, libraries, schools, commercial establishments, retail stores, bars, private clubs, and gaming facilities [5-6]. While many studies have examined the effect of smoking bans on hospitalizations, this study would be the first to examine the effect of the comprehensive smoking ban in IL on ED visits by utilizing chronic disease categories created with ED chief complaint data captured by syndromic surveillance [7]. The author hypothesizes that the comprehensive smoking ban in IL significantly reduced the ED visits associated with AMI, ACS, stroke, and COPD in adults in Cook County, IL. Methods ED visits with chief complaints consistent with categories for AMI, ACS, stroke and COPD captured by the Cook Co. Dept. of Public Health local instance of ESSENCE from Jan 1, 2006 – Dec 31, 2013 were included in the analysis. Proc Genmod with a log link and negative binomial distribution was utilized for the analysis. All data was aggregated at the monthly level. The total number of ED visits of the health effect of interest was the dependent variable. The total ED visits during the same period of time, was used as the offset variable, sub-grouped by age and gender where appropriate. A binary variable was utilized to capture the effect of the time period after the implementation of the statewide smoking ban; 0 for before the ban and 1 for after the ban. When examining the effect of the statewide ban, Cook Co. as an entirety was examined as well as ED visits stratified by zip codes that already had a smoking ban in place at that time point and those that did not, and stratifying by urban (Chicago) vs. suburban Cook Co. Seasonality was addressed by including month, month squared and month cubed in the model. Influenza was addressed by including a binary variable to indicate when influenza was occurring in the area based on percent influenza-like-illness ED visits that were occurring above the threshold for the area during that time period. Age and gender were also evaluated as confounders and effect modifiers. SAS 9.4 was utilized to perform the analyses. Results Results are presented in Table 1. Reductions of ED visits after the smoking ban implementation were seen in AMI and ACS disease categories for the overall adjusted model, at 3% and 3.5% respectively. Stroke associated ED visits were not affected by the smoking ban. COPD associated ED visits were not reduced immediately by the smoking ban, but did have a significant reduction 6 months after implementation of the ban at 3.6%. Stronger effects were seen in individuals 70 years and older, females, the urban population, and zip codes without a prior ban for AMI, ACS, and COPD. Conclusions An immediate, significant reduction in ED visits associated with AMI and ACS was associated with the IL statewide smoking ban in Cook Co., IL. COPD associated ED visits were significantly reduced 6 months after the ban implementation. The effect was greater in individuals 70 years and older, females, the urban population, and zip codes without a prior ban. References 1. WHO, WHO report on the global tobacco epidemic. Implementing smoke-free environments . 2009, WHO: Geneva, Switzerland. 2. DC, The health consequences of involuntary exposure to tobacco smoke : a report of the Surgeon General . 2006, U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, Coordinating Center for Health Promotion, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health: Atlanta, GA. 3. Eriksen, M. and F. Chaloupka, The economic impact of clean indoor air laws. CA Cancer J Clin, 2007. 57 (6): p. 367-78. 4. Foundation, A.N.R. Overview List - How many Smokefree Laws? 2015 10/2/2015 [cited 2015 10/5/2015]; Available from: http://www.no-smoke.org/pdf/mediaordlist.pdf. 5 . Smoke Free Illinois Act , in Public Act 095-0017 . 2007. 6. Goodman, P., et al., Effects of the Irish smoking ban on respiratory health of bar workers and air quality in Dublin pubs. Am J Respir Crit Care Med, 2007. 175 (8): p. 840-5. 7. Callinan, J.E., et al., Legislative smoking bans for reducing secondhand smoke exposure, smoking prevalence and tobacco consumption. Cochrane Database Syst Rev, 2010(4): p. CD005992.

Objective Plan, develop, and pilot an open source system that could be integrated into the PCORnet (PCORI) and Sentinel (FDA) national common data models (CDMs) to generate antimicrobial use (AU) reports submittable to CDC’s National Healthcare Safety Network (NHSN). The system included ancillary tables, and data quality and report generation queries. The DataMIME system will allow hospitals to generate comparable AU reports for hospital inpatients. Introduction Despite decades of attempts to promote judicious AU, the US has high rates of per-person antimicrobial consumption, and extremely high rates of carbapenem use 1 . Such profligate use is a key factor in the high rate of antimicrobial-resistant infections seen in US healthcare facilities 2 . Antimicrobial stewardship (AS) programs have been identified as a critical component of intervention strategies 3 . A core component of AS programs is tracking AU, which is needed to monitor trends in use, focus interventions on aberrant behaviors, promote judicious use, and evaluate the effectiveness of interventions. A system designed to extend two national data models would provide a scalable platform for rapid adoption of AU reporting. Methods Virtual meetings were held with all participating sites (five hospitals in IL, LA, and TN) to develop the ancillary tables to capture intrahospital patient movement, and administration of antimicrobial agents. Ancillary tables were designed & sites populated the tables with calendar year 2016 data. Data characterization was performed to assess overall table statistics, and verify mappings of facility unit locations to NHSN location codes, medications to RxNorm, and routes of administration to one of four SNOMED categories. Additional characterization focused on CDC’s NHSN Validation Protocol for the AU module. Analytical queries were developed to produce the output metrics required for submission to the NHSN AU module. Results Two ancillary tables and two look-up tables were developed: a bed information table (Table 1) to capture local location codes with a date-time stamp for precise tracking of patient location; a separate location look-up table allows mapping to other terminologies (Table 2); and, an inpatient drug administration table (Table 3) to capture data from the electronic medication administration record (eMAR) or bar coding medication administration (BCMA) system, utilizing a route of administration look-up table (Table 4). The data model was structured to accommodate use cases with alternative mapping terminologies for local location code, local term for route of administration, local codes for medication, and the option of including the NDC code. All sites populated the ancillary tables. For the bed information table, all sites utilized their ADT table information for patient movement. For the medication administration table, most sites limited the inclusion criteria to the 89 antimicrobial agents required for reporting to the NHSN AU module. Aggregate results from participating sites for facility-wide measures and select antimicrobial agents are presented in Table 5. Conclusions The data model developed was able to produce the metrics required for reporting to CDC’s NHSN AU module. The data dictionary language, implementation guidance, mappings, and queries will be distributed as a tool-kit for other PCORnet and Sentinel sites for reporting to the AU module. In addition, this CDM could allow for the development of additional metrics including excessive use of antibiotic combinations of redundant spectra, syndrome specific antibiotic use, or increased use of excessively broad spectrum antibiotic classes. References 1. Van Boeckel TP, Brower C, Gilbert M, Grenfell BT, Levin SA, Robinson TP, Teillant A, Laxminarayan R. Global trends in antimicrobial use in food animals. Proc Natl Acad Sci USA 2015; 112(18):5649-54. 2. CDC. Antibiotic resistance threats in the United States, 2013. Atlanta, GA: US Dept. of HHS, CDC; 2013. 3. Dellit TH, Owens RC, McGowan JE, Gerding DN, Weinstein RA, Burke JP, et al. IDSA and the SHEA Guidelines for Developing an Institutional Program to Enhance Antimicrobial Stewardship. Clin Infect Dis 2007; 44(2):159-77.

Potential human exposure to rabies is reportable in Illinois. A syndromic surveillance system containing emergency department (ED) records from 45 hospitals in Cook County was queried for visits pertaining to bat contact or rabies post exposure prophylaxis (PEP) from 1/1/2013 to 6/30/2015. The extracted records were matched on demographics to cases reported to the Cook County Department of Public Health (CCDPH). Of 241 individuals under CCDPH jurisdiction visiting local EDs for bat contact or rabies PEP, 63 (26%) were reported. Differential reporting completeness among institutions was observed. New procedures for active surveillance of potential rabies exposures were instituted at CCDPH.

Community health assessments rely on a number of indicators, many of which are readily available at the county level from public data sources. However, few commonly used sub-county level indicators of health outcomes and healthcare access have been identified. In suburban Cook County, data from a syndromic surveillance system was used to identify areas of geographic clustering and disproportionate use in emergency room visit rates. As syndromic surveillance reporting becomes standard among hospitals, emergency room visit rates may be a useful, sub county-level community health indicator that can be compared across jurisdictions.

Summary Background Enterovirus D68 (EV-D68) has been infrequently reported historically, and is typically associated with isolated cases or small clusters of respiratory illness. Beginning in August, 2014, increases in severe respiratory illness associated with EV-D68 were reported across the USA. We aimed to describe the clinical, epidemiological, and laboratory features of this outbreak, and to better understand the role of EV-D68 in severe respiratory illness. Methods We collected regional syndromic surveillance data for epidemiological weeks 23 to 44, 2014, (June 1 to Nov 1, 2014) and hospital admissions data for epidemiological weeks 27 to 44, 2014, (June 29 to Nov 1, 2014) from three states: Missouri, Illinois and Colorado. Data were also collected for the same time period of 2013 and 2012. Respiratory specimens from severely ill patients nationwide, who were rhinovirus-positive or enterovirus-positive in hospital testing, were submitted between Aug 1, and Oct 31, 2014, and typed by molecular sequencing. We collected basic clinical and epidemiological characteristics of EV-D68 cases with a standard data collection form submitted with each specimen. We compared patients requiring intensive care with those who did not, and patients requiring ventilator support with those who did not. Mantel-Haenszel χ 2 tests were used to test for statistical significance. Findings Regional and hospital-level data from Missouri, Illinois, and Colorado showed increases in respiratory illness between August and September, 2014, compared with in 2013 and 2012. Nationwide, 699 (46%) of 1529 patients tested were confirmed as EV-D68. Among the 614 EV-D68-positive patients admitted to hospital, age ranged from 3 days to 92 years (median 5 years). Common symptoms included dyspnoea (n=513 [84%]), cough (n=500 [81%]), and wheezing (n=427 [70%]); 294 (48%) patients had fever. 338 [59%] of 574 were admitted to intensive care units, and 145 (28%) of 511 received ventilator support; 322 (52%) of 614 had a history of asthma or reactive airway disease; 200 (66%) of 304 patients with a history of asthma or reactive airway disease required intensive care compared with 138 (51%) of 270 with no history of asthma or reactive airway disease (p=0·0004). Similarly, 89 (32%) of 276 patients with a history of asthma or reactive airway disease required ventilator support compared with 56 (24%) of 235 patients with no history of asthma or reactive airway disease (p=0·039). Interpretation In 2014, EV-D68 caused widespread severe respiratory illness across the USA, disproportionately affecting those with asthma. This unexpected event underscores the need for robust surveillance of enterovirus types, enabling improved understanding of virus circulation and disease burden. Funding None.

Four classifiers were evaluated to determine differences in case detection for heat related illness. The results of those classifiers, as applied to data from Northern Illinois ESSENCE participating hospitals from May 1, 2012 through August 31, 2012, will be discussed. Three refined syndrome definitions for heat related illness (chief complaint-based, discharge diagnosis-based, and both chief complaint and discharge diagnosis) were created based on the four evaluated classifiers and these classifiers were applied to current 2013 data. The application of the syndrome definition is dependent on a jurisdiction's purpose for conducting heat related illness surveillance.

Objective In May 2012, thousands of protesters, descended on Chicago during the NATO Summit to voice their concern about social and economic inequality. Given the increased numbers of international and domestic visitors to the Windy City and the tension surrounding protesting during the summit, increased monitoring for health events within the city and Chicago metropolitan region was advised. This project represents the first use of cloud technology to support monitoring for a high profile event. Introduction Hospital emergency departments in Cook and surrounding counties currently send data to the Cook County Department of Public Health (CCDPH) instance of ESSENCE on CCDPH servers. The cloud instance of ESSENCE has been enhanced to receive and export all meaningful use data elements in the meaningful use format. The NATO summit provided the opportunity for a demonstration project to assess the ability of an Amazon GovCloud instance of ESSENCE to ingest and process meaningful use data, and to export meaningful use surveillance data to the Cook County Locker in BioSense 2.0. Methods In the three weeks leading up to the NATO Summit, HL7 data extracts were sent to BioSense 2.0 and a data feed was established to the Amazon GovCloud instance of ESSENCE. Queries specific to anticipated health events associated with the summit such as injuries, tear gas exposure, and general exposure, were developed. Several features of the cloud instance of ESSENCE enhanced the ability of CCDPH staff epidemiologists to conduct analyses, including the sharing capabilities of queries and the myESSENCE dashboard feature. The sharing capabilities within the cloud instance of ESSENCE allowed queries to be easily shared with multiple staff epidemiologists and across health jurisdictions. The myESSENCE dashboard feature was used to create dashboards of surveillance results, including time series graphs, maps, and records of interest for relevant queries, that were shared with public health staff monitoring population health during the summit. This information was used to provide situational awareness on a daily basis in the Chicago Metropolitan region. Results Data feeds to BioSense 2.0 and the Amazon GovCloud instance of ESSENCE were successful. The NATO Summit did not produce any remarkable public health concerns in suburban Cook County. The use of the cloud instance of ESSENCE enhanced the timeliness of generating situational awareness reports for distribution to public health partners in the Chicago Metropolitan region. Conclusions While further evaluation of cloud resources to conduct syndromic surveillance is warranted, use of the cloud instance of ESSENCE during the NATO Summit demonstrated the ability of the cloud to support surveillance for both routine and high profile events.