Comparison of Sojourn Time Distributions in Modeling HIV/AIDS Disease Progression

Goshu, Ayele Taye; Asena, Tilahun Ferede

doi:10.4172/2155-6180.1000358

Cited by 2 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We note that this probability increases with the cumulative and worsening of the illness. [3,c(5,9,13,17,21,25,29,33,37)]) T2=as.data.frame(PE0 [3,c(6,10,14,18,22,26,30,34,38)]) T3=as.data.frame(PE0 [3,c(7,11,15,19,23,27,31,35,39)]) T4=as.data.frame(PE0 [3,c(8,12,16,20,24,28,29,36,40)]) plot(Month$`c (1,6,12,18,24,30,36,42,48)`, T1$`PE0 [3, c(5, 9, 13, 17, 21, 25, 29, 33, 37)]`, type = "b",col=6,lwd=2,ylim=c(0,1),pch=16,xlab="Months",ylab="Probability", main = "From state 3") lines(Month$`c(1,6,12,18,24,30,36,42,48)`, T2$`PE0 [3, c(6, 10, 14, 18, 22, 26, 30, 34, 38)]`, type = "b",col=3,lwd=2,pch=16) lines(Month$`c (1,6,12,18,24,30,36,42,48)`, T3$`PE0 [3, c(7, 11, 15, 19, 23, 27, 31, 35, 39)…”

Section: Fig 2 Conditional Probabilities For Each State 4 Discussionmentioning

confidence: 99%

“…Homogeneous semi-Markov processes have been used to estimate the proportion of individuals changing from one clinical state to another [9][10][11]. Semi-Markov models were applied to HIV disease evolution and compared sojourn time distributions, exponential and Weibull probability distributions [12]. Moreover, other authors applied the homogenous Markov process to HIV disease under a combination treatment therapy [13].…”

Section: Introductionmentioning

confidence: 99%

“…It is recommended to stick to ongoing ART treatment, with cautions to patients' recent disease status. To state 1", "To state 2","To state 3","To state 4"), col=c(6,3,4,2), lty=1:2, cex=0.8) #PLOTS FROM STATE3 TO STATES Month=as.data.frame(c(1,6,12,18,24,30,36,42,48)) T1=as.data.frame(PE0…”

mentioning

confidence: 99%

“…T3=as.data.frame(PE0[4,c(7,11,15,19,23,27,31,35,39)]) T4=as.data.frame(PE0[4,c(8,12,16,20,24,28,29,36,40)]) plot(Month$`c(1,6,12,18,24,30,36,42,48)`, T1$`PE0[4, c(5, 9, 13, 17, 21, 25, 29, 33, 37)]`, type = "b",col=6,lwd=2,ylim=c(0,1),pch=16,xlab="Months",ylab="Probability", main = "From state 4") lines(Month$`c(1,6,12,18,24,30,36,42,48)`, T2$`PE0[4, c(6, 10, 14, 18, 22, 26, 30, 34, 38)]`, type = "b",col=3,lwd=2,pch=16) lines(Month$`c(1,6,12,18,24,30,36,42,48)`, T3$`PE0[4, c(7, 11, 15, 19, 23, 27, 31, 35, 39)]`, type = "b",col=4,lwd=2,pch=16) lines(Month$`c(1,6,12,18,24,30,36,42,48)`, T4$`PE0[4, c(8, 12, 16, 20, 24, 28, 29, 36, 40)]`, type = "b",col=2,lwd=2,pch=16) legend(40, 1, legend=c("To state 1", "To state 2","To state 3","To state 4"), col=c(6,3,4,2), lty=1:2, cex=0.8) __________________________________________________________________________________________…”

mentioning

confidence: 99%

See 3 more Smart Citations

Estimating Sojourn Time and Transition between Clinical States of HIV Patients under ART Follow up in Namibia

Kashihalwa

Pazvakawambwa

Mwanyekange

2023

AJPAS

View full text Add to dashboard Cite

Background: Sojourn time refers to the amount of time a HIV patient spends in each clinical state in a single stay before he/she makes a transition to another state. HIV can be broken down into a number of intermediate states, based on CD4 counts. The four states of the Markov process of HIV are commonly deﬁned as: S1: CD4 count > 500 cells/microlitre of blood; S2: 350 < CD4 count ≤ 500 cells/microlitre of blood; S3: 200 < CD4 count ≤ 350 cells/microlitre of blood; S4: CD4 count ≤ 200 cells/microliter of blood. Aims: The aim of the study was to estimate sojourn and transition between clinical states of patients under ART in Namibia using homogenous semi-Markov processes, on data obtained from MoHSS. Methods: A retrospective study design was used to obtain data on 2422 patients who were observed 11028 times, during 2008 to 2017 follow up period. The four staged semi-Markov model was employed to estimate sojourn times and transition between clinical states. Results: Results indicates that 1637 (67.6%) were female and 785 (32.41%) were male .657(27.13%) patients started ART in state 1, 683(28.19%) patients started ART in state 2, 677(27.95%) patients started ART in state 3 and 405(16.72%) patients started ART in state 4, at treatment commencement (t = 0). As expected, the probabilities of transiting from good to worse states increased with time. After 6 months, the probabilities of transiting from state 1 to 3, and from state 1 to 4 are 0.023 and 0.004 respectively. Whereas after 12 months, the probabilities of transiting from state 1 to 3, and from state 1 to 4 are 0.059 and 0.010 respectively. As time increased the probabilities to remain in the same state is decreasing (probabilities of remaining in state 1 after 6, 12 and 18 months is 0.804, 0.698 and 0.633). Sojourn times for states 1, 2, 3 and 4 were 22, 8, 10 and 15 months respectively. Conclusions: Sojourn time is of interest in HIV modeling, as it gives a signal of how HIV is progressing. Longer sojourn times indicates slow HIV progression and shorter sojourn times indicates rapid HIV progression. As time increases, transition probabilities from good states to worse states increases.

show abstract

Section: Fig 2 Conditional Probabilities For Each State 4 Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Estimating Sojourn Time and Transition between Clinical States of HIV Patients under ART Follow up in Namibia

Kashihalwa

Pazvakawambwa

Mwanyekange

2023

AJPAS

View full text Add to dashboard Cite

show abstract

“…In a study of HIV progression, an illness-death multistate model was used to estimate the effects of TB, age, mode of transmission, marital status, gender and ART [9]. In 2017, semi-Markov models were applied to HIV/AIDS disease progression and compared two sojourn time distributions, in Ethiopia [10]. Furthermore, in 2018, a 7-staged continuous-time Markov model was used to assess the disease progression of HIV/AIDS patients receiving ART from a clinic in Bela-Bela, South Africa [3].…”

Section: Introductionmentioning

confidence: 99%

An Application of Homogenous Semi-Markov Model for Assessing HIV/AIDS Progression to ART Patients in Namibia

Kashihalwa,

Mwanyekange,

Pazvakawambwa

2023

AJPAS

View full text Add to dashboard Cite

Background: The progression of HIV infection to AIDS and then to death can be considered a stochastic process. Disease progression can be broken down into a ﬁnite number of intermediate states, based on CD4 counts. The five states of the Markov process of HIV/AIDS progression are commonly deﬁned as: S1: CD4 count > 500 cells/microliter; S2: 350 < CD4 count ≤ 500 cells/microliter; S3: 200 < CD4 count ≤ 350 cells/microliter; S4: CD4 count ≤ 200 cells/microliter; and D: Death. Objectives: The objective of this study was to model the progression of HIV/AIDS disease of patients under ART follow-up in Namibia using homogenous semi-Markov processes, using the data obtained from Ministry of Health and Social Services. Methods: A retrospective study design was used to obtain data on 2422 patients who were observed 11028 times. The semi-Markov model was employed to estimate the transition probabilities and transition intensity rate. Time Homogeneous Semi-Markov model was fitted to assess effectiveness of ART by comparing the forward transition and reverse transitions. Results: As expected the probabilities of transiting from good states to worse states increased with time (from state 1 to state 3 and 4 after 6 months is 0.023 and 0.004, after 12 months is 0.059 and 0.010 respectively). As time increase the probabilities of remaining in the same state is decreasing (probabilities of remaining in state 1 after 6, 12 and 18 months is 0.804, 0.698 and 0.633). As expected the intensity indicates that the rate of transiting from good states to worst states is decreasing (the intensity of transiting from state 1 to 3 and 4 is p<0.001). The strongest predictor of transition from state 1 to 2 is TDF/3TC/EFV, which has a hazard ratio of 1.338 (with p value of 0.002). Patients who were prescribed TDF/3TC/EFV, are over 1.338 times more likely to transit from state 1 to state 2 than patients who did not receive TDF/3TC/EFV. A hazard ratio of 0.678 for the predictor variable female shows that female were less likely to transit from state 2 to 3 than their male counterparts. The hazard ratios of females from a bad state to a better state are more than 1, which is an indication that females are less likely to respond to treatment compared to males. Conclusions: HIV can progress to AIDS without delay if there is no intervention. Early ART initiation is crucial to reduce the probabilities of transiting from good states to worse states.

show abstract

Comparison of Sojourn Time Distributions in Modeling HIV/AIDS Disease Progression

Cited by 2 publications

References 0 publications

Estimating Sojourn Time and Transition between Clinical States of HIV Patients under ART Follow up in Namibia

Estimating Sojourn Time and Transition between Clinical States of HIV Patients under ART Follow up in Namibia

An Application of Homogenous Semi-Markov Model for Assessing HIV/AIDS Progression to ART Patients in Namibia

Contact Info

Product

Resources

About