Public Health Impact of Syndromic Surveillance Data—A Literature Survey
Authors: Albert Stefanie P. , Ergas Rosa 
Published: October 2019

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.