COVID-19 pandemic in Czechia from geodemographic perspective
(September 2020 – March 2021)
This map application aims to present the regional variability of selected demographic and epidemiologic measures related to COVID-19 pandemic in Czechia. The regional level of administrative districts of municipalities with extended powers (MEPs, 206 units) is used. It is focused above all on the age structure of the population with confirmed infection and corresponding level of hospitalization. In the application, the most up-to-date data are presented, as well as their development in time in the half-month intervals starting from the beginning of September 2020.
For more effective planning of preventive measures, evaluation of the development, and risks for the health system or public health in general, the knowledge of sub-national differences of the COVID-19 statistics could be crucial.
The application offers three studied characteristics related to the issue:
1) Life expectancy at the age of 65
This characteristic could be understood as a simple representation of the general mortality conditions in the region (focused on the population aged 65 and more years only). Among others, it is highly affected by the living conditions, quality and approach to the health services, the average lifestyle of the population, quality of the environment, and social, demographic, or economic characteristics of the region (level of unemployment, education attainment, etc.). This measure represents above all the general conditions observable also in the pre-pandemic period.
2) The share of aged 65+ among the active cases (in %)
This characteristic represents one of the risk factors of the pandemic. As the risk of severe complications (or even death) increases with age on average, i.e., the higher proportion of older population among the confirmed cases of infection, the higher burden for the health system and the higher risk of death among the COVID-19 patients. The senior population (often defined by the age of 65 and more years) is taken as one of the more endangered sub-populations and preventive measures should aim to protect this sub-population from the infection. Active cases are defined as all the confirmed cases of infection which are by the time of analysis still active; the person is not recovered as well as not deceased.
3) The share of hospitalized among the active cases (in %)
This characteristic is the measure representing the impact of the infection for the population as well as the burden for the health system. It could be also understood as a proportion of confirmed cases with more severe complications. It could be supposed, that the share of hospitalized among the active cases is at least partly tied to the share of aged 65+ among the active cases.
All three characteristics are computed separately for males and females and visualized by choropleth maps and hot spot analysis cluster maps. The hot spot analysis, specifically the Getis–Ord (Gi*) spatial statistic, is used to identify statistically significant spatial pattern of hot spots and cold spots. To constitute a statistically significant hot spot, a district with a high value should also be surrounded by other districts with high values. The spatial weighting scheme based on queen contiguity is applied for calculation.
In the analysis, the public available data at the AD MEPs level (COVID ‑ 19: Data sets for predictive modeling) is used for calculation of presented COVID-19 characteristics. All computations are done for the 1st day and the 15th day of the month. The starting point is September 1st (time period 1), which could be roughly understood as the beginning of the second wave of the COVID-19 pandemics in Czechia. Before September (and partly also during the first weeks of the analyzed period), the numbers of registered cases and deaths at the AD MEPs level were so low so that the results are highly variable with non-systematic fluctuations. Life expectancy at the age of 65 is the contextual variable (published by the Czech Statistical Office in its Public Database) and is calculated as and average of the years 2015-2019.