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Pay-it-forward incentives for hepatitis virus testing in men who have sex with men: a cluster randomized trial

Nature Medicine volume 29, pages 2241–2247 (2023)Cite this article

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Abstract

Pay-it-forward incentives involve having a person receive a free test with community-generated messages and then asking if those who received a free test would like to donate money to support others to receive free testing. Here we undertook a two-arm cluster-randomized trial to evaluate pay-it-forward incentives with active community participation to promote hepatitis B virus (HBV) and hepatitis C virus (HCV) testing among men who have sex with men in China. Men randomized to the pay-it-forward arm received free HBV and HCV testing and were offered a chance to pay-it-forward by donating money to support the testing of another anonymous person. Each participant paid for their HCV and HBV test at 7.7 USD per test in the standard-of-care arm. The primary outcome was the proportion of men who tested for HBV and HCV. Between 28 March and 6 November 2021, 32 groups (10 men per group) of men were randomized to the pay-it-forward (n = 160, 16 clusters) and standard-of-care (n = 162, 16 clusters) arms, respectively. HBV and HCV rapid testing were higher in the pay-it-forward arm (59.4%) than in the standard-of-care arm (25.3%) (proportion difference 35.2%, 95% confidence interval 24.1–46.3%). No adverse events were reported. The community-led pay-it-forward incentives improved HBV and HCV testing among men who have sex with men. Clinical Trial registration: ChiCTR 2100046140.

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Fig. 1
Fig. 2: Multivariable logistic regression to compare HBV and HCV test uptake rates of two arms.

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Data availability

The data are not publicly available for everyone because making the data publicly available would require additional consent. If other investigators are interested in performing additional analysis, data requests can be submitted to the corresponding author, explaining the analyses planned. Access to data will be provided upon application, with a timeline of within 1 month determined in accordance with the request.

Code availability

All codes are available on GitHub. The code is freely accessible at https://github.com/PIFHepstudy/code.git.

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Acknowledgements

This work was supported by the Key Technologies Research and Development Program (2022YFC2304900-4 to W.T.), National Institute of Health (R34MH119963 to W.T., and R01AI158826), National Nature Science Foundation of China (81903371 to W.T.) and CRDF Global (G-202104-67775 to W.T.). We thank all study participants, staff members from Rainbow and Zhinanzhen groups in Jiangsu, the Social Entrepreneurship to Spur Health Global, and Jiangsu Center for Diseases Prevention and Control, who contributed to this study.

Author information

Author notes

  1. These authors contributed equally and are co-first authors: Ye Zhang, JianjunLi.

  2. These authors contributed equally: Gengfeng Fu, Weiming Tang.

Authors and Affiliations

  1. Kirby Institute, UNSW Sydney, Sydney, New South Wales, Australia

    Ye Zhang

  2. School of Public Health, Nanjing Medical University, Nanjing, China

    Ye Zhang & Dan Wu

  3. Department of HIV/STI Prevention and Control, Jiangsu Provincial Center for Diseases Prevention and Control, Nanjing, China

    Jianjun Li & Gengfeng Fu

  4. University of North Carolina Project-China, Guangzhou, China

    Yewei Xie, Gifty Marley, Joseph D. Tucker & Weiming Tang

  5. Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK

    Dan Wu & Joseph D. Tucker

  6. Central Clinical School, Monash University, Melbourne, Victoria, Australia

    Jason Ong

  7. Melbourne Sexual Health Centre, Alfred Health, Carlton, Victoria, Australia

    Jason Ong

  8. University of Malaysia, Kuala Lumpur, Malaysia

    Adeeba Kamarulzaman

  9. International AIDS Society, Geneva, Switzerland

    Adeeba Kamarulzaman

  10. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT, USA

    Haidong Lu

  11. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

    Fei Zou

  12. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

    Jennifer S. Smith

  13. Guangdong Second Provincial General Hospital, Guangzhou, China

    Weiming Tang

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Contributions

This manuscript is an original research paper that has not been published previously, nor is it under review with any other journal. W.T. and G.F. conceived the initial idea and designed this clinical trial. W.T. oversaw the study design, implementation, data analysis, results generation and manuscript write-up. J.L., Y.X., G.M. and G.F. implemented the study and collected data. W.T., G.F. and J.L. had access to the study’s raw data. Y.Z., J.L., Y.X. and H.L. were responsible for data cleaning and data analysis and generated the final analysis outputs. J.O. and F.Z. provided advice for data analysis. Y.Z., J.L. and Y.X. wrote the first draft of the paper, and J.D.T., J.O., D.W., A.K. and J.S.S. contributed to the interpretation of the results and provided expert advice on the draft. All co-authors provided constructive comments and approved the final draft of the submission.

Corresponding authors

Correspondence to Gengfeng Fu or Weiming Tang.

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The authors declare no competing interests.

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Nature Medicine thanks William Liu, Zixin Wang, Christian Bottomley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Ming Yang, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Decision tree model.

Start_up_C: The training cost to start the study; Capital_fixed_costs: The capital fixed cost of the study; Variable_cost_P/S: variable cost in the PIF/SOC group, variable cost means the fees of sample collection, transportation, and testing; Donation_P: The money donated to the PIF program by the participants; Payment_S: The fees paid for the testing service by participants in the SOC; PIF: pay-it-forward; SOC: standard-of-care.

Extended Data Fig. 2 Univariate sensitivity analysis compared to PIF vs. SOC’s cost-effectiveness (ICER of financial cost per additional person tested).

Start_up_C: The training cost to start the study; Capital_fc: The capital fixed cost of the study; Variable_cost_P/S: variable cost in the PIF/SOC group, variable cost means the fees of sample collection, transportation, and testing. Donation_P: The money donated to the PIF program by the participants; Payment_S: The fees paid for the testing service by participants in the SOC; Test_S: The probability of participants tested in the SOC; Test_P: The probability of participants tested in the PIF; Positive_P: The probability of participants tested positive in the PIF; Positive_S: The probability of participants tested positive in the SOC; PIF: pay-it-forward; SOC: standard-of-care; PIF: pay-it-forward; SOC: standard-of-care.

Extended Data Fig. 3 Univariate sensitivity analysis compared to the cost-effectiveness of PIF vs. SOC (ICER of financial cost per additional case identified).

Start_up_C: The training cost to start the study; Capital_fc: The capital fixed cost of the study; Variable_costs_P/S: variable cost in the PIF/SOC group, variable cost means the fees of sample collection, transportation, and testing. Donation_P: The money donated to the PIF program by the participants; Payment_S: The fees paid for the testing service by participants in the SOC; Test_S: The probability of participants tested in the SOC; Test_P: The probability of participants tested in the PIF; Positive_P: The probability of participants tested positive in the PIF; Positive_S: The probability of participants tested positive in the SOC; PIF: pay-it-forward; SOC: standard-of-care.

Extended Data Fig. 4 Cost-effectiveness acceptability curve of the financial cost per person tested.

the PIF has a greater probability of being more cost-effective than SOC if the willingness to pay is greater than $20 per person tested. PIF: pay-it-forward; SOC: standard-of-care.

Extended Data Fig. 5 Cost-effectiveness acceptability curve of the financial cost per case identified.

the probability of PIF being more cost-effective than SOC may decrease as the willingness to pay increases from $0 to $2000 per identified case. PIF: pay-it-forward; SOC: standard-of-care.

Extended Data Fig. 6

Community-led intervention implementation timeline.

Extended Data Fig. 7

Community-led intervention implementation procedures.

Extended Data Table 1 Unit costs of pay-it-forward and standard of care in the model
Full size table

Supplementary information

Supplementary Information

Consort 2010 checklist extension to cluster randomized trials, study ethical approval statement, randomization STATA code, study protocol, statistical analysis plan and EASE-SAGER Checklist.

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Zhang, Y., Li, J., Xie, Y. et al. Pay-it-forward incentives for hepatitis virus testing in men who have sex with men: a cluster randomized trial. Nat Med 29, 2241–2247 (2023). https://doi.org/10.1038/s41591-023-02519-w

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