Improving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settings.
BACKGROUND
Despite concerns about affordability and sustainability, many models of the lifetime costs of antiretroviral therapy (ART) used in resource limited settings are based on data from small research cohorts, together with pragmatic assumptions about life-expectancy. This paper revisits these modelling assumptions in order to provide input to future attempts to model the lifetime costs and the costs of scaling up ART.


METHODS
We analysed the determinants of costs and outcomes in patients receiving ART in line with standard World Health Organization (WHO) guidelines for resource poor settings in a private sector managed ART programme in South Africa. The cohort included over 5,000 patients with up to 4 years (median 19 months) on ART. Generalized linear and Cox proportional hazards regression models were used to establish cost and outcome determinants respectively.


RESULTS
The key variables associated with changes in mean monthly costs were: being on the second line regimen; receiving ART from 4 months prior to 4 months post treatment initiation; having a recent or current CD4 count <50 cells/microL or 50-199 cells/microl; having mean ART adherence <75% as determined by monthly pharmacy refill data; and having a current or recent viral load >100,000 copies/mL. In terms of the likelihood of dying, the key variables were: baseline CD4 count<50 cells/microl (particularly during the first 4 months on treatment); current CD4 count <50 cells/microl and 50-199 cells/microl (particularly during later periods on treatment); and being on the second line regimen. Being poorly adherent and having an unsuppressed viral load was also associated with a higher likelihood of dying.


CONCLUSIONS
While there are many unknowns associated with modelling the resources needed to scale-up ART, our analysis has suggested a number of key variables which can be used to improve the state of the art of modelling ART. While the magnitude of the effects associated with these variables would be likely to differ in other settings, the variables influencing costs and survival are likely to be generalizable. This is of direct relevance to those concerned about assessing the long-term costs and sustainability of expanded access to ART.
Leisegang
Rory
R
Division of Clinical of Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa. rory.leisegang@uct.ac.za
Maartens
Gary
G
Hislop
Michael
M
Regensberg
Leon
L
Cleary
Susan
S
D000353:Africa, Southern / Q000453:epidemiology; D044966:Anti-Retroviral Agents / Q000191:economics* / Q000627:therapeutic use*; D004180:Direct Service Costs; D016527:Drug Costs / Q000706:statistics & numerical data; D019317:Evidence-Based Medicine; D015658:HIV Infections / Q000188:drug therapy* / Q000401:mortality; D006284:Health Plan Implementation / Q000191:economics*; D006801:Humans; D008017:Life Expectancy; D008390:Markov Chains; D055118:Medication Adherence; D017149:Private Sector / Q000191:economics; D016016:Proportional Hazards Models
D044966:Anti-Retroviral Agents