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A Markov model examining intervention effects on the HIV prevalence/incidence amongst the overall population

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  • Ghayoori, Arash
  • Nagi, Rakesh

Abstract

Human Immunodeficiency Virus (HIV) remains a high burden in Africa. Several targeted prevention strategies are prevalent including the promotion of condom use, provision of anti-retroviral therapy (ART) and pre-exposure prophylaxis (PrEP). Optimal allocation of limited resources requires understanding how the HIV epidemic will be affected by these strategies. We develop a Markov model, considering four sub-populations: female sex workers (FSWs), general population (GP), sex clients (SCs), and men who have sex with men (MSM), which estimates the effects of these strategies on the HIV prevalence/incidence in Rwanda from 2017 to 2027. Using Rwanda's demographic, sexual risk behavior, and HIV-associated morbidity/mortality data, it was determined that by 2027, a 30% improvement in consistent condom use amongst female sex workers would reduce the HIV prevalence amongst female sex workers, sex clients, and the general population by 7.86%, 5.97%, and 0.17%, respectively. Secondly, while making improvements on the recurrent HIV testing as well as ART coverage only mildly reduces the prevalence/incidence amongst female sex workers, and sex clients, reducing the two would result in an increase in the HIV prevalence/incidence amongst FSWs and SCs. Lastly, our analysis shows that introduction of PrEP to female sex workers in 2019 is expected to reduce the HIV incidence amongst FSWs by 1.28% over the next decade. We further analyze scaling-up PrEP beyond the highest-risk population, i.e., FSWs, and weigh population-level impact with cost-effectiveness to make an informed priority-setting amongst women in the general population. We modeled the HIV risk to women, and computed the impact, cost, and cost-effectiveness of PrEP scale-up for younger women aged 15–25, women aged 25–34, and women aged 35–49 years, while accounting for the low program retention levels reported in demonstration projects. Perhaps, counter-intuitively, it is shown that PrEP could substantially avert more infections a year amongst women in the general population than amongst female sex workers. In summary, our models and analysis could help decision makers effectively allocate resources to each of these prevention/treatment strategies amongst different sub-populations.

Suggested Citation

  • Ghayoori, Arash & Nagi, Rakesh, 2022. "A Markov model examining intervention effects on the HIV prevalence/incidence amongst the overall population," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
  • Handle: RePEc:eee:soceps:v:79:y:2022:i:c:s0038012121001154
    DOI: 10.1016/j.seps.2021.101123
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    References listed on IDEAS

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    1. Carel Pretorius & John Stover & Lori Bollinger & Nicolas Bacaër & Brian Williams, 2010. "Evaluating the Cost-Effectiveness of Pre-Exposure Prophylaxis (PrEP) and Its Impact on HIV-1 Transmission in South Africa," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-10, November.
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    3. Yagci Sokat, Kezban & Armbruster, Benjamin, 2015. "Implications of switching away from injectable hormonal contraceptives on the HIV epidemic," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 1-9.
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    5. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    6. Gregory S. Zaric & Margaret L. Brandeau, 2001. "Optimal Investment in a Portfolio of HIV Prevention Programs," Medical Decision Making, , vol. 21(5), pages 391-408, October.
    7. Sabina S Alistar & Douglas K Owens & Margaret L Brandeau, 2011. "Effectiveness and Cost Effectiveness of Expanding Harm Reduction and Antiretroviral Therapy in a Mixed HIV Epidemic: A Modeling Analysis for Ukraine," PLOS Medicine, Public Library of Science, vol. 8(3), pages 1-15, March.
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    1. Gouveia, Catarina & Kalakou, Sofia & Cardoso-Grilo, Teresa, 2023. "How to forecast mental healthcare needs? Distinguishing between perceived and unperceived needs and their impact on capacity requirements," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).

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