Evaluating spillover of HIV knowledge from study participants to their network members in a stepped-wedge behavioural intervention in Tanzania

Lienert, Jeffrey; Fawzi, Mary C. Smith; Kaaya, Sylvia; Liu, Yuanyuan; Todd, Jim; Andrew, Irene; Onnela, Jukka Pekka

doi:10.1136/bmjopen-2019-033759

Cited by 4 publications

(3 citation statements)

References 61 publications

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“…When one participant’s exposure affects another participant’s health outcome, the effect is broadly known as spillover (alternatively, interference or dissemination) [ 39 ]. Spillover has been well studied to improve vaccine delivery and effectiveness in a population [ 40 , 41 , 42 ]; however, there have been limited evaluations of spillover for HIV interventions [ 43 ]. Spillover can generally exist in a network when an intervention is delivered to individuals connected through disease transmission risk or some other mechanism [ 44 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“… 6 Examples are trials to treat obstructive sleep apnoea, 7 chronic constipation, 8 malformations, 9 , 10 dementia, 11 to reduce household air pollution 12 and to evaluate self-management support programmes 13 and spillover of HIV knowledge. 14 The design of the individually randomized stepped-wedge design has not yet been thoroughly explored in the statistical literature. A recent study evaluated the efficiency of the individually randomized stepped-wedge design in trials with three time periods.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation to treatment sequences in individually randomized stepped-wedge designs with attrition

Moerbeek

2023

Clinical Trials

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Background/aims: The stepped-wedge design has been extensively studied in the setting of the cluster randomized trial, but less so for the individually randomized trial. This article derives the optimal allocation of individuals to treatment sequences. The focus is on designs where all individuals start in the control condition and at the beginning of each time period some of them cross over to the intervention, so that at the end of the trial all of them receive the intervention. Methods: The statistical model that takes into account the nesting of repeated measurements within subjects is presented. It is also shown how possible attrition is taken into account. The effect of the intervention is assumed to be sustained so that it does not change after the treatment switch. An exponential decay correlation structure is assumed, implying that the correlation between any two time point decreases with the time lag. Matrix algebra is used to derive the relation between the allocation of units to treatment sequences and the variance of the treatment effect estimator. The optimal allocation is the one that results in smallest variance. Results: Results are presented for three to six treatment sequences. It is shown that the optimal allocation highly depends on the correlation parameter [Formula: see text] and attrition rate [Formula: see text] between any two adjacent time points. The uniform allocation, where each treatment sequence has the same number of individuals, is often not the most efficient. For [Formula: see text] and [Formula: see text], its efficiency relative to the optimal allocation is at least 0.8. It is furthermore shown how a constrained optimal allocation can be derived in case the optimal allocation is not feasible from a practical point of view. Conclusion: This article provides the methodology for designing individually randomized stepped-wedge designs, taking into account the possibility of attrition. As such it helps researchers to plan their trial in an efficient way. To use the methodology, prior estimates of the degree of attrition and intraclass correlation coefficient are needed. It is advocated that researchers clearly report the estimates of these quantities to help facilitate planning future trials.

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“…Public health interventions in these populations often have disseminated effects, also known as indirect or spillover effects [9,10]. Recent work demonstrated that there are disseminated effects of HIV behavioral interventions, suggesting that intervening among highly-connected individuals may maximize spillover benefits to others [11]. Akin to other populations, PWID are embedded in social networks and communities (e.g., injection drug, non-injection drug, sexual risk network) in which they possibly exercise an influence upon other members [12,13].…”

Section: Introductionmentioning

confidence: 99%

Estimating Causal Effects of HIV Prevention Interventions with Interference in Network-based Studies among People Who Inject Drugs

Lee¹,

Buchanan²,

Katenka³

et al. 2021

Preprint

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Evaluating causal effects in the presence of interference is challenging in network-based studies of hard to reach populations. Like many such populations, people who inject drugs (PWID) are embedded in social networks and often exert influence on others in their network. In our setting, the study design is observational with a non-randomized network-based HIV prevention intervention. The information is available on each participant and their connections that confer possible shared HIV risk behaviors through injection and sexual risk behaviors. We consider two inverse probability weighted (IPW) estimators to quantify the population-level effects of non-randomized interventions on subsequent health outcomes. We demonstrated that these two IPW estimators are consistent, asymptotically normal, and derived a closed form estimator for the asymptotic variance, while allowing for overlapping interference sets (groups of individuals in which the interference is assumed possible). A simulation study was conducted to evaluate the finite-sample performance of the estimators. We analyzed data from the Transmission Reduction Intervention Project, which ascertained a network of PWID and their contacts in Athens, Greece, from 2013 to 2015. We evaluated the effects of community alerts on HIV risk behavior in this observed network, where the links between participants were defined by using substances or having unprotected sex together. In the study, community alerts were distributed to inform people of recent HIV infections among individuals in close proximity in the observed network. The estimates of the risk differences for both IPW estimators demonstrated a protective effect. The results suggest that HIV risk behavior can be mitigated by exposure to a community alert when an increased risk of HIV is detected in the network.

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Evaluating spillover of HIV knowledge from study participants to their network members in a stepped-wedge behavioural intervention in Tanzania

Cited by 4 publications

References 61 publications

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

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

Optimal allocation to treatment sequences in individually randomized stepped-wedge designs with attrition

Estimating Causal Effects of HIV Prevention Interventions with Interference in Network-based Studies among People Who Inject Drugs

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