Estimated mortality due to seasonal influenza in southeast of Iran, 2006/2007 to 2011/2012 influenza seasons.

BACKGROUND: Global estimates showed an estimate of up to 650,000 seasonal influenza-associated respiratory deaths annually. However, the mortality rate of seasonal influenza is unknown for most countries in the WHO Eastern Mediterranean Region, including Iran. We aimed to estimate the excess mortality attributable to seasonal influenza in Kerman province, southeast Iran for the influenza seasons 2006/2007-2011/2012. METHODS: We applied a Serfling model to the weekly total pneumonia and influenza (PI) mortality rate during winter to define the epidemic periods and to the weekly age-specific PI, respiratory, circulatory, and all-cause deaths during non-epidemic periods to estimate baseline mortality. The excess mortality was calculated as the difference between observed and predicted mortality. Country estimates were obtained by multiplying the estimated annual excess death rates by the populations of Iran. RESULTS: We estimated an annual average excess of 40 PI, 100 respiratory, 94 circulatory, and 306 all-cause deaths attributable to seasonal influenza in Kerman; corresponding to annual rates of 1.4 (95% confidence interval [CI] 1.1-1.8) PI, 3.6 (95% CI 2.6-4.8) respiratory, 3.4 (95% CI 2.1-5.2) circulatory, and 11.0 (95% CI 7.3-15.6) all-cause deaths per 100,000 population. Adults ≥75 years accounted for 56% and 53% of all excess respiratory and circulatory deaths, respectively. At country level, we would expect an annual of 1119 PI to 8792 all-cause deaths attributable to seasonal influenza. CONCLUSIONS: Our findings help to define the mortality burden of seasonal influenza, most of which affects adults aged ≥75 years. This study supports influenza prevention and vaccination programs in older adults.

All-cause versus cause-specific excess deaths for estimating influenza-associated mortality in Denmark, Spain, and the United States.

BACKGROUND: Seasonal influenza-associated excess mortality estimates can be timely and provide useful information on the severity of an epidemic. This methodology can be leveraged during an emergency response or pandemic. METHOD: For Denmark, Spain, and the United States, we estimated age-stratified excess mortality for (i) all-cause, (ii) respiratory and circulatory, (iii) circulatory, (iv) respiratory, and (v) pneumonia, and influenza causes of death for the 2015/2016 and 2016/2017 influenza seasons. We quantified differences between the countries and seasonal excess mortality estimates and the death categories. We used a time-series linear regression model accounting for time and seasonal trends using mortality data from 2010 through 2017. RESULTS: The respective periods of weekly excess mortality for all-cause and cause-specific deaths were similar in their chronological patterns. Seasonal all-cause excess mortality rates for the 2015/2016 and 2016/2017 influenza seasons were 4.7 (3.3-6.1) and 14.3 (13.0-15.6) per 100,000 population, for the United States; 20.3 (15.8-25.0) and 24.0 (19.3-28.7) per 100,000 population for Denmark; and 22.9 (18.9-26.9) and 52.9 (49.1-56.8) per 100,000 population for Spain. Seasonal respiratory and circulatory excess mortality estimates were two to three times lower than the all-cause estimates. DISCUSSION: We observed fewer influenza-associated deaths when we examined cause-specific death categories compared with all-cause deaths and observed the same trends in peaks in deaths with all death causes. Because all-cause deaths are more available, these models can be used to monitor virus activity in near real time. This approach may contribute to the development of timely mortality monitoring systems during public health emergencies.

Evaluation of data sources and approaches for estimation of influenza-associated mortality in India.

BACKGROUND: No estimates of influenza-associated mortality exist for India. OBJECTIVE: To evaluate national mortality and viral surveillance data from India for assessing their appropriateness in estimating influenza-associated mortality using varied analytic approaches. METHODS: We reviewed influenza virus surveillance data from a national influenza surveillance network. We also reviewed national mortality data from Civil Registration System (CRS), Medical Certification of Cause of Death (MCCD) and the Sample Registration System (SRS). We compared and scored the different sources of mortality data using specific criteria, including the process of cause of death assignment, sample size, proportion of ill-defined deaths, representativeness and availability of time series data. Each of these 5 parameters was scored on a scale from 1 to 5. To evaluate how to generate an influenza-associated mortality estimate for India, we also reviewed 4 methodologic approaches to assess the appropriateness of their assumptions and requirements for these data sets. RESULTS: The influenza virus surveillance data included year-round sample testing for influenza virus and was found to be suitable for influenza mortality estimation modelling. Based on scoring for the 5 mortality data criteria, the SRS data had the highest score with 20 of 25 possible score, whereas MCCD and CRS scored 16 and 12, respectively. The SRS which used verbal autopsy survey methods was determined to be nationally representative and thus adequate for estimating influenza-associated mortality. Evaluation of the modelling methods demonstrated that Poisson regression, risk difference and mortality multiplier methods could be applied to the Indian setting. CONCLUSION: Despite significant challenges, it is possible to estimate influenza-associated mortality in India.