To increase the urban and rural sub-region rates to 2011 estimates, we select a random set of households to also vaccinate. In the intervention scenarios, to scale up the coverage rates, the model makes additional households vaccination compliant. The method of selecting these extra households varies across scenarios (e.g., random or targeted by state and region). The model was programmed in C++. Analysis variables fall into four categories, which consider the intervention’s associated effect on disease burden, intervention costs, cost-effectiveness, and financial impact. The effect on disease burden

includes both deaths and disability-adjusted life years (DALYs) averted (we discount at 3% and use uniform age-weights that value any extra year of life equally). Cost-effectiveness is measured by dollars per DALY averted incremental to the baseline scenario. The financial impact measures follow Verguet et al. [23] and include the http://www.selleckchem.com/products/epz-6438.html out-of-pocket (OOP) expenditure averted from the baseline scenario, which measures the savings of the population that result from the intervention, and the money-metric value of insurance, which measures the value of protection from expenditure on disease treatment

(including the costs of seeking care). The money-metric value of insurance here differs slightly from Verguet et al.’s analysis. Our analysis period is one year as we study a cross-section of the under-five population, while they study a birth cohort, which is susceptible to disease over the first five years of life. Given this, we include only one year of disposable income in the calculation this website as opposed to five years. Additionally, we evaluate the value of insurance of an intervention with respect to the baseline by subtracting one from the other. Terminal deoxynucleotidyl transferase We analyze health and financial burden alleviated across India by wealth quintile, state, and rural versus urban areas. To quantify the uncertainty of the model, we conduct a 100-simulation Latin hypercube sampling (LHS) sensitivity analysis over a plausible range of the input parameters (Table 1). For each

disease, the parameters analyzed include the incidence, CFR, vaccine efficacy, vaccine cost, and treatment cost. Ninety-five percent uncertainty ranges for our mean estimated outcomes are calculated on the basis of this sensitivity analysis and reported in parentheses. In the baseline, immunization coverage is 77% for DPT3, 82% for measles, and there is no coverage for rotavirus. From DLHS-3 data, we find that baseline coverage increases by wealth for DPT3 and measles. The rural-to-urban immunization coverage ratio is 1.09 for DPT3 and 1.05 for measles (Fig. 1, row 1). Baseline DPT3 coverage is lowest in Arunachal Pradesh and Uttar Pradesh where 53% and 55% of under-fives are vaccinated (Fig. 2, column 1). Another nine states vaccinate less than 80% of their children; all of them are relatively poor states, with the exception of Gujarat (77% coverage). Eight states have DPT3 coverage above 90%.