Impacts of COVID-19 pandemic through decomposition of life expectancy according to leading causes and place of death in Czechia

Abstract

While the direct effects of the pandemic are well documented, less is known about the indirect ones, including changes in healthcare provision or human behavior. This paper aims to study the impact of indirect consequences on mortality, focusing on two leading causes (cardiovascular diseases, COVID-19) and places of death in Czechia, during the COVID-19 pandemic, one of the most severely affected European countries. The analysis was performed using data from the Czech Statistical Office and the Institute of Health Information and Statistics. The study compares annual mortality changes during three time periods: pre-pandemic (2018-2019), pandemic beginning and peaking (2020-2021), and pandemic fading (2022). Pandemic years were covered by the WHO public health emergency of international concern. Abridged life tables were computed, and Pollard's decomposition was used to calculate the contributions of causes and places of death on annual differences in life expectancy. Seasonal decomposition of monthly time series revealed an increase in cardiovascular mortality at home or in social care facilities corresponding to limitations in healthcare. While COVID-19 had a systemic negative effect on life expectancy during the pandemic, the impact of cardiovascular mortality according to place of death changed over time. This study contributes to the evidence base of systemic risks during health crises and emergency response.

Figure 1

Time series of monthly data adjusted for the length of particular months, January 2018–December 2022, CVDs hospitalizations (A), CVDs ambulant care (B), deaths in medical facilities (C), deaths at home and facilities of social care (D), Czechia. Source:^–, author’s calculation, output of the SAS software, version 6.4.

Figure 2

Seasonal component of time series decomposition (1 = average month corresponding to the overall trend of the time series), January 2018–December 2022, CVDs hospitalizations (A), CVDs ambulant care (B), deaths in medical facilities (C), deaths at home and facilities of social care (D), Czechia. Source:^–, author’s calculation, output of the SAS software, version 6.4.

Figure 3

Irregular component of time series decomposition (1 = average month corresponding to the overall trend and seasonal factor of the particular month of the time series), January 2018–December 2022, CVDs hospitalizations (A), CVDs ambulant care (B), deaths in medical facilities (C), deaths at home and facilities of social care (D), Czechia. Note: the dotted horizontal line at value one represents the reference level corresponding to an expected values of the time series for a particular month reflecting the trend and seasonal pattern. Source:^–, author’s calculation, output of the SAS software, version 6.4.