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The impact of social cohesion and risk communication on excess mortality due to COVID-19 in 213 countries: a retrospective analysis

BMC Public Health volume 24, Article number: 1598 (2024) Cite this article

Abstract

Background

Tools for assessing a country’s capacity in the face of public health emergencies must be reviewed, as they were not predictive of the COVID-19 pandemic. Social cohesion and risk communication, which are related to trust in government and trust in others, may have influenced adherence to government measures and mortality rates due to COVID-19.

Objective

To analyse the association between indicators of social cohesion and risk communication and COVID-19 outcomes in 213 countries.

Results

Social cohesion and risk communication, in their dimensions (public trust in politicians, trust in others, social safety nets, and equal distribution of resources index), were associated with lower excess mortality due to COVID-19. The number of COVID-19-related disorder events and government transparency were associated with higher excess mortality due to COVID-19. The lower the percentage of unemployed people, the higher the excess mortality due to COVID-19. Most of the social cohesion and risk communication variables were associated with better vaccination indicators, except for social capital and engaged society, which had no statistically significant association. The greater the gender equality, the better the vaccination indicators, such as the number of people who received all doses.

Conclusion

Public trust in politicians, trust in others, equal distribution of resources and government that cares about the most vulnerable, starting with the implementation of programs, such as cash transfers and combating food insecurity, were factors that reduced the excess mortality due to COVID-19. Countries, especially those with limited resources and marked by social, economic, and health inequalities, must invest in strengthening social cohesion and risk communication, which are robust strategies to better cope with future pandemics.

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Background

Risk communication is an important component of disaster risk management, as it influences risk perceptions and actions in the face of these events. The credibility of the information source impacts the decisions of individuals who receive the information. For example, in Japan, the level of trust in government and other official communications was severely tested in the Fukushima nuclear accident. Effective risk communication requires trust among involved parties, along with proper training and planning for execution [1].

Social cohesion refers to the extent of social connectedness and solidarity between different community groups within a society, and the level of trust and connectedness between individuals and across community groups. Social cohesion is a critical resource for disaster recovery planning and an important component of the pre-disaster, acute, post-disaster, and recovery phases. Community engagement and strong social networks are instrumental in identifying priorities and solutions that are more likely to be appropriate, lasting, and supported by the affected community. The COVID-19 pandemic has impacted social cohesion at various levels, and the strength of social cohesion before the pandemic is likely a strong indicator of recovery. The division between the public and government is of concern during a disaster and is largely in the hands of the government. This relationship is often cited as a key to social cohesion, with central concepts being inclusive leadership, legitimacy of authority, shared identity, and common goals [2].

A study that evaluated The Global Health Security Index (GHS), which measures a country’s capacity to prepare for and control epidemics and pandemics, pointed out that the inclusion of political, governance and social cohesion aspects in the GHS may benefit the ability to assess preparedness, detection and response, as these variables were significantly associated with excess mortality in countries over the first five hundred days of the COVID-19 pandemic. Additionally, in that same study, factors such as geographic connectivity and proportion of the elderly population increased the risk profile of countries and can be used to map the most vulnerable countries [3].

The Joint External Evaluation (JEE), which is a tool launched by the World Health Organization to evaluate a country’s capacity for ensuring health security threats, does not take into account indicators related to health inequalities. These indicators can be important in assessing countries’ preparedness and response capacity [4]. Especially at the beginning of the COVID-19 pandemic, when political factors and vulnerabilities not foreseen in the GHS and JEE influenced response actions and decreased the chances of success. Thus, the GHS and JEE need to be revised to assess the inclusion of new indicators that can better estimate the response capacity of countries in the face of public health threats [5].

A previous study found that the performance of countries’ health systems, as assessed by the GHS, during the COVID-19 pandemic, did not align with the outcomes of COVID-19 such as mortality. In other words, countries that were well-classified in the GHS had high mortality rates [6]. Investment in human resources is an important aspect of pandemic preparedness. The results of a study showed that countries with the highest scores in this regard, as measured by the GHS instruments and the International Health Regulation State Party Self-Assessment Annual Report (IHR-SPAR), had fewer COVID-19 cases and deaths in the first eight weeks of the pandemic [7].

The best-managed and most effective vaccination process is related to the highest levels of governance indicators. To better prepare for upcoming global health threats, countries need to increase investments in health infrastructure and rational use of resources and promote good public governance. Public trust in government interfered with decision-making for adherence to vaccination programs, which impacted the number of cases and deaths caused by COVID-19 [8].

Communication of reliable and timely information, for example, about the risks and vulnerabilities involved in the scenario, is an important aspect of increasing public trust in government, even when information is still limited. Investments in risk communication and societal engagement strategies are needed to increase adherence to government measures and increase trust [9].

The analysis of variables related to social cohesion and risk communication may contribute to improving the prediction of the available instruments concerning a country’s capacity to cope with emergencies, because, currently, these instruments do not include these variables. Therefore, the objective of this study was to analyse the association between indicators of social cohesion and risk communication and COVID-19 outcomes in 213 countries.

Methods

Study design

This was a documentary and descriptive study. It was a correlation analysis of secondary data in the public domain. The total number of countries analysed was 213. The complete list of countries is presented in supplementary material 1.

Data source

The databases used were: (i) GovData360 – World Bank; (ii) Data Futures Platform (United Nations Development Programme – UNDP); (iii) United Nations E-Government Knowledgebase; (iv) The Economist; (v) Oxford COVID-19 Government Response Tracker - University of Oxford; (vi) Our World in Data – University of Oxford and Global Change Data Lab and (vii) Vaccine Equity Dashboard Data (United Nations Development Programme – UNDP).

GovData360 [10] is a World Bank initiative and presents data on governance-related indicators relating to State capacity, efficiency, accountability, integrity and levels of trust in government.

The Data Futures Platform brings together data from the United Nations (UN) system [11] and from partners to advance integrated development solutions in support of the 2030 Agenda.

United Nations E-Government Knowledgebase [12] is a database that provides public data regarding the governments’ capacity to develop digital solutions for access to information and citizen participation.

The Economist [13] presents data on excess deaths during the pandemic in 223 countries, based on artificial intelligence.

The Oxford COVID-19 Government Response Tracker [14] provides data from over 180 countries on government response capacities in the COVID-19 pandemic.

Our World in Data [15] corresponds to a cooperation between scientists from the University of Oxford and a non-profit organization, Global Change Data Lab., which presents data from indicators related to social disparities, health, climate change and also COVID-19 pandemic indicators.

Vaccine Equity Dashboard Data (United Nations Development Programme – UNDP) [16] is conceived by the United Nations, the World Health Organization and the University of Oxford, which provides data on the progress of vaccination in countries.

Selection of variables

Independent variables

Social cohesion

Social cohesion variables were selected based on the concept defined by the United Nations in the Data Futures Platform [11]. Variables: (i) Social capital (Score) (World Bank, 2020); (ii) Media corrupt (Score) (World Bank, 2019); (iii) Engaged society (Score) (World Bank, 2019); (iv) Social safety nets (Score) (World Bank, 2020); (v) Gender equality (Score) (World Bank, 2019); and (vi) Unemployment, youth total (% of the total labour force ages 15–24) (United Nations, 2019); (vii) Equal access index (United Nations, 2019); (viii) Equal distribution of resources index (United Nations, 2019); (ix) Public trust in politicians (Score) (World Bank, 2017–2018); (x) COVID-19-related disorder events (Total number) (United Nations, 2020).

Risk communication

The risk communication variables were selected based on previous studies [2, 17,18,19] and the WHO guide [20], which guides risk communication in public health emergencies. This guide presents different aspects related to this type of communication, including relationships of public trust, transparency, access to information, leadership, social participation, community engagement and political coordination. A study defined four components of risk communication: news media exposure, information-gathering ability, trust in the government and trust in news media [21].

In addition to the variables (corrupt media, engaged society and public trust in politicians) already selected to analyse social cohesion, other variables were selected: (i) E-Participation Index (United Nations, 2020); (ii) Transparency of government policymaking (Score) (World Bank, 2017); (iii) Right to information (Score) (World Bank, 2019) and (iv) Policy Coordination (Score) (World Bank, 2020).

Risk communication has 9 principles:1) Timeliness, 2) Transparency, 3) Coordination, 4) Accuracy and consistency, 5) Accountability and integrity, 6) Independence from politics, 7) Responsiveness, 8) Equity, 9) Trust and empathy. Concerning principle 3, effective risk communication depends on good coordination between the different levels of government responsible for disseminating information to the public [22].

Furthermore, the government must create strategies to engage the public and understand their information needs [23].

The demographic, environmental, health, economic and ethnic variables were selected from previously published studies [9, 24, 25]. The variables considered were:

  • % population aged > = 65 [26].

  • % population living in areas where elevation is < 5 m [26].

  • Population density (people per sq. km of land area) [26].

  • GDP per capita (current US$) [26].

  • % population living in areas where elevation is < 500 m [27].

  • Ambient particulate matter pollution (micrograms per cubic meter) [28].

  • Age-standardized smoking prevalence (> 15 years) [28].

  • High body-mass index [28].

  • Asthma prevalence [28].

  • Total cancer prevalence [28].

  • Chronic obstructive pulmonary disease (COPD) prevalence [28].

  • Diabetes mellitus prevalence [28].

  • Cardiovascular diseases prevalence [28].

  • Tuberculosis prevalence [28].

  • Alzheimer’s disease and other dementias prevalence [28].

  • Ethnic fractionalization [29].

  • People fully vaccinated.

  • Delivered population.

  • Vaccination policy.

  • Administration of the first dose in the country.

  • The total number of vaccination doses administered per hundred people at the country level) [30,31,32,33].

Factors related to the increased likelihood of patients progressing to the severe form of COVID-19 have been evaluated as possible factors that influence excess mortality due to COVID-19 [34].

Also, the correlation between social cohesion, risk communication variables and vaccination variables was evaluated.

The full list of the variables used is presented in the supplementary material 2.

Dependent variables

Adjusted cumulative infection rate per thousand people and adjusted infection-fatality ratio per thousand infections

This study evaluated two dependent variables related to infection: (i) Adjusted Cumulative infection rate per thousand people and (ii) Adjusted infection-fatality ratio per thousand infections. These variables were assessed for January 1, 2020, until September 30, 2021. One study evaluated the association between cumulative infection rates and infection-fatality ratio due to COVID-19 in 2021 and governance, health, and economic indicators [9]. In this study, accumulated infection rates were adjusted, taking into account possible confounding factors. Therefore, these adjusted rates were used in the present study.

Daily and cumulative excess deaths per one hundred thousand inhabitants

Excess mortality estimates how many people died during the COVID-19 pandemic, relative to the expected number of deaths, regardless of cause, under normal conditions [3]. Excess mortality corresponds to a more robust assessment of the effects of the pandemic on mortality compared to the number of deaths caused by COVID-19, as it also considers misdiagnosed or misreported deaths and mortality resulting from overburdened health services or exacerbated poverty [13, 35]. For this variable, three periods were used: (i) January 1 to December 7, 2020; (ii) January 1, 2020 to December 6, 2021 and (iii) January 1, 2020 to December 5, 2022. Data from 2017 to 2019 were selected, as they corresponded to the most recent ones, for some indicators. The choice of 2020 is justified by the declaration of a public health emergency by the World Health Organization. Considering the launch and use of the COVID-19 vaccine in 2021 and 2022, these years were included.

Statistical analysis

Statistical analysis consisted of adjusting a multiple linear regression model for the dependent variables: (i) daily estimated cumulative excess deaths per one hundred thousand inhabitants, in 2020–2022; (ii) adjusted infections per thousand people and (iii) the adjusted infection-fatality ratio per thousand infections), associated with the independent variables: sociodemographic, vaccination, health, social cohesion and risk communication.

The analysis occurred in two stages i.e., (i) simple and (ii) multiple. The simple linear regression was used to select variables to be included in the multiple linear regression model. Initially, simple linear regression models were adjusted for each independent variable. Those in which the p-value < 0.05 were included in the multiple linear regression analysis. In the second stage, these variables were adjusted using a multiple linear regression model with a stepwise procedure. Only those variables with p < 0.05 remained in the final model. Pearson correlation and biserial correlation coefficients of the variables (social cohesion and communication) and vaccination variables were calculated. In the case of the vaccination policy variable, it was dichotomised as 0 if the vaccination policies were from the year 2021, and 1 if the vaccination policies were from January to June 2022. The variable administration of the first dose in the country was dichotomised into two values: zero if the administration of the first dose occurred from July to December 2020; and one if the administration took place from January to October 2021.

Assumptions of normality, homoscedasticity and linearity were verified through the analysis of residual graphs and probabilistic normal and the identification of possible outliers and leverage points. The basis for the linearity of the independent variables in the linear regression model [36] was presented in supplementary material 3.

Multicollinearity between independent variables was evaluated. It was considered a limit for multicollinearity if the tolerance indicator assumed values greater than 0.60.

The results of the simple linear regression model analysis were not presented.

Analyses were conducted using the SAS 9.4 application [37].

Results

The adjustment of the stepwise multiple regression model showed that the estimated percentage of the population aged 65 and above, high body mass index, and COVID-19-related disorder events correlate significantly and positively with the daily estimated cumulative excess deaths per one hundred thousand inhabitants in 2020 (p < 0.0001, p < 0.0001 andp = 0.0018, respectively). On the other hand, the estimated population living in areas where elevation is < 500 m, social safety nets in 2020 and unemployment in 2019 correlate negatively and in a significant way with the daily estimated cumulative excess deaths per one hundred thousand inhabitants in 2020 (p = 0.0036,p = 0.0047 andp = 0.0449, respectively) (Table 1). The adjusted model provided an R2 = 0.4915.

Table 1 Multiple linear regression models for daily and cumulative excess deaths due to COVID-19 per one hundred thousand inhabitants in 2020 by sociodemographic data, vaccination status, health, social cohesion and risk communication variables (N = 81)
Full size table

The adjustment of the stepwise multiple regression model showed that the variables high body mass index, cardiovascular disease prevalence and transparency government 2017 correlated significantly and positively with daily excess deaths per one hundred thousand inhabitants in 2021 (p = 0.0338, p < 0.0001 and p = 0.0417, respectively). On the other hand, the estimated percentage of the population living in areas where elevation is < 5 m, social capital 2020 and public trust in politicians in 2017 correlated negatively and in a significant way with the daily estimated cumulative excess deaths per one hundred thousand inhabitants in 2021 (p = 0.0456,p = 0.0064 and p = 0.0008, respectively) (Table 2). The adjusted model provided an R2 = 0.6407.

Table 2 Multiple linear regression models for daily and cumulative excess deaths due to COVID-19 per one hundred thousand inhabitants in 2021 by sociodemographic data, vaccination status, health, social cohesion and risk communication variables (N = 87)
Full size table

The adjustment of the stepwise multiple regression model showed that the percentage of the population aged 65 and above and high body mass correlated significantly and positively with daily excess deaths per one hundred thousand inhabitants in 2022 (p < 0.0001 andp = 0.0004, respectively) (Table 3). The adjusted model provided an R2 = 0.5982.

Table 3 Multiple linear regression models for daily and cumulative excess deaths due to COVID-19 per one hundred thousand inhabitants in 2022 by sociodemographic data, vaccination status, health, social cohesion and risk communication variables (N = 92)
Full size table

The adjustment of the stepwise multiple regression model showed that the variables age-standardised, smoking prevalence, high body mass and COVID-19-related disorder events correlated significantly and positively with the adjusted infections per thousand people (p = 0.0076,p < 0.0001 and p = 0.0028, respectively), with an N = 107. On the other hand, total cancer prevalence correlated significantly and negatively with the adjusted infections per thousand people (p = 0.0350). As for the social cohesion and risk communication variables, it was positively associated with the variable COVID-19-related disorder events (p = 0.0028). The adjusted model provided an R2 = 0.3761. The adjusted infection-fatality ratio per thousand infections had no statistically significant association with the independent variables.

Most social cohesion and risk communication variables were associated with better vaccination indicators (Table 4), except for social capital and engaged society, which had no statistically significant association. There was only an association between social capital and delivered population (p = 0.0002) and social capital and administration first dose (January to October 2021) (p < 0.0001). The higher the gender equality, the better the vaccination indicators, such as people fully vaccinated and the total number of vaccination doses. However, gender equality was associated with the worst indicators of vaccination policies (January to June 2022) and administration first dose (January to October 2021).

Table 4 Pearson Correlation and Biserial Correlation coefficients of variables (social cohesion and communication) and vaccination variables, with p < 0.0001
Full size table

Discussion

One of the key findings of the present study was that the greater the number of disorder events related to COVID-19, the greater the excess mortality, in 2020, and the adjusted cumulative infection rate per thousand people due to COVID-19. It may be that this fact became more evident in 2020 because at the beginning of the pandemic, the virus and the disease were not known and people saw their jobs and lives threatened, due to government measures, which influenced waves of protests. A study revealed that in India, Israel and Mexico, where the largest number of protests in the world occurred during the COVID-19 pandemic, restrictive government measures to contain the spread of the COVID-19 virus, such as quarantine, may have influenced people to organise themselves to protest against these measures, because they felt entitled to go out to work to maintain basic conditions, such as to purchase food products. Thus, people have organised themselves to protest. In the crowds at these events, the infection rate may have increased. These events can impact adherence to government measures and, consequently, increase the number of cases or deaths due to COVID-19, in agreement with the results presented by the present research. Strengthening trust between government and citizens can improve citizens’ perception regarding the brevity of measures and the importance of achieving the good of all [38].

The COVID-19-related disorder events may also be related to the country’s economic situation, aggravated by the pandemic. The results of the present research showed that there was a negative association between the unemployment rate and excess mortality due to COVID-19. This was not an expected result, as in an unstable economic scenario, one could think that unemployment could force families to expose themselves to more risks, such as going out to look for a job or continuing in the informal market. This can reduce adherence to recommendations. According to a study, unemployment is a factor that impacted the efficiency of countries in the face of the difficulties imposed by the pandemic [39]. Alternatively, it is possible that providing economic support in countries with limited resources and high unemployment rates may have boosted public trust in the government. Initially, it may have given the impression that the government was prioritising the most vulnerable members of society. This confidence can affect acceptance and adherence to governments’ measures to control the pandemic.

Social safety nets are negatively proportional and associated with excess mortality due to COVID-19, according to the results of the present study. This was an expected result, as these safety nets are policy-related, for example, cash or food transfer programs or education-related initiatives, to reduce social risks such as unemployment and poverty. A study conducted in Malawi showed that individuals benefiting from social safety nets were less likely to reduce food consumption and need to use up investments (savings) during the COVID-19 pandemic [40]. Another study conducted in nine African countries, showed an association, albeit weak, between social safety nets and reduced food deprivation [41]. In the concept of social safety nets, defined by the World Bank, the highest score corresponds to the compensation of social risks through programs or policies, especially concerning health care. Healthcare safety nets were defined by one study as the provision of health support for those with difficulties in accessing health services. According to the definition proposed by this study, which was a systematic literature review, healthcare safety nets can result in decreased morbidity and mortality [42]. Social safety nets can be strategies for coping with a pandemic, as they can reduce social and economic vulnerability, which can impact following governmental measures and, consequently, better controlling a pandemic.

The results of this study showed that the higher the level of interpersonal trust in the government, the lower the excess mortality due to COVID-19. They agree with the findings from previous studies about the level of trust in the government. However, the dimensions of social capital influenced mortality due to COVID-19 differently. The literature showed that trust in government and politicians was associated with better adherence to government measures to control the pandemic [43,44,45] and reduced mortality rates due to COVID-19 [46,47,48]. On the other hand, a high level of trust in the government can make it difficult to enforce strict measures. For example, it may be that countries with a higher level of trust in government are those with strong liberal tendencies, which can make it difficult to enforce these measures, as individuals may find their freedoms threatened. The implementation of very strict measures can influence a population’s level of confidence in the government and reduce adherence to these measures [46].

Social capital is related to the level of trust between people and the need for mutual help in a crisis. This can impact compliance, such as wearing a mask in public, as it is related to concern for the other and the good of all. One study showed that in Japan, different dimensions of social capital had different effects on mortality due to COVID-19. The level of reciprocity and trust in the government were associated with a lower mortality rate due to COVID-19, from October to December 2020 and from January to March 2021. The other evaluated dimensions such as trust in neighbours, neighbourhood ties and social participation, were not associated with COVID-19 deaths [49]. On the other hand, another study indicated that societies with a greater capacity to organise themselves into groups had more deaths due to COVID-19. One explanation is that the identification and proximity of the associated groups may compromise physical distancing. Participation in these groups, makes people feel welcomed and protected [50]. Therefore, social capital influences the behaviour of people during a pandemic in different ways.

Although the results of the present study did not indicate an association between the adjusted cumulative infection rate per thousand people due to COVID-19 and social capital, a study, which assessed data from 177 countries, showed that trust in the government and the people around us is correlated with lower infection rates due to COVID-19. The results suggest that, if countries achieved trust in government as can be seen in Denmark, there would be 13% fewer infections on the planet. Regarding the ability to trust the other, based on Denmark’s level, there would be 40% fewer infections [51].

The present study showed that the greater the transparency of the government, the greater the excess mortality due to COVID-19. A transparent government tends to disclose data, such as the number of cases and deaths, regardless of whether this can compromise the government’s image concerning its performance and efficiency in controlling the pandemic. In contrast, a study showed a different result, which indicated that the lack of information or disclosure of unreliable information was associated with a greater number of cases of respiratory infections, including COVID-19. However, this same study showed no association between mortality related to these infections and the transparency of information [52]. This may be due to the difference between transparency and reliability, as a government can be transparent, but not use strategies to convey information in a way that is clear or that reaches the target population. Furthermore, transparency does not mean the dissemination of reliable information.

Choosing a strategy for communicating reliable information transparently, including through social media, strengthens people’s trust in government. Also, the creation of mechanisms for the active participation of individuals in the evaluation of government actions improves confidence and can help countries increase their capacity to prepare for future pandemics [17, 44, 53].

Despite the results of the present study showing that there was no significant association between the equal access index and excess mortality due to COVID-19, a study suggested that equity of access to health care implies a 0.38% reduction in the mortality rate due to COVID-19 when considering an increase in the standard deviation greater than the average value of this index. Still, this study indicates that social aspects such as trust in government and social capital are less important than the infrastructure of health systems and access to services, as countries that are largely egalitarian, where higher levels of trust are suggested, had greater difficulty in balancing different interests, such as reducing food insecurity, implementing more restrictive measures and making measures more flexible to guarantee individual rights [54].

The definition of the equal access index from the United Nations, which measures how power is distributed to needy and minority populations, may not have a positive relationship to access to health care. Furthermore, one does not know the exact measure of this power. Thus, it may have impacted the study results. A higher index of equal distribution of resources in a country, on the other hand, had a negatively proportional association with excess mortality due to COVID-19, according to the results of this study. The definition of this index corresponds to the measure of perception of how well resources are distributed in a country. This measure may have a closer relationship with the resources used in the pandemic, such as those related to health infrastructure (number of hospital beds, equipment, health professionals, etc.).

The variables of social capital and engaged society were not associated with most vaccination variables, including the total number of doses and fully vaccinated people. One study found that the greater the trust in government, the greater the vaccination. However, it was negatively proportional when the association between trust in others and vaccination was analysed. One hypothesis is that other factors influence this relationship, such as religion [55]. Another study, which was conducted in the United States, suggested that social capital has strongly contributed to the inequalities of vaccination uptake in the USA. Furthermore, this study reinforces that other factors influence this relationship, such as level of education and race [56]. By not identifying the association between the various sources, this present study cannot be controlled by factors that influence this relationship. The relationship between the population and the government may be less influenced by other factors.

Most social cohesion and risk communication variables were associated with better vaccination indicators, including the people fully vaccinated. Not only do social cohesion and risk communication seem to influence vaccination indicators, but also countries with better governance indicators had a higher number of people fully vaccinated [57].

According to the results of the present study, variables related to risk communication, such as corrupt media, right to information and social participation index, were not associated with excess mortality and adjusted cumulative infection rate per thousand people. However, government transparency was related to excess mortality. Other factors related to communication may not have been analyzed in the study. From this, more research on the role of risk communication in pandemics must be carried out.

One published study left an important recommendation, that risk communication is a process of exchange between government and the citizen, and that citizen engagement in this process, their assessment and perceptions are vital [58].

The present study has some limitations. The ecologic design of the study implies that the evidence may suffer from ecological bias. Thus, it was not possible to infer causal relationships and the correlation coefficient analysis does not imply a cause-and-effect analysis. Other variables, which were not considered in the analysis, may have influenced the results of the study. For example, the COVID-19 infection rate is not comparable over time or between states, as it was influenced by the testing strategy or testing capacities. The reliability of the data sources raises some concerns. The data may be influenced by government officials. The study results may have been impacted by missing data and temporal variation in COVID-19 outcomes. Risk perception is a factor that influences the risk communication process and the behaviour of individuals when faced with threats [21]. Therefore, these factors do not pretend to represent the whole complexity of the topic. It has to be emphasized that our analysis did not take into account relevant socio-economic indicators and cultural [59]. The results may have been affected by missing data and temporal variation in COVID-19 outcomes.

Conclusions

Social cohesion and risk communication, in their dimensions (public trust in politicians, trust in others, social safety nets, and equal distribution of resources index), were associated with lower excess mortality due to COVID-19. The number of COVID-19-related disorder events and government transparency, on the other hand, were associated with higher excess mortality due to COVID-19. Regarding unemployment, the lower the percentage of people, the higher the excess mortality due to COVID-19. Most of the social cohesion and risk communication variables were associated with better immunization indicators, except for social capital and engaged society, which had no statistically significant association. Public trust in politicians and others, equal distribution of resources and a government that cares about the most vulnerable starting with the implementation of programs, such as cash transfers and combating food insecurity, were factors that reduced the excess mortality due to COVID-19. Future studies are important to investigate other social cohesion and risk communication variables that may have contributed to effective responses in the COVID-19 pandemic. The instruments for assessing countries’ preparedness and response capacity of countries facing public health emergencies should be reviewed, and these factors of social cohesion and risk communication should be considered as potential predictors of preparedness and response. Trust and government transparency were crucial factors in tackling the pandemic, but due to the complexity of the topic, the results cannot be generalised. Future studies can investigate other factors, which may have impacted Covid-19 outcomes, such as social, economic, and cultural differences. Countries, especially those with limited resources and marked by social, economic, and health inequalities, should invest in strengthening social cohesion and risk communication, which are strategies to better cope with future pandemics.

The complete list of 213 countries is presented in supplementary material 1.

The definitions, database and year of the variables are presented in supplementary material 2.

The basis for the linearity of the independent variables in the linear regression model is presented in supplementary material 3.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. All data are available in publicly accessible databases, which are reported in the methods section.

References

  1. Shaw R, Takeuchi Y, Matsuura S, Saito K. 2013. Risk Communication. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/16147 (Accessed 01 October 2023).

  2. Jewett RL, Mah SM, Howell N, Larsen MM. Social Cohesion and Community Resilience during COVID-19 and Pandemics: a Rapid Scoping Review to inform the United Nations Research Roadmap for COVID-19 recovery. J Health Serv. 2021;51(3):325–36. https://doi.org/10.1177/0020731421997092.

    Article  Google Scholar 

  3. Rose SM, Paterra M, Isaac C, Bell J, Stucke A, Hagens A, et al. Analysing COVID-19 outcomes in the context of the 2019 Global Health Security (GHS) Index. BMJ Glob Health. 2021;6(12):e007581. https://doi.org/10.1136/bmjgh-2021-007581.

    Article  PubMed  Google Scholar 

  4. Stowell D, Garfield R. How can we strengthen the Joint External evaluation? BMJ Glob Health. 2021;6:e004545. https://doi.org/10.1136/bmjgh-2020-004545.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Haider N, Yavlinsky A, Chang YM, Hasan MN, Benfield C, Osman AY, et al. The Global Health Security index and joint external evaluation score for health preparedness are not correlated with countries’ COVID-19 detection response time and mortality outcome. Epidemiol Infect. 2020;148(e210):1–8. https://doi.org/10.1017/S0950268820002046.

    Article  CAS  Google Scholar 

  6. Abbey EJ, Khalifa BA, Oduwole MO, Ayeh SK, Nudotor RD, Salia EL, et al. The Global Health Security Index is not predictive of coronavirus pandemic responses among Organization for Economic Cooperation and Development countries. PLoS ONE. 2020;15(10):e0239398. https://doi.org/10.1371/journal.pone.0239398.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Duong DB, King AJ, Grépin KA, Hsu LY, Lim JF, Phillips C. Strengthening national capacities for pandemic preparedness: a cross-country analysis of COVID-19 cases and deaths. Health Policy Plan. 2022;37(1):55–64. https://doi.org/10.1093/heapol/czab122.

    Article  PubMed  Google Scholar 

  8. Coccia M. Preparedness of countries to face COVID-19 pandemic crisis: strategic positioning and factors supporting effective strategies of prevention of pandemic threats. Environ Res. 2022;203:111678. https://doi.org/10.1016/j.envres.2021.111678.

    Article  CAS  PubMed  Google Scholar 

  9. Bollyky TJ, Hulland EN, Barber RM, Collins JK, Kiernan S, Moses M, et al. Pandemic preparedness and COVID-19: an exploratory analysis of infection and fatality rates, and contextual factors associated with preparedness in 177 countries, from Jan 1, 2020, to Sept 30, 2021. Lancet. 2022;399(10334):1489–512. https://doi.org/10.1016/S0140-6736(22)00172-6.

    Article  Google Scholar 

  10. The World Bank. GovData360. https://govdata360.worldbank.org (Accessed 20 september 2022).

  11. The United Nations Development Programme (UNDP). 2022. A Data Futures Platform. https://data.undp.org (Accessed 30 september 2022).

  12. United Nations E-Government Knowledgebase. 2022. https://publicadministration.un.org/egovkb/en-us/About/Overview/E-Participation-Index (Accessed 30 september 2022).

  13. The Economist. 2022. Tracking COVID-19 excess deaths across countries. https://www.economist.com/graphic-detail/coronavirus-excess-deaths-tracker (Accessed 30 september 2022).

  14. The Oxford COVID-19 Government Response Tracker. 2022. https://www.bsg.ox.ac.uk/research/covid-19-government-response-tracker (Accessed 30 september 2022).

  15. Our World in Data. 2022. https://ourworldindata.org/about# (Accessed 30 september 2022).

  16. The United Nations Development Programme (UNDP). 2022. Vaccine Equity Dashboard Data. https://github.com/UNDP-Data/Vaccine-Equity-Dashboard-Data/blob/main/Data.csv (Accessed 30 september 2022).

  17. Hyland-Wood B, Gardner J, Leask J, Ecker UKH. Toward effective government communication strategies in the era of COVID-19. Humanit Soc Sci Commun. 2021;8:30. https://doi.org/10.1057/s41599-020-00701-w.

    Article  Google Scholar 

  18. Schiller C, Hellmann T, Pieper A. Liberal democracies must demonstrate long-term thinking. Bertelsmann Stiftung Carl-Bertelsmann-Straße 25633311 Gütersloh Ger. 2022. https://doi.org/10.11586/2022147.

    Article  Google Scholar 

  19. Kim DKD, Kreps GL. An Analysis of Government Communication in the United States during the COVID-19 pandemic: recommendations for effective Government Health Risk Communication. World Med Health Policy. 2020;12(4):398–412. https://doi.org/10.1002/wmh3.363.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Communicating risk in public health emergencies: a WHO guideline for emergency risk communication (ERC) policy and practice. Geneva: World Health Organization. 2017.https://www.who.int/publications/i/item/9789241550208 (Accessed 20 october 2022).

  21. Heydari ST, Zarei L, Sadati AK, Moradi N, Akbari M, Mehralian G, et al. The effect of risk communication on preventive and protective behaviours during the COVID-19 outbreak: mediating role of risk perception. BMC Public Health. 2021;21(1):54. https://doi.org/10.1186/s12889-020-10125-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Savoia E, Piltch-Loeb R, Stanton EH, Koh HK. Learning from COVID-19: government leaders’ perspectives to improve emergency risk communication. Global Health. 2023;19(1):86. https://doi.org/10.1186/s12992-023-00993-y.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Park D, Ha J. Comparison of COVID-19 and MERS Risk Communication in Korea: A Case Study of TV Public Service Advertisements. Risk Manag Healthc Policy. 2020;13:2469–2482. https://doi.org?10.2147/RMHP.S269230.

  24. Mathur R, Rentsch CT, Morton CE, Hulme WJ, Schultze A, MacKenna B, et al. Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: an observational cohort study using the OpenSAFELY platform. Lancet. 2021;397(10286):1711–24. https://doi.org/10.1016/S0140-6736(21)00634-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Office for National Statistics (ONS) and Department for Health and Social Care (DHSC). 2022.International comparisons of possible factors affecting excess mortality. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthandwellbeing/articles/internationalcomparisonsofpossiblefactorsaffectingexcessmortality/2022-12-20 (Accessed 15 february 2023).

  26. The World Bank. Databank. 2023. https://data.worldbank.org (Accessed 15 february 2023).

  27. Tremblay JC, Ainslie PN. Global and country-level estimates of human population at high altitude. PNAS. 2021;118(18):e2102463118. https://doi.org/10.1073/pnas.2102463118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Institute for Health Metrics and Evaluation – IHME. 2019. Global Burden of Disease Study 2019 (GBD) Data Resources. https://vizhub.healthdata.org/gbd-compare/# (Accessed 15 february 2023).

  29. Drazanova L. 2019. Historical Index of Ethnic Fractionalization Dataset (HIEF). https://doi.org/10.7910/DVN/4JQRCL (Accessed 15 february 2023).

  30. Sobral MFF, Oliveira BRB, Sobral AIGP, Marinho MLM, Duarte GB, Melo AS. What indicators are Associated with the Worldwide distribution of the first doses. Inq (INQ). 2021;58:1–7. https://doi.org/10.1177/00469580211060184.

    Article  Google Scholar 

  31. Bayati M, Noroozi R, Ghanbari–Jahromi M, Jalali FS. Inequality in the distribution of COVID–19 vaccine: a systematic review int. J Equity Health. 2022;21:122. https://doi.org/10.1186/s12939-022-01729-x.

    Article  Google Scholar 

  32. Oliveira BRB, Sobral AIGP, Marinho MLM, Sobral MFF, Melo AS, Duarte GB. Determinants of access to the SARS-CoV-2 vaccine: a preliminary approach. Int Int J Equity Health. 2021;20:183. https://doi.org/10.1186/s12939-021-01520-4.

    Article  PubMed  Google Scholar 

  33. Watson OJ, Barnsley G, Toor J, Hogan AB, Winskill P, Ghani AC. Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect Dis. 2022;22:1293–302. https://doi.org/10.1016/S1473-3099(22)00320-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Underlying Medical Conditions Associated with Higher Risk for Severe COVID-19. Information for Healthcare Professionals. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/underlyingconditions.html (Accessed 14 October 2023).

  35. Wang H, Paulson KR, Pease SA, Watson S, Comfort H, Zheng P, et al. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. Lancet. 2022;399:1513–36. https://doi.org/10.1016/S0140-6736(21)02796-3.

    Article  CAS  Google Scholar 

  36. Kutner M, Nachtsheim C, Neter J, Li W. Applied Linear Statistical Models (5th Edition). Chapter 10 pags 384–390. McGraw-Hill/Irwin – New York, 2005.

  37. Statistical Analysis System. Version 9.4 https://www.sas.com/pt_br/home (Accessed 27 January 2023).

  38. Campedelli GM, D’Orsogna MR. Temporal clustering of disorder events during the COVID-19 pandemic. PLoS ONE. 2021;16(4):e0250433. https://doi.org/10.1371/journal.pone.0250433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Martínez-Córdoba PJ, Benito B, García-Sánchez IM. Efficiency in the governance of the COVID-19 pandemic: political and territorial factors. Global Health. 2021;17(1):113. https://doi.org/10.1186/s12992-021-00759-4.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Mnyanga M, Chirwa GC, Munthali S. Impact of Safety nets on Household Coping mechanisms for COVID-19 pandemic in Malawi. Front Public Health. 2022;9:806738. https://doi.org/10.3389/fpubh.2021.806738.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Dasgupta S, Robinson EJZ, Food Insecurity. Safety nets, and coping strategies during the COVID-19 Pandemic: Multi-country evidence from Sub-saharan Africa. Int J Environ Res Public Health. 2021;18(19):9997. https://doi.org/10.3390/ijerph18199997.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Park BM, Lee HJ. Healthcare Safety Nets during the COVID-19 pandemic based on double Diamond Model: a Concept Analysis. Healthcare. 2021;9(8):1014. https://doi.org/10.3390/healthcare9081014.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Fridman I, Lucas N, Henke D, Zigler CK. Association between Public Knowledge about COVID-19, Trust in Information sources, and adherence to Social Distancing: cross-sectional survey. JMIR Public Health Surveill. 2020;6(3):e22060. https://doi.org/10.2196/22060.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Grogan J. 2022. Impact of the COVID-19 measures on fundamental rights and democracy, Publication for the special committee on the COVID-19 pandemic: lessons learned and recommendations for the future (COVI), Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament, Luxembourg. https://www.europarl.europa.eu/thinktank/en/document/IPOL_STU(2022)734010 (Accessed 10 October 2023).

  45. Weinberg J. Trust, Governance, and the COVID-19 pandemic: an explainer using Longitudinal Data from the United Kingdom. Political Q. 2022;93(2):316–25. https://doi.org/10.1111/1467-923X.13131.

    Article  Google Scholar 

  46. Zaki BL, Nicoli F, Wayenberg E, Verschuere B. In trust we trust: the impact of trust in government on excess mortality during the COVID-19 pandemic. Public Policy Adm. 2022;37(2):226–52. https://doi.org/10.1177/09520767211058003.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Reiersen J, Romero-Hernández M, Adán-González R. Government reactions, citizens’ responses, and COVID-19 around theWorld. Int J Environ Res Public Health. 2022;19(9):5667. https://doi.org/10.3390/ijerph19095667.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Chang D, Chang X, He Y, Tan KJK. The determinants of COVID-19 morbidity and mortality across countries. Sci Rep. 2022;12(1):5888. https://doi.org/10.1038/s41598-022-09783-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Murayama H, Nakamoto I, Tabuchi T. Social Capital and COVID-19 deaths: an ecological analysis in Japan. Int J Environ Res Public Health. 2021;8(20):10982. https://doi.org/10.3390/ijerph182010982.

    Article  CAS  Google Scholar 

  50. Elgar FJ, Stefaniak A, Wohl MJA. The trouble with trust: time-series analysis of social capital, income inequality, and COVID-19 deaths in 84 countries. Soc Sci Med. 2020;263:113365. https://doi.org/10.1016/j.socscimed.2020.113365.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Thornton J. COVID-19: trust in government and other people linked with lower infection rate and higher vaccination uptake. BMJ. 2022;376:o292. https://doi.org/10.1136/bmj.o292.

    Article  PubMed  Google Scholar 

  52. Lin TH, Chang MC, Chang CC, Chou YH. Government-sponsored disinformation and the severity of respiratory infection epidemics including COVID-19: a global analysis, 2001–2020. Soc Sci Med. 2022;296:114744. https://doi.org/10.1016/j.socscimed.2022.114744.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Mansoor M. Citizens’ trust in government as a function of good governance and government agency’s provision of quality information on social media during COVID-19. Gov Inf Q. 2021;38(4):101597. https://doi.org/10.1016/j.giq.2021.101597.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Vadlamannati KC, Cooray A, de Soysa I. Health-system equity, egalitarian democracy and COVID-19 outcomes: an empirical analysis. Scand J Public Health. 2021;49(1):104–13. https://doi.org/10.1177/1403494820982106.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Viskupič F, Wiltse DL, Meyer BA. Trust in physicians and trust in government predict COVID-19 vaccine uptake. Soc Sci Q. 2022;103(3):509–20. https://doi.org/10.1111/ssqu.13147.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Qiao S, Li Z, Zhang J, Sun X, Garrett C, Li X, Social Capital. Urbanization level, and COVID-19 vaccination uptake in the United States: A National Level Analysis. Vaccines (Basel). 2022;10(4):625. https://doi.org/10.3390/vaccines10040625.

    Article  CAS  PubMed  Google Scholar 

  57. da Silva RE, Novaes MRCG, de Oliveira C, Guilhem DB. National governance and excess mortality due to COVID-19 in 213 countries: a retrospective analysis and perspectives on future pandemics. Global Health. 2023;19(1):80. https://doi.org.10.1186/s12992-023-00982-1.

  58. Khan S, Mishra J, Ahmed N, Onyige CD, Lin KE, Siew R, et al. Risk communication and community engagement during COVID-19. Int J Disaster Risk Reduct. 2022;74:102903. https://doi.org/10.1016/j.ijdrr.2022.102903.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Chen D, Peng D, Rieger MO. Institutional and cultural determinants of speed of government responses during COVID-19 pandemic. Humanit Soc Sci Commun. 2021;8(171):1–9. https://doi.org/10.1057/s41599-021-00844-4.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the Economic and Social Research Council, Brazilian Health Regulatory Agency – Anvisa and University of Brasília – UnB.

Funding

This work was supported by the Economic and Social Research Council [grant number ES/T008822/1.

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Authors and Affiliations

  1. Brazilian Health Regulatory Agency - Anvisa, Setor de Indústrias, Trecho 5, Área Especial 57, Brasília, 71205-050, DF, Brazil

    Ricardo Eccard da Silva

  2. Faculty of Health Sciences, University of Brasília – UnB, Campos Univ. Darcy Ribeiro, Asa Norte, Brasília, 70910-900, DF, Brazil

    Ricardo Eccard da Silva, Maria Rita Carvalho Garbi Novaes & Dirce Bellezi Guilhem

  3. Department of Epidemiology & Public Health, University College London (UCL), 1-19 Torrington Place, London, WC1E 6BT, UK

    Cesar de Oliveira

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  1. Ricardo Eccard da Silva
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  4. Dirce Bellezi Guilhem
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Contributions

R.E.S., M.R.C.G.N and D.B.G conceived of this study. C.M.O secured the funding. R.E.S was responsible by data collection. All authors analyzed and interpreted the data and critically reviewed the article. Furthermore, all authors reviewed drafts and approved the final manuscript.

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Correspondence to Cesar de Oliveira.

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The study did not involve human beings. Ethical approval for this type of study is not required by our institute.

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The authors declare no competing interests.

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da Silva, R.E., Novaes, M.R.C.G., de Oliveira, C. et al. The impact of social cohesion and risk communication on excess mortality due to COVID-19 in 213 countries: a retrospective analysis. BMC Public Health 24, 1598 (2024). https://doi.org/10.1186/s12889-024-19076-7

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