equation(4) Covd,r,q,s,t=Dosed,r,q,s⋅Timed,r,q,s,tCovd,r,q,s,t=Dosed,r,q,s⋅Timed,r,q,s,t This model is intended to be generalized, rather than pertaining to a single particular vaccine. As a result, we assumed efficacy that is similar to recent published estimates [10] and assumed the same efficacy in each subgroup. Vaccine efficacy was estimated for 1, 2, and 3 doses to account for incomplete courses and rotavirus events that might occur between doses. During the first year we assumed an efficacy of 50% for a full course, and 10% and 25% efficacy for 1 and 2 doses [5] and [38]. We also assumed a 10% waning in efficacy

(to 45%) during subsequent years [39]. Full assumptions are shown in Table 1. Vaccination effectiveness and benefit were estimated for each subpopulation

by combining information on the coverage and efficacy of each INK 128 manufacturer dose by time period with information on the expected burden over time. equation(5) VacBenefitr,q,s=∑d,tCovd,r,q,s,t⋅VacEffd,t⋅RVBurderr,q,s,twhere VacEffd,t is the incremental protection of each dose d during time period t. The method described above accounts for the correlation between individual risk and vaccine access at the Selleck BIBW2992 region-quintile-sex sub-group level, however it implicitly assumes that risk and access are not correlated within each subgroup. We tested this assumption by examining the correlation of DTP2 coverage and risk index because within each subgroup. Estimating the expected benefits at current coverage levels, we also estimated the potential benefits if all geographic-economic sub-groups had the same mortality reduction as the highest coverage group (South, middle quintile, 40%). The difference between these potential benefits and expected benefits were defined

as the health consequence of coverage disparities. Patterns of healthcare utilization for diarrheal treatment vary geographically and by socio-economic status. As a result, direct medical costs for rotavirus treatment are expected to vary as well. However, limited data are currently available on the extent of variability. In order to account for this heterogeneity in cost we combined published estimates of overall rotavirus direct medical costs [40] and [41] per child with an estimate of the relative cost per child in each geographic and economic setting [42] (Table 1). We estimated the distribution of costs among children based on the pattern of care seeking (NFHS-3) weighted by estimated cost of each treatment type (Table 2). While consistent data are not available for all of these categories we estimated the relative costs based on available published data (Table 1) and applied cost estimates to reported categories of treatment facility or provider in NFHS-3. Relative costs were then rescaled to have a mean of 1 and multiplied by the average cost per child from the literature (to ensure the same mean cost per child).