Comment 112 www.thelancet.com Vol 386 July 11, 2015 Public health interventions rarely introduce health innovations to every individual in a population all at once. Rather, practitioners target some people for early adoption, hoping that the innovation will spread by word of mouth through social networks. Selection of optimum targets for health interventions in social networks is difficult, because little is known about the spread of health innovations in real-world social networks. 1 In The Lancet, David Kim and colleagues 2 deliver the first randomised comparison of multiple network-targeting strategies to promote the spread of health innovations in real-world face-to-face social networks. The authors establish two practically important results. First, on the encouraging side, they show that a new and cheap targeting strategy can substantially improve the spread of health innovations in social networks compared with a conventional and expensive targeting strategy. In 32 villages in rural Honduras, with a total population of 5773, villages were randomly assigned to receive one, both, or neither of two interventions (chlorine for water purification or multivitamins, each accompanied by vouchers which could be used by others to obtain further quantities of the same intervention). In each village, interventions were introduced to target groups composed either of randomly selected villagers, the best-connected villagers, or the friends of randomly selected villagers. As judged by redemption of vouchers, asking the friends of a random sample of villagers to distribute vouchers for multivitamins to other villagers led to a greater diffusion of multivitamins throughout the villages than asking the best-connected people in the villages to distribute the vouchers (p<0·01), and to an increase of 12·2% (95% CI 6·9–17·9) compared with a randomly targeted intervention. Targeting friends of a random sample of villagers is fairly cheap because it does not require a mapping of the entire social network, as would finding the most connected villagers. Getting more for less is always good news. Second, on the cautionary side, Kim and colleagues 2 establish that the efficacy of different targeting strategies is highly context dependent: the targeting strategy that most improved the spread of multivitamins made no difference to the spread of chlorine for water purification. For any specific innovation, it will be difficult to predict which targeting strategy will produce the best results in practice. Yet Kim and colleagues’ study marks real progress. Empirical confirmation that targeting the most-connected people in a network does not guarantee that a health innovation will ultimately reach the greatest number of people in the network challenges the conventional practice of focusing innovations on so- called opinion leaders or hubs. 3 This study 2 should motivate further empirical research on how best to exploit face-to-face social networks for the seeding of health innovations. Among other things, future research should probe whether other network targeting strategies might reach even more people while maintaining cost savings. The difficulty of this Public health: real-world network targeting of interventions Timothy A Rockall Royal Surrey County Hospital NHS Trust, Guildford, Surrey GU2 7XX, UK tim.rockall@btinternet.com I declare no competing interests. 1 Jairath V, Kahan BC, Gray A, et al. Restrictive versus liberal blood transfusion for acute upper gastrointestinal bleeding (TRIGGER): a pragmatic, open-label, cluster randomised feasibility trial. Lancet 2015; published online May 6. http://dx.doi.org/10.1016/S0140- 6736(14)61999-1. 2 Jairath V, Kahan BC, Logan RF, Travis SP, Palmer KR, Murphy MF. Red blood cell transfusion practice in patients presenting with acute upper gastrointestinal bleeding: a survey of 815 UK clinicians. Transfusion 2011; 51: 1940–48. 3 Jairath V, Hearnshaw S, Brunskill SJ, et al. Red cell transfusion for the management of upper gastrointestinal haemorrhage. Cochrane Database Syst Rev 2010; 9: CD006613. 4 Rockall TA, Logan RF, Devlin HB, et al. Risk assessment after acute upper gastrointestinal haemorrhage. Gut 1996; 38: 316–21. Published Online May 5, 2015 http://dx.doi.org/10.1016/ S0140-6736(15)60503-7 See Articles page 145 Alex Slobodkin/Getty Images 5 Al-Jaghbeer M, Yende S. Blood transfusion for upper gastrointestinal bleeding: is less more again? Crit Care 2013; 17: 325. 6 Villanueva C, Colomo A, Bosch A, et al. Transfusion strategies for acute upper gastrointestinal bleeding. N Engl J Med 2013; 368: 11–21. 7 Malone DL, Dunne J, Tracy JK, Putnam AT, Scalea TM, Napolitano LM. Blood transfusion, independent of shock severity, is associated with worse outcome in trauma. J Trauma 2003; 54: 898–905. 8 Herbert PC, Wells G, Blajchman MA, et al. A multicenter, randomized, controlled trial of transfusion requirements in critical care. N Engl J Med 1999; 340: 409–17. 9 Hajjar LA, Vincent JL, Galas FR, et al. Transfusion requirements after cardiac surgery: the TRACS randomized controlled trial. JAMA 2010; 304: 1559–67. 10 Carson JL, Terrin ML, Noveck H, et al. Liberal or restrictive transfusion in high-risk patients after hip surgery. N Engl J Med 2011; 365: 2453–62. 11 Holst LB, Haase N, Wetterslev J, et al. Lower versus higher hemoglobin thresholds for transfusion in septic shock. N Engl J Med 2014; 371: 1381–91. 12 Pape A, Stein P, Horn O, et al. Clinical evidence of blood transfusion effectiveness. Blood Transfus 2009; 7: 250–58.