Demographic aspects of the COVID-19 pandemic in Italy, Spain, Germany, and South Korea

Tesárková, Klára Hulíková

doi:10.37040/geografie2020125020139

Cited by 8 publications

(10 citation statements)

References 26 publications

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“…At the same time, Italy has a high share of elderly, with the percentage of people over 65 years being 22.1% (compared to 17.58% in Romania) (“ Romania Demographics Profile, 2020 ). This likely has led to a greater casualty rates in Italy, as the elderly are the most vulnerable segment of the population ( Hulíková Tesárková, 2020 ). Furthermore, Italy is characterized by extended families ( Caserta et al, 2021 ), which facilitates contacts between young and old people, therefore accelerating likely transmission of the virus.…”

Section: Theoretical and Practical Backgroundmentioning

confidence: 99%

Testing the Effectiveness of the Health Belief Model in Predicting Preventive Behavior During the COVID-19 Pandemic: The Case of Romania and Italy

et al. 2022

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We use a cultural psychology approach to examine the relevance of the Health Belief Model (HBM) for predicting a variety of behaviors that had been recommended by health officials during the initial stages of the COVID-19 lockdown for containing the spread of the virus and not overburdening the health system in Europe. Our study is grounded in the assumption that health behavior is activated based on locally relevant perceptions of threats, susceptibility and benefits in engaging in protective behavior, which requires careful attention to how these perceptions might be structured and activated. We assess the validity of the HBM in two European countries that have been relatively understudied, using simultaneous measurements during acute periods of infection in Romania and Italy. An online questionnaire provided a total of (N = 1863) valid answers from both countries. First, to understand individual difference patterns within and across populations, we fit a General Linear Model in which endorsement was predicted by behavior, country, their interaction, and a random effect for participants. Second, we assess the effect of demographics and health beliefs on prevention behaviors by fitting a multi-group path model across countries, in which each behavior was predicted by the observed health belief variables and demographics. Health beliefs showed stronger relationships with the recommended behaviors than demographics. Confirming previously reported relationships, self-efficacy, perceived severity, and perceived benefits were consistently related to the greater adoption of individual behaviors, whereas greater perceived barriers were related to lower adoption of health behaviors. However, we also point to important location specific effects that suggest that local norms shape protective behavior in highly contextualized ways.

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Section: Theoretical and Practical Backgroundmentioning

confidence: 99%

Testing the Effectiveness of the Health Belief Model in Predicting Preventive Behavior During the COVID-19 Pandemic: The Case of Romania and Italy

et al. 2022

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“…Soon it was proven that the age structure of the population with confirmed infection (confirmed cases) influenced the observed overall case fatality rate (CFR) (i.e. the number of deaths per confirmed cases) more than the age structure of the population as a whole [5–8].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it helps to identify vulnerable populations and assesses the quality or efficiency of healthcare and treatment. However, it may be also biased by some of its components entering the calculation [8].…”

Section: Introductionmentioning

confidence: 99%

“…During the early stages of the pandemic, when there was a lack of detailed data, the estimations were based on crude rates [9] not adjusted for the influence of age structure [7,8] or any other disturbing factors (time delay between the confirmation of the infection and death). Those crude CFRs vary widely in different populations, geographical locations, and stages of the ongoing pandemic [9–11]: based on the preliminary study published in The Lancet on January 24, 2020, from 3% in overall China [12] to 25% in the Italian region of Lombardy [1].…”

Section: Introductionmentioning

confidence: 99%

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The age structure of cases as the key of COVID-19 severity: Longitudinal population-based analysis of European countries during 150 days

Tesárková

Dzúrová

2021

Scand J Public Health

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Aims: Over a million confirmed cases of the coronavirus disease (COVID-19) across 16 European countries were observed during the first wave of the pandemic. Epidemiological measures like the case fatality rate (CFR) are generally used to determine the severity of the illness. The aim is to investigate the impact of the age structure of reported cases on the reported CFR and possibilities of its demographic adjustment for a better cross-country comparison (age-standardized CFRs, time delay between cases detection and death). Methods: This longitudinal study uses prospective, population-based data covering 150 days, starting on the day of confirmation of the 100th case in each country. COVerAGE-DB and the Human Mortality Database were used in this regard. The age-standardized CFRs were calculated with and without the time delay of the number of deaths after the confirmation of the cases. Results: The observed decline in the CFRs at the end of the first wave is partly given by the changes in the age structure of confirmed cases. Using the adjusted (age-standardized) CFRs with time delay, the risk of death among confirmed cases is much more stable in comparison to crude (observed) CFRs. Conclusions: Preventing the spread of COVID-19 among the elderly is an important way to positively influence the overall fatality rate, decrease the number of deaths, and not overload the health systems. The crude CFRs (still often presented) are not sufficient for a proper evaluation of the development across populations nor as a means of identifying the influencing factors.

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“…The investigated period was chosen for the analysis also because the mortality level in the Czech Republic did not fluctuate sharply. For example, it would not be possible to perform the analysis in the current situation where the mortality level is affected by COVID-19 disease and can be problematic in the future as well [39,40].…”

Section: Introductionmentioning

confidence: 99%

Circulatory and Nervous Diseases Mortality Patterns—Comparison of Geomagnetic Storms and Quiet Periods

Podolská

2021

Atmosphere

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The aim of this paper is to statistically examine whether there are different patterns in daily numbers of deaths during the quiet periods of solar activity, in contrast to the periods of the strong solar storms. We considered three periods of solar storms (storm of 14 July 2000 Bastille Day Event, storm of 28 October 2003 Halloween Solar Storms, and storm of 17 March 2015 St. Patrick’s Day event) and three periods of continuous very low solar activity (13 September–24 October 1996, 21 July–20 August 2008, and 31 July–31 August 2009) during the Solar Cycles No. 23 and No. 24. In particular, we focus on diseases of the nervous system (group VI from ICD-10) and diseases of the circulatory system (group IX from ICD-10) separately for both sexes and two age groups (under 39 and 40+). We demonstrate that in the resulting graphical models there was a connection between the daily number of deaths and all indices of solar and geomagnetic activity in periods of low solar activity in contrast to periods of strong solar storms in some monitored groups according to age, sex, and group of diagnosis.

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Demographic aspects of the COVID-19 pandemic in Italy, Spain, Germany, and South Korea

Cited by 8 publications

References 26 publications

Testing the Effectiveness of the Health Belief Model in Predicting Preventive Behavior During the COVID-19 Pandemic: The Case of Romania and Italy

Testing the Effectiveness of the Health Belief Model in Predicting Preventive Behavior During the COVID-19 Pandemic: The Case of Romania and Italy

The age structure of cases as the key of COVID-19 severity: Longitudinal population-based analysis of European countries during 150 days

Circulatory and Nervous Diseases Mortality Patterns—Comparison of Geomagnetic Storms and Quiet Periods

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