About 20.6 of 37.7 million adults and children living with HIV are from Southern and Eastern Africa.

Southern and Eastern Africa have 50% of the world’s new HIV infections among children. 45% of all AIDS-related deaths are occurring in Southern and Eastern Africa (UNAIDS).

IeDEA-SA includes six of the countries most affected by HIV worldwide: South Africa, Lesotho, Malawi, Mozambique, Zambia and Zimbabwe.

The IeDEA-SA database contains over 3,615,860 person-years of observation.

Correcting overall

programme­ specific mortality for loss to follow­up

This calculator provides estimates of mortality (with 95% confidence intervals) in ART programmes in sub­Saharan Africa that are corrected for loss to follow­up.The calculation is based on the fact that overall corrected mortality (MC) in patients that started ART is the average of mortality in patients retained in care (not lost to follow­up, MNL) and mortality in patients lost to follow­up (ML), weighted by the proportion of patients lost to follow­up (r):MC = (1­r)*MNL + r*MLMNL and r can be observed directly in the ART programme at a given point in time (for example at one or two years after starting ART). ML can then be estimated by tracing a representative sample of patients lost to follow­up to ascertain their vital status (by tracing method). Note that MNL, ML and r must refer to the same time period. See Yu et al, 2007 for an example of a tracing study.

Alternatively, in the absence of local tracing information, ML at one year can be estimated based on a meta­analysis (Brinkhof et al, 2009) of published mortality data from tracing studies in sub ­Saharan Africa (by meta method). Mortality in patients lost to follow­up was inversely associated with the rate of loss to follow up in the programme: it declined from around 60% to 20% as the percentage of patients lost increased from 5% to 50%. Note that when using the meta method MNL, and r must refer to one year after starting ART.

Further technical details

Estimates of Mc with 95% confidence intervals are derived using Monte Carlo simulations with 10,000 iterations. These simulations allow for uncertainty in MNLby sampling from a normal distribution of predicted Kaplan­Meier survival estimates (log­log transformed) at one year, and in r by sampling from a binomial distribution of the number of patients lost to follow­up (NL) given the number at risk of loss to follow­up (NR). The tracing method estimates ML similar to MNL, by sampling from the predicted KaplanMeier survival estimates in the sample of successfully traced patients; the meta method samples from the normal distribution of the predicted logit of death in patients lost to follow­up, given the rate of loss to follow­up in the programme.

Calculations of the proportion lost to follow­up should be based on the patients at risk of loss to follow­up (NR), i.e. patients with sufficient potential follow­up time to allow their status to be determined. For example, assuming the definition for loss to follow­up is “no clinic visit for 6 months or more”, then patients starting ART less than 6 months before the end of the observation period can by definition not be lost to follow­up and should be excluded from NR.

Prognostic model for mortality

This calculator provides estimates of mortality at one year (with 95% confidence intervals, 95% CI) for patients starting ART and remaining in the ART programme in sub ­Saharan Africa.

The CD4 cell count and HIV­1 viral load are important prognostic factors in patients on ART, but in many clinics in sub­Saharan Africa neither CD4 counts nor viral load are routinely available. We therefore developed two prognostic models, one including the CD4 count (the CD4 model) and an alternative model where CD4 count was replaced by total lymphocyte count and haemoglobin (the TLC/Hb model). We developed the model based on over 10,000 adult patients who started ART between 2004 and 2007 in ART scale­up programmes in Côte d’Ivoire, South Africa and Malawi. Mortality in the first year of ART was analysed by intention­to­continue­treatment, ignoring treatment changes and interruptions. We excluded patients lost to follow­up in order to reduce bias due to under­ascertainment of death.

The CD4 model includes CD4 count, clinical stage, body weight, age and sex (160 risk strata). In the TLC/Hb model the CD4 count was replaced with TLC and the degree of anaemia (288 risk strata). With the CD4 model the probability of death in the first year of ART ranges from 0.9% (95% CI 0.6­1.4%) in patients in the lowest risk stratum to 53% (95% CI 44­62%) in patients in the highest risk stratum. The corresponding probabilities for the TLC/Hb model were 0.9% (95% CI 0.5­1.4%) and 60% (95% CI 48­71%).

Generalized Multistate Simulation Model (gems):

simulate and analyze multistate models with general hazard functions

Gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition­specific hazard function, whose parameters are multivariable normally distributed.

A Cost­-effectiveness Tool

This tool allows you to compare the cost­effectiveness of a range of monitoring strategies of ART, including clinical monitoring, CD4 monitoring, targeted viral load monitoring, and routine viral load monitoring either with a qualitative POC test or a fully quantitative laboratory test. Download a user­friendly Excel spreadsheet tool, where the user can vary the unit costs and compare the costeffectiveness of the monitoring strategies in a specific setting.