All‐cause versus cause‐specific excess deaths for estimating influenza‐associated mortality in Denmark, Spain, and the United States. Issue 4 (23rd February 2022) - Schmidt, Sebastian S. S.; Iuliano, Angela Danielle; Vestergaard, Lasse S.; Mazagatos‐Ateca, Clara; Larrauri, Amparo; Brauner, Jan M.; Olsen, Sonja J.; Nielsen, Jens; Salomon, Joshua A.; Krause, Tyra G.
Abstract: 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.