Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-22T22:12:52.320Z Has data issue: false hasContentIssue false

National and Regional Representativeness of Hospital Emergency Department Visit Data in the National Syndromic Surveillance Program, United States, 2014

Published online by Cambridge University Press:  17 February 2016

Ralph J. Coates*
Affiliation:
Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia
Alejandro Pérez
Affiliation:
Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia
Atar Baer
Affiliation:
Department of Public Health – Seattle & King County, Seattle, Washington.
Hong Zhou
Affiliation:
Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia
Roseanne English
Affiliation:
Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia
Michael Coletta
Affiliation:
Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia
Achintya Dey
Affiliation:
Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia
*
Correspondence and reprint requests to Ralph J. Coates, PhD, Division of Health Informatics and Surveillance, Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, GA 30333 (e-mail: [email protected]).

Abstract

Objective

We examined the representativeness of the nonfederal hospital emergency department (ED) visit data in the National Syndromic Surveillance Program (NSSP).

Methods

We used the 2012 American Hospital Association Annual Survey Database, other databases, and information from state and local health departments participating in the NSSP about which hospitals submitted data to the NSSP in October 2014. We compared ED visits for hospitals submitting data with all ED visits in all 50 states and Washington, DC.

Results

Approximately 60.4 million of 134.6 million ED visits nationwide (~45%) were reported to have been submitted to the NSSP. ED visits in 5 of 10 regions and the majority of the states were substantially underrepresented in the NSSP. The NSSP ED visits were similar to national ED visits in terms of many of the characteristics of hospitals and their service areas. However, visits in hospitals with the fewest annual ED visits, in rural trauma centers, and in hospitals serving populations with high percentages of Hispanics and Asians were underrepresented.

Conclusions

NSSP nonfederal hospital ED visit data were representative for many hospital characteristics and in some geographic areas but were not very representative nationally and in many locations. Representativeness could be improved by increasing participation in more states and among specific types of hospitals. (Disaster Med Public Health Preparedness. 2016;10:562–569)

Type
Original Research
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Baer, A, Elbert, Y, Burkom, HS, et al. Usefulness of syndromic data sources for investigating morbidity resulting from a severe weather event. Disaster Med Public Health Prep. 2011;5(01):37-45. http://dx.doi.org/10.1001/dmp.2010.32.Google ScholarPubMed
2. Bellazzini, MA, Minor, KD. ED syndromic surveillance for novel H1N1 spring 2009. Am J Emerg Med. 2011;29(1):70-74. http://dx.doi.org/10.1016/j.ajem.2009.09.009.Google ScholarPubMed
3. Fan, S, Blair, C, Brown, A, et al. A multi-function public health surveillance system and the lessons learned in its development: the Alberta Real Time Syndromic Surveillance Net. Can J Public Health . 2010;101:454-458.CrossRefGoogle ScholarPubMed
4. Josseran, L, Fouillet, A, Caillère, N, et al. Assessment of a syndromic surveillance system based on morbidity data: results from the Oscour® Network during a heat wave. PLoS One . 2010;5(8):e11984. http://dx.doi.org/10.1371/journal.pone.0011984.Google ScholarPubMed
5. Hope, KG, Merritt, TD, Durheim, DN, et al. Evaluating the utility of emergency department syndromic surveillance for a regional public health service. Commun Dis Intell . 2010;34:310-318.Google ScholarPubMed
6. O’Connell, EK, Zhang, G, Leguen, F, et al. Innovative uses for syndromic surveillance. Emerg Infect Dis . 2010;16(4):669-671. http://dx.doi.org/10.3201/eid1604.090688.CrossRefGoogle ScholarPubMed
7. Hall, G, Krahn, T, Majury, A, et al. Emergency department surveillance as a proxy for the prediction of circulating respiratory viral disease in Eastern Ontario. Can J Infect Dis Med Microbiol . 2013;24:150-154.Google ScholarPubMed
8. Hiller, KM, Stoneking, L, Min, A, et al. Syndromic surveillance for influenza in the emergency department – a systematic review. PLoS One. 2013;8(9):e73832. http://dx.doi.org/10.1371/journal.pone.0073832.CrossRefGoogle ScholarPubMed
9. Smith, GE, Bawa, Z, Macklin, Y, et al. Using real-time syndromic surveillance systems to help explore the acute impact of the air pollution incident of March/April 2014 in England. Environ Res. 2015;136:500-504. http://dx.doi.org/10.1016/j.envres.2014.09.028.CrossRefGoogle ScholarPubMed
10. Ziemann, A, Rosenkötter, N, Garcia-Castrillo Riesgo, L, et al. A concept for routine emergency-care data-based syndromic surveillance in Europe. Epidemiol Infect. 2014;142(11):2433-2446. http://dx.doi.org/10.1017/S0950268813003452.CrossRefGoogle ScholarPubMed
11. Chretien, J-P, Tomich, NE, Gaydos, JC, et al. Real-time public health surveillance for emergency preparedness. Am J Public Health. 2009;99(8):1360-1363. http://dx.doi.org/10.2105/AJPH.2008.133926.CrossRefGoogle ScholarPubMed
12. Watkins, SM, Perrotta, DM, Stanbury, M, et al. State-level emergency preparedness and response capabilities. Disaster Med Public Health Prep. 2011;5(S1):S134-S142. http://dx.doi.org/10.1001/dmp.2011.26.CrossRefGoogle ScholarPubMed
13. Buehler, JW, Whitney, EA, Smith, D, et al. Situational uses of syndromic surveillance. Biosecur Bioterror. 2009;7(2):165-177. http://dx.doi.org/10.1089/bsp.2009.0013.CrossRefGoogle ScholarPubMed
14. Paterson, BJ, Durrheim, DN. The remarkable adaptability of syndromic surveillance to meet public health needs. J Epidemiol Glob Health. 2013;3(1):41-47. http://dx.doi.org/10.1016/j.jegh.2012.12.005.CrossRefGoogle ScholarPubMed
15. McCloskey, B, Endericks, T, Catchpole, M, et al. London 2012 Olympic and Paralympic Games: public health surveillance and epidemiology. Lancet. 2014;383(9934):2083-2089. http://dx.doi.org/10.1016/S0140-6736(13)62342-9.CrossRefGoogle ScholarPubMed
16. Samoff, 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 Rep. 2012;127(3):310-317.CrossRefGoogle ScholarPubMed
17. Centers for Disease Control and Prevention. Meaningful Use. CDC website. http://www.cdc.gov/ehrmeaningfuluse/introduction.html. Last updated October 22, 2012. Accessed August 17, 2015.Google Scholar
18. Centers for Disease Control and Prevention. National Syndromic Surveillance Program. CDC website. http://www.cdc.gov/nssp/. Last updated December 4, 2015. Accessed March 20, 2015.Google Scholar
19. Wenger, E, McDermott, R, Snyder, WM. Cultivating Communities of Practice. Boston, MA: Harvard Business School Press; 2002.Google Scholar
20. European Centre for Disease Prevention and Control. Data Quality Monitoring and Surveillance System Evaluation–A Handbook of Methods and Applications. Stockholm, Sweden: ECDC; 2014.Google Scholar
21. Centers for Disease Control and Prevention. Updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. MMWR Recomm Rep. 2001;50(RR-13):1-35.Google Scholar
22. American Hospital Association (AHA) ANNUAL SURVEY DATABASETM, FY2012. Copyrighted and licensed by Health Forum LLC, an American Hospital Association company, Chicago, 2014. Fiscal Year 2012 Documentation Manual. http://www.ahadataviewer.com/book-cd-products/AHA-Survey/. Accessed March 2014.Google Scholar
23. Dartmouth Atlas of Healthcare. Geographic Crosswalks and Boundary Files, 2012 Zip Code Cross Walks, Lebanon, NH. http://www.dartmouthatlas.org/tools/downloads.aspx?tab=39/. Accessed November 7, 2014.Google Scholar
24. US Census Bureau. American Community Survey, 2012 American Community Survey 5-Year Estimates, Tables B01001, B01002, B01003, B03002, S1701, and S1702; generated by Alejandro Pérez using American FactFinder. http://factfinder2.census.gov. Accessed September 22, 2014.Google Scholar
25. Area Health Resources Files (AHRF). 2013-2014. US Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Workforce website. http://ahrf.hrsa.gov/download.htm. Accessed September 23, 2014.Google Scholar
26. HCUP Nationwide Emergency Department Sample (NEDS). Healthcare Cost and Utilization Project (HCUP). 2011. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/nedsoverview.jsp/. Accessed January 2014.Google Scholar
27. Wang, D, Zhao, W, Wheeler, K, et al. Unintentional fall injuries among US children: a study based on the National Emergency Department Sample. Int J Inj Contr Saf Promot. 2013;20(1):27-35. http://dx.doi.org/10.1080/17457300.2012.656316.CrossRefGoogle ScholarPubMed