UC Santa Barbara

Spatially explicit impact assessment analyses such as vulnerability studies often require spatially detailed population distribution as input. Downscaled population datasets otherwise known as disaggregated, gridded or fine resolution population datasets are becoming increasingly available at global and regional scales and are based on information from various sources with varying spatial and temporal resolutions as well as reliabilities. Uncertainty is endemic in such downscaled population estimates, particularly in developing countries yet it is hardly assessed. Consequently, decision-makers are potentially faced with a bias problem whereby uncertainties are masked and estimates are presented as unique or expected values even after being derived in a probabilistic context. This research explores how HIV prevalence in three districts of the United Republic of Tanzania might vary with the utilization of simulations of downscaled population estimates. In so doing, this study explores some scenarios in which HIV prevalence that corresponds to minimum expected cost of antiretroviral (ARV) treatment is estimated under three different decision-making attitudes, namely minimax regret, maximin and maximax, followed by a discussion of some implications of any variation in best estimates of HIV prevalence corresponding to the least impact on ARV cost.

Our findings show that for effective decision analysis, rather than using coarse aggregated values such as census data at the district level, decision-makers may benefit from the application of multiple simulated spatial distributions of fine scale population along with the associated ARV cost estimates. From this distribution of ARV cost estimates, decision-makers could select best estimates based on an explication of risk attitude, thus avoiding unforeseeable consequences of underestimating or overestimating impact assessment outcomes of HIV prevalence.