Application of Social Network Analysis to a Public Health Emergency Preparedness-Funded Workforce Program

Transcription

1 North Carolina Preparedness & Emergency Response Research Center (NCPERRC) Application of Social Network Analysis to a Public Health Emergency Preparedness-Funded Workforce Program Christine A. Bevc, PhD, MA, 1 Milissa L. Markiewicz, MPH, MIA, 1 Jennifer Hegle, MPH, 1 Jennifer A. Horney, PhD, MPH, 1 Lana Deyneka, 2 and Pia D.M. MacDonald, PhD, MPH 1 1 University of North Carolina - Chapel Hill 2 North Carolina Division of Public Health Prepared for Academy Health Public Health Systems Research Interest Group Meeting, Seattle, WA June 14-15, 2011

2 Background Social network analysis seeks to understand individual actions in the context of structured relationships (or the structures directly). 1,2 Considerable work on disease epidemics and transmission networks 3,4 Examples: HIV/AIDS, 5 STDs (Chlamydia, Gonorrhea, Syphilis), 6 SARS, 7 H1N1, 8 Smoking, 9 and Obesity 10 Shift focus towards public health surveillance system Detection, monitoring, and reporting of possible public health outbreaks and emergencies, including bioterrorism [1] Wasserman S, Faust, K. Social Network Analysis: Methods and Applications, Cambridge, UK: Cambridge University Press [2] Luke DA, Harris JK. Network analysis in public health: History, methods, and applications. Annual Review of Public Health : [3] Morris M, Kretzschmar M. Concurrent partnerships and transmission dynamics in networks. Soc. Netw. 1995; 17: [4] Friedman SR, Aral S. Social networks, risk-potential networks, health, and disease. J. Urban Health: Bull. N. Y. Acad. Med. 2001; 78: [5] Goodreau SM, Golden MR. Biological and demographic causes of high HIV and sexually transmitted disease prevalence in men who have sex with men. Sex Transm Infect. 2007; 83: [6] Laumann EO, Youm Y. Racial/ethnic group differences in the prevalence of sexually transmitted diseases in the United States: a network explanation. Sex. Transm. Dis. 1999; 26(5): [7] Meyers LA, Pourbohloul B, Newman MEJ, Skowronski DM, Brunham RC. Network theory and SARS: predicting outbreak diversity. J. Theoret. Biol. 2005; 232:71 81.[8] [9] Christakis NA, Fowler JH. The collective dynamics of smoking in a large social network. N Engl J Med. 2008; 358(21): [10] Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med 2007; 357:370-9.

3 Public Health Epidemiologists Introduced in 2003 by North Carolina Division of Public Health (DPH) CDC PHEP-Funded Workforce Program Provide LHDs and NC DPH with a dedicated point of contact within 11 major hospitals Primary tasks and responsibilities 46% time spent surveillance, detection, and monitoring 21% time spent assisting LHDs Educating and communicating with clinicians, hospitals, and the public health system

4 Study Objectives Examine the relationship and patterns of interaction among hospital-based public health epidemiologists (PHEs) and their partners related to public health surveillance activities To identify key actors and partnerships in the public health epidemiologist (PHE) network to understand how these relationships impact current and potential flows of information and communication To determine the extent to which the program fulfills its intended liaison role between LHDs and local hospitals

5 Methodology Defining the Network 11,12 Actors Public health departments/districts, hospitals, and public health epidemiologists Edges Relationship and patterns of reported interaction (e.g. info requests, lab results, disease outbreak) among PHEs and their partners related to CD surveillance activities Collecting the Data Survey Roster checklist, open-ended - LHDs Qualitative interviews with PHE and hospital staff Network analysis was conducted in R using statnet package. 13 [11] Wasserman S, Faust, K. Ibid. [12] Butts CT. Social network analysis: A methodological introduction. Asian Journal of Social Psychology. 2008; 11(1): [13] Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M. statnet: Software tools for the Statistical Modeling of Network Data URL:

6 Interactions within PHE Program Common Reported Interactions among PHEs-LHDs-Hospitals Based on Survey, Interview, and Program Data Support and Services Case Investigations/Event Information PHEs Program Catchment Area Referral Hospitals Preferred Partners Regional Epi-Teams State Preparedness Regions Geographic Contiguity LHDs Jurisdiction Public Health Reporting Hospitals

7 Interactions within PHE Program Basic Network Descriptives Geographic Projection of PHE Interactions with LHDs and Hospitals Nodes weighted by betweenness centrality Actors 11 PHEs 85 LHDs (100 counties) 109 Hospitals Edges 2,264 reported interactions

8 Interactions within PHE Program Basic Network Descriptives Interactions among PHEs-LHDs-Hospitals Fruchterman-Reingold projection, nodes weighted according to betweenness Graph-Level 14,15 Density = Reciprocity = Node-Level 16 Degree, = 20.6 PHE =15.1 LHD =18.8 Hospitals =1.9 Betweenness, = PHE = LHD =814.3 Hospitals =7.4 Proportion of all possible ties present Proportion of ties reciprocated (i.e. two-way) Number of nodes connected to each actor (i.e. popularity ) Number of times a node serves as a go-between [14] Wasserman S, Faust, K. Ibid. [15] Butts CT. Ibid. [16] Marsden PV. Egocentric and sociocentric measures of centrality. Social Networks, 2002; 24:

9 Measurement and Assessment Validate qualitative findings assoc w/ PHEs Providing a communication channel between LHDs and clinicians/other hospital staff Providing a bridge 17 between local/state public health Gould-Fernandez brokerage analysis 18,19,20 Differentiate specific roles in the public health surveillance system Determine whether PHEs fulfill liaison role [17] Williams P. The competent boundary spanner. Public Administration, 2002; 80(1): [18] Gould GV, Fernandez RM. Structure of mediation: A formal approach to brokerage in transaction networks. Sociological Methodology. 1989; 19: [19] Lind BE, Tirado M, Butts CT, Petrescu-Prahova M. Brokerage roles in disaster response: Organisational mediation in the wake of Hurricane Katrina. Int J Emergency Management. 2008; 5(1/2): [20] Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M. Ibid.

10 Brokerage Analysis Coordinator Itinerant Broker Gatekeeper Representative Liaison Hospital PHE LHD IMPORTANT NOTE Each organizational actor in the network may fulfill any (or all) of these roles as a result of their structural position in the network. 21 The liaison role identifies the frequency in which PHEs (or others) may serve as a go-between for hospitals and LHDs. 22,23 [21] Gould GV, Fernandez RM. Ibid. [22] Williams P. Ibid. [23] Lind BE, Tirado M, Butts CT, Petrescu-Prahova M. Ibid.

11 Brokerage Results Coordinator Itinerant Broker Gatekeeper Representative Liaison Source: Lind et al Brokerage Properties Frequency Distribution Coordinator Itinerant Gatekeeper Representative Liaison PHEs (n=11) LHDs (n=85) 2, ,557 2, Hospitals (n=109) Total 2,896 1,024 3,783 2,768 1,245 [24] Gould GV, Fernandez RM. Ibid.

12 Brokerage Results Coordinator Itinerant Broker Gatekeeper Representative Liaison Source: Lind et al Brokerage Properties (within Groups) Coordinator Itinerant Gatekeeper Representative Liaison PHEs (n=11) 0.6% 37.3% 11.0% 8.7% 42.5% LHDs (n=85) 30.0% 2.5% 37.0% 26.9% 3.5% Hospitals (n=109) % % [24] Gould GV, Fernandez RM. Ibid.

13 Brokerage Results Coordinator Itinerant Broker Gatekeeper Representative Liaison Source: Lind et al Brokerage Properties (across Groups) Coordinator Itinerant Gatekeeper Representative Liaison PHEs (n=11) 0.4% 74.8% 6.0% 6.5% 70.1% LHDs (n=85) 99.6% 23.6% 94.0% 93.5% 27.3% Hospitals (n=109) % % Overall 24.7% 8.7% 32.3% 23.6% 10.6% [24] Gould GV, Fernandez RM. Ibid.

14 Brokerage by PHEs Geographic Projection of PHE Interactions with LHDs and Hospitals Nodes weighted according to brokerage score, liaison role

15 Conclusions Brokerage analysis provides one approach to help assess and validate a PHEP-funded program Limitations Does not explore factors/attributes influencing roles Limited only to public health and program-related activities Snapshot w/in past year (May-Sept 2010) PHEs improves information and communication in routine/non-routine public health events Greatly enhanced completeness (58.9%) and timeliness (66.1%) of H1N1 reporting in the community Greatly enhanced (62.7%) communication between hospitals and local health departments, i.e. CD reporting/investigation

16 Implications and Directions Informing stakeholder decision-making Reports and follow-up discussions w/in 60 days Evaluating the hospital catchment area State-wide system coverage Assessing the time-lag reduction Timeliness of info requests* PHE Hospitals (39.8%) Non-PHE (15.7%) Building a robust model Identifying program factors Incorporation of actor attributes * Response time of immediately - when LHDs asked about time to receive requested information

17 North Carolina Preparedness & Emergency Response Research Center (NCPERRC) Application of Social Network Analysis to a Public Health Emergency Preparedness-Funded Workforce Program Christine A. Bevc, PhD, MA Phone: (919) University of North Carolina - Chapel Hill bevc@unc.edu Milissa L. Markiewicz, MPH, MIA University of North Carolina - Chapel Hill Jennifer Hegle, MPH University of North Carolina - Chapel Hill Jennifer A. Horney, PhD, MPH University of North Carolina - Chapel Hill Lana Deyneka North Carolina Division of Public Health Pia D.M. MacDonald, PhD, MPH University of North Carolina - Chapel Hill This research is a part of the North Carolina Preparedness and Emergency Response Research Center (NCPERRC) which is part of the UNC Center for Public Health Preparedness at the University of North Carolina at Chapel Hill s Gillings School of Global Public Health and was supported by the Centers for Disease Control and Prevention (CDC) Grant 1PO1 TP The contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC. For additional information: