key: cord-0939869-6nb9jk8d authors: Fountoulakis, Konstantinos N; Fountoulakis, Nikolaos K; Koupidis, Sotirios A; Prezerakos, Panagiotis E title: Factors determining different death rates because of the COVID-19 outbreak among countries date: 2020-07-30 journal: J Public Health (Oxf) DOI: 10.1093/pubmed/fdaa119 sha: 0bc85dbbb7a3afa7b2758052a1e1164a1d83c6d9 doc_id: 939869 cord_uid: 6nb9jk8d BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, all European countries were hit, but mortality rates were heterogenous. The aim of the current paper was to identify factors responsible for this heterogeneity. METHODS: Data concerning 40 countries were gathered, concerning demographics, vulnerability factors and characteristics of the national response. These variables were tested against the rate of deaths per million in each country. The statistical analysis included Person correlation coefficient and Forward Stepwise Linear Regression Analysis (FSLRA). RESULTS: The FSLRA results suggested that ‘days since first national death for the implementation of ban of all public events’ was the only variable significantly contributing to the final model, explaining 44% of observed variability. DISCUSSION: The current study suggests that the crucial factor for the different death rates because of COVID-19 outbreak was the fast implementation of public events ban. This does not necessarily mean that the other measures were useless, especially since most countries implemented all of them as a ‘package’. However, it does imply that this is a possibility and focused research is needed to clarify it, and is in accord with a model of spreading where only a few superspreaders infect large numbers through prolonged exposure. During the coronavirus disease 2019 (COVID-19) pandemic, all European countries were hit, but mortality rates were heterogenous, with some countries like Italy being hit very hard (1), whereas others like Latvia, Greece or Bulgaria had much lower death rate. It is important to understand why such differences exist, in order to learn how to best prepare for future pandemics and how to plan for optimal actions. A number of factors have been proposed as determining the outcome, including the age composition in combination with background disease in the population (especially asthma), smoking habits, hosting of big public events, socializing habits or the capacity of the health care system. A significant number of deaths seem to have occurred in elderly nursing homes, and the number of these beds in a country might also constitute a risk factor. Some authors suggest that social inequality and social determinants of health can have a considerable effect on COVID-19 outcomes (2, 3) . These include poverty, race or ethnicity, homelessness (4), malnutrition, health-related habits and behaviors (including smoking (5) ) and others. Additionally, socially vulnerable populations might find it much more difficult to follow prophylactic and hygiene measures (2) . However, although the above have been extensively discussed, there is no empirical verification of these claims. The aim of the current paper was to identify which of these factors exerted an effect on the mortality rate because of the COVID-19 outbreak and its difference among countries, with data at the population (country) level. Data concerning 40 European countries were gathered. The variables utilized, concerned demographics with emphasis on population density and aging, general health vulnerability factors including smoking, obesity and asthma-related conditions, international connectivity of the country in terms of flights, tourists and local Chinese community, social and economic vulnerability factors (poverty rate and GINI index) as well as characteristics of the outbreak and of the national response to it. The Gini index, is a measure of statistical dispersion intended to represent the income inequality or wealth inequality within a nation or any other group of people. It was developed by the Italian statistician and sociologist Corrado Gini. The national response was assessed on the basis of the time latency in days since the first death in Europe (in France on February 16) and since the first death in the specific country and the implementation of specific measures. Sensitivity analysis included the use of the time latency since 10th death in the specific country. For Malta and Montenegro the time since ninth death was calculated. The time since 100th death was also considered but almost half of countries had >100 deaths at the time of the current study. These variables were tested against the rate of deaths per million in each country, which were obtained from https://www.worldo meters.info/coronavirus/#countries at 1 June 2020, at 23.59, Coordinated Universal Time. The complete list of variables is shown in Table 1 and the complete dataset along with sources of data are shown in the appendix. Not all data were available for all countries. Specifically concerning the measures, the response of countries was heterogenous. Thus, the analysis was made twice, first with the original dataset and then with the imputation of a latency time of 100 days for those countries they did not implement a specific measure. This is as if their latency time to adopt the specific measure was 100 days after the first death in the specific country or in Europe. The analysis included two stages for continuous variables. At the first screening stage, Pearson correlation coefficients were calculated for all variables with rates of death per million population. At the second stage, which was also screening, Forward Stepwise Linear Regression Analysis (FSLRA) was performed with deaths per million as the dependent variable and all the variables with significant correlations at the first stage as independent variables (predictors). At this stage separate FSLRA were performed for each group of variables. At the third and final stage, a single FSLRA was performed with deaths per million as the dependent variable and all the variables which were selected during the previous stage as predictors. At each stage sensitivity analysis was performed on the basis of including or excluding the variable 'Days of first death in country since first death in Europe' and imputed data. The complete dataset concerning the current paper is based on, together with the sources, are shown in the web appendix. In terms of measures taken, 30 countries implemented a ban for public gatherings, 25 for use of public transportation, 28 a full lockdown, 29 banned domestic travel and 35 banned international travel. Data were not available in a uniform way from all countries and from a minority they were completely missing. The correlation analysis suggested that Pearson coefficients between the death rate and the various variables were significant in a number of variables at P < 0.05 (Table 1) . The use of these significant variables only, in the second step in FSLRA is separately for groups of variables suggested that from the demographic variables only male life expectancy at the age of 65 was predicting the death rates, and explained 32% of the observed variance. From the somatic vulnerability variables, only wheezing symptoms had a significant contribution and explained 28% of the observed variance. The Air Connectivity Index (ACI %) was the only variable significantly contributing from the cluster reflecting international connectivity and explained 31% of observed variance. Interestingly none of the social vulnerability factors was significant. The national response and outbreak characteristics were entered in two separate FSLRA. In the first, only those countries that implemented these measures were utilized, since the cells concerning latency time from the other countries were empty. In the second analysis, imputation values were also utilized. Depending on the analysis, two variables contributed significantly, that is 'Days of first death in country since first death in Europe' and 'Public events ban-Days since first national death'. The final FSLRA included 'male life expectancy at the age of 65', 'Wheezing symptoms', ACI, 'Days of first death in country since first death in Europe' and 'Public events ban-Days since first national death' as predictors. No matter whether there was imputation of data for the 'days since first national death for the implementation of ban of all public events' or not, the results were identical and this later variable was the only significantly contributing to the final model, explaining 44% of observed variability. Sensitivity analysis produced the same results at the final stage, suggesting also that speed of banning of public events was the important factor (Table 1 ). The results of the current study suggest that the crucial factor was the fast implementation of public events ban (Fig. 1) . It has been discussed widely and especially in the media that different backgrounds and different country reactions to the outbreak have determined the final outcome, but opinions are conflicting as to which factors are important. An interesting comparison would be that of Sweden versus Greece. The two countries are similar in population size but with completely different characteristics, but Greece was the fastest to implement measures (Fig. 1) whereas in contrast Sweden adopted the 'herd immunity' approach. Greece had most of the risk factors against as compared with Sweden, that is, 3.6 times higher population density, 1.6 times higher urban population density, more aged population, 4-times more tourist visits, probably a larger Chinese community, 2-times more Chinese tourists, earlier first death in comparison to first death in Europe, higher smoking and obesity rates and more adverse socioeconomic environment. The only higher risk factors for Sweden were two to three times higher asthmarelated conditions. The critical difference was that Greece was one of the fastest countries to implement all measures (Fig. 1 ) Sweden implemented only a ban on international travel. The result was a 25-times higher mortality rate ( Table 2) . The current study is the first to quantify the effect of various risk factors on the different mortality rates across European countries. The findings do not necessarily mean that the other measures were useless, especially since most countries implemented all of them as a 'package'. However, it does imply that this is a possibility and focused research is needed to clarify it. It is in relative contrast with a report suggesting that the lockdown was the strongest measure with 81% or R 0 reduction attributed to it, but the methodology of that study was quite different from ours and based on self-report data (6) . That study also suggested that that only multiple measures implemented simultaneously could reduce R 0 below 1. Our results do not preclude such an assumption, they are however in sharp contrast with the analysis by the ICL (only in 11 countries) which suggests that lockdown was the only efficacious measure (7) . The results are in accord with a model of spreading where only a few superspreaders infect large numbers through prolonged exposure (superspreading events) (8, 9) . Finally, it is interesting to inspect the map of Europe as it is colored on the basis of first death in each country in relationship to the first death in Europe, which occurred in France (Fig. 2) . It is evident that geography played a significant role, with countries neighboring France suffering a heavier burden because of the outbreak. Geography also suggests that COVID-19 spread in Europe probably through Germany and Italy. This is not in contrast with reports of isolated instances of COVID-19 contamination by e.g. Chinese visitors to Germany since the characteristics of the virus demand that the virus should be introduced at least four to five times in the country in order to trigger an outbreak. These suggest that some countries seem to be 'unlucky' since they were struck very early and therefore they were too late in implementing measures. On the other hand, some other countries were too slow in spite of having enough time and information on the outbreak and its consequences (Fig. 1) . The data utilized in the current paper are at the population/country level. They are heterogenous in the sense that there are different ways of registering and reporting deaths because of COVID-19 (10) and different ways of practically implementing measures and to different extent (11, 12) . The strength of the current study is that it is the first based on the statistical analysis of quantified data reported by third-parties. The current study suggests that the crucial factor that determined the difference in the death rates because of COVID-19 among European countries was the latency time in the implementation of public events ban specifically. This does not necessarily mean that the other measures were useless, but it does imply that this is a possibility, superspreader events might be very important rather than a wider way of infecting. Focused research is needed to clarify it. What other countries can learn from Italy during the COVID-19 pandemic COVID-19 and the impact of social determinants of health Why inequality could spread COVID-19 COVID-19: a potential public health problem for homeless populations COVID-19 and smoking: a systematic review of the evidence Modelling the SARS-CoV-2 first epidemic wave in Greece: social contact patterns for impact assessment and an exit strategy from social distancing measures Report 13: estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries Superspreading and the effect of individual variation on disease emergence Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China How comparable is COVID-19 mortality across countries? How do countries control the entry of travellers during the COVID-19 pandemic? How do measures for isolation, quarantine and contact tracing differ among countries? None. None pertaining to the current paper. None. The copyright of the paper is granted to the journal. Supplementary data mentioned in the text are available to subscribers in Journal of Public Health online. All authors contributed equally to the paper. KNF conceived and designed the study. The other authors participated formulating the final protocol, designing and supervising the data collection and creating the final dataset. KNF did the data analysis and wrote the first draft of the paper. All authors participated in interpreting the data and developing further stages and the final version of the paper.