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Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study.

Estill J, Egger M, Johnson LF, Gsponer T, Wandeler G, Davies MA, Boulle A, Wood R, Garone D, Stringer JS, Hallett TB, Keiser O, IeDEA Southern Africa Collaborati - PLoS ONE (2013)

Bottom Line: We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years.Eleven percent was explained by non-HIV related mortality.VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.

View Article: PubMed Central - PubMed

Affiliation: Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland. jestill@ispm.unibe.ch

ABSTRACT

Objectives: Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference.

Design: Mathematical modelling study based on data from ART programmes.

Methods: We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained.

Results: RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality.

Conclusions: VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.

Show MeSH
Comparison of all-cause mortality based on model predictions and observed data.Orange lines show Kaplan-Meier estimates from ART programmes in South Africa, Malawi and Zambia [12] and blue lines the model predictions. Solid lines represent routine viral load monitoring (South Africa) and broken lines CD4 cell monitoring (Malawi, Zambia).
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pone-0057611-g001: Comparison of all-cause mortality based on model predictions and observed data.Orange lines show Kaplan-Meier estimates from ART programmes in South Africa, Malawi and Zambia [12] and blue lines the model predictions. Solid lines represent routine viral load monitoring (South Africa) and broken lines CD4 cell monitoring (Malawi, Zambia).

Mentions: Modelled mortality and Kaplan-Meier estimates of observed mortality in the South African VL programmes were 9.1% and 8.6% at 3 years of ART, respectively. In Malawi and Zambia, with CD4 monitoring only, the corresponding modelled estimates ranged from 9.5% (scenarios A and B) to 10.1% (scenario B combined with C, assuming a 3-times higher virologic failure rate). The Kaplan-Meier estimate of mortality at 3 years in the CD4 monitoring only cohorts was 10.9% (Figure 1). During the first 1.5 years on ART, the modelled mortality was higher than the observed morality, and little difference was seen between the three scenarios. After 1.5 years differences in mortality between modelled scenarios increased gradually.


Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study.

Estill J, Egger M, Johnson LF, Gsponer T, Wandeler G, Davies MA, Boulle A, Wood R, Garone D, Stringer JS, Hallett TB, Keiser O, IeDEA Southern Africa Collaborati - PLoS ONE (2013)

Comparison of all-cause mortality based on model predictions and observed data.Orange lines show Kaplan-Meier estimates from ART programmes in South Africa, Malawi and Zambia [12] and blue lines the model predictions. Solid lines represent routine viral load monitoring (South Africa) and broken lines CD4 cell monitoring (Malawi, Zambia).
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585414&req=5

pone-0057611-g001: Comparison of all-cause mortality based on model predictions and observed data.Orange lines show Kaplan-Meier estimates from ART programmes in South Africa, Malawi and Zambia [12] and blue lines the model predictions. Solid lines represent routine viral load monitoring (South Africa) and broken lines CD4 cell monitoring (Malawi, Zambia).
Mentions: Modelled mortality and Kaplan-Meier estimates of observed mortality in the South African VL programmes were 9.1% and 8.6% at 3 years of ART, respectively. In Malawi and Zambia, with CD4 monitoring only, the corresponding modelled estimates ranged from 9.5% (scenarios A and B) to 10.1% (scenario B combined with C, assuming a 3-times higher virologic failure rate). The Kaplan-Meier estimate of mortality at 3 years in the CD4 monitoring only cohorts was 10.9% (Figure 1). During the first 1.5 years on ART, the modelled mortality was higher than the observed morality, and little difference was seen between the three scenarios. After 1.5 years differences in mortality between modelled scenarios increased gradually.

Bottom Line: We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years.Eleven percent was explained by non-HIV related mortality.VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.

View Article: PubMed Central - PubMed

Affiliation: Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland. jestill@ispm.unibe.ch

ABSTRACT

Objectives: Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference.

Design: Mathematical modelling study based on data from ART programmes.

Methods: We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained.

Results: RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality.

Conclusions: VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.

Show MeSH