1. Adjusting U5M for AIDS Issues and suggested answers

2. How does AIDS bias our estimates? Unreported child deaths due to death of the mother Changes in prevalence and treatment make projections Iffy

3. Unreported child deaths due to death of the mother Correlation between child and maternal deaths due to AIDS This means we may have under-reporting of under-five deaths due to AIDS Artzrouni and Zaba –Only is important in very high HIV prevalence settings (say above 10%) –Error is greater with longer recall periods

4. DHS Percent of U5M due to AIDS Corrected for kids and mothersBias 1987592.8%601.7% 19927729.4%8611.7% 19935835.4%7224.1% 199710243.4%1085.9% 19985442.7%6214.8% 20038233.9%864.9% Example with Zimbabwe

5. So, should we do this? There are about 10 countries with HIV prevalence above 10% Most of the corrections would be on the 10-14 period Probably not so important for point estimates, but would help with trend fit Would not require a lot of work for past data sets

6. Changes in prevalence and treatment make projections Iffy When AIDS explains a large proportion of U5M, then projecting without accounting for HIV prevalence trends can be very dangerous both for LOESS and SPLINE Examples –Projections for Botswana (Spline) – It just kept going up –Past trends for Botswana (LOESS) – It doesnt go up with AIDS mortality

7. Zimbabwe, again

8. Suggestions Identify countries where AIDS prevalence curves (and U5M deaths) are included in the estimation process Make these curves available in U5M database (from UN/Country estimates) Allows estimated AIDS mortality curves and perhaps HIV prevalence curves to be overlaid with U5M curves Perhaps not algorithmic, but these data should help with projecting either by person or by adjusting knot size