Preamble

Analysis of the psychological effects of Covid-19 in Europe during the first weeks of the pandemic.The survey on which this study is based was conducted by international researchers around the world, trying to map out all the factors, that might affects people’s psychological wellbeing and their ability to make good decisions during the COVID-19 (or “Coronavirus”) outbreak. Researchers from many countries are collaborating on this project, to help scientists and decision makers to help and communicate.

Why this project?

The project was chosen as it deals with analyzing all the factors that in this emergency situation can have threatening consequences in people’s lives: the isolation, the economic situation and the stress due to the stringent measures to stop the pandemic are part of the side effects of covid-19. The survey on which the next analyzes are based was designed by social scientists and psychologists from more than 50 different universities. The study in question presents more than 150,000 individual respondents from more than 50 different countries. The following analyzes describe a series of variables selected in the survey, taking as a sample the responses provided by citizens located in Europe who answered the survey between March 30, 2020 and May 30, 2020.

For more informations check this Nature article.

Characteristics of the Respondents

As said in the ‘Preamble’ this project is focuses on the responses provided by citizens located in Europe’s countries.

#filtering by EU Countries
target <- c("Austria","Belgium","Bulgaria","Croatia","Cyprus","Czech Republic",
            "Denmark","Estonia","Finland","France","Germany","Greece","Hungary",
            "Ireland","Italy","Latvia","Lithuania","Luxembourg","Malta",
            "Netherlands","Poland","Portugal","Romania","Slovakia","Slovenia",
            "Spain","Sweden")

EU <- filter(cleaned_d, Country %in% target) 

#fixing date visualization
EU$RecordedDate <- as.Date(EU$RecordedDate , format= "%Y-%m-%d")

The majority of respondents are aged between 20 years old and 40 years old.

Age of the Respondents

The majority of the answers come from women.

Gender of the Respondents

Full time employed lead the survey.

Employment of the Respondents

Stress level in Europe

An important point of the project is based on the analysis of perceived individual stress, obtained through the Perceived Stress Scale, based on the answers to 10 questions, invented by psychologists Cohen, Karmak, and Mermelstrein (1983), using indicators of stress responses, for instance, perceived lack of control over events, pressure from mounting difficulties and feeling upset about unexpected changes. Scores are considered moderate above 2.4, and high above 3.7. More informations about PS Scale here

PSS scores are obtained by reversing responses (e.g., 0 = 4, 1 = 3, 2 = 2, 3 = 1 & 4 = 0) to the four positively stated items (items 4, 5, 7, & 8) and then summing across all scale items. A short 4 item scale can be made from questions 2, 4, 5 and 10 of the PSS 10 item scale.

#note that the na values are omitted
EU$Scale_PSS10_UCLA_4[EU$Scale_PSS10_UCLA_4 == 1] <- 5
EU$Scale_PSS10_UCLA_4[EU$Scale_PSS10_UCLA_4 == 2] <- 4
EU$Scale_PSS10_UCLA_4[EU$Scale_PSS10_UCLA_4 == 4] <- 2
EU$Scale_PSS10_UCLA_4[EU$Scale_PSS10_UCLA_4 == 5] <- 1
EU$Scale_PSS10_UCLA_5[EU$Scale_PSS10_UCLA_5 == 1] <- 5
EU$Scale_PSS10_UCLA_5[EU$Scale_PSS10_UCLA_5 == 2] <- 4
EU$Scale_PSS10_UCLA_5[EU$Scale_PSS10_UCLA_5 == 4] <- 2
EU$Scale_PSS10_UCLA_5[EU$Scale_PSS10_UCLA_5 == 5] <- 1
EU$Scale_PSS10_UCLA_7[EU$Scale_PSS10_UCLA_7 == 1] <- 5
EU$Scale_PSS10_UCLA_7[EU$Scale_PSS10_UCLA_7 == 2] <- 4
EU$Scale_PSS10_UCLA_7[EU$Scale_PSS10_UCLA_7 == 4] <- 2
EU$Scale_PSS10_UCLA_7[EU$Scale_PSS10_UCLA_7 == 5] <- 1
EU$Scale_PSS10_UCLA_8[EU$Scale_PSS10_UCLA_8 == 1] <- 5
EU$Scale_PSS10_UCLA_8[EU$Scale_PSS10_UCLA_8 == 2] <- 4
EU$Scale_PSS10_UCLA_8[EU$Scale_PSS10_UCLA_8 == 4] <- 2
EU$Scale_PSS10_UCLA_8[EU$Scale_PSS10_UCLA_8 == 5] <- 1

eu_stress <- select(EU,Country, Scale_PSS10_UCLA_1:Scale_PSS10_UCLA_10) %>%
  ddply( .(Country), summarize,
         Rate_PSS10_UCLA1=mean(Scale_PSS10_UCLA_1,na.rm=TRUE),
         Rate_PSS10_UCLA2=mean(Scale_PSS10_UCLA_2,na.rm=TRUE),
         Rate_PSS10_UCLA3=mean(Scale_PSS10_UCLA_3,na.rm=TRUE),
         Rate_PSS10_UCLA4=mean(Scale_PSS10_UCLA_4,na.rm=TRUE),
         Rate_PSS10_UCLA5=mean(Scale_PSS10_UCLA_5,na.rm=TRUE),
         Rate_PSS10_UCLA6=mean(Scale_PSS10_UCLA_6,na.rm=TRUE),
         Rate_PSS10_UCLA7=mean(Scale_PSS10_UCLA_7,na.rm=TRUE),
         Rate_PSS10_UCLA8=mean(Scale_PSS10_UCLA_8,na.rm=TRUE),
         Rate_PSS10_UCLA9=mean(Scale_PSS10_UCLA_9,na.rm=TRUE),
         Rate_PSS10_UCLA10=mean(Scale_PSS10_UCLA_10,na.rm=TRUE))%>%
  mutate(total_stress = rowMeans(select(., -Country)))

Level of stress by Country

Levels of stress were moderate or lower in many countries. Poland and Croatia reported the highest levels of stress in Europe, Denmark and Netherlands the lowest.

Are men or women more stressed?

eu_stress_gender <- select(filter(EU,Dem_gender=="Male" | Dem_gender=="Female"), Dem_gender,
                           Scale_PSS10_UCLA_1:Scale_PSS10_UCLA_10) %>%
  ddply( .(Dem_gender), summarize,
         Rate_PSS10_UCLA1=mean(Scale_PSS10_UCLA_1,na.rm=TRUE),
         Rate_PSS10_UCLA2=mean(Scale_PSS10_UCLA_2,na.rm=TRUE),
         Rate_PSS10_UCLA3=mean(Scale_PSS10_UCLA_3,na.rm=TRUE),
         Rate_PSS10_UCLA4=mean(Scale_PSS10_UCLA_4,na.rm=TRUE),
         Rate_PSS10_UCLA5=mean(Scale_PSS10_UCLA_5,na.rm=TRUE),
         Rate_PSS10_UCLA6=mean(Scale_PSS10_UCLA_6,na.rm=TRUE),
         Rate_PSS10_UCLA7=mean(Scale_PSS10_UCLA_7,na.rm=TRUE),
         Rate_PSS10_UCLA8=mean(Scale_PSS10_UCLA_8,na.rm=TRUE),
         Rate_PSS10_UCLA9=mean(Scale_PSS10_UCLA_9,na.rm=TRUE),
         Rate_PSS10_UCLA10=mean(Scale_PSS10_UCLA_10,na.rm=TRUE))%>%
  mutate(total_stress = rowMeans(select(., -Dem_gender)))

Level of stress by Gender

From what emerges women result more stressed then men. In line with this Psychological article.

Which is the most stressed employment?

eu_stress_empl <- select(EU, Dem_employment,Scale_PSS10_UCLA_1:Scale_PSS10_UCLA_10) %>%
  ddply( .(Dem_employment), summarize,
         Rate_PSS10_UCLA1=mean(Scale_PSS10_UCLA_1,na.rm=TRUE),
         Rate_PSS10_UCLA2=mean(Scale_PSS10_UCLA_2,na.rm=TRUE),
         Rate_PSS10_UCLA3=mean(Scale_PSS10_UCLA_3,na.rm=TRUE),
         Rate_PSS10_UCLA4=mean(Scale_PSS10_UCLA_4,na.rm=TRUE),
         Rate_PSS10_UCLA5=mean(Scale_PSS10_UCLA_5,na.rm=TRUE),
         Rate_PSS10_UCLA6=mean(Scale_PSS10_UCLA_6,na.rm=TRUE),
         Rate_PSS10_UCLA7=mean(Scale_PSS10_UCLA_7,na.rm=TRUE),
         Rate_PSS10_UCLA8=mean(Scale_PSS10_UCLA_8,na.rm=TRUE),
         Rate_PSS10_UCLA9=mean(Scale_PSS10_UCLA_9,na.rm=TRUE),
         Rate_PSS10_UCLA10=mean(Scale_PSS10_UCLA_10,na.rm=TRUE))%>%
  mutate(total_stress = rowMeans(select(., -Dem_employment)))

Stress level by employment

The most stressed category is the one of the students, as expected, due to isolation and social distancing. This article stated that Polish University’s students recorded an higher level of stress during the Covid-19 pandemic. Notably, Poland is also the most stressed Country.

How stress level changed in Europe during the first weeks of Covid pandemic?

eu_stress_2months <- select(filter(EU,RecordedDate=="2020-03-30"|
                                     RecordedDate=="2020-04-06"|
                                     RecordedDate=="2020-04-12"|
                                     RecordedDate=="2020-04-18"|
                                     RecordedDate=="2020-04-24"|
                                     RecordedDate=="2020-04-30"|
                                     RecordedDate=="2020-05-05"|
                                     RecordedDate=="2020-05-11"|
                                     RecordedDate=="2020-05-17"|
                                     RecordedDate=="2020-05-23"|
                                     RecordedDate=="2020-05-30"), 
                            RecordedDate,Scale_PSS10_UCLA_1:Scale_PSS10_UCLA_10) %>%
  ddply( .(RecordedDate), summarize,
         Rate_PSS10_UCLA1=mean(Scale_PSS10_UCLA_1,na.rm=TRUE),
         Rate_PSS10_UCLA2=mean(Scale_PSS10_UCLA_2,na.rm=TRUE),
         Rate_PSS10_UCLA3=mean(Scale_PSS10_UCLA_3,na.rm=TRUE),
         Rate_PSS10_UCLA4=mean(Scale_PSS10_UCLA_4,na.rm=TRUE),
         Rate_PSS10_UCLA5=mean(Scale_PSS10_UCLA_5,na.rm=TRUE),
         Rate_PSS10_UCLA6=mean(Scale_PSS10_UCLA_6,na.rm=TRUE),
         Rate_PSS10_UCLA7=mean(Scale_PSS10_UCLA_7,na.rm=TRUE),
         Rate_PSS10_UCLA8=mean(Scale_PSS10_UCLA_8,na.rm=TRUE),
         Rate_PSS10_UCLA9=mean(Scale_PSS10_UCLA_9,na.rm=TRUE),
         Rate_PSS10_UCLA10=mean(Scale_PSS10_UCLA_10,na.rm=TRUE))%>%
  mutate(total_stress = rowMeans(select(., -RecordedDate)))

Level of stress during the first weeks of the pandemic

Level of stress remained fairly stable over the middle of April, with a negligible decrease in the second half of the month. Notably, stress level increased at the end of May and that is odd because with the beginning of summer, people should be less stressed. We do not have any data that will confirm this trend.

Geo Map of stress level

Visualizing a detailed map of stress level in Europe.

Level of stress during the first weeks of the pandemic, based on PSS Scale

Sources of Distress

The analysis will focus on the cause of stress of the respondents, obtaining the data through asking them questions regarding various factors that represent a source of stress.

eu_stress_source <- select(EU, Expl_Distress_1:Expl_Distress_24)%>%
                    na.omit()

eu_stress_source = data.frame(apply(eu_stress_source,2,function(x)mean(x[x<99]))) %>%
  tibble::rownames_to_column(var = "col1") %>%
  `colnames<-`(c("stress_source", "mean")) 
 
listlab<-(c("No religious activities", "Not knowing how to stop covid-19",
            "Feeling ashamed for acting differently","Adapt work to Digital Platforms",
            "Access to necessities(food etc..)","Behavior of adults I live with",
            "Behavior of childrens I live with","Adapt to social life on digital platforms",
            "No trovels outside my Country","Loneliness","Time i spend in proximity to others",
            "Children's education","Civil services(Police,sanitations..)","Income",
            "Not knowing about developments with Covid-19","Time I spend inside","Work",
            "Job prospect","No social activities","Worry over friends and relatives who live far away",
            "Not knowing how long the measures will last","Risk of catching covid-19",
            "Risk of being hospedalized or dying","National economy"))

$Sources of Distress among Europeans during the COVID-19 Pandemic$

Sources of Distress among Europeans during the COVID-19 Pandemic

The results indicate that most respondents are concerned about the state of the national economy. Economic considerations were followed closely by health-related risks, such as the risks of being hospitalized and of contracting the new disease.

Trust in institutions

Partecipants were asked how much they trusted six major institutions in relation to the Covid-19 emergency (on a scale from 1 = not at all to 10 = completely), in particular partecipants were asked about their trust towards World Health Organization (WHO), the national governments’ efforts to tackle the COVID19, the Police, the civil service, the national government and the healthcare systems.

Here there are OECD guidelines on measuring institutions trust.

eu_trust <- select(EU, OECD_insititutions_1:OECD_insititutions_6)%>%
  na.omit()

eu_trust = data.frame(apply(eu_trust,2,function(x)mean(x))) %>%
  tibble::rownames_to_column(var = "col1") %>%
  `colnames<-`(c("trust", "mean")) 
lablist<-(c("Country's Governments","Country's civil Service","Country's Police",
            "Governments effort against covid-19","WHO","Country's Healthcare system"))

Trust in Institutions

Europeans reported only medium level of trust at all, with the highest levels of confidence for their countries’ healthcare system and the WHO. Notably, Countries governments reported the lowest level of trust.

How much citizens trust their own governments?

eu_mean_oecd1 <- select(EU,Country,OECD_insititutions_1) %>%
  ddply( .(Country), summarize,
         Rate_OECD_insititutions_1=mean(OECD_insititutions_1,na.rm=TRUE))%>%
        arrange(desc( Rate_OECD_insititutions_1))

Trust towards governments

Notably, Poland is the most stressed Nation but it is also one of the country in which the population trust least in their own government. Finland, on the other hand, is the nation with the least stress related to Covid, and it is also one of the countries in which the population places an high level of compliance for their own government.

How much citizens trust the government’s effort to handle the pandemic?

 eu_mean_oecd4 <- select(EU,Country,OECD_insititutions_4) %>%
   ddply( .(Country), summarize,
          Rate_OECD_insititutions_4=mean(OECD_insititutions_4,na.rm=TRUE))%>%
   arrange(desc( Rate_OECD_insititutions_4))

Trust towards government’s effort to handle Coronavirus

Similar patterns as the trust level in the governments, with Bulgaria that reported the lowest level of trust for the national effort to handle the pandemic.

Trust in Countries’ Measure

It was also asked participants to judge the appropriateness of the countries’ measures in response to the COVID-19 on a scale from 0 (too little), to 5 (appropriate, the black dashed line in the graph), to 10 (too much).

eu_trustinc <- select(EU,Country,Trust_countrymeasure) %>%
  group_by(Country) %>%
  dplyr::summarize(Mean = mean(Trust_countrymeasure, na.rm=TRUE))

Appropriateness of the countries’ measures

Slovakia and Slovenia were those countries where citizens considered the measures stronger than necessary; in the plot, the vertical mid-line indicates that the measures are appropriate. France and Hungary citizens judged their governments’ response less than appropriate.

There is correlation between confidence in the measures taken by governments to tackle Covid and the actions that the population has taken to reduce transmission?

eu_conf_corr <- select(EU,Country,OECD_insititutions_6,Compliance_1:Compliance_6) %>%
                ddply( .(Country), summarize,
         Rate_OECD=mean(OECD_insititutions_6,na.rm=TRUE),
         Rate_Compliance_1=mean(Compliance_1,na.rm=TRUE),
         Rate_Compliance_2=mean(Compliance_2,na.rm=TRUE),
         Rate_Compliance_3=mean(Compliance_3,na.rm=TRUE),
         Rate_Compliance_4=mean(Compliance_4,na.rm=TRUE),
         Rate_Compliance_5=mean(Compliance_5,na.rm=TRUE))%>%
  mutate(total_Compl = rowMeans(select(., -Country,-Rate_OECD)))

mod = lm( total_Compl ~Rate_OECD, data = eu_conf_corr)
summary(mod)


Call:
lm(formula = total_Compl ~ Rate_OECD, data = eu_conf_corr)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.27076 -0.09898  0.03277  0.09216  0.26395 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.20673    0.15954  26.367   <2e-16 ***
Rate_OECD    0.03408    0.02464   1.383    0.179    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1386 on 25 degrees of freedom
Multiple R-squared:  0.07106,   Adjusted R-squared:  0.0339 
F-statistic: 1.912 on 1 and 25 DF,  p-value: 0.1789

mod$coefficients

(Intercept)   Rate_OECD 
 4.20672802  0.03407768

Trust towards the governments’handling of the pandemic

From what emerges Poland not having confidence in the measures taken by governments and does not even try to reduce transmission, while Denmark is in the opposite situation. In countries with higher trust towards governments ’efforts, citizens were also more likely to report higher levels of compliance with directives aimed at controlling the spread of the virus. Notably, Portugal reported higher levels of compliance than the model would predict based on the acceptance of government efforts. An outlier in the other direction, Latvia reported lower levels of compliance than those the model predicted, given levels of trust.

Will be government’s effort to handle Coronavirus more trusted over the time?

eu_trust2m <- select(filter(EU,RecordedDate=="2020-03-30"|
                       RecordedDate=="2020-04-06"|
                       RecordedDate=="2020-04-12"|
                       RecordedDate=="2020-04-18"|
                       RecordedDate=="2020-04-24"|
                       RecordedDate=="2020-04-30"|
                       RecordedDate=="2020-05-05"|
                       RecordedDate=="2020-05-11"|
                       RecordedDate=="2020-05-17"|
                       RecordedDate=="2020-05-23"|
                       RecordedDate=="2020-05-30"),RecordedDate,OECD_insititutions_6) %>%
  ddply( .(RecordedDate), summarize,
         Rate_OECD=mean(OECD_insititutions_6,na.rm=TRUE))

Trust towards the governments’handling of the pandemic over the first weeks

In the first month the trust for government’s effort to handle Coronavirus appears to increase, as a result of more stringent measures. With the beginning of the summer season trust level appears to decrease; probably because in many countries, including ours, Italy, the measures were almost null.

Conclusions

Correlation between level of stress and trust in governments

(r = cor(p$total_stress, p$Rate_OECD_insititutions_1))

[1] -0.7951361

r^2

[1] 0.6322414

In conclusions, Student is the most stressed category, as expected, due to isolation and social distancing; Poland is the most stressed Nation in Europe and as reported in this article Polish University’s Students recorded an highly level of stress during the Covid-19 pandemic. Women result more stressed than men, as stated in this Psychological article. Remarkably, around the Europe the Country’s healthcare system is trusted followed by the WHO. There is a strong negative correlation between an high level of stress and a lack of trust in governments, indeed citizens of northern Europe’s countries are less stressed and they have also an high level of confidence in their governments.

Presentation

Click Here to start.

About

Author of this project: Andrea Turchet
Author of the original work: The Covidistress Team
- Andrea’s GitHub
- Covidistress GitHub
- Original report

Psychological impact of Covid-19