Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Feb 5, 2024
Date Accepted: Oct 2, 2024
Assessing COVID-19 Mortality in Serbia’s Capital: A Model-Based Analysis of Excess Deaths
ABSTRACT
Background:
There is a lack of data regarding the impact of COVID-19 on densely populated cities with heavily centralized healthcare. Additionally, some researchers have raised concerns about COVID-19 cause-of-death misattribution in Serbia’s death reports.
Objective:
The objective of our study was to quantify the relative impact of COVID-19 on the death rate in the population of the capital of Serbia. In the process, we aimed to explore if any evidence of cause-of-death misattribution exists in the published datasets.
Methods:
We utilized standardization and modeling techniques to quantify the direct impact of COVID-19 and estimate excess deaths in the urban territory of Belgrade, the capital of the Republic of Serbia. We developed and fit a model to the acquired data for the period of 2015-2019 and then used the model to generate mortality predictions for 2020 and 2021. Reported causes of death were then compared to obtained predictions.
Results:
The total number of excess deaths, calculated from model estimates, were 3,175 deaths, 99% CI [1715, 4094] for 2020, and 8321 deaths, 99% CI [6975, 9197] for 2021, respectively. The most impacted age groups were people in their seventh and eighth decades. People aged 60-79 suffered an estimated 1,279 to 2,143 and 3,594 to 4,423 excess deaths in 2020 and 2021, respectively. People aged 80 and over suffered an estimated 504 to 1,625 and 2,895 to 3,858 excess deaths in 2020 and 2021.
Conclusions:
COVID-19 significantly contributed to the increased death toll during 2020 and 2021. Death count predictions derived from the pre-pandemic period matched official reported deaths when COVID-19 as a cause was removed, providing evidence that no significant cause-of-death misattribution occurred in the final published dataset. There seemed to be fewer deaths than expected in children and young people by predicted trends, which is congruent with raw observed data.
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