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Informal Networks in Disaster Medicine

Published online by Cambridge University Press:  08 December 2016

Fadl Bdeir
Affiliation:
Faculty of Engineering and IT, The University of Sydney, New South Wales, Australia
John W Crawford
Affiliation:
Sustainable Systems, Rothamsted Research West Common, Harpenden, Hertfordshire, United Kingdom
Liaquat Hossain*
Affiliation:
Library and Information Management Program, Division of Information and Technology Studies, The University of Hong Kong, Hong Kong
*
Correspondence and reprint requests to Liaquat Hossain, PhD, Professor and Director, Library and Information Management Program, Division of Information and Technology Studies, Room 113, Runme Shaw Building, The University of Hong Kong, Pokfulam Road, Hong Kong (e-mail: [email protected]).

Abstract

Objective

Our study of informal networks aimed to explore information-sharing environments for the management of disaster medicine and public health preparedness. Understanding interagency coordination in preparing for and responding to extreme events such as disease outbreaks is central to reducing risks and coordination costs.

Methods

We evaluated the pattern of information flow for actors involved in disaster medicine through social network analysis. Social network analysis of agencies can serve as a basis for the effective design and reconstruction of disaster medicine response coordination structures. This research used new theoretical approaches in suggesting a framework and a method to study the outcome of complex inter-organizational networks in coordinating disease outbreak response. We present research surveys of 70 health professionals from different skill sets and organizational positions during the swine influenza A (H1N1) PDM09 2009 pandemic. The survey and interviews were designed to collect both qualitative and quantitative data in order to build a comprehensive and in-depth understanding of the dynamics of the inter-organizational networks that evolved during the pandemic.

Results

The degree centrality of the informal network showed a positive correlation with performance, in which the ego’s performance is related to the number of links he or she establishes informally—outside the standard operating structure during the pandemic. Informal networks facilitate the transmission of both strong (ie, infections, confirmed cases, deaths in hospital or clinic settings) and weak (ie, casual acquaintances) ties.

Conclusions

The results showed that informal networks promoted community-based ad hoc and formal networks, thus making overall disaster medicine and public health preparedness more effective. (Disaster Med Public Health Preparedness. 2017;11:343–354)

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

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References

1. Hossain, L, Uddin, S. Design patterns: coordination in complex and dynamic environments. Disaster Prev Manag. 2012;21(3):336-350. http://dx.doi.org/10.1108/09653561211234516.CrossRefGoogle Scholar
2. Jackson, BA, Buehler, JW, Cole, D, et al. Bioterrorism with zoonotic disease: public health preparedness lessons from a multiagency exercise. Biosecur Bioterror. 2006;4(3):287-292. http://dx.doi.org/10.1089/bsp.2006.4.287.CrossRefGoogle ScholarPubMed
3. Bdeir, F, Hossain, L, Crawford, J, et al. Dynamics of complex coordination during disease outbreak. Safety and Security Engineering IV . 2011;117:429.Google Scholar
4. Balicer, R, Omer, S, Barnett, D, et al. Local public health workers’ perceptions toward responding to an influenza pandemic. BMC Public Health. 2006;6(1):99. http://dx.doi.org/10.1186/1471-2458-6-99.CrossRefGoogle Scholar
5. Babbie, E. The practice of social research. Boston: Cengage Learning; 2015.Google Scholar
6. Kermack, WO, McKendrick, AG. Contributions to the mathematical theory of epidemics. II. The problem of endemicity. Proc R Soc A. 1932;138(834):55-83. http://dx.doi.org/10.1098/rspa.1932.0171.Google Scholar
7. Cleary, PD, Angel, R. The analysis of relationships involving dichotomous dependent variables. J Health Soc Behav. 1984;25(3):334-348. http://dx.doi.org/10.2307/2136429.CrossRefGoogle ScholarPubMed
8. Freeman, LC. Centrality in social networks conceptual clarification. Soc Networks. 1978;1(3):215-239. http://dx.doi.org/10.1016/0378-8733(78)90021-7.CrossRefGoogle Scholar
9. Hossain, L, Wu, A, Chung, KKS. Actor centrality correlates to project based coordination. In: Proceedings of the 2006 20th anniversary Conference on Computer Supported Cooperative Work . New York: ACM; 2006:363-372. doi: 10.1145/1180875.1180930.Google Scholar
10. Granovetter, MS. The strength of weak ties. Am J Sociol. 1973;78(6):1360-1380. http://dx.doi.org/10.1086/225469.CrossRefGoogle Scholar
11. Uddin, MS, Hossain, L. Social networks enabled coordination model for cost management of patient hospital admissions. J Healthc Q. 2011;33(5):37-48. doi: 10.1111/j.1945-1474.2011.00118.x.CrossRefGoogle ScholarPubMed
12. Hossain, L, Kuti, M. Disaster response preparedness coordination through social networks. Disasters. 2010;34(3):755-786. doi: 10.1111/j.1467-7717.2010.01168.x.CrossRefGoogle ScholarPubMed
13. Burt, RS. Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press; 1992.CrossRefGoogle Scholar
14. Mintzberg, H. The Structuring of Organizations. Upper Saddle River, NJ: Prentice Hall; 1979.Google Scholar
15. Waldstrøm, C. Informal Networks in Organizations - A Literature Review. DDL Working Paper no. 2. Aarhus School of Business; February 2001.Google Scholar
16. Krackhardt, D, Stern, RN. Informal networks and organizational crises: an experimental simulation. Soc Psychol Q. 1988;51(2):123-140.CrossRefGoogle Scholar
17. Miles, MB, Huberman, AM. Qualitative Data Analysis. Thousand Oaks, CA: Sage Publications; 1999.Google Scholar
18. Sudman, S, Bradburn, NM. Asking Questions: A Practical Guide to Questionnaire Design. San Francisco, CA: Jossey-Bass; 1982.Google Scholar
19. Luke, DA, Harris, JK. Network analysis in public health: history, methods, and applications. Annu Rev Public Health. 2007;28:69-93.CrossRefGoogle ScholarPubMed
20. Riley, KJ, Hoffman, B. Domestic Terrorism: A National Assessment of State and Local Law Enforcement Preparedness. Santa Monica, CA: RAND Corporation; 1995.Google Scholar
21. Davis, LM, Riley, KJ, Ridgeway, G, et al. When Terrorism Hits Home. Santa Monica, CA: RAND Corporation; 2004.Google Scholar
22. Fricker, RD, Jacobson, JO, Davis, LM. Measuring and Evaluating Local Preparedness for a Chemical or Biological Terrorist Attack. Santa Monica, CA: RAND Corporation; 2002.CrossRefGoogle Scholar
23. Streftaris, G, Gibson, G. Statistical inference for stochastic epidemic models. In Proc. of the 17th Int’l Workshop on Statistical Modelling. Chania; 2002;609:616.Google Scholar
24. Krackhardt, D, Hanson, JR. Informal networks: the company behind the chart. Harv Bus Rev. 1993;71(4):104-111.Google ScholarPubMed
25. Groat, M. The informal organization: ride the headless monster. Management Accounting. 1997;75(4):40-43.Google Scholar
26 Brannen, DE, McDonnell, MA, Schmitt, A. Organizational culture on community health outcomes after the 2009 H1N1 pandemic. Journal of Organizational Culture, Communication and Conflict. 2013;17(1):1-18.Google Scholar