title: Incorporating social network effects into cost-effectiveness analysis: a methodological contribution with application to obesity prevention.
abstract: OBJECTIVE
  To develop a methodology for integrating social networks into traditional cost-effectiveness analysis (CEA) studies. This will facilitate the economic evaluation of treatment policies in settings where health outcomes are subject to social influence.
  
  
  METHODS
  This is a simulation study based on a Markov model. The lifetime health histories of a cohort are simulated, and health outcomes compared, under alternative treatment policies. Transition probabilities depend on the health of others with whom there are shared social ties.
  
  
  METHODS
  The methodology developed is shown to be applicable in any healthcare setting where social ties affect health outcomes. The example of obesity prevention is used for illustration under the assumption that weight changes are subject to social influence.
  
  
  METHODS
  Incremental cost-effectiveness ratio (ICER).
  
  
  RESULTS
  When social influence increases, treatment policies become more cost effective (have lower ICERs). The policy of only treating individuals who span multiple networks can be more cost effective than the policy of treating everyone. This occurs when the network is more fragmented.
  
  
  CONCLUSIONS
  (1) When network effects are accounted for, they result in very different values of incremental cost-effectiveness ratios (ICERs). (2) Treatment policies can be devised to take network structure into account. The integration makes it feasible to conduct a cost-benefit evaluation of such policies.
authors.affiliation: Clinical Analytics Department, NorthShore University Health System, Evanston, Illinois, USA.
authors.forename: Chad
authors.initials: C
authors.lastname: Konchak
authors.forename: Kislaya
authors.initials: K
authors.lastname: Prasad