Algorithms for seeding social networks can enhance the adoption of a public health intervention in urban India

Targeting structurally influential individuals within social networks can enhance adoption of health interventions within populations. We tested the effectiveness of two algorithms to improve social contagion that do not require knowledge of the whole network structure. We mapped the social interactions of 2,491 women in 50 residential buildings (chawls) in Mumbai, India. The buildings, which are social units, were randomized to (1) targeting 20% of the women at random, (2) targeting friends of such randomly chosen women, (3) targeting pairs of people composed of randomly chosen women and a friend, or (4) no targeting. Both targeting algorithms, friendship nomination and pair targeting, enhanced adoption of a public health intervention related to the use of iron-fortified salt for anemia. In particular, the targeting of pairs of friends, which is relatively easily implementable in field settings, enhanced adoption of novel practices through both social influence and social reinforcement.

Since knowledge, attitudes, and behavior can spread across interpersonal ties, and since the networks formed by such ties tend to amplify this spread, changes in one person’s behavior can cascade out across a social network, producing behavioral changes in the larger population in which a person is embedded (1–3). Such cascades offer the prospect of increasing the effectiveness of public health campaigns, which could be especially beneficial in low-resource settings (4, 5). While there is a widespread interest in using social contagion to amplify the adoption of health interventions, there are currently few established network-targeting strategies available for straightforward implementation in practice. Here, we report a randomized controlled trial (RCT) of two easy-to-implement strategies designed to improve the likelihood of such behavioral cascades.Deliberately fostering cascade effects requires the identification of a subset of potentially influential individuals among whom to launch an intervention. Simulation results suggest that targeting highly connected individuals in networks could enhance the diffusion of interventions (6–8), and other research suggests more complex methods for the selection of optimal targets (9–11). However, such methods typically require mapping whole social networks in order to identify the targets who might exercise special influence. Such sociocentric data may not be available, or certain algorithms may be simpler or more efficient than relying on whole network data.Previous research has identified two types of seed-selection strategies that do not require knowledge of the underlying social network but that can still significantly accelerate adoption. An evaluation in India found that gossip nomination strategies (asking randomly selected people which community members are good sources of trustworthy gossip and then recruiting the gossipers for the public health intervention) can result in faster diffusion of information, driving up childhood vaccination rates (12). A study in Honduras showed that recruiting the friends of randomly selected residents can effectively accelerate adoption (13), based on the property that, on average, friends have more connections than respondents themselves (14). We refer to this approach as "friend targeting" (we use the term "targeting" as a shorthand for network seed selection more generally).However, in many real-world situations involving public health interventions, it may be unethical or impractical to deny treatment to the original randomly-chosen informants while offering it instead to people they identify. For instance, one might not want to go into a village, ask people who their friends or gossipers are, and then proceed to offer something of value to those other people but not to the people who identified them. Here, we therefore present and evaluate the effectiveness of a seed-selection strategy, whereby both randomly chosen people and also their friends (who, in expectation, are actually more socially influential) are treated with a public health intervention. We refer to this seed-selection approach as pair targeting. The pair may reinforce each other’s behavior and work in tandem (thus potentially raising their own adoption of an intervention) as well as enhance their social influence on others to whom they are connected who did not receive the intervention.We used iron-deficiency anemia in India as our testbed. Iron-deficiency anemia can lead to cognitive impairment in childhood and in old age, physical disability and diminished work capacity in adults, and adverse outcomes of pregnancy for mothers and newborns (15–17). Prevention of anemia in childbearing women is key to preventing low birth weight and perinatal and maternal mortality (18–21). Iron-deficiency anemia among Indian women remains a key public health problem, with 53% of Indian women affected by the condition (22). Unfortunately, recent government attempts to promote nationwide use of iron and folic acid supplements have had limited reach. However, since its introduction to the market, iron-fortified salt has emerged as the major new way to help reduce the prevalence of iron-deficiency anemia in India and achieve the goals of the Anemia-Free India Initiative (Anemia Mukt Bharat), a collaboration between the Indian Ministry of Health and Family Services and UNICEF (23).