While universal equitable coverage would reduce disparities, an alternative would be to target accelerated introduction or expanded coverage of high-risk children, based on geography or other population characteristics. The cost-effectiveness and impact estimates in Table 4 and Fig. 2 and Fig. 4 can be interpreted as the incremental cost-effectiveness of introducing the vaccine into higher risk populations first. The results Veliparib mouse suggest that it would be most cost-effective to target these children first. Although few countries are considering sub-national introduction, this could be done to target high-risk regions. In order to be most effective, these regions would also need to have adequate levels of vaccine

coverage. Geographic targeting could also focus on more remote areas

where access to timely treatment of diarrhea is lower. For other infections with clear geographic hotspots (e.g., malaria and soil transmitted helminthes) this is a clear strategy for improving value for money [30] and [31]. Although it can be more difficult to target children based on socio-economic characteristics, there are examples of programs CX-5461 designed to do this, such as conditional cash transfer programs that target low-income communities and households [32] and [33]. A related approach would be to target based on other risk factors such as nutritional status by coordinating with maternal and newborn nutrition programs. These targeting strategies would increase the likelihood that investments go disproportionately to the areas Methisazone or children where they provide the greatest value for money. While these targeting strategies would create challenges, the level of potential benefit (a 38% increase in mortality reduction) is too great to ignore. The current study is a preliminary assessment of the distributional effects and, as such, it has a number of limitations. First, no systematic data are available for directly estimating rotavirus mortality or burden by wealth quintile or sub-national

regions. As a result, we aggregated data on post-neonatal infant mortality and low weight-for-age as a proxy measure. It is important to note that there is variability in estimated mortality disparities, depending on which proxy measure is used. For example, in Table 3 post-neonatal mortality is highest in the second poorest quintile, rather than the poorest. This may be the product of higher neonatal mortality among the poorest, differences in reporting biases or other factors. This suggests that better proxy measures, at the level of quintiles or individuals could provide more accurate estimates of disparities. In addition, the analysis only explores one dimension of equity at a time (either socio-economic status or geographic location) without exploring the interaction between them or whether other factors such as maternal education may explain both reduced vaccination and increased mortality risk.