DISCUSSION
The HITS study confirms that a modest community‐wide financial incentive increases the uptake of HIV testing. Our study establishes that an individual offered a financial incentive is more likely to take up testing when family members have received the same offer prior to or at the same time as them. The strength of the effect appears to increase with the count of family members in receipt of the offer.This finding adds to a growing body of evidence from randomized control trials demonstrating that economic incentives increase the uptake of HIV testing [Though prior studies are often not explicit about the causal mechanism through which incentives are hypothesized to shape behaviour, several explanations do appear in the epidemiologic literature. Incentives can change the structural environment in which behaviour unfolds (for instance by alleviating poverty); they can affect the price of some behaviour or good, or the income of the recipient in relation to that good or behaviour; and they can intervene on the psychological processes that shape behaviour [There are some notable exceptions. Several trials have shown that incentivizing close social contacts—most commonly romantic [Developing and applying theory that reflects the interdependence of individuals could enable the development of new interventions. For instance, family‐based intervention strategies might be effective at reaching groups which otherwise have low access to health services, such as young people [We make a novel contribution to the fields of study design and applied causal inference. Our results show empirical evidence for the violation of the assumption of “partial interference” in the context of a large‐scale cluster‐randomized trial [It is likely that there are important social relationships that are relevant to HIV testing that were not captured in population surveillance data. This is a limitation of our study. Further research should develop methods to account for missing network data and design new approaches to measuring socio‐centric networks. A further limitation is that we used the assumption of partial interference to calculate standard errors, though we show this assumption to be violated. This was motivated by the fact that network connections are much denser within clusters than they are across them; we do not expect this analytic decision to lead to anti‐conservative estimates of uncertainty. Finally, we did not adjust for multiple testing, though we note that using the Bonferroni correction (i.e. using a nominal Type I error rate of 0.05/3 = 0.017) would not have altered the main conclusions of this study.A major strength of our findings is that they are not susceptible to homophily bias—bias that arises because of the tendency for people with similar unmeasured characteristics to form relationships based on those characteristics [Understanding humans in the context of their relationships can lead to improvements in population health. There is an urgent need to cultivate robust social network data for epidemiologic analysis—whether by collecting them, constructing them from already collected study data as we did here or connecting passively collected information, such as social media data, with large public health datasets.