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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.

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Possible explanations for the difference in mortality at three years of antiretroviral therapy between South Africa and Malawi and Zambia.The graph shows the proportion that different causes may contribute to the higher mortality observed in Malawi and Zambia (CD4 cell count monitoring) compared to South Africa (VL monitoring). The estimates are based on the mathematical model. The effect of a higher risk of virologic failure in sites with CD4 count monitoring is shown for a 2-times higher risk (dark blue) and 3-times higher risk (light and dark blue combined).
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pone-0057611-g002: Possible explanations for the difference in mortality at three years of antiretroviral therapy between South Africa and Malawi and Zambia.The graph shows the proportion that different causes may contribute to the higher mortality observed in Malawi and Zambia (CD4 cell count monitoring) compared to South Africa (VL monitoring). The estimates are based on the mathematical model. The effect of a higher risk of virologic failure in sites with CD4 count monitoring is shown for a 2-times higher risk (dark blue) and 3-times higher risk (light and dark blue combined).

Mentions: The absolute difference in observed mortality between Malawi and Zambia and South Africa was 2.3% (10.9%–8.6%). Approximately 4% of the observed difference in mortality could be explained by more complete detection of virologic failure with VL monitoring (Figure 2). The delay from failure to switching explained only 1% of the difference. When we assumed that VL monitoring decreased rates of virologic failure (through improved adherence) the percentage of the mortality difference explained by viral load monitoring increased to 19% (assuming a 2-times higher failure rate) or 32% (3-times higher failure rate). Differences in HIV-unrelated background mortality explained 11% of the observed difference (Figure 2).


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)

Possible explanations for the difference in mortality at three years of antiretroviral therapy between South Africa and Malawi and Zambia.The graph shows the proportion that different causes may contribute to the higher mortality observed in Malawi and Zambia (CD4 cell count monitoring) compared to South Africa (VL monitoring). The estimates are based on the mathematical model. The effect of a higher risk of virologic failure in sites with CD4 count monitoring is shown for a 2-times higher risk (dark blue) and 3-times higher risk (light and dark blue combined).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0057611-g002: Possible explanations for the difference in mortality at three years of antiretroviral therapy between South Africa and Malawi and Zambia.The graph shows the proportion that different causes may contribute to the higher mortality observed in Malawi and Zambia (CD4 cell count monitoring) compared to South Africa (VL monitoring). The estimates are based on the mathematical model. The effect of a higher risk of virologic failure in sites with CD4 count monitoring is shown for a 2-times higher risk (dark blue) and 3-times higher risk (light and dark blue combined).
Mentions: The absolute difference in observed mortality between Malawi and Zambia and South Africa was 2.3% (10.9%–8.6%). Approximately 4% of the observed difference in mortality could be explained by more complete detection of virologic failure with VL monitoring (Figure 2). The delay from failure to switching explained only 1% of the difference. When we assumed that VL monitoring decreased rates of virologic failure (through improved adherence) the percentage of the mortality difference explained by viral load monitoring increased to 19% (assuming a 2-times higher failure rate) or 32% (3-times higher failure rate). Differences in HIV-unrelated background mortality explained 11% of the observed difference (Figure 2).

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