An Application of Social Network Theory to Optimize Influenza Vaccination Among Healthcare Workers
P.M. Polgreen,
T. Tassier,
S. Pemmaraju,
A.M. Segre
International Conference on Emerging Infectious Diseases (March 2008).
Background: Influenza vaccination is the most effective measure for preventing nosocomial spread of
influenza, and the CDC recommends vaccination for all healthcare workers. Yet, in the US, only 36% of workers with
direct patient contact are immunized annually. Interventions exist to increase vaccination rates, but they are costly to
implement, and there are no data to identify the groups of healthcare workers who should be the primary focus of
such interventions. Similarly, there are no data to guide vaccination efforts in the event of a vaccine shortage, nor is
there a theoretical framework to inform such decisions.
Methods: At the University of Iowa Hospital and Clinics (UIHC) we shadowed individuals from16 different healthcare-worker groups for 40 hours over different times of day and counted all contacts of the observed healthcare workers with patients and other healthcare workers. All contacts (direct touch and within three feet) were recorded. Using these data, we constructed a network representative of the contact structure at UIHC. We then performed an agent based SIR model of influenza transmission across the network assuming no vaccination, in order to observe baseline infection rates among worker groups. We then introduced vaccinations, varying the vaccination rates of healthcare worker groups in order to measure the marginal effect of each vaccination (the number of secondary infections prevented by inoculating a given individual in a given group).
Results: In the simulation we observe a large degree of heterogeneity in the infection rates of worker groups. Thus, not surprisingly, the effectiveness of vaccinations also varies greatly. We find that vaccinating individuals from groups whose members have large numbers of contacts (such as residents, medical students, and floor nurses) or groups whose members have contacts within many different hospital groups (such as unit clerks) provides the greatest benefit.
Conclusions: The degree and structure of contacts among healthcare workers contribute greatly to the size of outbreaks in our simulations. Our results suggest that social network theory can help inform interventions to target and optimize vaccination strategies to protect patients against nosocomial spread of influenza.