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Application Note

A three-state continuous time Markov chain model for HIV disease burden

, &
Pages 1671-1688 | Received 21 Aug 2017, Accepted 30 Nov 2018, Published online: 10 Dec 2018
 

ABSTRACT

Plasma HIV viral load (VL) is the clinical indicator used to evaluate disease burden for HIV-infected patients. We developed a covariate-adjusted, three-state, homogenous continuous time Markov chain model for HIV/AIDS disease burden among subgroups. We defined Detectable and Undetectable HIV VL levels as two transient states and Death as the third absorbing state. We implemented the exact maximum likelihood method to estimate the parameters with related asymptotic distribution to conduct hypothesis testing. We evaluated the proposed model using HIV-infected individuals from South Carolina (SC) HIV surveillance data. Using the developed model, we estimated and compared the transition hazards, transition probabilities, and the state-specific duration for HIV-infected individuals. We examined gender, race/ethnicity, age, CD4 count, place of residence, and antiretroviral treatment regimen prescribed at the beginning of the study period. We found that patients with a higher CD4 count, increased age, heterosexual orientation, white, and single tablet regimen users were associated with reduced risk of transitioning to a Detectable VL from an Undetectable VL, whereas shorter time since diagnosis, being male, and injection drug use increased the risk of the same transition.

2010 MATHEMATICS SUBJECT CLASSIFICATIONS:

Acknowledgments

We acknowledge Terri G. Stephens, from the HIV Surveillance Program at the South Carolina Department of Health and Environmental Control, for assistance with data management. We also acknowledge the anonymous reviewers for their insightful comments and suggestions that improved this article greatly.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the Duke University Center for AIDS Research (CFAR), an NIH funded program (5P30 AI064518).

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