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Assessing Potential Outcomes Mediation in HIV Interventions

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Abstract

Knowledge of causal processes through mediation analysis can help improve the effectiveness and reduce costs of public health programs, like HIV prevention and treatment interventions. Advancements in mediation using the potential outcomes framework provide a method for estimating the causal effect of interventions on outcomes via a mediating variable. The purpose of this paper is to provide practical information about mediation and the potential outcomes framework that can enhance data analysis and causal inference for intervention studies. Causal mediation effects are defined and then estimated using data from an HIV intervention randomized trial among people who inject drugs (PWID) in Ukraine. Results from a potential outcomes mediation analysis show that the intervention had a total causal effect on incident HIV infection such that participants in the experimental group were 36% less likely to become infected during the 12-month study than those in the control arm, but that neither self-efficacy nor network communication mediated this effect. Because neither putative mediator was significant, measurement and confounding issues should be investigated to rule out these mediators. Other putative mediators, such as injection frequency, route of administration, or HIV knowledge can be considered. Future research is underway to examine additional, multiple mediators explaining efficacy of the current intervention and sensitivity to confounding effects.

Resumen

El conocimiento de procesos causales a través del análisis de mediación puede ayudar a mejorar la eficiencia y reducir los costos de programas de salud pública, incluyendo la prevención y el tratamiento del VIH. Avances en mediación, utilizando el enfoque de resultados potenciales ofrece un método para estimar los efectos causales de las intervenciones en variables dependientes a través de variables mediadoras. El objetivo de este artículo es ofrecer información acerca del análisis de mediación y del enfoque de resultados potenciales, el cual permite el análisis y la inferencia causal de las intervenciones. Los efectos causales de mediación son definidos y estimados utilizando los datos de un ensayo clínico con asignación aleatoria para disminuir riego del VIH entre usuarios de drogas inyectables (UDI) en Ucrania. Los resultados del análisis de mediación desde el enfoque de resultados potenciales muestran que la intervención tuvo un efecto causal total en la incidencia de infección del VIH, tal que, durante los 12 meses de estudio, fue 36% menos probable que los participantes del grupo experimental se infectaran en comparación con aquellos en el grupo control. Sin embargo, ni la autoeficacia ni la red comunicación mediaron el efecto. Dado que ninguno de los mediadores resultó ser significativo, sería necesario investigar problemas con la medición y efectos de confusión para poder descartarlos. Otros mediadores podrían ser considerados, tales como frecuencia de la inyección, ruta de administración, o el conocimiento acerca del VIH. Futuras investigaciones podrían estudiar diferentes y múltiples mediadores para explicar la eficacia de esta intervención y realizar un análisis de sensibilidad de efectos de confusión.

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Code Availability

A SAS program for analysis is available in the appendix, or by contacting the first author.

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Acknowledgements

This research was supported in part by the National Institute on Drug Abuse (NIDA; R37DA09757) and (R01DA042666). Data are from a NIDA-funded randomized trial (R01DA026739). We acknowledge the staff and directors of the organization who participated in the original intervention and data collection, including Dimitry Kryzhko with Health of Nation in Makeyevka/Donetsk; Olga Kostyuk and Tatiana Semikop with Faith, Hope and Love in Odessa; and Elena Goryacheva with the Charity Foundation Vykhod in Nikolayev. We would also like to thank the participants who gave their time for the project.

Funding

This research was supported in part by the National Institute on Drug Abuse (NIDA; R37DA09757) and (R01DA042666). Data are from a NIDA-funded randomized trial (R01DA026739).

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Authors and Affiliations

  1. Department of Psychology, Arizona State University, PO Box 871104, Tempe, AZ, 85287-1104, USA

    Heather L. Smyth & David P. MacKinnon

  2. School of Social Work, San Diego State University, San Diego, CA, USA

    Eileen V. Pitpitan

  3. Department of Psychiatry, University of Colorado, Denver, CO, USA

    Robert E. Booth

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  1. Heather L. Smyth
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  2. Eileen V. Pitpitan
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  3. David P. MacKinnon
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  4. Robert E. Booth
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Contributions

Heather Smyth was responsible for conducting the statistical analysis. David MacKinnon and Eileen Pitpitan assisted with the analysis and data interpretation. Heather Smyth, Eileen Pitpitan, and David MacKinnon wrote the initial draft of the manuscript. Robert Booth secured funding for and led the original intervention trial. All authors contributed to and approved the final manuscript.

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Correspondence to Heather L. Smyth.

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This study represents a secondary data analysis of a study, including informed consent, that was approved by the Colorado Multiple Institutional Review Board at the University of Colorado Denver and by the Ukrainian Institute on Public Health Policy. As a secondary analysis of de-identified data, the current analysis was not deemed as Human Subjects research by the Institutional Review Board at San Diego State University.

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Appendix

Appendix

PROC CAUSALMED Program

/*The following program performs a causal mediation analysis*/

/*X is treatment (0=control, 1=treatment), M is a continuous mediator measured at time 2, Y is a binary outcome (0=no disease, 1=disease), and C is the baseline measurement of the mediator as a covariate*/

/*The class statement specifies the two categorical variables. The descending option is used to predict the probability Y=1*/

/*A binary distribution for Y and the log link function are specified in the model statement. The log link is used because the outcome is not rare.*/

/*bootstrap confidence intervals are specified using 1000 bootstraps and a specified random seed.*/

Title 'Single mediator with baseline covariate';

proc causalmed data=use.data pall alpha = .05;

class X Y/descending;

model Y = X|M / dist=bin link=log;

mediator M = X;

covar C;

bootstrap CI (all) nboot = 1000 seed = 08012019;

run;

quit;

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Smyth, H.L., Pitpitan, E.V., MacKinnon, D.P. et al. Assessing Potential Outcomes Mediation in HIV Interventions. AIDS Behav 25, 2441–2454 (2021). https://doi.org/10.1007/s10461-021-03207-x

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