The underlying factors of the COVID‐19 spatially uneven spread. Initial evidence from regions in nine EU countries
2020; Elsevier BV; Volume: 12; Issue: 6 Linguagem: Inglês
10.1111/rsp3.12340
ISSN1757-7802
Autores Tópico(s)Health disparities and outcomes
ResumoThe novel coronavirus COVID-19 was brought to the global spotlight in early 2020 and has already had significant impacts on daily life, while the effects could last for a long period. However, these impacts appear to have been regionally differentiated, since similar to previous pandemics, geography plays an important role in viruses' diffusion. This paper enriches our knowledge about the initial territorial impact of the pandemic, from January to May 2020, studying the spread of COVID-19 across 119 regional economies in nine EU countries and explaining its underlying factors. Air quality, demographics, global interconnectedness, urbanization trends, historic trends in health expenditure as well as the policies implemented to mitigate the pandemic were found to have influenced the regionally uneven mortality rate of COVID-19. El reciente coronavirus COVID-19 se convirtió en el foco de atención mundial a principios de 2020 y ya ha tenido importantes repercusiones en la vida cotidiana, y es posible que sus efectos duren por un largo período. Sin embargo, estos impactos parecen ser diferentes por regiones, ya que, al igual que en pandemias anteriores, la geografía desempeña un papel importante en la difusión de los virus. Este artículo enriquece el conocimiento sobre el impacto territorial inicial de la pandemia entre enero y mayo de 2020, mediante el estudio de la propagación de COVID-19 en 119 economías regionales de nueve países de la UE y una explicación de sus factores subyacentes. Se comprobó que la calidad del aire, la demografía, la interconexión mundial, las tendencias hacia la urbanización, las tendencias históricas del gasto sanitario y las políticas aplicadas para mitigar la pandemia influyeron en la desigualdad por regiones de la tasa de mortalidad por COVID-19. 新型コロナウイルス感染症 (COVID-19)は2020年初頭に世界的な注目を集め、すでに日常生活に大きな影響を与えているが、その影響は長期間持続する可能性がある。しかし、これらの影響は、過去のパンデミックと同様、地理学がウイルスの拡散に重要な役割を果たしおり、地域的な識別が行われているようである。本稿では、2020年の1月から5月の、EUの9か国における119の地域の経済におけるCOVID-19の拡散を検討し、その基礎となる要因を解明し、パンデミックの初期の地域的影響に関する知識を強化する。大気質、人口統計、グローバル経済の相互関連性、都市化傾向、医療費の歴史的傾向、そしてパンデミックを緩和するために実施された政策が地域的に不均一なCOVID-19による死亡率に影響を及ぼしたことが認められる。 Since December 2019, the novel coronavirus COVID-19 has entered our lives and, starting from China, has spread across 215 countries, by the end of May 2020. Although the fatality rate cannot be estimated yet, there are two elements that make COVID-19 a serious threat to human life. First, the high amount of asymptomatic COVID-19 carriers and, second, the high reproduction rate (R0), which shows the number of people being infected by a single patient, being above 2.5 in the early stages of the current outbreak (Benvenuto et al., 2020). Coronavirus and the subsequent policies adopted to address its impacts shook up all aspects of life. However, these effects appear to have been geographically differentiated. Space has played an important role in the development of previous pandemics, with geographical proximity being key to viruses' spread (McLafferty, 2010). Regions across the European Union have recorded different levels of COVID-19 transmission, adding a layer to the complex mosaic of factors that determine the spatial inequalities in terms of growth (Woods, 2020a). Most patients and deaths caused by COVID-19 have been concentrated in specific regions of Europe, such as Lombardia, Madrid and Paris. Examining the COVID-19 impact at the regional level could provide useful insights, with regions recording different levels of exposure to the virus, even within the same country. This analytical paper contributes to the study of the initial territorial impact of COVID-19, seeking to examine its spread across regional economies in Western Europe and explain its underlying factors. It focuses on 119 regions in nine EU countries, being among those which so far have been most adversely affected across the globe. The paper examines distinct regional economic, social, demographic and environmental factors and highlights the various policy responses, such as lockdown and social distancing measures (Torre, 2020). Considering the novelty of the situation with COVID-19, the paper tests the role of specific regional features in the spread of the virus already examined in the literature, such as environmental pollution (Zhu, Xie, Huang, & Cao, 2020) and demographics (Dowd et al., 2020), but also sheds light on factors that have not been studied yet, including agglomeration and global interconnectedness. Chiefly, it examines the wider impact of all these factors based on their interaction, employing econometric analysis (Section 4), following the formation of the conceptual framework (Section 2) and the explanation of the methodology and variables employed (Section 3). The last section concludes. Human societies have historically suffered from serious pandemics, whose emergence and growth have increased in the last decades, due to various socio-economic restructurings and behavioural changes. Major examples include changing production and consumption patterns, climate change, demographic transition, growth in international mobility and trade (McLafferty, 2010), as well as the implementation of rolling-back welfare and privatization policies, and the subsequent weakening of health systems (Forster, Kentikelenis, & Bambra, 2018; Humer, Rauhut, & Marques da Costa, 2013). SARS and Ebola are important cases of recent infectious diseases. Geography has been highlighted as a major explanatory factor of the nature and spread of previous pandemics. Geographers have used the concept of spatial diffusion concept to analyze the geographies of pandemics (Cliff & Haggett, 2006; Sabel, Pringle, & Schaerstrom, 2010). By expanding locally, leading to high regional concentration, or transferring over longer distances, resulting in international spread, viruses diffuse across space, following certain routes, either at the regional level, through commuting, or at the national and international level, via trade and air travel routes (Lai et al. 2009). The deepening of globalization and the increase in urbanization in the last decades have been important for the pandemics. Globalization has been accompanied by significant advances in communications and medical treatments, that are capable of mitigating the pandemic spread (McLafferty, 2010). Notwithstanding that, globalization comes together with a considerable rise in mobility and global interconnectedness, as well as the deepening of climate emergency, favouring the emergence and growth of pandemics (King, 2009). Indeed, the increasing trends in global air transport have been key drivers of the spread of previous viruses, such as SARS (Bowen & Laroe, 2006). The growth of urbanized areas and the development of "satellite" towns in peri-urban zones, accelerated by the vast rural-to-urban migration, is an important driver for the spread of a contagious disease, mainly due to the increase of population density and commuting (Connolly, Keil, & Ali, 2020). It should be born in mind that the origins of both SARS and Ebola are to be found in urbanized areas (Keil & Alim, 2007). Against these pandemics, governments have applied mitigation policies, whose nature is largely geographical, seeking to intervene in local environments and interactions among people within them (McLafferty, 2010). These spatial factors are underpinned by specific regional features to enhance the explanatory framework of the geographical evolution of COVID-19, accounting for the unequal spatial growth of pandemics (Huang & Smith, 2010). As aforementioned, geography and spatial proximity are deemed to be crucial in the COVID-19 spread, due to its high R0 (Torre, 2020), with high levels of contagion being observed in cases where great concentration of people occurs, such as metropolises. To draw a detailed picture of the initial spread of the pandemic outbreak across EU regions, this paper estimates the hitherto number of deaths caused by COVID-19 per 100,000 inhabitants in each region as of 1 May 2020 (mortality rate), based on data sourced from the national statistical agency in each country. This is the dependent variable and was preferred to "people tested positive to COVID-19 per capita" (transmission rate), since data on deaths ease comparisons among countries whilst testing policies vary significantly. Some countries decided to conduct aggressive testing, trace the patients and their contacts, and isolate them, while others tested just the serious cases (Ren, 2020). The paper interrogates the way that various regional features interact with the mortality associated with COVID-19. While some of these elements possibly affect only the transmission rate, the selected index is argued to be effective capturing such effects, considering that high COVID-19 transmission is linked to high death rates (Woods, 2020a). Tests are undertaken for nine countries (Germany, France, Greece, Portugal, Italy, Austria, the Netherlands, Sweden, and Spain). These countries provide a cross section of regions with different levels of deaths caused by COVID-19, as well as different types of government measures to mitigate the pandemic. Based on the territorial level that data for deaths from COVID-19 are available, all the variables are estimated at NUTS 2 level, except for Germany and France, that are calculated at NUTS 1 (figures are available at this level). This research strategy, combining different NUTS regions, has been employed by other works, when comprehensive data were not available (Martin & Tyler, 2006). Regarding the data limitations, first, there has not been a consensus around the most appropriate way of measuring COVID-19 related deaths to be shared among different countries. Some countries publish deaths only from victims in the hospitals, while others also include deaths outside hospitals (Comas-Herrera et al., 2020). Second, there are different criteria for death registrations, with some countries including deaths from COVID-19 after a test has been made, and others publishing figures about deaths of persons suspected to have been infected by the virus. Third, there is a timing issue, with some countries recording severe delays to publish deaths caused by COVID-19 that occurred weeks before the publication day. Overall, examining the impact of a pandemic in its early stages entails insufficient data availability. The following explanatory variables were chosen to examine the regional features that determined the extent of COVID-19 spread. The three key factors include demographics, global interconnectedness and mitigation measures. Table 1 demonstrates the descriptive statistics of all the variables used in this paper. The demographic profile of regions could be insightful about the spatial distribution of COVID-19, given that the fatality rate (deaths per COVID-19 patients) is much higher in elderly people, particularly in the age groups above 65 years old (Dowd et al., 2020). This paper employs the share of people above 65 years old in the total population of each region (YEAR65). It is hypothesized that regions of aging population demonstrate higher mortality. The COVID-19 pandemic has significantly spread through mass travel and transport (Hall, Scott, & Gössling, 2020). This is the reason that EU governments decided to close the intra-EU borders, seeking to mitigate the spread of the coronavirus (Renda & Castro, 2020). On these grounds, regions that are globally interconnected, with high levels of "openness," through a hundred thousand movements of goods and people on a daily basis, are likely to have recorded a more severe impact. To test global interconnectedness, from the perspective of international trade, the value of exports per capita in 2019 (EXP) is estimated, based on data from national statistical agencies. Moreover, motorway density (MOTORD), expressed in kilometres of motorways per thousand square kilometres in 2018, is employed as an index of land freight transport in the region. These variables are expected to be positively correlated to COVID-19 mortality. Another transmission channel is tourism, which has demonstrated strong growth since the 1980s, based on increased global mobility through time and space compression (Niewiadomski, 2020). Two variables are used to examine the impact of tourism. The number of available beds in collective tourist accommodation per 100,000 inhabitants in 2018 (BEDS), and the arrivals of air transport passengers per 100,000 inhabitants in 2018 (AIRPASS). Both predictors are hypothesized to lead to an increase in mortality. The various policies seeking to mitigate the COVID-19 pandemic may have affected its spread, presenting significant differentiation across space and time (Ren, 2020). While some EU countries decided to follow the "herd immunity" strategy (Sweden and the Netherlands to a certain extent), governments in other countries (such as Spain, Italy and Greece) imposed strict horizontal lockdown and quarantine measures across all their regions (Renda & Castro, 2020). Although they lacked co-ordination (Jordana & Triviño-Salazar, 2020), these lockdown measures prevented or delayed millions of COVID-19 infections (Hsiang et al., 2020). Mitigation policies could include mass gatherings restrictions, home isolation and general lockdown. To test the effects of mitigation policies, two variables are employed, both at the national level, since most EU countries implemented uniform nation-wide lockdown measures. The number of days between the first death caused by COVID-19 and the beginning of lockdown (DEATHL) as well as the number of COVID-19 patients when the lockdown restrictions were applied (CASES). Given that Sweden and the Netherlands have not applied lockdown measures, DEATHL for the regions in these countries is estimated from day of the first death to 1 May 2020, while CASES refer to the same day. It is hypothesized that both variables are positively correlated to COVID-19 mortality. Air quality is likely to affect the impact of COVID-19 (Zhu et al., 2020). This paper employs the variable AIR that describes the average weekly concentration (ug/m3) of PM10, a particulate matter that has a negative impact on air quality, in the week starting 10 February 2020, according to the measurement stations of the European Environment Agency (2020). In the event that more than one station is present in each region, the average is calculated. It is expected that regions with a high concentration of PM10 demonstrate higher mortality from COVID-19. Air pollution links to the presence and the level of manufacturing activity in a region. Besides, manufacturing was subject to moderate lockdown restrictions, involving several "essential" economics branches, such as food processing and manufacture of pharmaceutical products. Regions with higher manufacturing share in total economic activity are more likely to have been adversely affected by COVID-19, since more workers continued going to work, keeping the infection rate high, either through commuting to work via public transport or from physical interactions at the workplace. The paper estimates the share of manufacturing firms in regional business stock in 2017 (MANUF), which is expected to be positively correlated to mortality. Considering the high risk of COVID-19 contagion in cases of individuals' concentration (Walker et al., 2020), the "size" of sites where people spend a significant part of their day could be crucial. Two sites are significant in this field: the household and the workplace. The author estimates the average number of people living in a house (HOUSEH), based on census data of 2011, and the average size of businesses in terms of employment (BUSZ) in 2017. Extending the argument of high concentration of people at a larger scale, the role of agglomerations is examined. Urban areas are expected to demonstrate higher death rates, due to the higher concentration of individuals and greater intraregional transportation than rural regions (Connolly et al., 2020). The index of population density ignores the fact that administrative units are largely heterogeneous and unevenly sized across countries, with some municipalities covering larger surfaces than other urban administrative units (OECD, 2012). Therefore, following suggestions made by Zenou (2000), this paper employs the density of firms as an agglomeration index, calculated as the number of businesses per 100,000 inhabitants in 2017 (FIRMD). Moreover, to test the impact of the metropolises on the mortality, the author uses a dummy variable (METROP), related to the presence of a metropolitan or large metropolitan functional urban area in the region (1 = Yes, 0 = No). This paper uses the data and the terminology of OECD which defines metropolis as a densely populated city and a suburban area with a labour market that is largely integrated with the city, which has a total population above 500,000 people (OECD, 2012). In this instance, regions containing a metropolis, according to OECD, are considered as urban. To further investigate the impact of agglomeration dynamics and considering the lack of available data related to urban/rural typology at the NUTS 2 level, GDP per capita is perceived as an additional index of urbanization of a region (de Beer, van der Gaag, & van der Erf, 2014). Regions considered as urban by Eurostat had 21% higher GDP per capita than the EU average in 2014, with rural regions recording 28% lower GDP per head (DG Agriculture and Rural Development, 2018). GDP per capita (GDP), for 2018, being an index of economic dynamism, combines issues of agglomeration, high global interconnectedness, large firms, and strong tendency for long commutes (Woods, 2020a). Therefore, regions with higher GDP per capita are expected to demonstrate greater mortality. However, areas of lower GDP per head are likely to be linked to greater deprivation of the population, with this implying worse health conditions, restricted access to health services, and more people being unable to cease economic activity due to low income, while also limiting the possibility of working remotely, due to the nature of their jobs or access to 'home office' (Torre, 2020). Besides the environmental, demographic and economic factors, institutional aspects, either formal or informal, need to be considered, as they significantly influence the regional socio-economic context (Rodríguez-Pose, 2020). Regarding norms, the lifestyle of elderly people could have affected the regionally uneven impact of the pandemic. There are societies, especially in Northern Europe, within which a culture of formal care for the elderly (care homes) is dominant (Eurofound, 2017). By contrast, in several countries in Southern Europe, care homes are less common, with elderly people either living independently or with their families. Due to the high concentration of elderly people, sharing common facilities, care homes have facilitated COVID-19 transmission (Comas-Herrera et al., 2020). Based on 2011 census data, the author uses the index of share of people above 65 years old living in 'collective living quarters' (COLLECT), i.e. premises where large groups of individuals live all together. Alongside the norms, formal institutions are crucial, with welfare provision and policies related to the health system possibly affecting the impact of COVID-19. This could be more important considering the cuts in health expenditures by EU countries to resolve the 2007/08 global economic crisis, that implied more centralized health systems (Woods, 2020b). The allocation of health expenditure is geographically uneven across the EU, leading to persistent socio-spatial inequalities in terms of access to health services (Forster et al., 2018). Better resourced health systems may have responded more efficiently to the pandemic, minimizing the risk of mortality. To evaluate the impact of the health system conditions in each region, the variables of hospital beds (HOSB) and medical doctors (DOC) per 100,000 inhabitants are employed (2017 data). They are expected to be negatively correlated to COVID-19 mortality. The model has five different versions to capture the aggregate impact of the explanatory variables on COVID-19 mortality (Appendix A). Each of the five simulations includes different predictors. The author builds on the main assumption that demographics, global interconnectedness and mitigation measures are the key factors that explain the regionally uneven impact of COVID-19. Therefore, to test these three key factors, the variables related to them (YEAR65, EXP, MOTORD, DEATHL, CASES) are included in every version of the model. Different control variables are progressively added in each version of the model to test their impact, but also to indicate that the results related to the key factors do not change. Alternative estimation strategies were employed to test the effects of a broad set of key independent variables and to assess the robustness and stability of the key regressor across various estimation methods. Therefore, different variables are used for specific factors (such as HOSB and DOC to test the impact of health funding). In this way, the bias of the results is minimized and their validity increases. For the cross-section regression, the degree of correlation between the explanatory variables should be tested in order to avoid biased estimates. The pairwise correlation matrix did not indicate high correlation between the explanatory variables included in the same version of the model. Finally, the parameter estimates of the control variables in all regression models have been tested for potential multicollinearity. The standard tests based on the variance inflation factor (VIF) reject any degree of multicollinearity, with VIF having values below 10 in all the five simulations (Wooldridge, 2012). To further increase the robustness of the results, the author checked for the presence of outliers that could bias the results, the normality in the distribution of the variables and heterogeneity of the observations in each variable based on z-score (Wooldridge, 2012). While many variables followed the normal distribution, several were identified to include outliers. To correct heterogeneity and address the issue of outliers driving the significance and direction of the results, the author ran the model without the observations that were identified as outliers (presented in Table 3). The results did not have significant changes with the estimations including the outliers (Appendix B). Moreover, the log was taken to normalize the variable and the model was re-run with no significant changes (Appendix C). Overall, the outliers have been either omitted from the model or the model was run without these observations, with no significant changes of the results. These controls confirm that the validity of the model is not threatened from issues stemming from either a highly skewed distribution of the variables or the presence of outliers. Regarding the flow of the data, the first part of the analytical section presents the descriptive statistics of the dependent and independent variables (Table 1), including the outliers, before illustrating the COVID-19 mortality rate across the 119 regions (Figure 1). Table 2 presents the findings about the top 10 and bottom 10 regions in terms of COVID-19 mortality, while Figure 2 illustrates evidence on the mean and coefficient of variation (CV) of COVID-19 mortality across the regions in each country. Table 3 shows the results of the OLS regression. Correlation between mortality rate and density of firms across selected EU regions Source: National statistical agencies and own elaboration. Notes: Regions of each country have different shape. ItalySpainFranceNetherlandsSwedenGreeceGermanyPortugalAustria Source: Own elaboration The pandemic evolution has been regionally uneven. Table 1 shows that the CV of mortality was quite high (1.33) across the 119 regions. Figure 1 illustrates the mortality rate, and its positive correlation to agglomeration (firms per 100,000 inhabitants), providing initial insights for the significant impact of the pandemic on regions with large urban centercentres. Lombardia was the EU NUTS 2 region with the highest mortality (136 deaths per 100,000 inhabitants), followed by Communidad de Madrid (122). Unsurprisingly, these regions are in Italy and Spain, two of the countries experiencing the most severe impact of the pandemic. Italy recorded more than 28,000 deaths from COVID-19 and Spain almost 25,000 victims as of 1 May 2020. These regions are largely urbanized, containing two of the most important metropolises in the EU (Milan and Madrid). By contrast, five regions have not recorded any deaths, with four of them being in Greece (Ipeiros, Thessalia, Sterea Ellada, Notio Aigaio) and one in Portugal (Região Autónoma da Madeira), which are among the countries with a limited impact from COVID-19. Greece had 140 deaths from COVID-19 and Portugal almost 1,000 as of 1 May 2020. Table 2 illustrates the top 10 and bottom 10 regions in terms of mortality. Eight of the bottom 10 regions are in Greece and two in Portugal, while six of the top 10 regions are in Italy and four in Spain. Significant differences are also observed within countries. For instance, in Spain, Castilla-la Mancha recorded 120 deaths per 100,000 inhabitants and La Rioja 106, while Galicia saw only 20 deaths and Comunidad Valenciana 24. In Italy, the mortality rate in Valle d' Aosta was 109 and in Emilia-Romagna 79, whereas in Calabria only four and Umbria seven. In France, Île de France recorded 49 deaths per 100,000 people, while Bretagne only seven. Finally, in Germany, the mortality in Bayern was 14, whereas in Sachsen-Anhalt was only two. Figure 2 provides more information about the regionally uneven distribution of COVID-19 deaths within the nine states, illustrating evidence on the mean and CV of COVID-19 mortality across the regions in each country. Spain presented the highest average mortality with 45 deaths per 100,000 residents, followed by Italy (39) and the Netherlands (23). By contrast, Greece and Portugal had the lowest average mortality (1 and 6, accordingly). Notwithstanding the above, Greece demonstrated the highest CV of mortality, with this demonstrating the greatest regional concentration of deaths per 100,000 inhabitants. In fact, 41% of COVID-19 deaths were recorded in the capital region of Attiki. Italy had the second highest CV, as 49% of deaths occurred in Lombardia, the EU region suffering the most severe impact. Following Italy, Portugal exhibited a CV of 0.98, with 57% of the deaths being recorded in Norte region. By contrast, Sweden, Austria and Germany witnessed the most equal distribution of deaths across their regions. It is considered that different socio-economic characteristics identified in each region have driven the uneven trajectory of growth of COVID-19. Table 3 demonstrates the results of the OLS model. The first value shows the coefficient of the independent variable, while the value in the parenthesis indicates the probability value, which determines the statistical significance of each independent variable at each of the three levels (1%, 5%, or 10%). In the different forms of the model, the explanatory variables that are statistically significant include: air pollution, motorway density, value of exports per capita, household, size, business size, business density, presence of a metropolitan area, share of people above 65 years old in the regional population, GDP per capita, number of days between the first death and the beginning of lockdown, number of COVID-19 patients when the lockdown restrictions were applied, number of medical doctors per 100,000 inhabitants, and hospital beds per 100,000 inhabitants. Confirming the proposition of this paper, Table 3 indicates that the share of people above 65 years old in the regional population exhibits a high and positive coefficient. That is, the age structure of a region is significant for the COVID-19 impact, considering that the mortality rate could diverge between two areas with a similar population size but of different age composition (Dowd et al., 2020). The importance of aging population structures in the EU regions is highlighted by the finding that mortality in territories with a share of elderly people in the total population being higher than the average (18.2%) was 23 deaths, while the regions with that share below the average recorded 18 deaths per 100,000 inhabitants. Closely related to the age structure, the proportion of people aged over 65 residing in care homes was found to exhibit a statistically significant, positive and high coefficient. Associated with aspects of norms and social life, that have a major impact on the regional socio-economic milieu (Rodríguez-Pose, 2020), regions exhibiting a high proportion of elderly individuals living in care homes are likely to be accompanied by higher mortality from COVID-19. Considering the rapid transmission of the virus in places of high concentration of people along with the high fatality risk in elderly population, care homes constitute significant hotspots of COVID-19 transmission (Comas-Herrera et al., 2020). The top 20 regions in terms of rate of elderly people residing in care homes had an average mortality of 16 people. The 20 regions exhibiting the lowest proportion of elderly population living in care homes recorded an average mortality of four people. Several variables were employed to investigate the role of global interconnectedness. In terms of trade, the variables of motorway density and value of exports per capita were found, as expected, to exhibit a positive coefficient, although the exports' coefficient is small. Regions with an important position in the global production networks, being largely interconnected to other places across the globe, r
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