A new method for estimating national and regional ART need Basia Zaba, Raphael Isingo, Alison Wringe, Milly Marston, and Mark Urassa TAZAMA / NACP seminar Dar-es-Salaam, September 19 th 2008

Outline Why do we need a new method for estinmating ART need? Explanation of the new method Results for Kisesa Producing national estimates

Forecasting national ART need using “EPP” and “Spectrum” packages EPP (no age & sex structure) ANC surveillance data entered to get prevalence trend DHS prevalence data added to correct overall level Spectrum (age, sex and incidence modelling) Population age structure added to EPP results Prevalence age and sex patterns modelled by year Incidence pattern estimated from prevalence changes Prevalence projected using survival models Output estimates of new infections, AIDS deaths, new orphans, treatment need

EPP / Spectrum inputs National ANC all age prevalence DHS all age prevalence Census population age and sex distribution Regional ANC all age prevalence? DHS all age prevalence Census population age distribution Model (international) prevalence age and sex distribution deriving incidence from prevalence survival post infection Process “Black box” with user friendly inputs and nice graph outputs

TAZAMA method inputs National DHS prevalence of HIV by age and sex Census population age and sex distribution Regional DHS prevalence of HIV by age and sex Census population age and sex distribution Model (Mwanza) age pattern of incidence age-specific mortality rates of HIV infected population Process Spreadsheet – not yet user friendly, but has nice graphs …

Assumptions behind new method People need to start ART 3 years before they would have died if they didn’t have treatment Age-specific patterns of mortality for HIV infected persons not on treatment are the same for both sexes all over the country The Kisesa cohort age patterns of incidence can be scaled up or down to represent incidence in different parts of the country

Why we think these assumptions are reasonable Joint studies in the ALPHA HIV cohort study network showed that: –from CD4 count of 350 (new UNAIDS treatment start recommendation) people survive for a median period of 3 years without treatment before they die –age-specific mortality patterns of people infected with HIV in the pre-treatment era did not vary much from one place to another –HIV incidence has very different levels from one country to another, but the age- and sex- specific patterns are very similar from one place to the next

Example calculation x =- = sum of previous column

How it all adds up The calculation is repeated for infected people at every single year of age in the start year We generate expected new infections at each age by multiplying the uninfected by the incidence rate We work out expected HIV deaths in those not yet infected in the start year, and their ART need

Impact of incidence decline on treatment need Declining incidence only affects treatment need in those not yet infected in the baseline year

Impact of incidence decline on prevalence Declining incidence means that in future there will be smaller proportion of infected people in the “not yet needing treatment” category

Sex differences in number needing treatment Overall more women than men will need treatment as there are more infected women than men, because of their earlier average age at infection

Sex differences in proportion of infected needing treatment However, women’s earlier age at infection will mean that there will be proportionately more of them in the “not yet needing treatment” category

To get national or regional estimates The only new input needed is the smoothed single year age distribution of the population by HIV infection status. Mortality and incidence age patterns can be taken from the Kisesa cohort, with a suitable scaling factor for incidence

How different is HIV prevalence in Kisesa?

HIV incidence patterns in Kisesa, by age and sex

Conclusions The ART need estimation method developed and tested on Kisesa cohort data is easy to adapt for national and regional estimates It allows us to model various assumptions: –future incidence trends –current prevalence patterns –mortality of those not yet on treatment –years prior to death that treatment should start It still needs to incorporate: –mortality of those already on treatment –number already receiving treatment in the base year