Statistical Modelling of Disease Progression among HIV-Infected Patients on Antiretroviral Therapy at Howard Opportunistic Infection Clinic

Background: Sustaining high rates of adherence to antiretroviral therapy (ART) is critical to suppressing HIV viral replication, delaying disease progression, and improving survival. While prior research indicates that treatment adherence and mortality patterns vary across demographic factors such as marital status, the specific differential trajectories of immunologic indicators and clinical disease progression between married and non-married patients remain under-investigated in sub-Saharan Africa. This study evaluated patterns of change in immunologic markers (CD4+ T-cell count) and clinical parameters (haemoglobin and weight) over time according to marital status among HIV-infected adults initiating ART.

Antiretroviral Therapy (ART); CD4 Lymphocyte Trajectory; Statistical Modelling; Random Effects Model; Hierarchical Linear Model; Marital Status; HIV Disease Progression; Zimbabwe.

1. Sepkowitz KA. AIDS—the first 20 years. N Engl J Med. 2001;344(23):1764–1772. [Google Scholar] [Crossref]

2. UNAIDS. Report on the global AIDS epidemic: executive summary. Geneva, Switzerland: Joint United Nations Programme on HIV/AIDS; 2009. [Google Scholar] [Crossref]

3. Ministry of Health and Child Welfare (MOHCW). National survey of HIV and syphilis prevalence among women attending antenatal clinics in Zimbabwe. Harare, Zimbabwe: MOHCW; 2009. [Google Scholar] [Crossref]

4. Adler MW. ABC of AIDS. 5th ed. London: BMJ Publishing Group; 2001. [Google Scholar] [Crossref]

5. Nwauche CA, Erhabor O, Ejele OA, Akani CI. Adherence to antiretroviral therapy among HIV-infected patients in resource-constrained settings in the Niger Delta of Nigeria. Pharm World Sci. 2006;28(4):210–215. [Google Scholar] [Crossref]

6. Anguko AA. Effect of marital status and education level on HIV/AIDS mortality in adults in rural KwaZulu-Natal, South Africa. Durban: University of KwaZulu-Natal; 2009. [Google Scholar] [Crossref]

7. Peltzer K, Preez NF, Ramlagan S, Anderson J. Antiretroviral treatment adherence among HIV patients in KwaZulu-Natal, South Africa. BMC Public Health. 2010;10:111. [Google Scholar] [Crossref]

8. Binquet C, Chene G, Jacqmin-Gadda H, Journot V, Saves M, Lacoste D, et al. Modelling changes in CD4-positive T-lymphocyte counts after the start of highly active antiretroviral therapy and the relation with risk of opportunistic infection. Am J Epidemiol. 2001;153(4):386–393. [Google Scholar] [Crossref]

9. Bangsberg DR, Perry S, Charlebois ED, Clark AR, Roberston M, Zolopa AR, et al. Non-adherence to highly active antiretroviral therapy predicts progression to AIDS. AIDS. 2000;14(4):357–366. [Google Scholar] [Crossref]

10. Feeney G. The impact of HIV/AIDS on adult mortality in Zimbabwe. Popul Dev Rev. 2001;27(4):771–780. [Google Scholar] [Crossref]

11. Burga M, Regidor E, Rodriguez C, Juan L. Mortality by cause of death and marital status in Spain, 1991. Eur J Public Health. 1998;8(1):37–42. [Google Scholar] [Crossref]

12. Lund R, Holstein B, Osler M. Marital history from age 15 to 40 years and subsequent 10-year mortality: a longitudinal study of Danish males born in 1953. Int J Epidemiol. 2004;33(2):389–397. [Google Scholar] [Crossref]

13. Shisana O, Zungu-Dirwayi N, Toefy Y, Simbayi L, Malik S, Zuma K. Marital status and risk of HIV infection in South Africa. S Afr Med J. 2004;94(7):537–543. [Google Scholar] [Crossref]

14. World Health Organization (WHO). Patient evaluation and antiretroviral treatment for adults and adolescents: global recommendations. Geneva: WHO; 2009. [Google Scholar] [Crossref]

15. De Beaudrap P, Etard JF, Diouf A, Ndiaye I, Gueye NF, Sow PS, et al. Modelling CD4 T-cell count recovery in HIV-1-infected adults receiving highly active antiretroviral therapy in Senegal. Am J Trop Med Hyg. 2008;79(3):430–436. [Google Scholar] [Crossref]