SOCIOECONOMIC, DEMOGRAPHIC AND ENVIRONMENTAL FACTORS AND COVID-19 VACCINATION: INTERACTIONS AFFECTING EFFECTIVENESS

Kargı Bilal; Coccia Mario; Uçkaç Bekir Cihan

Date of publication: September 26, 2023 DOI: 10.52270/26585561_2023_19_21_83

SOCIOECONOMIC, DEMOGRAPHIC AND ENVIRONMENTAL FACTORS AND COVID-19 VACCINATION: INTERACTIONS AFFECTING

EFFECTIVENESS

Kargi, Bilal1, Coccia, Mario2, Uçkaç, Bekir Cihan3

1PhD (Economics), Professor (Associate) at Ankara Yildirim Beyazit University, Ankara, Turkey, E-mail: [email protected] 2National Research Council of Italy (CNR), First Researcher, 44, Via San Martino Della Battaglia,

Roma, Italy

3PhD (Finance), Independent Researcher, Istanbul, Turkey, E-mail: [email protected]

Abstract

This study analyses the relation between people fully vaccinated and mortality to assess the effectiveness of this health policy to cope with COVID-19 pandemic between a sample of 150 countries. Statistical analyses show a positive correlation between share of people fully vaccinated and total COVID-19 mortality in early 2022 (r= 0.65, p-value <.01). These results suggest that COVID-19 vaccinations cannot be a sufficient policy response to eradicate the overall negative impact of the new infectious disease in society. Although high levels of vaccinations in some countries, many demographic (density of population), environmental (air pollution), technological (equipment of non-invasive ventilators), biological (new variants), socioeconomic (health expenditures) factors, etc., influence the diffusion and negative effects of COVID-19 pandemic society. This study can provide new knowledge to improve crisis management and the preparedness of countries to cope with or prevent future pandemic crisis and negative effects in socioeconomic systems.

Keywords: COVID-19 pandemic; Vaccination campaign; Health policy; Innovative technology; Fatality rate; Policy responses; Air pollution; Temperature; Wind speed; Variants; Health expenditures; Density of people; Crisis management.

JEL: O33; Q01; Q16; Q18.

I. INTRODUCTION

We are still in the throes in 2022 of negative socioeconomic effects of the pandemic of Coronavirus Disease 2019 (COVID-19), an infectious illness generated by (novel) mutant viral agent of the Severe Acute Respiratory Syndrome Coronavirus 2/SARS-CoV-2 (Bontempi et al., 2021; Bontempi & Coccia, 2021; Coccia, 2020, 2021; Johns Hopkins Center for System Science and Engineering, 2022; Vinceti et al., 2021).

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Initially, in 2020, countries apply non-pharmaceutical interventions (e.g., lockdown and quarantine) to cope with COVID-19 pandemic crisis; a later time, in 2021 and 2022, the most applied health policy worldwide is the administration of new types of vaccines based on viral vector, protein subunit, and nucleic acid-RNA (Abbasi, 2020; Coccia, 2020, 2021a; Mayo Clinic, 2021). The vaccination plans have the potential goal to reduce the diffusion of COVID-19, to relax non-pharmaceutical measures and maintain a low basic reproduction number, but an important problem is whether these novel types of vaccines are really effective to reduce high numbers of COVID-19 related infected individuals and deaths between countries to control and/or eradicate the pandemic diffusion and to reduce negative effects in society (Aldila et al., 2021; Prieto Cruriel, et al. 2021; Saadi et al., 2021). Akamatsu et al. (2021) argue the vital role of governments to implement an efficient campaign of vaccination to reduce infections in society, and avoid the collapse of the healthcare system (cf., Coccia, 2021, 2022). Shattock et al. (2021) argue that a rapid vaccination rollout can allow the sooner relaxation of non-pharmaceutical interventions, but emerging viral variants of SARS-CoV-2 create new scenarios and problems for epidemic control (Fontanet et al., 2021; Papanikolaou et al., 2021). Shattock et al. (2021) also find that a gradual phased relaxation can reduce population-level morbidity and mortality and that a faster vaccination campaign can offset the size of the pandemic wave, allowing more flexibility for non-pharmaceutical control measures to be relaxed sooner. Aldila et al. (2021) maintain that higher levels of vaccination rate can eradicate COVID-19 in the population by approaching herd immunity to protect vulnerable individuals (cf., Anderson et al., 2020; de Vlas & Coffeng, 2021; Randolph & Barreiro, 2020).

However, Aschwanden (2020, 2021) raised many doubts about the achievement of herd immunity, which is a "false promise" because of manifold factors affecting the transmission dynamics of COVID-19 (cf., Moore et al., 2021). Seligman et al. (2021) analyze the COVID-19 pandemic in the United States and show that social determinants can affect COVID-19 mortality at the individual level. Results of demographics of deaths reveal a mean age of 71.6 years, 45.9% female, and 45.1% non-Hispanic white. They found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity, individuals with income below the median, individuals with less than a high school level of education, and veterans (cf., Davies et al., 2021; Wolf et al., 2021). In general, substantial inequalities in COVID-19 mortality are due to racial/ethnic minorities and poor people having less education. Garber (2021) for the US case study maintains that mortality from COVID-19 rises steeply with advancing age, in a pattern that parallels overall mortality. Age-specific mortality rates increased in the US more for groups that already experienced greater mortality, such as non-Hispanic Black people, as reflected in projections of life expectancy at birth. Ackley et al. (2022), investigating the impact of the COVID-19 pandemic in the US, show that a significant percentage of excess deaths associated with the pandemic were not directly assigned to COVID-19. Across the U.S.A., the estimates of the model indicate about 438,386 excess deaths occurred in 2020, among which 87.5% were assigned to COVID-19. Some regions of Mideast, Great Lakes, New England, etc. had the most excess deaths in large central metropolitan areas, whereas other regions (Southwest, Southeast, Rocky Mountains, Great Plains, etc.) reported the highest excess mortality in non-metropolitan areas. Stokes et al., (2021) found that direct COVID-19 death counts in the US in 2020 underestimate the total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered (cf., Stokes et al., 2021a). Sanmarchi et al. (2021) argue that many countries experienced an increase in mortality during 2020. Several Latin American and East European countries exhibit a large gap between Excess Mortality (EM) and COVID-19 Confirmed Mortality (CCM), such as Mexico; other countries showed a moderate EM beyond CCM (e.g., Greece). Countries with negative EM also had extremely low CCM and were located in East Asia. Islam et al. (2021) point out that about one million excess deaths occurred in 2020 in many high-income countries. Age-standardized excess death rates were higher in men than women in all countries. Excess deaths exceeded reported deaths from COVID-19 in many countries, indicating that determining the full impact of the pandemic on mortality requires assessment of excess deaths. Kiang et al. (2020) argue that the true number of deaths resulting from COVID-19, both directly and indirectly, is likely to be much higher, and correct analysis and evaluation of excess mortality are critical goals to understanding this pandemic and its effect on human life and overall society.

In general, these studies clearly show that the mortality of COVID-19 pandemic is a critical indicator associated with manifold factors (Barnard et al., 2021; Garber, 2021; Islam et al., 2021; Stokes et al., 2021, 2021a; Woolf et al., 2021).

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In this context, the study aims to conduct a statistical analysis to understand the relationships between the vaccination rates and COVID-19 mortality rates across different countries. The goal is to shed light on the complex factors that influence the spread of the pandemic and its impact on society. The findings from this analysis can provide valuable insights for the development of effective crisis management practices to deal with ongoing and future pandemics similar to COVID-19. This research is part of a larger project that seeks to explore the drivers of COVID-19 transmission dynamics and formulate policies to address and prevent pandemic threats in society.

II. STUDY DESIGN AND METHODS

Sample

The total sample of this study is N=151 countries worldwide. For some statistical analyses based on different confounding factors, the sample can be lower for missing data of some variables.

The study incorporates several key data sources to conduct its analysis:

• Vaccination Data: The measurement of vaccination progress is based on the percentage of individuals who have been fully vaccinated against COVID-19 as of January 11, 2022. While the primary data reference point is January 2022, some countries may have reported their data for December 2021 due to challenges in data collection and reporting. However, this minor temporal variation for certain countries does not significantly impact the statistical analyses, as the study utilizes a large sample size of more than 100 countries. The dataset accounts for the use of various COVID-19 vaccines, including those developed by Johnson & Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinovac, Sputnik V, and Moderna (Ritchie et al., 2020). Data source: Our World in Data (2022).

• Gross Domestic Product (GDP) per Capita in 2020: GDP per capita, measured in constant 2010 US dollars, is utilized as an economic indicator for the year 2020. This metric represents the GDP divided by the midyear population and serves as a measure of the economic well-being and productivity of a country's residents. GDP reflects the total value added by all resident producers within an economy, accounting for product taxes and subsidies. The calculations are performed without deductions for asset depreciation or natural resource depletion and degradation. Data source: The World Bank (2022).

• Total Population in 2020: The total population figures for the year 2020 are based on the de facto definition of population, encompassing all residents irrespective of legal status or citizenship. These population estimates are midyear values and provide insights into the demographic composition of various countries. Data source: The World Bank (2022a).

• COVID-19 Deaths in January 2022: The dataset includes the total number of deaths attributed to COVID-19 that occurred in January 2022. These figures serve as a crucial indicator of the severity of the COVID-19 pandemic within different socioeconomic systems. Additionally, the study calculates the mortality rate per 100,000 individuals to facilitate comparative analyses between countries. Data source: Johns Hopkins Center for System Science and Engineering (2022).

The combination of these diverse datasets enables a comprehensive analysis of the relationships between COVID-19 vaccination rates, economic factors (GDP per capita), population demographics, and the impact of the pandemic (COVID-19 deaths) across a broad spectrum of countries. These data sources are instrumental in assessing the complex dynamics surrounding the COVID-19 pandemic and its implications for society and public health.

Model and data analysis procedure

The study employs a systematic approach to analyze the data, beginning with descriptive statistics. The primary statistical measures used are the arithmetic mean and the standard error of the mean. The normality of the distribution of variables under examination is assessed using skewness and kurtosis coefficients. In cases where the distribution of variables is found to deviate from normality, appropriate transformations are applied.

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Specifically, a logarithmic scale transformation is used to achieve normality, facilitating subsequent parametric analyses as per established statistical practices (Coccia, 2021a).

The primary focus of this study is to calculate and analyze a specific ratio related to COVID-19 deaths, which is computed for all countries under investigation:

Bivariate Pearson Correlation: This method is used to examine the relationship between pairs of continuous variables. In this case, the correlation coefficient 'r' is calculated to measure the strength and direction of the linear relationship between the percentage of people fully vaccinated against COVID-19 and the mortality rate (expressed as deaths per population per 100,000) for all countries in the study. Partial Correlation: This analysis goes a step further by assessing the linear relationship between the same continuous variables (vaccination rate and mortality rate), while also accounting for the potential influence of GDP per capita. It allows for a more nuanced understanding of these relationships while controlling for the economic factor. Multiple Regression Analysis: In this step, a multiple regression model is employed to predict the mortality rate (considered the dependent or response variable) based on the values of two independent variables: the share of people fully vaccinated against COVID-19 and GDP per capita. This analysis helps in understanding how these two predictors collectively influence the mortality rate, providing insights into their relative importance.

The specification of log-log model is given by:

log (1)

where:

• y, t- = Mortality rate of COVID-19 in January 2022.

• xit = Share % of people fully vaccinated against COVID-19 in January 2022

• z, t-1 = GDP per capita in 2020

• uit = Error term

country i=1, ..., n; t=time

The results of the regression analysis include several key statistical measures:

R-squared (R2): This value, also known as the coefficient of determination, represents the proportion of variance in the dependent variable (mortality rate) that can be explained by the independent variables (vaccination rate and GDP per capita). It measures how well the regression model fits the data, with higher R2 values indicating a better fit. Standard Error of the Estimate: This metric quantifies the accuracy of the predictions made by the regression model. A lower standard error suggests that the model's predictions are closer to the actual values. F-ratio (ANOVA): The F-ratio is used to test whether the overall regression model is a good fit for the data. It assesses whether the independent variables collectively have a significant effect on the dependent variable. Unstandardized Coefficients: These coefficients provide insights into how much the dependent variable (mortality rate) changes when one independent variable (e.g., vaccination rate) changes while holding the other independent variable (e.g., GDP per capita) constant. They help understand the individual impact of each predictor. Statistical Significance: The t-tests associated with each independent variable determine whether they are statistically significant in predicting the dependent variable. A significant result suggests that the independent variable has a meaningful impact on the dependent variable. These statistical analyses were conducted using SPSS version 26 to assess the relationships between vaccination rates, GDP per capita, and COVID-19 mortality rates across different countries.

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Statistical analysis

Table 1 shows descriptive statistics and that variables with logarithmic transformation have a normal distribution (coefficients of skewness and kurtosis have values in the correct range) to perform appropriate and robust parametric analyses.

Table 1. Descriptive statistics_

Variables N Mean Std. Error of Mean Skewness Kurtosis

GDPPC 2020, GDP per capita $ 151 14,457.69 1,716.74 2.68 9.64

MOR2022, Mortality rate per 100 000 people (number) 151 111.43 9.75 1.33 1.69

VAC2022, Share % of people fully vaccinated 144 44.14 2.26 -0.13 -1.29

Log GDPPC2020 149 8.68 0.12 0.07 -0.90

LogM0R2022 151 3.82 0.13 -0.58 -0.68

LogVAC2022 144 3.40 0.09 -1.44 1.39

ble 2. Bivariate correlation

Pearson Correlation LogVAC2022 LogMOR2022

LogVAC2022 1 .646**

N 144 144

Note: MOR2022, Mortality rate per 100 000 people in 2022, VAC2022, Share % of people fully vaccinated in 2022; ** Correlation is significant at the 0.01 level (1 -tailed).

The bivariate Pearson Correlation produces, in the sample of N=144 countries, a positive coefficient r=.65 (p-value<0.01), which indicates a strong correlation between mortality rate per 100 000 people and share % of people fully vaccinated. This finding is confirmed in table 3 with the partial correlation that indicates the moderate linear relationship between continuous variables just mentioned, controlling for the effect of GDP per capita (r partiai = .44, p-value=.001).

Table 3. Partial correlation

Control variable: GDPPC2020 Partial Correlation LogVAC2022 LogM0R2022

LogVAC2022 1 .443***

N 135 135

Note: GDPPC 2020, GDP per capita; MOR2022, Mortality rate per 100 000 people in 2022; VAC2022, Share % of people fully vaccinated in 2022.

*** Correlation is significant at the 0.001 level (1 -tailed).

Table 4. Regression analyses of mortality rate in 2022 on people fully vaccinated in 2022 (and GDP per capita 2020), log-log model [1]_

Simple Regression Multiple regression

Constant 0.754* 0.542

(St. Err) (0.325) (0.665)

VAC2022, Coefficient j 0 917*** 0.713***

(St. Err.) (0.091) (0.132)

GDPPC2020, Coefficient 2 -- 0.228*

(St. Err.) (0.103)

R2 .42 .43

(St. Err. of Estimate) (1.23) (1.22)

F 101.70*** 52.80***

Note: Dependent (response) variable is: MOR2022, Mortality rate per 100 000 people in 2022; Explanatory variables are: VAC2022, Share (%) of people fully vaccinated against COVID-19 in 2022 and GDPPC2020, Gross Domestic Product per capita in 2020. Significance; ***p-value<0.001; p-value<0.05

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Table 4 summarizes the results of both simple and multiple regression analyses. While the outcomes are consistent, we will focus on describing the estimated multivariate relationship using Equation [1], which includes two explanatory variables: the percentage share of people fully vaccinated against COVID-19 in 2022 and the Gross Domestic Product (GDP) per capita in 2020. The partial coefficient of regression (p1) in the model reveals that, while controlling for GDP per capita, a 1% increase in the share of people fully vaccinated against COVID-19 is associated with a 0.7% increase in the expected mortality rate of COVID-19 per 100,000 people (p-value < 0.001). Conversely, the partial coefficient of regression (p2) indicates that a 1% higher GDP per capita, while controlling for the vaccination rate, leads to a 0.2% increase in the expected mortality rate of COVID-19 per 100,000 people (p-value < 0.05). The F-test, which assesses the overall fit of the regression model, is highly significant (p-value < 0.001), indicating that the model is a good fit for the data. The R-squared (R2) value of the multiple regression model suggests that approximately 53% of the variation in the COVID-19 mortality rate can be attributed, in a linear fashion, to the share (%) of people fully vaccinated against COVID-19 in 2022 and the Gross Domestic Product per capita in 2020. Figure 1 visually represents the regression line for COVID-19 deaths per 100,000 people based on a log-log model, illustrating the relationship between the mortality rate and the percentage of people vaccinated against COVID-19. These findings indicate that, while vaccination rates and GDP per capita both have statistically significant associations with COVID-19 mortality rates, the direction of their influence is counterintuitive, with higher vaccination rates associated with higher mortality rates and higher GDP per capita associated with higher mortality rates, albeit to a lesser extent.

Figure 1. Relation of COVID-19 deaths per 100 000 people on share of people vaccinated against COVID-19

(%) based on log-log model.

These significant findings underscore the complexity of the COVID-19 pandemic and suggest that increasing the percentage of people vaccinated against the virus, while necessary, may not be a sufficient measure to fully mitigate its negative impact in terms of reducing mortality. The dynamics of viral transmission are influenced by a multitude of environmental and socioeconomic factors that extend beyond vaccination rates.

The findings suggest that controlling the pandemic and minimizing mortality rates necessitates a more comprehensive and multifaceted approach that considers the interplay of various factors. In the following section, we will delve into the complexities of the transmission dynamics, examining the environmental and socioeconomic elements that contribute to the pandemic's course and impact.

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III. DISCUSSIONS TO EXPLAIN THE RELATION

The key findings of this study highlight a significant correlation between the mortality rate per 100,000 people and the percentage of people fully vaccinated against COVID-19, even after accounting for the influence of GDP per capita. This result underscores the importance of COVID-19 vaccinations as a critical strategy in reducing the pandemic's negative impact on society. However, it also emphasizes that vaccination alone is not a sufficient solution to fully mitigate the effects of the novel coronavirus.

The study reveals that the dynamics of the pandemic are influenced by a multitude of factors beyond vaccination rates. These factors contribute to the continued transmission and mortality of COVID-19, even in countries with a high percentage of fully vaccinated individuals. Thus, a comprehensive approach is essential to address the complexities of the pandemic and effectively reduce its impact on public health.

In the following section, we will explore these multifaceted factors, shedding light on the various environmental and socioeconomic elements that play a role in the transmission and mortality dynamics of COVID-19.

■ High air pollution and exposure of population to days exceeding levels of PM2.5 air pollution (e.g.. max 50 days of high levels of air pollution per year)

■ Low "wind speed, low temperature and high atmospheric humidity7

■ New SARS-CoV-2 variants of concern (e g . Delta, etc.)

■ Low health expenditure as % of GDP

■ Low7 government health expenditure per capita

■ Lower investments in innovative technology7, such as lugh-tecli medical ventilators

■ Delayed application of containment policies

■ Unsustainable policies for economic development

■ High density7 and intensive commercial activity

Figure 2. Factors determining high mortality rates, though a high share of vaccinated people between countries. Factors to be considered to design general guidelines to constrain pandemic crises of novel viral agents like Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2).

Determinants of the pervasive diffusion of COVID-19 in society, which vaccinations cannot stop, are: new variants, high air pollution and density of people in cities, intensive commercial activities of countries, low investments in healthcare sectors and little new technology (such as non-invasive medical ventilation), etc. (Figure 2).

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• High level of air pollution

Coccia (2020, 2021) finds out, based on a case study of Italy, that the number of infected people was higher in cities with >100 days per year exceeding limits set for PM10 or ozone and cities located in hinterland zones (i.e., away from the coast). In hinterland cities (mostly those bordering large urban conurbations) with a high number of days exceeding PM10 and ozone limits, coupled with low wind speed, the average number of infected people in April 2020 more than doubled that of more windy coastal cities in Italy that had also exceeded the air pollution limits (Coccia, 2020, 2021). These findings provide valuable insight into geo-environmental factors that may accelerate the diffusion of COVID-19 and similar viral agents. Studies show that a sustainable environment plays a vital role in reducing COVID-19-related infected individuals and deaths; in particular, a low rate of fatality is associated with a low level of air pollution (Coccia, 2020a, 2021a, 2022). In fact, the average population exposed to levels exceeding WHO guideline value (% of total) is 72% in countries with a lower level of fatality rate, whereas in countries with a higher incidence of mortality of COVID-19, it is almost 98% (Coccia, 2020; 2021, 2022). Coccia (2020; 2021, 2022) maintains that a proactive strategy to cope with future epidemics should concentrate on reducing levels of air pollution in hinterland and polluted cities. Copat et al. (2020),

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Factors determining high mortality rates, though a high share of vaccinated people (factors to be considered when shaping m: general strategies to mitigate case fatality rates of future waves of COYID-19 and similar pandemics)

considering different studies about the relation between air pollution and the spread of COVID-19, suggest that PM2.5 and NO2 can support the spread and lethality of COVID-19, but additional analyses are needed to confirm this relation concerning transmission dynamics and negative effects of the SARS-CoV-2 in society (Coccia, 2021).

• Climate factors: low wind speed and temperature, high humidity

Studies suggest that the concentration of atmospheric pollutants is a main driver of the spread of SARS-CoV-2 (Coccia, 2020a), but a high wind speed sustains clean days from air pollution, reducing whenever possible the spread of COVID-19 (Coccia, 2020a, 2021, 2021a; Caliskan et al., 2020). To put it differently, a low wind speed in cities prevents the dispersion of air pollutants that can include bacteria and viruses, such as SARS-CoV-2, and can increase the incidence of COVID-19, such as in some European regions (Coccia, 2020, 2020a, 2021; 2022). Rosario et al. (2020, p. 4) suggest that wind improves the circulation of air and increases the exposure of the novel coronavirus to the solar radiation effects, a factor having a negative correlation in the diffusion of COVID-19. Nicastro et al. (2021) also analyze the spatial aspects of SARS-CoV-2 in response to UV light and solar irradiation measurements on Earth. The results of the study show that UV-B/A photons have a powerful virucidal effect on the single-stranded RNA virus of COVID-19. Moreover, the solar radiation that reaches temperate regions of the Earth at noon during summers is a sufficient condition to inactivate 63% of virions in open-space concentrations in less than 2 minutes.

• High density of cities and intensive commercial activities

Coccia (2020a, 2022) showed, with a case study of Italy, that the average number of infected individuals increases with the average density of people/km2. In fact, the density of the population per km2 is a principal factor for transmission dynamics of infectious diseases, and studies confirm that high population density increases the probability of interpersonal contacts and viral transmission of COVID-19 in cities (Coccia, 2020a, 2022). Moreover, Bontempi & Coccia (2021) and Bontempi et al. (2021) find out that intensive commercial activity, measured by the level of import and export, can be a main predictor of the diffusion of COVID-19 in society. In particular, the study suggests that the total import and export of Italian provinces have a high association with confirmed cases over time, with an average r > 0.78 and a p-value < 0.001 (Bontempi & Coccia, 2021). Another study based on three large countries in Europe (Italy, France, and Spain) suggests a positive association between trade and pandemic diffusion. In general, international trade data is supposed to be a complex parameter of the transmission dynamics of COVID-19 that includes many factors related to economic, demographic, environmental, and climate aspects (Bontempi et al., 2021).

• New SARS-Co V-2 variants of concern

The novel coronavirus in the environment and the human ecosystem constantly changes through mutations. A new mutation generates a variant of the original SARS-CoV-2 virus. Fontanet et al. (2021) argue that in December 2020, an unexpected rise in reported COVID-19 cases was attributed to the emergence of new SARS-CoV-2 variants, including Alfa (B.1.1.7) in the UK and Beta (B.1.351) in South Africa. Both variants had mutations in the receptor-binding domain of the spike protein that are reported to increase transmission, ranging between 40% and 70%. Davies et al. (2021) conducted a study that revealed the Alpha variant (B.1.1.7) of SARS-CoV-2 is more transmissible than pre-existing variants. This study estimates that the hazard of death associated with B.1.1.7 is 61% higher than pre-existing variants. In short, the analysis suggests that B.1.1.7 is not only more transmissible than previous SARS-CoV-2 variants but may also cause more severe illness. Other two variants of the novel coronavirus (SARS-CoV-2) that cause coronavirus disease 2019 (COVID-19) and subsequent health and socioeconomic problems are (Mayo Clinic, 2022):

• Delta (B.1.617.2): This variant is nearly twice as contagious as earlier variants and might cause more severe illness. The greatest risk of transmission is among unvaccinated people. People who are fully vaccinated can get vaccine breakthrough infections and spread the virus to others. However, it appears that vaccinated people spread COVID-19 for a shorter period than do unvaccinated people. Research suggests that COVID-19 vaccines are slightly less effective against the delta variant (Mayo Clinic, 2022).

• Omicron (B.1.1.529): This variant might spread more easily than other variants, including delta. But it is not yet clear if omicron causes more severe disease. It is expected that people who are fully vaccinated can

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get breakthrough infections and spread the virus to others. However, the COVID-19 vaccines are expected to be effective at preventing severe illness. This variant also reduces the effectiveness of some monoclonal antibody treatments (Mayo Clinic, 2022).

The alpha, gamma, and beta variants of SARS-CoV-2 continue to be monitored but are spreading at much lower levels. The mu variant is also being monitored. Of course, these variants of the novel coronavirus change the transmission dynamics and negative effects in society (Mayo Clinic, 2022).

• Health investments and innovative technology of medical ventilators

Coccia (2022) highlights that countries with lower fatality rates allocate a substantial portion of their GDP to healthcare, around 7.6%, and their government health expenditure per capita is approximately $2,300. Conversely, countries with higher COVID-19 fatality rates tend to have a lower health expenditure, about 6% of GDP, and a significantly lower government health expenditure per capita, averaging about $243 per inhabitant. This suggests a weaker healthcare sector in these nations, making it less prepared to handle pandemics and other healthcare challenges within their societies. Regarding COVID-19 treatment in Intensive Care Units (ICUs), a key technology used for managing seriously ill patients is mechanical ventilation. These artificial breathing devices, including both positive and negative mechanical ventilators, are employed in various healthcare settings such as ICUs, neonatal care centers, and ambulances. They function by inserting a hollow tube into the patient's trachea, establishing a stable airway, and helping maintain appropriate oxygen and carbon dioxide levels in the body. This aids in relieving respiratory distress, reversing respiratory muscle fatigue, and initiating lung healing (IMARC, 2022). However, prolonged use of invasive ventilation can lead to lung problems and infections, particularly when treating COVID-19 patients. Ventilator-associated lung injury, also known as ventilator-induced lung injury, is a condition characterized by damage to the alveoli and small airways due to mechanical ventilation. The mechanisms involved include alveolar overdistension and shear forces generated by the repeated opening and collapsing of alveoli. These processes lead to the release of inflammatory mediators, resulting in increased alveolar permeability and fluid accumulation. To address these challenges, there has been a growing shift toward Non-Invasive Ventilation (NIV) as a technological advancement. NIV involves providing ventilatory support without the use of invasive artificial airways like endotracheal tubes or tracheostomy tubes. The use of NIV has significantly increased over the past two decades and has become a critical tool for managing both acute and chronic respiratory failure, both in home settings and critical care units. This innovative technology is emerging as an alternative to invasive ventilation, offering greater flexibility in patient management (Soo Hoo, 2020, 2010). The development of new Non-Invasive Ventilation (NIV) technologies has brought about significant improvements in patient care. These advancements in NIV technology include the accurate measurement of airway pressure, the use of respiratory abdominal sensors and transducers to allow patient-triggered pressure assistance, and the provision of adequate humidification to maintain airway clearance and optimize ventilation while enhancing patient comfort. Normal respiratory functions involve the warming, moistening, and filtering of inhaled gases by the nasal mucosa and upper airways before they reach the lungs. In regular respiration, these mechanisms supply 75% of the heat and moisture required for the smaller airways and alveoli. By the time inhaled air reaches the alveoli, it warms to 37°C at 100% relative humidity. Therefore, NIV technology aims to replicate these natural processes to improve patient outcomes. The benefits of NIV technology extend to both patients and healthcare facilities. Some advantages include cost-effectiveness, the avoidance of sedation, patient comfort, the elimination of the need for intubation and airway skills, and time efficiency for healthcare facilities. During the COVID-19 pandemic, countries like Germany, which had a substantial number of medical ventilators (approximately 30,000 in 2020), were better equipped to manage the crisis. This capacity allowed for the treatment of patients with severe respiratory distress caused by the virus. As a result, despite Germany's larger population of 83.24 million, the COVID-19 death toll (117,318) remained lower than in countries with fewer ventilators, such as Argentina, which reported 120,019 deaths with a population of about 45 million. Scholars like Kapitsinis (2020) emphasize that investments in the healthcare sector are crucial public policies for mitigating the mortality rate of COVID-19 and addressing future public health threats. To prepare for such threats, countries should invest in healthcare infrastructure expansion, research and development for innovative technologies, and the development of effective vaccines, antiviral drugs, and high-tech medical devices (Ardito et al., 2021). These investments play a critical role in bolstering healthcare systems and improving preparedness for future epidemics like COVID-19.

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IV. CONCLUSIVE OBSERVATIONS

Lau et al. (2021) argue that in the presence of a continuous global COVID-19 pandemic threat, the mortality rate is a main indicator to evaluate the real effects of COVID-19 in society (cf., Liu et al., 2021).

In this context, one of the goals of nations to cope with COVID-19 pandemic crisis is to mitigate mortality and case fatality rate (cf., Coccia, 2020a, 2021e). Initially, in 2020, countries apply non-pharmaceutical interventions (e.g., lockdown) to cope with COVID-19 pandemic crisis; a later time, in 2021 and 2022, the most applied health policy worldwide is the administration of vaccinations on a vast population (Coccia, 2022b).

Findings here reveal that the increase of vaccinated people (%) against COVID-19 is not associated with a reduction of mortality of COVID-19 between countries because manifold factors can affect the complex dynamics of diffusion of COVID-19 pandemic in environment and society. Although this study has provided interesting results, that are of course tentative, it has several limitations. First, a limitation of the study is the lack of data about total vaccinations in manifold countries. Second, not all the possible confounding factors that affect the diffusion of vaccination and mortality of COVID-19 are taken into consideration and in future these factors deserve to be controlled for supporting results here. Third, the lack of integration of data with socioeconomic aspects of countries may influence the results of mortality, making comparative analyses a problematic approach (Angelopoulos et al., 2020). Fourth, country-specific health investments may affect the vaccination, management of healthcare and mortality of people and have to be controlled in future development of this study. Finally, the estimated relationships in this study focus on variables in specific months (based on recent data available) but an extension of the period under study is needed to reinforce results here. Thus, the generalization of this results should be done with caution. Future research should consider new data, when available, and when possible, to examine also other variables between countries to explain the interaction between vaccination, mortality and other socioeconomic factors between countries. Despite these limitations, results presented here suggest that the vaccination is a health policy not enough to reduce mortality of COVID-19, control and stop the pandemic diffusion and subsequent negative effects in society. Hence, there is need for much more detailed research in these topics and this study encourages further investigations to clarify complex factors driving pandemics in environment and ecosystems also considering the interaction between restrictions, vaccinations and general investments in healthcare. To conclude, varied factors between countries that are not only parameters related to medicine but also to social, economic and innovation studies can explain the mortality of COVID-19 pandemic in society and should be accurately considered to control future negative impact of pandemic crisis on public health, economy and society. Hence, results here have to be reinforced with much more follow-up investigation concerning detailed research into the relations between negative effects of pandemic in society, health system, public health capacity and pandemic response of countries.

Overall, then, this study suggests that an effective strategy to reduce the negative impact (in terms of mortality) of future pandemic threats similar to COVID-19, it has to be based on high investments in the health system and the design of comprehensive health, social, and economic policy responses of crisis management, not only a vaccination-based approach, considering that complex environmental and socioeconomic factors guide transmission dynamics of COVID-19 and negative effects in society. To conclude, this study here suggests analyzing further socio-economic factors that may shape and support general health strategy, beyond vaccinations, to cope with future pandemic crises by creating appropriate ecosystems and socioeconomic systems of countries that improve public health and the overall well-being of people.

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СОЦИАЛЬНО-ЭКОНОМИЧЕСКИЕ, ДЕМОГРАФИЧЕСКИЕ И ЭКОЛОГИЧЕСКИЕ ФАКТОРЫ И ВАКЦИНАЦИЯ ПРОТИВ COVID-19: ВЗАИМОДЕЙСТВИЕ, ВЛИЯЮЩЕЕ НА ЭФФЕКТИВНОСТЬ

Kargi, Bilal1, Coccia, Mario2, Uçkaç, Bekir Cihan3

1Доктор экономических наук, доцент, университет Йылдырым Беязит, Анкара, Турция, E-mail: [email protected] Национальный исследовательский совет Италии (CNR), исследователь, 44, улица Сан-Мартино-делла-Батталья, Рим, Италия 3Доктор экономических наук, независимый исследователь, Стамбул, Турция,

E-mail: [email protected]

Аннотация

В настоящем исследовании проведен анализ взаимосвязи между количеством полностью вакцинированных людей и смертностью для оценки эффективности этой политики здравоохранения в борьбе с пандемией COVID-19 в выборке из 150 стран. Статистический анализ показал положительную корреляцию между долей полностью вакцинированных людей и общей смертностью от COVID-19 в начале 2022 года (r= 0,65, p-value <.01). Полученные результаты свидетельствуют о том, что вакцинация против COVID-19 не может быть достаточным политическим ответом для искоренения общего негативного влияния нового инфекционного заболевания на общество. Несмотря на высокий уровень вакцинации в некоторых странах, на распространение и негативные последствия пандемии COVID-19 в обществе влияют многие демографические (плотность населения), экологические (загрязнение воздуха), технологические (оснащение неинвазивными аппаратами искусственной вентиляции легких), биологические (новые варианты), социально-экономические (расходы на здравоохранение) и другие факторы. Данное исследование может дать новые знания для совершенствования антикризисного управления и повышения готовности стран к преодолению или предотвращению будущих пандемических кризисов и негативных последствий в социально-экономических системах.

Ключевые слова: Пандемия COVID-19; кампания вакцинации; политика здравоохранения; инновационные технологии; смертность; политические меры; загрязнение воздуха; температура; скорость ветра; варианты; расходы на здравоохранение; плотность населения; антикризисное управление.

JEL: O33; Q01; Q16; Q18.

СПИСОК ЛИТЕРАТУРЫ

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97

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