title: Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
authors.affiliation: Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland. jestill@ispm.unibe.ch
authors.forename: Janne
authors.initials: J
authors.lastname: Estill
authors.forename: Matthias
authors.initials: M
authors.lastname: Egger
authors.forename: Leigh F
authors.initials: LF
authors.lastname: Johnson
authors.forename: Thomas
authors.initials: T
authors.lastname: Gsponer
authors.forename: Gilles
authors.initials: G
authors.lastname: Wandeler
authors.forename: Mary-Ann
authors.initials: MA
authors.lastname: Davies
authors.forename: Andrew
authors.initials: A
authors.lastname: Boulle
authors.forename: Robin
authors.initials: R
authors.lastname: Wood
authors.forename: Daniela
authors.initials: D
authors.lastname: Garone
authors.forename: Jeffrey S A
authors.initials: JS
authors.lastname: Stringer
authors.forename: Timothy B
authors.initials: TB
authors.lastname: Hallett
authors.forename: Olivia
authors.initials: O
authors.lastname: Keiser
mesh_terms: D000328:Adult; D019380:Anti-HIV Agents / Q000494:pharmacology / Q000627:therapeutic use*; D015658:HIV Infections / Q000188:drug therapy* / Q000401:mortality*; D006801:Humans; D008295:Malawi / Q000453:epidemiology; D008875:Middle Aged; D015233:Models, Statistical; D013019:South Africa / Q000453:epidemiology; D019562:Viral Load / Q000187:drug effects; D015024:Zambia / Q000453:epidemiology
chemical_list: D019380:Anti-HIV Agents