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Background: Influenza causes substantial morbidity and mortality worldwide, however, reliable burden estimates from developing countries are limited, including India. We aimed to quantify influenza-associated mortality for India utilizing 2010-2013 nationally representative data sources for influenza virus circulation and deaths.Methods: Virological data were obtained from the influenza surveillance network of 10 laboratories led by National Institute of Virology, Pune covering eight states from 2010-2013. Death data were obtained from the nationally representative Sample Registration System for the same time period. Generalized linear regression with negative binomial distribution was used to model weekly respiratory and circulatory deaths by age group and proportion of specimens positive for influenza by subtype; excess deaths above the seasonal baseline were taken as an estimate of influenza-associated mortality counts and rates. Annual excess death rates and the 2011 India Census data were used to estimate national influenza-associated deaths.Results: Estimated annual influenza-associated respiratory mortality rates were highest for those ≥65 years (51.1, 95% confidence interval (CI) = 9.2-93.0 deaths/100 000 population) followed by those <5 years (9.8, 95% CI = 0-21.8/100 000). Influenza-associated circulatory death rates were also higher among those ≥65 years (71.8, 95% CI = 7.9-135.8/100 000) as compared to those aged <65 years (1.9, 95% CI = 0-4.6/100 000). Across all age groups, a mean of 127 092 (95% CI = 64 046-190,139) annual influenza-associated respiratory and circulatory deaths may occur in India.Conclusions: Estimated influenza-associated mortality in India was high among children <5 years and adults ≥65 years. These estimates may inform strategies for influenza prevention and control in India, such as possible vaccine introduction. [ABSTRACT FROM AUTHOR]

Background: Different statistical approaches for estimating excess deaths due to coronavirus disease 2019 (COVID-19) pandemic have led to varying estimates. In this study, we developed and validated a covariate-based model (CBM) with imputation for prediction of district-level excess deaths in India., Methods: We used data extracted from deaths registered under the Civil Registration System for 2015-19 for 684 of 713 districts in India to estimate expected deaths for 2020 through a negative binomial regression model (NBRM) and to calculate excess observed deaths. Specifically, we used 15 covariates across four domains (state, health system, population, COVID-19) in a zero inflated NBRM to identify covariates significantly (P < 0.05) associated with excess deaths estimate in 460 districts. We then validated this CBM in 140 districts by comparing predicted and estimated excess. For 84 districts with missing covariates, we validated the imputation with CBM by comparing estimated with predicted excess deaths. We imputed covariate data to predict excess deaths for 29 districts which did not have data on deaths., Results: The share of elderly and urban population, the under-five mortality rate, prevalence of diabetes, and bed availability were significantly associated with estimated excess deaths and were used for CBM. The mean of the CBM-predicted excess deaths per district (x̄ = 989, standard deviation (SD) = 1588) was not significantly different from the estimated one (x̄ = 1448, SD = 3062) (P = 0.25). The estimated excess deaths (n = 67 540; 95% confidence interval (CI) = 35 431, 99 648) were similar to the predicted excess death (n = 64 570; 95% CI = 54 140, 75 000) by CBM with imputation. The total national estimate of excess deaths for all 713 districts was 794 989 (95% CI = 664 895, 925 082)., Conclusions: A CBM with imputation can be used to predict excess deaths in an appropriate context., Competing Interests: Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests., (Copyright © 2024 by the Journal of Global Health. All rights reserved.)