tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2025 Apr 17:11:e56877.
doi: 10.2196/56877. Assessing COVID-19 Mortality in Serbia's Capital: Model-Based Analysis of Excess Deaths
Dane Cvijanovic[SUP] #[/SUP][SUP] 1 [/SUP], Nikola Grubor[SUP] #[/SUP][SUP] 2 [/SUP], Nina Rajovic[SUP] 2 [/SUP], Mira Vucevic[SUP] 3 [/SUP], Svetlana Miltenovic[SUP] 3 [/SUP], Marija Laban[SUP] 4 [/SUP], Tatjana Mostic[SUP] 5 [/SUP], Radica Tasic[SUP] 6 [/SUP], Bojana Matejic[SUP] #[/SUP][SUP] 7 [/SUP], Natasa Milic[SUP] #[/SUP][SUP] 2 8 [/SUP]
Affiliations
Background: Concerns have been raised about discrepancies in COVID-19 mortality data, particularly between preliminary and final datasets of vital statistics in Serbia. In the original preliminary dataset, released daily during the ongoing pandemic, there was an underestimation of deaths in contrast to those reported in the subsequently released yearly dataset of vital statistics.
Objective: This study aimed to assess the accuracy of the final mortality dataset and justify its use in further analyses. In addition, we quantified the relative impact of COVID-19 on the death rate in the Serbian capital's population. In the process, we aimed to explore whether any evidence of cause-of-death misattribution existed in the final published datasets.
Methods: Data were sourced from the electronic databases of the Statistical Office of the Republic of Serbia. The dataset included yearly recorded deaths and the causes of death of all citizens currently living in the territory of Belgrade, the capital of the Republic of Serbia, from 2015 to 2021. Standardization and modeling techniques were utilized to quantify the direct impact of COVID-19 and to estimate excess deaths. To account for year-to-year trends, we used a mixed-effects hierarchical Poisson generalized linear regression model to predict mortality for 2020 and 2021. The model was fitted to the mortality data observed from 2015 to 2019 and used to generate mortality predictions for 2020 and 2021. Actual death rates were then compared to the obtained predictions and used to generate excess mortality estimates.
Results: The total number of excess deaths, calculated from model estimates, was 3175 deaths (99% CI 1715-4094) for 2020 and 8321 deaths (99% CI 6975-9197) for 2021. The ratio of estimated excess deaths to reported COVID-19 deaths was 1.07. The estimated increase in mortality during 2020 and 2021 was 12.93% (99% CI 15.74%-17.33%) and 39.32% (99% CI 35.91%-39.32%) from the expected values, respectively. Those aged 0-19 years experienced an average decrease in mortality of 22.43% and 23.71% during 2020 and 2021, respectively. For those aged up to 39 years, there was a slight increase in mortality (4.72%) during 2020. However, in 2021, even those aged 20-39 years had an estimated increase in mortality of 32.95%. For people aged 60-79 years, there was an estimated increase in mortality of 16.95% and 38.50% in 2020 and 2021, respectively. For those aged >80 years, the increase was estimated at 11.50% and 34.14% in 2020 and 2021, respectively. The model-predicted deaths matched the non-COVID-19 deaths recorded in the territory of Belgrade. This concordance between the predicted and recorded non-COVID-19 deaths provides evidence that the cause-of-death misattribution did not occur in the territory of Belgrade.
Conclusions: The finalized mortality dataset for Belgrade can be safely used in COVID-19 impact analysis. Belgrade experienced a significant increase in mortality during 2020 and 2021, with most of the excess mortality attributable to SARS-CoV-2. Concerns about increased mortality from causes other than COVID-19 in Belgrade seem misplaced as their impact appears negligible.
Keywords: COVID-19; COVID-19 impact; SARS-Cov-2; Serbia; centralized health care; coronavirus; death rate; death toll; dense population; excess mortality; infectious disease; pandemic; public health; pulmonary; respiratory; surveillance; urban.
. 2025 Apr 17:11:e56877.
doi: 10.2196/56877. Assessing COVID-19 Mortality in Serbia's Capital: Model-Based Analysis of Excess Deaths
Dane Cvijanovic[SUP] #[/SUP][SUP] 1 [/SUP], Nikola Grubor[SUP] #[/SUP][SUP] 2 [/SUP], Nina Rajovic[SUP] 2 [/SUP], Mira Vucevic[SUP] 3 [/SUP], Svetlana Miltenovic[SUP] 3 [/SUP], Marija Laban[SUP] 4 [/SUP], Tatjana Mostic[SUP] 5 [/SUP], Radica Tasic[SUP] 6 [/SUP], Bojana Matejic[SUP] #[/SUP][SUP] 7 [/SUP], Natasa Milic[SUP] #[/SUP][SUP] 2 8 [/SUP]
Affiliations
- PMID: 40246590
- DOI: 10.2196/56877
Background: Concerns have been raised about discrepancies in COVID-19 mortality data, particularly between preliminary and final datasets of vital statistics in Serbia. In the original preliminary dataset, released daily during the ongoing pandemic, there was an underestimation of deaths in contrast to those reported in the subsequently released yearly dataset of vital statistics.
Objective: This study aimed to assess the accuracy of the final mortality dataset and justify its use in further analyses. In addition, we quantified the relative impact of COVID-19 on the death rate in the Serbian capital's population. In the process, we aimed to explore whether any evidence of cause-of-death misattribution existed in the final published datasets.
Methods: Data were sourced from the electronic databases of the Statistical Office of the Republic of Serbia. The dataset included yearly recorded deaths and the causes of death of all citizens currently living in the territory of Belgrade, the capital of the Republic of Serbia, from 2015 to 2021. Standardization and modeling techniques were utilized to quantify the direct impact of COVID-19 and to estimate excess deaths. To account for year-to-year trends, we used a mixed-effects hierarchical Poisson generalized linear regression model to predict mortality for 2020 and 2021. The model was fitted to the mortality data observed from 2015 to 2019 and used to generate mortality predictions for 2020 and 2021. Actual death rates were then compared to the obtained predictions and used to generate excess mortality estimates.
Results: The total number of excess deaths, calculated from model estimates, was 3175 deaths (99% CI 1715-4094) for 2020 and 8321 deaths (99% CI 6975-9197) for 2021. The ratio of estimated excess deaths to reported COVID-19 deaths was 1.07. The estimated increase in mortality during 2020 and 2021 was 12.93% (99% CI 15.74%-17.33%) and 39.32% (99% CI 35.91%-39.32%) from the expected values, respectively. Those aged 0-19 years experienced an average decrease in mortality of 22.43% and 23.71% during 2020 and 2021, respectively. For those aged up to 39 years, there was a slight increase in mortality (4.72%) during 2020. However, in 2021, even those aged 20-39 years had an estimated increase in mortality of 32.95%. For people aged 60-79 years, there was an estimated increase in mortality of 16.95% and 38.50% in 2020 and 2021, respectively. For those aged >80 years, the increase was estimated at 11.50% and 34.14% in 2020 and 2021, respectively. The model-predicted deaths matched the non-COVID-19 deaths recorded in the territory of Belgrade. This concordance between the predicted and recorded non-COVID-19 deaths provides evidence that the cause-of-death misattribution did not occur in the territory of Belgrade.
Conclusions: The finalized mortality dataset for Belgrade can be safely used in COVID-19 impact analysis. Belgrade experienced a significant increase in mortality during 2020 and 2021, with most of the excess mortality attributable to SARS-CoV-2. Concerns about increased mortality from causes other than COVID-19 in Belgrade seem misplaced as their impact appears negligible.
Keywords: COVID-19; COVID-19 impact; SARS-Cov-2; Serbia; centralized health care; coronavirus; death rate; death toll; dense population; excess mortality; infectious disease; pandemic; public health; pulmonary; respiratory; surveillance; urban.