Antimicrobial resistance surveillance in the AFHSC-GEIS network

International infectious disease surveillance has been conducted by the United States (U.S.) Department of Defense (DoD) for many years and has been consolidated within the Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System (AFHSC-GEIS) since 1998. This includes activities that monitor the presence of antimicrobial resistance among pathogens. AFHSC-GEIS partners work within DoD military treatment facilities and collaborate with host-nation civilian and military clinics, hospitals and university systems. The goals of these activities are to foster military force health protection and medical diplomacy. Surveillance activities include both community-acquired and health care-associated infections and have promoted the development of surveillance networks, centers of excellence and referral laboratories. Information technology applications have been utilized increasingly to aid in DoD-wide global surveillance for diseases significant to force health protection and global public health. This section documents the accomplishments and activities of the network through AFHSC-GEIS partners in 2009

Public Health Impact of Syndromic Surveillance Data—A Literature Survey

ObjectiveTo assess evidence for public health impact of syndromic surveillance.IntroductionSystematic syndromic surveillance is undergoing a transition. Building on traditional roots in bioterrorism and situational awareness, proponents are demonstrating the timeliness and informative power of syndromic surveillance data to supplement other surveillance data.MethodsWe used PubMed and Google Scholar to identify articles published since 2007 using key words of interest (e.g., syndromic surveillance in combinations with emergency, evaluation, quality assurance, alerting). The following guiding questions were used to abstract impact measures of syndromic surveillance: 1) what was the public health impact; what decisions or actions occurred because of use of syndromic surveillance data?, 2) were there specific interventions or performance measures for this impact?, and 3) how, and by whom, was this information used?ResultsThirty-five papers were included. Almost all articles (n=33) remarked on the ability of syndromic surveillance to improve public health because of timeliness and/or accuracy of data. Thirty-four articles mentioned that syndromic surveillance data was used or could be useful. However, evidence of health impact directly attributable to syndromic surveillance efforts were lacking. Two articles described how syndromic data were used for decision-making. One article measured the effect of data utilization.ConclusionsWithin the syndromic surveillance literature instances of a conceptual shift from detection to practical response are plentiful. As the field of syndromic surveillance continues to evolve and is used by public health institutions, further evaluation of data utility and impact is needed.ReferencesAyala, A., Berisha, V., Goodin, K., Pogreba-Brown, K., Levy, C., McKinney, B., Koski, L., & Imholte, S. (2016). Public health surveillance strategies for mass gatherings: Super Bowl XLIX and related events, Maricopa County, Arizona, 2015. Health Security, 14(3), 173-84. doi: 10.1089/hs.2016.0029.Bermis, K., Frias, M., Patel, M.T., & Christiansen, D. (2017). Using an Emergency Department Syndromic Surveillance System to Evaluate Reporting of Potential Rabies Exposures, Illinois, 2013-2015. Public Health Reports 132(Supplement 1) 59S-64S."Borroto, R., Williamson, B., Pitcher, P., Ballester, L., Smith, W., Soetebier, K., & Drenzek, C. (2016). Using Syndromic Surveillance Alert Protocols for Epidemiologic Response in Georgia. Online Journal of Public Health Informatics 9(1):e123. doi:10.5210/ojphi.v9i1.7707."Daly, E.R., Dufault, K., Swenson, D.J., Lakevicius, P., Metcalf, E., & Chan, B.P. (2017). Use of emergency department data to monitor and respond to an increase in opioid overdoses in New Hampshire 2011-2015. Public Health Reports 132(Supplement 1) 73S-79S. doi: 10.1177/0033354917707934Deyneka, L., Hakenewerth, A., Faigen, Z., Ising, A., & Barnett, C. (2017). Using syndromic surveillance data to monitor endocarditis and sepsis among drug users. Online Journal of Public Health Informatics, (9)1. doi: http://dx.doi.org/10.5210/ojphi.v9i1.7708DeYoung, K., Chen, Y., Beum, R., Askenazi, M., Zimmerman, C., & Davidson, A. J. (2017). Validation of a syndromic case definition for detecting emergency department visits potentially related to marijuana. Public Health Reports, epublication.doi: 10.1177/0033354917708987"Dinh, M.M., Kastelein, C., Bein, K.J., Bautovich, T., & Ivers, R. (2015). Use of a syndromic surveillance system to describe the trend in cycling-related presentations to emergency departments in Sydney. Emergency Medicine Australasia, 27(4), 343-7. doi: 10.1111/1742-6723.12422Gevitz, K., Madera, R., Newbern, C., Lojo, J., & Johnson, C. Risk of Fall-Related Injury due to Adverse Weather Events, Philadelphia, Pennsylvania, 2006-2011. Public Health Reports (132) 53S-58S. doi: 10.1177/0033354917706968"Gonzales-Colon, F.J., Lake, I., Barker, G., Smith, G.E., Elliot, A.J., & Morbey, R. (2016). Using Bayesian Networks to assist decision-making in syndromic surveillance. Online Journal of Public Health Informatics, 8(1), e15. doi:10.5210/ojphi.v8i1.6415"Harmon, KJ., Proescholdbell, S., Marshall, S., & Waller, A. (2014). Utilization of emergency department data for drug overdose surveillance in North Carolina. Online Journal of Public Health Informatics 6(1), e174. doi: 10.5210/ojphi.v6i1.5200Harris, J.K., Mansour, R., Choucair, B., Olson, J., Nissen, C., & Bhatt, J. (2014). Health department use of social media to identify foodborne illness—Chicago, Illinois, 2013-2014. MMWR Morbidity and Mortality Weekly Report 63(32), 681-685. Retrieved from: https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6332a1.htm"Harrison, C., Jorder, M., Stern, H., Stavinksy, F., Reddy, V., Hanson, H., Waechter, H., Lowe, L., Gravano, L., & Balter, S. (2014). Using online reviews by restaurant patrons to identify unreported cases of foodborne illness — New York City, 2012–2013. MMWR Morbidity and Mortality Weekly Report 63(20), 441-445. Retrieved from:https://www.cdc.gov/MMWr/preview/mmwrhtml/mm6320a1.htm"Hawkins, J.B., Tuli, G., Kluberg, S., Harris, J., Brownstein, J.S., & Nsoesie, E. (2016). A digital platform for local foodborne illness and outbreak surveillance. Online Journal of Public Health Informatics 8(1), e60. http://dx.doi.org/10.5210/ojphi.v8i1.6474Hines, J.Z., Bancroft, J., Powell, M., & Hedberg, K. (2017). Case finding using syndromic surveillance data during an outbreak of Shiga Toxin–Producing Escherichia coli O26 infections, Oregon, 2015. Public Health Reports, epublication. https://doi.org/10.1177/0033354917708994Hudson, L. T., Klekamp, B.G., & Matthews, S.D. (2017). Local Public Health Surveillance of Heroin-Related Morbidity and Mortality, Orange County, Florida, 2010-2014. Public Health Reports (132), 80S-87SHughes, H.E., Morbey, R., Hughes, T.C., Locker, T.E., Pebody, R., Green, H.K., Ellis, J., Smith, G.E., & Elliot, A.J. (2016). Emergency department syndromic surveillance providing early warning of seasonal respiratory activity in England. Epidemiology and Infection, 144(5), 1052-64. doi: 10.1017/S0950268815002125Hughes, H.E., Morbey, R., Hughes, T.C., Locker, T.E., Shannon, T., Carmichael, C., Murray, V., Ibbotson, S., Catchpole, M., McCloskey, B., Smith, G., & Elliot, A.J. (2014). Using an emergency department syndromic surveillance system to investigate the impact of extreme cold weather events. Public Health, 128(7), 628-635. doi: 10.1016/j.puhe.2014.05.007Ising, A., Proescholdbell, S., Harmon, K.J., Sachdeva, N., Marshall, S.W., & Waller, A.E. (2016). Use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies. Injury Prevention 22:i43-i49.http://dx.doi.org/10.1136/injuryprev-2015-041821"Johnson, J. I., & Brown, K. (2015). Validation of emergency department and outpatient data using ILI syndrome classifiers. Online Journal of Public Health Informatics, 7(1), e83. http://doi.org/10.5210/ojphi.v7i1.5749Lall, R., Abdelnabi , J., Ngai, S., Parton, H.B., Saunders, K., Sell, J., Wahnich, A., Weiss, D., Marthes, R.W. (2017). Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016. Public Health Reports (132), 23S-30SLiljeqvist, H. T., Muscatello, D., Sara, G., Dinh, M., & Lawrence, G. L. (2014). Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance. BMC Medical Informatics and Decision Making, 14(84). http://doi.org/10.1186/1472-6947-14-84Lober, W. B., Reeder, B., Painter, I., Revere, D., Goldov, K., Bugni, P. F., & Olson, D. R. (2014). Technical description of the Distribute Project: a community-basedsyndromic surveillance system implementation. Online Journal of Public Health Informatics, 5(3), 224. http://doi.org/10.5210/ojphi.v5i3.4938Mathes, R. W., Ito, K., & Matte, T. (2011). Assessing syndromic surveillance of cardiovascular outcomes from emergency department chief complaint data in New York City. Public Library of Science ONE, 6(2), e14677. http://doi.org/10.1371/journal.pone.0014677O’Connell, E. K., Zhang, G., Leguen, F., Llau, A., & Rico, E. (2010). Innovative uses for syndromic surveillance. Emerging Infectious Diseases, 16(4), 669–671. http://doi.org/10.3201/eid1604.090688Rumoro, D.P., Hallock, M.M., Silva, J., Shah, S.C., Gibbs, G., Trenholme G.M., & Waddell, M.J. (2013). Why does Influenza-Like Illness surveillance miss true influenza cases in the emergency department?: Implications for health care providers. Annals of Emergency Medicine, 62(4), S75. https://doi.org/10.1016/j.annemergmed.2013.07.024Samoff E, Waller A, Fleischauer A, et al. Integration of Syndromic Surveillance Data into Public Health Practice at State and Local Levels in North Carolina. Public Health Reports. 2012;127(3):310-317.Savard, N., Bédard, L., Allard, R., & Buckeridge, D.L. (2015). Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-Like Illness surveillance. Journal of the American Medical Informatics Association, 22(3): 688-696. doi: 10.1093/jamia/ocu002Seil, K., Marcum, J., Lall, R., & Stayton, C. (2015). Utility of a near real-time emergency department syndromic surveillance system to track injuries in New York City. Injury Epidemiology, 2(1), 11. http://doi.org/10.1186/s40621-015-0044-5Smith, S., Elliot, A. J., Hajat, S., Bone, A., Smith, G. E., & Kovats, S. (2016). Estimating the burden of heat illness in England during the 2013 summer heatwave using syndromic surveillance. Journal of Epidemiology and Community Health, 70(5), 459–465. http://doi.org/10.1136/jech-2015-206079Stephens, E. (2017). Development of syndrome definitions for acute unintentional drug and heroin overdose. Online Journal of Public Health Informatics, (9)1. http://dx.doi.org/10.5210/ojphi.v9i1.7593.Stigi, K., Baer, A., Duchin, J., & Lofy, K. (2014). Evaluation of electronic ambulatory care data for Influenza-Like Illness surveillance, Washington state. Journal of Public Health Management & Practice, 20(6)580-582.doi: 10.1097/PHH.0b013e3182aaa29bVilain, P., Larrieu, S., Mougin-Damour, K., Marianne Dit Cassou, P.J., Weber, M., Combes, X., & Filleul, L. (2017). Emergency department syndromic surveillance to investigate the health impact and factors associated with alcohol intoxication in Reunion Island. Emergency medicine journal 34(6), 386-390. doi: 10.1136/emermed-2015-204987Walsh, A. (2017). Going beyond chief complaints to identify opioid-related emergency department visits. Online Journal of Public Health Informatics, (9)1. http://dx.doi.org/10.5210/ojphi.v9i1.7617.White, J.R., Berisha, V., Lane, K., Menager, H., Gettel, A., & Braun, C.R. (2017). Evaluation of a Novel Syndromic Surveillance Query for Heat-Related Illness Using Hospital Data From Maricopa County, Arizona, 2015. Public Health Reports (132), 31S-39SYih WK, Deshpande S, Fuller C, et al. Evaluating Real-Time Syndromic Surveillance Signals from Ambulatory Care Data in Four States. Public Health Reports. 2010;125(1):111-120

Syndrome Development to Assess IDU, HIV, and Homelessness in MA Emergency Departments

ObjectiveWe sought to measure the burden of emergency department (ED) visits associated with injection drug use (IDU), HIV infection, and homelessness; and the intersection of homelessness with IDU and HIV infection in Massachusetts via syndromic surveillance data.IntroductionIn Massachusetts, syndromic surveillance (SyS) data have been used to monitor injection drug use and acute opioid overdoses within EDs. Currently, Massachusetts Department of Public Health (MDPH) SyS captures over 90% of ED visits statewide. These real-time data contain rich free-text and coded clinical and demographic information used to categorize visits for population level public health surveillance.Other surveillance data have shown elevated rates of opioid overdose related ED visits, Emergency Medical Service incidents, and fatalities in Massachusetts from 2014-20171,2,3. Injection of illicitly consumed opioids is associated with an increased risk of infectious diseases, including HIV infection. An investigation of an HIV outbreak among persons reporting IDU identified homelessness as a social determinant for increased risk for HIV infection.MethodsTo accomplish our objectives staff used an existing MDPH SyS IDU syndrome definition4, developed a novel syndrome definition for HIV-related visits, and adapted Maricopa County's homelessness syndrome definition. Syndromes were applied to Massachusetts ED data through the CDC’s BioSense Platform. Visits meeting the HIV and homelessness syndromes were randomly selected and reviewed to assess accuracy; inclusion and exclusion criteria were then revised to increase specificity. The final versions of all three syndrome definitions incorporate free-text elements from the chief complaint and triage notes, as well as International Statistical Classification of Diseases and Related Health Problems, 9th (ICD-9) and 10th Revision (ICD-10) diagnostic codes. Syndrome categories were not mutually exclusive, and all reported visits occurring at Massachusetts EDs were included in the analysis.Syndromes CreatedFor the HIV infection syndrome definition, we incorporated the free-text term “HIV” in both the chief complaint and triage notes. Visit level review demonstrated that the following exclusions were needed to reduce misspellings, inclusion of partial words, and documentation of HIV testing results: “negative for HIV”, “HIV neg”, “negative test for HIV”, “hive”, “hivies”, and “vehivcle”. Additionally, the following diagnostic codes were incorporated: V65.44 (Human immunodeficiency virus [HIV] counseling), V08 (asymptomatic HIV infection status), V01.79 (contact with or exposure to other viral diseases), 795.71 (nonspecific serologic evidence of HIV), V73.89 (special screening examination for other specified viral diseases), 079.53 (HIV, type 2 [HIV-2]), Z20.6 (contact with and (suspected) exposure to HIV), Z71.7 (HIV counseling), B20 (HIV disease), Z21 (asymptomatic HIV infection status), R75 (inconclusive laboratory evidence of HIV), Z11.4 (encounter for screening for HIV), and B97.35 (HIV-2 as the cause of diseases classified elsewhere).Building on the Maricopa County homeless syndrome definition, we incorporated a variety of free-text inclusion and exclusion terms. To meet this definition visits had to mention: “homeless”, or “no housing”, or, “lack of housing”, or “without housing”, or “shelter” but not animal and domestic violence shelters. We also selected the following ICD-10 codes for homelessness and inadequate housing respectively, Z59.0 and Z59.1.We analyzed MDPH SyS data for visits occurring from January 1, 2016 through June 30, 2018. Rates per 10,000 ED visits categorized as IDU, HIV, or homeless were calculated. Subsequently, visits categorized as IDU, HIV, and meeting both IDU and HIV syndrome definitions (IDU+HIV) were stratified by homelessness.ResultsSyndrome Burden on EDThe MDPH SyS dataset contains 6,767,137 ED visits occurring during the study period. Of these, 82,819 (1.2%) were IDU-related, 13,017 (0.2%) were HIV-related, 580 (<0.01%) were related to IDU + HIV, and 42,255 visits (0.6%) were associated with homelessness.The annual rate of IDU-related visits increased 15% from 2016 through June of 2018 (from 113.63 to 130.57 per 10,000 visits); while rates of HIV-related and IDU + HIV-related visits remained relatively stable. The overall rate of visits associated with homelessness increased 47% (from 49.99 to 73.26 per 10,000 visits).Rates of IDU, HIV, and IDU + HIV were significantly higher among visits associated with homelessness. Among visits that met the homeless syndrome definition compared to those that did not: the rate of IDU-related visits was 816.0 versus 118.03 per 10,000 ED visits (X2= 547.12, p<0. 0001); the rate of visits matching the HIV syndrome definition was 145.54 versus 18.44 per 10,000 ED visits (X2= 99.33, p<0.0001); and the rate of visits meeting the IDU+HIV syndrome definition was 15.86 versus 0.76 per 10,000 visits (X2= 13.72, p= 0.0002).ConclusionsMassachusetts is experiencing an increasing burden of ED visits associated with both IDU and homelessness that parallels increases in opioid overdoses. Higher rates of both IDU and HIV-related visits were associated with homelessness. An understanding of the intersection between opioid overdoses, IDU, HIV, and homelessness can inform expanded prevention efforts, introduction of alternatives to ED care, and increase consideration of housing status during ED care.Continued surveillance for these syndromes, including collection and analysis of demographic and clinical characteristics, and geographic variations, is warranted. These data can be useful to providers and public health authorities for planning healthcare services.References1. Vivolo-Kantor AM, Seth P, Gladden RM, et al. Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses — United States, July 2016–September 2017. MMWR Morbidity and Mortality Weekly Report 2018; 67(9);279–285 DOI: http://dx.doi.org/10.15585/mmwr.mm6709e12. Massachusetts Department of Public Health. Chapter 55 Data Brief: An assessment of opioid-related deaths in Massachusetts, 2011-15. 2017 August. Available from: https://www.mass.gov/files/documents/2017/08/31/data-brief-chapter-55-aug-2017.pdf3. Massachusetts Department of Public Health. MA Opioid-Related EMS Incidents 2013-September 2017. 2018 Feb. Available from: https://www.mass.gov/files/documents/2018/02/14/emergency-medical-services-data-february-2018.pdf4. Bova, M. Using emergency department (ED) syndromic surveillance to measure injection-drug use as an indicator for hepatitis C risk. Powerpoint presented at: 2017 Northeast Epidemiology Conference. 2017 Oct 18 – 20; Northampton, Massachusetts, USA

Community Engagement among the BioSense 2.0 User Group

This roundtable will provide a forum for the syndromic surveillance Community of Practice (CoP) to learn about activities of the BioSense 2.0 User Group (BUG) workgroups that address priority issues in syndromic surveillance. The goals of the workgroups are to coordinate efforts nationwide, better inform development of BioSense 2.0 to the Governance Group and CDC, and achieve high-quality outcomes for the practice of syndromic surveillance. Representatives from each workgroup will describe their efforts to date so participants can discuss key challenges and best practices in the areas of data quality, data sharing, onboarding, and developing syndrome definitions

The impact of the national stay-at-home order on emergency department visits for suspected opioid overdose during the first wave of the COVID-19 pandemic

•Declaration of the COVID-19 national public health emergency impacted emergency department encounters for opioid overdose.•An immediate decline in ED rates for opioid overdose occurred after March 14, 2020 in New York, Massachusetts, and Ohio.•Kentucky and Ohio saw a significant increase in opioid overdose after the emergency declaration.•The impact of the COVID-19 pandemic on encounters for suspected opioid overdose was highly heterogeneous across the 4 states. Although national syndromic surveillance data reported declines in emergency department (ED) visits after the declaration of the national stay-at-home order for COVID-19, little is known whether these declines were observed for suspected opioid overdose. This interrupted time series study used syndromic surveillance data from four states participating in the HEALing Communities Study: Kentucky, Massachusetts, New York, and Ohio. All ED encounters for suspected opioid overdose (n = 48,301) occurring during the first 31 weeks of 2020 were included. We examined the impact of the national public health emergency for COVID-19 (declared on March 14, 2020) on trends in ED encounters for suspected opioid overdose. Three of four states (Massachusetts, New York and Ohio) experienced a statistically significant immediate decline in the rate of ED encounters for suspected opioid overdose (per 100,000) after the nationwide public health emergency declaration (MA: -0.99; 95 % CI: -1.75, -0.24; NY: -0.10; 95 % CI, -0.20, 0.0; OH: -0.33, 95 % CI: -0.58, -0.07). After this date, Ohio and Kentucky experienced a sustained rate of increase for a 13-week period. New York experienced a decrease in the rate of ED encounters for a 10-week period, after which the rate began to increase. In Massachusetts after a significant immediate decline in the rate of ED encounters, there was no significant difference in the rate of change for a 6-week period, followed by an immediate increase in the ED rate to higher than pre-COVID levels. The heterogeneity in the trends in ED encounters between the four sites show that the national stay-at-home order had a differential impact on opioid overdose ED presentation in each state