ObjectiveTo utilize New Jersey’s syndromic surveillance data in the study and comparison of trends in injection opioid use and infection with selected bloodborne pathogens (BBPs) over the years 2013-2017.IntroductionWhen the opioid epidemic began in the early 1990s, pills such as oxycodone were the primary means of abuse. Beginning in 2010, injection use of, first, heroin and then synthetic opioids dramatically increased, which led the number of overdose deaths involving opioids to increase fivefold between 1999 and 2016.1 It would be expected that BBP rates would rise with this increase in injection use, and, nationally, there has been a rise in acute hepatitis C (HCV) rates, although the other two main BBPs, acute hepatitis B (HBV) and acute human immunodeficiency virus (HIV) have been flat and declining, respectively.2,3In this study, we compared New Jersey’s reported incidence of these three BBPs (acute HBV, acute HCV, and HIV) over five years (2013-2017) with syndromic surveillance data for opioid use over the same time period in order to test the hypothesis that emergency department (ED) visits for opioid use could be used as a predictor of BBP infection.MethodsTo indirectly track the number of injection opioid users, we wrote a custom classifier for EpiCenter, New Jersey's statewide syndromic surveillance system, to search ED chief complaints for the number of visits related to injection opioid usage. Our custom classifier creation started with the CDC’s National Syndromic Surveillance Program (NSSP) ESSENCE Chief Complaint Category classifier for opioid overdose.4 As we were looking to count not just overdoses but all visits likely to be associated with injection drug use, we chose not to omit the keyword “withdrawal,” differing from CDC’s classifier in which it is a negative indicator.Positive indicator keywords included “opioid,” “heroin,” “narcot,” “opiate,” “fentanyl,” “naloxo,” “narcan,” "ivdu," and the ICD-9 and ICD-10 codes e850.[0-2], 304, 305.5, f11, t40.[0-6], and 965. These keywords were used to target the chief complaints of people using injection opioids. Negative indicators included “patch,” “allerg,” and “med” to eliminate medical opioid use. Negative indicators also included “vicodin,” “tramadol,” “percocet,” “oral,” and t40.5 to filter out opioids most commonly used in pill form, as well as other drugs.Cases of acute HCV and acute HBV were totaled using CDRSS, New Jersey's Communicable Disease Reporting and Surveillance System. In order to maintain consistency, we used the respective 2012 case definition for each disease. Numbers of new HIV infections were accessed from NJ’s reportable disease list.5 All of the data sets followed the epidemiologic years 2013–2017 (based upon MMWR weeks).ResultsHIV diagnosis rates slightly decreased over time. HBV rates trend upwards, similar to the rates of injection drug use (IDU) for the first three years but start to drop after 2015. Aside from an unexplained dip in 2016, the HCV rates generally track the EpiCenter data for IDU (Figure 1). On a regional scale, NJ’s Northwest region had the highest rates per capita of the five NJ regions and the most similar trending between the HCV and EpiCenter data sets (Figure 2). This result follows the nationwide trend of the opioid epidemic occurring more widely in rural areas, as this region is the most rural region in New Jersey.6In figures 1 & 2, IDU (EpiCenter) and HIV are plotted on the primary (left) axis and HCV and HBV are plotted on the secondary (right) axis.ConclusionsBoth IDU related visits and cases of acute HCV show an ongoing upward trend. This result agrees with the initial hypothesis. However, the association between acute HBV cases and IDU wasn’t as strong. This finding can be attributed to the fact that while HBV is a BBP, it is most commonly transmitted vertically from an infected mother to her child at birth, whereas HCV is primarily transmitted through the sharing of needles or syringes.7,8 There is no apparent relationship between HIV rates and injection drug rates, likely because HIV has a 0.3% infection risk rate from a single infected needlestick versus the 1.8% risk of acquiring HCV and 22-31% risk of acquiring HBV.9References1. Centers for Disease Control and Prevention (CDC), Understanding the Epidemic; August 30, 2017. https://www.cdc.gov/drugoverdose/epidemic/index.html. Accessed 13 July 2018.2. CDC, Viral Hepatitis; May 19, 2016. https://www.cdc.gov/hepatitis/hbv/statisticshbv.htm. Accessed 24 July 2018.3. CDC, HIV in the United States: At A Glance; June 26, 2018. https://www.cdc.gov/hiv/statistics/overview/ataglance.html. Accessed 24 July 2018.4. CDC, NSSP Update; May, 2017. https://www.cdc.gov/nssp/documents/nssp-update-2017-05.pdf. Accessed 23 July 2018.5. NJDOH, HIV, STD, and TB Services; December, 2016. https://www.cdc.gov/hiv/statistics/overview/ataglance.html Accessed 27 July 20186. Schranz, A.J., Barrett, J., Hurt, C.B. et al. Challenges Facing a Rural Opioid Epidemic: Treatment and Prevention of HIV and Hepatitis C. Curr HIV/AIDS Rep. 2018;15(3):245-254.7. Rolls, David A. et al. Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs. PLoS ONE. 2013;8(11).8. Perrillo, R.P. Hepatitis B: transmission and natural history. Gut. 1993;34(2):S48-S49.9. Berry, Arnold J. Needle stick and other safety issues. Anesthesiology Clinics of North America. 2004; 22(3):493-508.

Objective: Medical notes provide a rich source of information that can be used as additional supporting information for healthcare-associated infection (HAI) investigations. The medical notes from 10 New Jersey (NJ) emergency departments (ED) were searched to identify cases of surgical-site infections (SSI).Introduction: EpiCenter, NJ’s statewide syndromic surveillance system, collects ED registration data. The system uses chief complaint data to classify ED visits into syndrome categories and provides alerts to state and local health departments for surveillance anomalies.After the 2014 Ebola outbreak in West Africa, the New Jersey Department of Health (NJDOH) started collecting medical notes including triage notes, which contain more specific ED visit information than chief complaint, from 10 EDs to strengthen HAI syndromic surveillance efforts.In 2017, the NJDOH was aware of one NJ resident whose surgical site was infected following a cosmetic procedure outside of the US. This event triggered an intensive data mining using medical notes collected in EpiCenter. The NJDOH staff searched one week of medical notes data in EpiCenter with a specific keyword to identify additional potential cases of surgical-site infections (SSI) that could be associated with medical tourism.Methods: The NJ resident whose surgical site was infected following a cosmetic procedure outside of the US was interviewed by NJDOH staff for details about their procedure. First, the patient’s interview results were reviewed to prepare a set of SSI and travel related keywords to be used in performing data mining in medical notes collected in EpiCenter. The interviewed patient had tummy tuck and liposuction surgeries; therefore, it was decided to search for “tummy tuck” as a keyword in EpiCenter. The medical notes from August 31, 2017 through September 8, 2017 were reviewed to identify patients who developed SSI following a cosmetic procedure outside of the US.Results: The search yielded 8 ED visits, one of which was identified as possible surgical site infection. The medical notes details indicated that the ED patient, a 21-year old female who had abdominoplasty (tummy tuck) and liposuction surgeries about a month prior, presented with post-surgical complaints such as pain, surgical dehiscence, and purulent drainage at the surgery site. Chief complaint text for the same ED patient indicated the patient had headache and dizziness which were less specific than medical notes.The NJDOH staff contacted the ED to obtain additional information regarding the infection. The lab results from the ED showed that the patient was identified as having a post-surgery infection, which prompted public health to follow-up whether it was an HAI.Conclusions: The limitation for this project was that the keyword search was conducted only on one week of data. The timeframe was kept short to pilot testing the keyword identified. The Centers for Disease Control and Prevention suggests clinicians should consider nontuberculous mycobacteria (NTM) infections in the differential diagnosis for all people who have wound infections after surgery abroad, including surgery that has occurred weeks to months previously (1). Future studies will explore larger data sets with additional keywords (e.g. country and organism) to see if potential cases can be identified as possible HAI and/or outbreak that will lead to public health investigations.

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ObjectiveDescribe the inclusion of triage notes into a syndromic surveillance system to enhance population health surveillance activities.

ObjectiveEvaluate the usage of triage note data from EpiCenter, a syndromicsurveillance system utilized by New Jersey Department of Health(NJDOH), to enhance Healthcare-Associated Infections (HAIs)surveillance for infections following a surgical procedure.IntroductionIn New Jersey, Health Monitoring Systems Inc.’s (HMS) EpiCentercollects chief complaint data for syndromic surveillance from 79 of80 emergency departments (ED). Using keyword algorithms, thesevisits are classified into syndrome categories for monitoring unusualhealth events.HAIs are infections that patients acquire while they are receivingtreatment for a health condition in a health care setting. Followingthe 2014 Ebola outbreak in West Africa, the New Jersey Departmentof Health (NJDOH) Communicable Disease Service (CDS) startedrecruiting EDs to include triage note data in addition to chiefcomplaint data to enhance surveillance capability for Ebola and otherHAIs. Research by the University of North Carolina suggests triagenote data improve the ability to detect illness of interest by fivefold1.Currently, there are three NJ EDs with triage note data in EpiCenteralong with ICD 10 codes which can be used for comparison.This pilot study will assess whether infections following a surgicalprocedure can be captured in triage note data along with ICD codes.Also, this evaluation will determine if triage note data can be usedto create HAI custom classifications for syndromic surveillance.These classifications can potentially be used by surveillanceand/or preparedness personnel and local health departments, as wellas hospitals, to better prepare for detecting and preventing HAIs thatare a significant cause of morbidity and mortality in the U.S.2MethodsThree NJ facilities with triage notes information sending toEpiCenter were included in this study. ED visits occurred from10/23/2015 to 10/29/2015 and from 2/2/2016 to 2/10/2016 in thesefacilities with available ICD 10 codes information in EpiCenter wereevaluated.This analysis focused on sepsis and post-surgery infections relatedICD 10 codes: A400, A401, A402, A403, A408, A409, A410, A411,A412, A414, A4150, A4151, A4152, A4158, A418, A419, R571,R578, R579, T811, T81.43. The keywords tested in triage notesare abdominal pain, redness, fev, fver, pyrexia, temp, elev temp,elevated temp, temp elev, hi temp, high temp, temp hi, temp10, temp10, feeling hot, feels hot, feel hot, fuo, febr, cloudy fluid, cfluid,drainage, abscess, wound, tenderness, swelling, erythema, red, pain,post surgery, fever.The sensitivity, specificity and positive predictive value (PPV)of selected keywords applied in the triage notes were evaluated bycomparing to patient’s ICD 10 codes.ResultsThere were 2757 ED visits with triage notes and ICD 10 codes from10/23/2015 to 10/29/2015 and from 2/2/2016 to 2/10/2016. Duringthese time frames, one ED visit matched with both selected keywordsand ICD codes, five matched with ICD 10 codes only, 59 matchedwith keywords only, and 2692 did not match with either keywordsor ICD 10 codes. In Table 1, it indicates that selected keywordshave a high specificity (97.9 %) but with a relatively low sensitivity(16.7 %) and PPV (1.7%).ConclusionsSelected keywords and ICD 10 codes from facilities sending triagenotes were used to evaluate the surveillance system on identifyinginfections following a surgical procedure through analysis of EDtriage note field. We also reviewed all NJ ED data during the samestudy period for other facilities not sending triage notes. It indicatedthat several key ICD codes, e.g. ICD code T81.4, infections followinga surgical procedure, have been included in many facilities. Thisanalysis will be repeated as more EDs participate in EpiCenterwith triage notes and other data fields to refine the keywords and toimprove the sensitivity and PPV.Table 1: Sensitivity, specificity and PPV calculations of selected keywordsapplied in triage notes based on the ICD 10 codes related to infectionsfollowing a surgical procedure.