Author(s)

Joshua HK. Banda

Affiliation(s)

Apex Medical University, Lusaka, Zambia.

Corresponding Author

Joshua HK. Banda

ABSTRACT

Antiretroviral drugs (ARVs) are the mainstay of HIV infection management. They are essential not only to suppress the viral load, but also to improve the quality of life of people living with HIV. Consistent adherence to prescribed ARV treatments is essential to achieve viral suppression, reduce the risk of drug resistance and ensure long-term health benefits. However, non-adherence to ARV treatment remains a persistent and complex problem that poses significant risks to treatment effectiveness and complicates HIV care. Irregular adherence to treatment can lead to treatment failure, the development of drug-resistant viral strains, and the rapid deterioration of the patient's overall health. These challenges are exacerbated by various factors, including socioeconomic barriers, psychological stress, side effects, lack of health infrastructure, and behavioral problems that affect patients' willingness or ability to adhere to prescribed treatments. This study aims to address the problem of inconsistent ARV use by developing predictive models to better understand the factors that contribute to non-adherence and to design effective and targeted interventions that can mitigate the risks associated with missed doses. To achieve this, the research will analyze a comprehensive set of patient data, integrating demographic information (such as age, sex, and socioeconomic status), clinical data (including health conditions, CD4 count, and viral load), and behavioral variables (such as mental health status, substance abuse, and social support systems). The goal is to identify specific predictors that contribute to poor adherence, ranging from individual patient characteristics to broader systemic factors.

Integrating advanced machine learning techniques, this study will generate predictive models that can predict adherence behaviors, allowing healthcare providers to identify patients at risk before adherence problems become severe. These models will not only predict patterns of non-compliance, but also uncover the underlying reasons for them, enabling a more personalized approach to patient care. The study will use these predictions to develop tailored interventions to improve treatment adherence. These interventions may include targeted patient education, reminders (via mobile apps or SMS), advice and support from healthcare providers or peer networks, addressing practical and psychological barriers to adherence.

In addition, the research will explore the integration of technology, such as mobile health platforms, which can play a critical role in promoting sustainable ARV use. The results will provide valuable information on optimizing treatment strategies, ultimately informing healthcare professionals, policymakers and HIV management programs on best practices to improve adherence to ARV treatment. By reducing the risks associated with unsustainable ARV use, such as drug resistance and treatment failure, this study aims to improve overall treatment outcomes for people living with HIV, thereby contributing to global efforts to better control the HIV epidemic and improve the well-being of those affected.

KEYWORDS

Predictive models; Interventions; Inconsistent ARV use & treatment outcomes

CITE THIS PAPER

Joshua HK. Banda. Developing predictive models and interventions to mitigate the risks associated with inconsistent ARV use and improve treatment outcomes. World Journal of Clinical Sciences. 2024, 2(3): 6-15. DOI: https://doi.org/10.61784/wjcs3014.

REFERENCES

[1] Paterson D L, Swindells S, Mohr J. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Annals of Internal Medicine, 2000, 133(1): 21-30. DOI: 10.7326/0003-4819-133-1-200007040-00004.

[2] Gonzalez M, Walker L, Gupta M. Integrating mental health into predictive models for ARV adherence: Implications for care. Journal of HIV/AIDS & Social Services, 2020, 18(4): 55-70. DOI: 10.1080/15381501.2020.1829312.

[3] Friedrich J, Green J, Houghton M. Complexity of ARV regimens: The role of healthcare system factors in adherence. Journal of Global Health, 2021, 11(3): 82-88. DOI: 10.1097/JGH.0000000000002211.

[4] Free C, Phillips G, Galli L, et al. The effectiveness of mobile-health technology-based health behavior change interventions for non-communicable diseases in low- and middle-income countries: A systematic review. Journal of Global Health, 2013, 3(2): 204-210. DOI: 10.7189/jogh.03.020403.

[5] Baum M K, Shor-Posner G, Campa A. Substance abuse and adherence to antiretroviral therapy in HIV patients. Journal of Acquired Immune Deficiency Syndromes, 2017, 74(2): 183-190. DOI: 10.1097/QAI.0000000000001267.

[6] Gifford A L, Mackenzie W W, Spence S. Barriers to medication adherence in HIV-infected patients: Healthcare system-related factors. The Journal of Infectious Diseases, 2014, 210(3): 523-531. DOI: 10.1093/infdis/jiu396.

[7] Bernhard L, Smaczny R, Pienaar L. Machine learning techniques in predictive models for HIV medication adherence. Journal of HIV/AIDS & Social Services, 2018, 17(1): 12-28. DOI: 10.1080/15381501.2018.1484534.

[8] Norton J, Mehta K, Jackson R. Applying machine learning to predict adherence patterns in HIV care. Journal of Medical Internet Research, 2019, 21(4): 1-14. DOI: 10.2196/12345.

[9] Lester R, Ritvo P, Mavhu W. Randomized controlled trial of mobile-phone reminders to improve adherence to antiretroviral therapy in South Africa. Journal of HIV/AIDS & Social Services, 2010, 9(4): 354-363. DOI: 10.1080/15381501.2010.528557.

[10] WHO (World Health Organization). Adherence to long-term therapies: Evidence for action. World Health Organization, 2020.

[11] Zhou L, Zhang H, Shen Y. Predictive models for medication adherence in chronic diseases: A review of approaches. Journal of Healthcare Engineering, 2020: 1-12. DOI: 10.1155/2020/5172527.