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    • Fig shows results over two years after

    2018-11-05

    Fig. 4 shows results over two years after implementation of a physical activity intervention in 10% of the population. The model dictates a 17% increase in physical activity in agents chosen for the intervention. On average, the average decline in body weight was -1.77kg across the five scenarios ranging from -1.17 (Random) to -2.79 (IM). After physical activity intervention, the random, vulnerable and high risk targeting strategies performed similarly. Targeting based on network centrality yielded a 25% better average decline in weight. Again, the best performing targeting approach was IM, which generated the largest population-wide impact (-2.9kg CTNI), which was 57 percent more than average. In these results, vulnerable and random methods had overlapping CIs, while other CIs were separate. In addition to five targeting methods discussed here, four other network-based targeting methods are presented in the supplemental material. These four methods, include three different ways of measuring the centrality of nodes (degree centrality was discussed here), and a cluster based targeting method. We also studied the changes in the prevalence of overweight (25≤BMI≤29.9), and obese individuals (BMI≥30) in the population. Tables 3 and 4 show the performance of various targeting methods on changing these prevalences. When the targeted agents are receiving an EI intervention, network centrality and high-risk methods yield lower percentages of obese individuals in the estriol (with 28.9% and 28.3%) than random and vulnerable methods (with 29% for both). Using IM targeting, the number of individuals with obesity drops to 26.6%. Similar patterns were observed for the physical activity intervention. In this case, the IM method yielded the greatest reduction in obesity prevalence (-4.8% for EI and -6% for PA CTNI).
    Discussion Harnessing information on the social characteristics of individuals in designing interventions may lead to greater population-level impact (Bahr et al., 2009). However, the impact of harnessing social influence as a basis for intervention targeting is difficult or impossible to estimate using traditional methods. Previous studies about the potential advantages of using social network structure for targeting have been inconclusive. This becomes more important when we consider the relative cost and difficulty of gathering social network information in real human populations. Few interventions have been designed and implemented by analyzing the network structure of the population of interest. The purpose of this paper was to develop and evaluate an agent-based model (ABM) to evaluate performance of 5 possible intervention targeting regimes. Computational methods provide powerful tools to study the effectiveness of alternative targeting strategies by conducting experiments in a so-called in silico environment. Our main finding is that subject to the limits and assumptions of our model, we find evidence that using network information to inform targeting outperforms more standard targeting approaches including random selection, or selecting high-risk individuals, or vulnerable contexts. Our simulations showed that targeting individuals based on their network position leads to greater population effectiveness in obesity interventions, holding the efficacy of the intervention for an individual constant. This is consistent with some existing findings (Bahr et al., 2009; Hammond & Ornstein, 2014; Sangachin et al., 2014; Trogdon & Allaire, 2014). However, these results conflict with other studies. El-Sayed et al. (2013) and Zhang et al. (2015) found that network-based obesity interventions have little or no added value compared with at-random interventions. El-Sayed et al. (2013) assumed that risk of obesity if an individual’s contact becomes obese is 1.6 times higher. Sangachin et al. (2014) used a linear threshold model to implement diffusion of obesity. Differences in the choice of diffusion model and key model parameter decisions might explain these inconsistencies. In this study, the usage of a threshold model for simulating obesity-related behavior and its diffusion is based on the model proposed by Giabbanelli et al. (2012). We believe we have improved their approach by replacing a single fixed population threshold with threshold values drawn from a distribution. In addition, we performed sensitivity analysis over the range of threshold values to demonstrate that results are consistently plausible, and not extreme. Our final results are not strongly sensitive to these threshold values. The results of these experiments are reported in supplemental materials.