Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs

Abstract

Human Immunodeficiency Virus (HIV) interventions among people who use drugs (PWUD) often have spillover, also known as interference or dissemination, which occurs when one participant’s exposure affects another participant’s outcome. PWUD are often members of networks defined by social, sexual, and drug-use partnerships and their receipt of interventions can affect other members in their network. For example, HIV interventions with possible spillover include educational training about HIV risk reduction, pre-exposure prophylaxis, or treatment as prevention. In turn, intervention effects frequently depend on the network structure, and intervention coverage levels and spillover can occur even if not measured in a study, possibly resulting in an underestimation of intervention effects. Recent methodological approaches were developed to assess spillover in the context of network-based studies. This tutorial provides an overview of different study designs for network-based studies and related methodological approaches for assessing spillover in each design. We also provide an overview of other important methodological issues in network studies, including causal influence in networks and missing data. Finally, we highlight applications of different designs and methods from studies of PWUD and conclude with an illustrative example from the Transmission Reduction Intervention Project (TRIP) in Athens, Greece.

Population Health Management: Leveraging it for Better Patient Outcome’s

“…Metrics including precision, recall, precision, accuracy, and F1 score are used in the evaluation of the Naive Bayes model to gauge how well it performs in classifying foetal health. By evaluating the model over several dataset subsets, cross-validation approaches like k-fold improve reliability and guarantee ability to be generalised in healthcare predictions [13]. This assessment procedure is streamlined by Python packages such as scikit-learn.…”

Network and covariate adjusted response‐adaptive design for binary response

“…Existing trials either consider a network-based intervention, 18,21 evaluate network interference by assessing outcomes of both treated participants and their network neighbors, 22,23 or statistically adjust for network interference in data analysis. 24,25 As an alternative, there have been new designs that incorporate network information in participants allocation. 19,26 Compared to trials with social network interventions that generally employ a block randomization scheme, the new designs apply to general interventions and are able to recruit individual participants sequentially, thus have advantages in terms of application.…”

“…Although the concept of network interference is relatively new, there has been an increasing interest in understanding the effects of of social networks in randomized controlled trials. Existing trials either consider a network‐based intervention, 18,21 evaluate network interference by assessing outcomes of both treated participants and their network neighbors, 22,23 or statistically adjust for network interference in data analysis 24,25 . As an alternative, there have been new designs that incorporate network information in participants allocation 19,26 .…”