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
Summary
Background
National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics.
Methods
Daily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation’s modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates.
Findings
The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5·4% [4·0–7·9] of variation), GDP per capita (4·2% [1·8–6·6] of variation), and the proportion of infections attributable to seasonality (2·1% [95% uncertainty interval 1·7–2·7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46·7% [18·4–67·6] of variation), GDP per capita (3·1% [0·3–8·6] of variation), and national mean BMI (1·1% [0·2–2·6] of variation). 44·4% (29·2–61·7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as well as less government corruption, were also associated with higher COVID-19 vaccine coverage among middle-income and high-income countries where vaccine availability was more widespread, and lower corruption was associated with greater reductions in mobility. If these modelled associations were to be causal, an increase in trust of governments such that all countries had societies that attained at least the amount of trust in government or interpersonal trust measured in Denmark, which is in the 75th percentile across these spectrums, might have reduced global infections by 12·9% (5·7–17·8) for government trust and 40·3% (24·3–51·4) for interpersonal trust. Similarly, if all countries had a national BMI equal to or less than that of the 25th percentile, our analysis suggests global standardised IFR would be reduced by 11·1%.
Interpretation
Efforts to improve pandemic preparedness and response for the next pandemic might benefit from greater investment in risk communication and community engagement strategies to boost the confidence that individuals have in public health guidance. Our results suggest that increasing health promotion for key modifiable risks is associated with a reduction of fatalities in such a scenario.
Funding
Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.
Evidence before this study
Responsive policies such as physical distancing and mask mandates were important in shaping outcomes during the COVID-19 pandemic. Yet, the conditions associated with cross-country variation in infection and fatality rates during the COVID-19 pandemic are not well understood. In the aftermath of the 2013–16 Ebola epidemic in west Africa, WHO launched a voluntary Joint External Evaluation (JEE) process to track adoption of core capacities required under the 2005 International Health Regulations and to assess national capacity to prevent, detect, and respond to disease with potential for pandemic spread. WHO’s April 2021 interim assessment did not find JEE scores from the 100 countries that had conducted voluntary assessments to be correlated with COVID-19 outcomes, although such metrics were designed as benchmarking exercises for National Action Plans rather than cross-country comparators. Preliminary analysis of COVID-19 outcomes in relation to other health-system capacity indices, such as the Global Health Security Index and the index of effective coverage of universal health coverage produced by the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) have also been found not to be predictive of COVID-19 outcomes. Other exploratory research on COVID-19 outcomes has had a regional focus or has focused on a small number of country experiences.
Added value of this study
We analysed measures of pandemic preparedness. 12 indicators of preparedness and response and seven indicators of health-system capacity were considered, in addition to ten other demographic, social, and political conditions that previous research suggests might be relevant. Associations with both incidence and mortality from SARS-CoV-2 infections were investigated. We controlled for demographic, biological, economic, and environmental variables associated with COVID-19 outcomes, including population age structure and environmental seasonality, population density, national income, and population health risks, to identify contextual factors subject to policy control. This research considerably expands on the scope of previous research by investigating correlates of pandemic preparedness and mitigation in 177 countries between Jan 1, 2020, and Sept 30, 2021, and includes inputs that have been adjusted for problems associated with under-reporting of COVID-19 outcomes. This expanded scope was possible because of inputs from COVID-19 research produced by the Institute for Health Metrics and Evaluation and mortality and population estimates generated by GBD.
Implications of all the available evidence
The existing metrics for health-system capacity and national pandemic preparedness and response have been poor predictors of pandemic outcomes, suggesting other areas might merit greater weight in future preparedness efforts. Not all of the correlates that account for some variation in infections per capita and infection-to-fatality ratios, such as age structure, altitude at which a population lives, and environmental seasonality, are easy for policy makers to control. Yet, other factors are within the policy realm, including preventive health measures focused on population health fundamentals: encouraging healthy bodyweight and reducing smoking might be helpful in averting morbidity and mortality in future pandemic scenarios. Moreover, the level of trust is something that a government can prepare for and earn in a crisis, and our analysis suggests doing so may be crucial to mount a more effective response to future pandemic threats. Large unexplained variation in differences in SARS-CoV-2 infections across countries speaks to the importance of further research in this area.
Introduction
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Policy makers have begun developing new global and national pandemic preparedness proposals to ensure the world is better prepared when the next deadly and fast-spreading novel pathogen emerges.
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Policy responses such as mask mandates and physical-distancing measures have been key to shaping outcomes.
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However, identifying the contextual factors associated with reduced infection and fatality rates is important to guide the long-term path to addressing future threats.
Reported incidence and mortality from SARS-CoV-2 have not followed the pattern of many other communicable diseases; wealthier countries with more health-care resources have had a greater burden from COVID-19 than have low-income countries with fewer health-care resources. Upper-middle-income and high-income countries have 48% of the global population but 53% of the total estimated excess mortality-adjusted cumulative deaths from COVID-19 as of Sept 30, 2021, despite having much higher COVID-19 vaccination rates since December, 2020, compared with those in low-income and lower-middle-income countries.
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Also, national cumulative mortality rates have varied dramatically, even among countries within close geographical proximity. Bulgaria, Namibia, and Bolivia have COVID-19 mortality rates greater than 4 deaths per 1000 people, whereas geographical neighbours Turkey, Angola, and Colombia, respectively, have fewer than half as many deaths per capita, with mortality rates at or below 2 per 1000. Moreover, countries that experts believed before the pandemic to be most prepared to mitigate the effects of a pandemic have not been the most successful at doing so.
A preliminary analysis in June, 2020, examined the Global Health Security (GHS) Index, the WHO Joint External Evaluation (JEE), and a measure of universal health coverage and found no connection between those capacity measures and COVID-19 deaths, even when accounting for differences in population age structure.
The report of the Independent Oversight and Advisory Committee for the WHO Health Emergencies Program in May, 2021, also did not find evidence of a relationship between JEE scores and COVID-19 outcomes.
used somewhat limited correlational and descriptive analyses without controlling for known key determinents,
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focused exclusively on cumulative deaths,
or had small sample sizes and missing data.
One analysis investigated some political, social, and governmental correlates with cumulative deaths per capita,
and another found a relationship between trust in government and reduced death rates.
Our analysis builds on this research by incorporating results from the Institute for Health Metrics and Evaluation (IHME) on estimated infections built from hospitalisations, reported cases, and deaths accounting for excess mortality due to the COVID-19 pandemic,
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and additional covariates of interest such as metrics of health system and pandemic preparedness and response capacity. Additionally, we controlled for key covariates associated with age structure of the population and environmental seasonality, among other factors.
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Without controlling for these factors, an analysis risks confounding from other deterministic drivers that are outside the control of policy makers. Also, we differentiated between infection rates and infection-fatality ratios (IFRs) to assess the differences in prevention and treatment of COVID-19. Finally, we incorporated subnational data where available.
The aim of this research was to complete an exploratory analysis of potential correlates of COVID-19 prevention and treatment across 177 countries and territories. We investigated these correlates in relation to both SARS-CoV-2 infections and IFRs to disentangle the factors that prevented the spread of the virus from the health-system factors that prevented death from disease. We controlled for known factors of SARS-CoV-2 infection and mortality that are generally considered outside the control of policy makers (such as altitude, age profile, and seasonality of the disease) and explored associations with 28 factors that policy makers can control. Variables explored were associated with pandemic preparedness indices; health-system capacity indicators; governance variables; and measures of economic inequality and societies’ trust in their government, science, and their communities.
Methods
Overview
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This research was done in three stages. In stage 1, we standardised the national infection rates and IFRs by estimating what the infection rate and IFR would be if each country had the global mean value of key known drivers of infection and IFR. This process included adjusting national infection rates for environmental seasonality, altitude, and income, among other factors, and standardising IFRs to the global age distribution and the prevalence of competing risks. In stage 2, we measured the cross-country association of these standardised infection rates and IFRs against health-system policy variables, such as measures of pandemic preparedness, health-system capacity, governance factors, and several measures of social and governmental trust, to identify which of these policy factors, if any, were associated with fewer infections and lower IFR. In stage 3, we investigated how reduction in mobility and vaccine coverage might be pathways for more distal policy variables to affect infection rates and IFR. For stages 1 and 2, we assessed two time periods. To assess the full span of the pandemic (until present), we assessed cumulative infection and IFR for Jan 1, 2020, until Sept 30, 2021. As a sensitivity analysis, we also assessed the time period before vaccines and disease variants were known to have spread, Jan 1, 2020, until Oct 15, 2020. All analyses were done using R (version 4.0.3).
Code used to produce this analysis is available online.
COVID-19 infection and mortality estimates
To estimate the number of COVID-19 deaths, IHME extracted data from the Johns Hopkins University Center for Systems Science and Engineering COVID-19 database, supplemented these data with additional data from national and subnational ministries and departments of health, and adjusted them to correct for missing data and reporting lags. The resulting mortality rates were then adjusted for under-reporting on the basis of the ratio of excess deaths attributable to COVID-19 versus reported deaths, a ratio that was modelled using spatial correlation and additional covariates.
To estimate the number of SARS-CoV-2 infections, IHME estimated infections from the number of deaths, hospital census, and reported cases occurring in each location, again extracted from the Johns Hopkins COVID-19 database and adjusted for missing data and reporting lags. The infections estimate was based on IFR, infection to hospitalisation ratios, and infection to detection ratios, respectively, estimated for each population. Ratio observations were derived by matching the parameter (eg, deaths) to the number of infections occurring in the population according to seroprevalence surveys, the results of which have been adjusted for waning sensitivity of antibody tests and other known biases. The IFR, infection to hospitalisation ratio, and infection to detection ratio were then modelled as a function of covariates to obtain predictions for all locations and days. Underlying data uncertainty and modelling uncertainty were propagated at each stage and incorporated into the quantification of the estimates’ uncertainty. Full details of the modelling approaches are provided elsewhere.
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The IFRs were calculated by applying a 9-day lag to our daily infections to account for the delay between infection and death, calculating the sum of infections and deaths, and then dividing the cumulative deaths over the cumulative lagged infections.
Variable selection
Stage 1: standardising infection rates and IFRs
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), and population density. We produced standardised infection rates by estimating the seasonally adjusted infection rates for each location had each had the global mean of each of these factors.
(COPD, cancer, diabetes [of any type], cardiovascular disease, and chronic kidney disease), the age-standardised rates of chronic kidney and diabetes were correlated, and cardiovascular disease and diabetes were correlated with BMI, leading to multicollinearity. Consequently, age-standardised prevalence of chronic kidney disease, age-standardised cardiovascular disease, and age-standardised diabetes (the factors with the largest variance inflation factors) were removed from the model.
Stage 2: exploring health care, governance, and social associations with standardised COVID-19 outcomes
that imputed values for countries with missing information (appendix p 45). To estimate the fraction of variance explained by each indicator, we noted the sum of squares explained by each factor and combined each value with the raw sum of squares of cumulative infections per capita or of IFR.
Stage 3: the association between key factors and intermediate health outputs
and the maximum achieved vaccine coverage (at least one dose) as of Sept 30, 2021. Given the lack of access to vaccinations and vaccine supplies in many low-income and lower-middle-income countries during our study period, the analysis of vaccine coverage was only on upper-middle-income and high-income countries.
Uncertainty and sensitivity analyses
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Additionally, for each draw and each linear regression we took a random sample draw from the estimated variance-covariance matrix to incorporate model uncertainty. Similarly, the PCA-based summary indicators were completed 100 times to capture uncertainty from the imputation process. Here, we report the mean of the 100 estimates, with uncertainty intervals (UIs) spanning for the 2·5th and 97·5th percentiles of the 100 estimates.
Role of the funding source
Funding was provided by the Bill & Melinda Gates Foundation, Bloomberg Philanthropies, J Stanton, T Gillespie, and J and E Nordstrom. The funders of the study had no role in the study design, data collection, data analysis, data interpretation, or the writing of the report. Members of the core research team for this topic area had full access to the underlying data used to generate estimates presented in this paper. All other authors had access, and reviewed, estimates as part of the research evaluation process, which includes additional stages of formal review.
Results
Discussion
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For IFR, the dominant significant factors explaining cross-country variation are age structure, GDP per capita, and BMI. Age structure alone explains the largest proportion of cross-country variation in IFR globally. Much research on COVID-19 has shown that with each year of life lived, the relative risk of mortality with infection increases dramatically,
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and this increase in risk goes beyond simply increased rates of other competing risks. Some research has shown that lower mortality for younger patients might be driven by molecular differences that allow younger patients to control the viral load, one predictor of mortality.
Despite the importance of age, the effect of modifiable health risks in this analysis confirms previous research findings on the important role of high BMI,
and suggests changes to smoking prevalence
and ambient air pollution
could influence outcomes during this pandemic. The prevalence of these risks related to non-communicable diseases is amenable to national and international health policy and represents an area of potential investment for mitigating the effects of future pandemics.
Tobacco control policies, including increases in taxes and bans on advertising, have proven effective and cost-efficient in poor and wealthy nations alike.
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Strategies to promote lower BMI might include subsidising healthy food, such as fresh fruits, nuts, and vegetables, and taxing unhealthy foods such as sugar-sweetened beverages.
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Increasing reliance on non-polluting, renewable energy sources and ramping up air-quality monitoring have been central to progress in reducing ambient air pollution, even in difficult settings.
In addition to risk-specific interventions, progress might also be advanced by working with WHO and other intergovernmental institutions on advancing international, national, and community-level strategies to counter the common commercial drivers behind tobacco use prevalence, obesity, and air pollution.
The second major conclusion of this study is that important indicators of health-care capacity (the UHC effective coverage index and the HAQ Index) and of pandemic preparedness and response (JEE and GHS Index) were not correlated with cross-country variation in SARS-CoV-2 infections or IFR. The disconnect that exists between COVID-19 outcomes and the composite estimates of JEE and GHS Index scores also holds when the analysis is done on index components devoted to detection, prevention, and response, as well as the GHS Index health sector, commitment to international norms, and risk environment indices.
This analysis suggests that the JEE, GHS Index, and the UHC effective coverage index do not simply reflect capacities otherwise required for effective pandemic prevention and response, but fail to account for the consequences of poor leadership and dysfunctional political environments. A high ranking on the leading measures of health system capacity and pandemic preparedness has not only been insufficient for success in this pandemic, but also unnecessary. Various countries, including Burundi, the Philippines, and the Dominican Republic, are all examples of countries that rank low in GHS Index and JEE overall scores, and UHC effective coverage and HAQ Index scores, but so far have maintained low rates of standardised infections and IFR. We can further conclude that whatever proportion of cross-country variation in infections and IFR might be policy amenable, these existing measures of health-care and pandemic preparedness capacity offer no explanation.
The JEE, GHS Index, and measures of UHC are intended to be tools for identifying gaps in national capacity to direct financial and political support appropriately and were never intended to predict pandemic outcomes. JEEs were developed as a mechanism to identify gaps in a country’s preparedness for developing National Action Plans and were not designed for cross-country comparability. Additionally, the aggregate measure is a weighting of the components and was not scored by the countries themselves. More than 100 nations have undertaken voluntary JEEs, and more than 60 countries developed National Action Plans for Health Security; the benefits of such exercises extend beyond preparing for the COVID-19 pandemic. Similarly, the 2019 GHS Index focuses on a national capacity and preparedness to prevent, limit, and respond to epidemic spread, with what the scores measures potentially having benefits associated with future pandemics. Measures assessed by these metrics, such as laboratory capacity, that have not yet been shown to drive outcomes in a pandemic, might well prove important against future emerging disease threats because such measures were not intended to predict outcomes for any one specific pandemic.
The third major conclusion of this study is that higher levels of trust (government and interpersonal) had large, statistically significant associations with fewer infections for the entire study period, but not with global variation in IFR. Less government corruption had a smaller but still statistically significant association with fewer infections and has no association with global variation in IFR. No other social factors (economic inequality or trust in science), state capacity measures (government effectiveness or state fragility), or features of political systems (electoral democracy or populism) had a statistically significant association with cross-country variation in infections or IFR. One way to quantify the contribution of trust to COVID-19 outcomes is with a counterfactual: if these associations are causal and all countries improved trust in government to the level of Denmark (approximately the 75th percentile of measured countries), this analysis suggests 12·9% fewer global infections would have occurred. Similarly, if all countries improved interpersonal trust to the same level (the 75th percentile of measured countries), the effect would be even larger—40·3% fewer global infections would have occurred.
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When a virus emerges with high potential for spread, governments must be able to convince citizens to adopt essential public health measures. Doing so often requires behaviour change, from mask wearing and physical-distancing rules to following quarantine policies. This study accords with previous research that suggests that the success of that effort depends on two forms of trust: trust in governments
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and interpersonal trust. Collectively, trust among people and in their government can change behaviour such that if people respond to directives and take protective health measures, they might expect other members of the community to do the same.
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In much of the world, public health is a local, community-based endeavour and, in that context, the outsized role of interpersonal trust in this pandemic is unsurprising. Previous research has shown that public corruption contributes to lower trust in government and social institutions, which might reduce compliance with public health guidance and policies.
Other smaller and preliminary studies have suggested links between trust in government, interpersonal trust, and public corruption and COVID-19 outcomes.
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Surveys from Italy, the Netherlands, and Switzerland during the 2009 H1N1 influenza pandemic found that government trust was associated with increased handwashing, physical distancing, vaccination, and other recommended behaviours.
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The identity of the messenger in risk communication can also improve or damage trust.
The Ebola epidemic in west Africa was curtailed by rebuilding the public’s trust in the government response. In Liberia, Ebola survivors were celebrated in communities, while community youth leaders, pastors, and imams were trained to conduct daily household surveillance and identify infected patients. In Sierra Leone, local community-liaison teams working in collaboration with WHO increased acceptance of the Ebola vaccine trials during and after the outbreak. In the COVID-19 pandemic, by contrast, some nations with historically low levels of government trust opted to promote politicians over public health experts for risk communication in the crisis, which might have contributed to reduced compliance with public health guidance and worsening health outcomes.
It can be fostered in between crises through sustained investment. Previous research has assessed that trusting relations affect health outcomes through various forms of social capital, including bonding social capital (among networks of people who consider themselves to be similar), bridging social capital (among members of a network who perceive themselves as differing by age, racial or ethnic group, class, or other sociodemo-graphic characteristic), and linking social capital (across power or authority gradients such as the relationships between people and their law enforcement, health-care providers, medical researchers, or bankers).
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, Trusting relationships in all three of these forms of social capital have clearly affected health outcomes in the COVID-19 pandemic. Identifying which forms of social capital the role of trust has been the most important in this pandemic is beyond the scope of this paper, but the appendix (p 14) shows the correlations among the health-care, governance, and social indicators considered in this analysis and might provide some preliminary clues. For instance, low interpersonal trust is most correlated with income inequality and government corruption, suggesting those who are economically and socially disadvantaged and confront a society stacked against them might be naturally less inclined to trust others.
The findings of future research should inform the longer-term potential application of the strategies used to promote resilience in disaster response and recovery by deepening trust within and between communities, from the economically and social disadvantaged and up to those in authority.
Uncertainty over the conditions that contribute to variation across countries in COVID-19 outcomes undermines efforts to convince global partners and policy makers to invest in preparing for future pandemics. Large, unexplained variation in differences in infections across countries speaks to the importance of further research in this area. In the meantime, this analysis identifies factors that explain some of the variation in the COVID-19 pandemic and suggests areas for potential investment in preparing for the next pandemic threat. Governments should invest in risk communication and community engagement strategies to boost the confidence that individuals have in government guidance in public health crises, especially in settings with historically low levels of government and interpersonal trust. Additionally, health promotion to address key modifiable risks might be an important condition for reducing fatalities in some pandemic scenarios.
COVID Pandemic Preparedness Collaborators
Thomas J Bollyky*, Erin N Hulland*, Ryan M Barber, James K Collins, Samantha Kiernan, Mark Moses, David M Pigott, Robert C Reiner Jr, Reed J D Sorensen, Cristiana Abbafati, Christopher Adolph, Adrien Allorant, Joanne O Amlag, Aleksandr Y Aravkin, Bree Bang-Jensen, Austin Carter, Rachel Castellano, Emma Castro, Suman Chakrabarti, Emily Combs, Xiaochen Dai, William James Dangel, Carolyn Dapper, Amanda Deen, Bruce B Duncan, Lucas Earl, Megan Erickson, Samuel B Ewald, Tatiana Fedosseeva, Alize J Ferrari, Abraham D Flaxman, Nancy Fullman, Emmanuela Gakidou, Bayan Galal, John Gallagher, John R Giles, Gaorui Guo, Jiawei He, Monika Helak, Bethany M Huntley, Bulat Idrisov, Casey Johanns, Kate E LeGrand, Ian D Letourneau, Akiaja Lindstrom, Emily Linebarger, Paulo A Lotufo, Rafael Lozano, Beatrice Magistro, Deborah Carvalho Malta, Johan Månsson, Ana M Mantilla Herrera, Fatima Marinho, Alemnesh H Mirkuzie, Ali H Mokdad, Lorenzo Monasta, Paulami Naik, Shuhei Nomura, James Kevin O’Halloran, Christopher M Odell, Latera Tesfaye Olana, Samuel M Ostroff, Maja Pasovic, Valeria Maria de Azeredo Passos, Louise Penberthy, Grace Reinke, Damian Francesco Santomauro, Maria Inês Schmidt, Aleksei Sholokhov, Emma Spurlock, Christopher E Troeger, Elena Varavikova, Anh T Vo, Theo Vos, Rebecca Walcott, Ally Walker, Simon D Wigley, Charles Shey Wiysonge, Nahom Alemseged Worku, Yifan Wu, Sarah Wulf Hanson, Peng Zheng, Simon I Hay, Christopher J L Murray, and Joseph L Dieleman. *Joint first authors, contributed equally.
Affiliations
Council on Foreign Relations, Washington, DC, USA (T J Bollyky JD, S Kiernan BA); Institute for Health Metrics and Evaluation (E N Hulland MPH, R M Barber BS, J K Collins BS, M Moses MHS, D M Pigott PhD, R C Reiner Jr PhD, R J D Sorensen MPH, A Allorant PhD, J O Amlag MPH, A Y Aravkin PhD, B Bang-Jensen MA, A Carter MPH, R Castellano MA, E Castro MS, S Chakrabarti MA, E Combs BA, X Dai PhD, W J Dangel MD, C Dapper MA, A Deen MPH, L Earl MSc, M Erickson MA, S B Ewald MS, T Fedosseeva MSc, A J Ferrari PhD, A D Flaxman PhD, N Fullman MPH, Prof E Gakidou PhD, J Gallagher MPA, J R Giles PhD, G Guo MPH, J He MSc, M Helak BA, B M Huntley BA, C Johanns BS, K E LeGrand MPH, I D Letourneau BA, E Linebarger BA, Prof R Lozano MD, J Månsson MS, A H Mokdad PhD, P Naik MSPH, J K O’Halloran MA, C M Odell MPP, S M Ostroff PhD, M Pasovic MEd, L Penberthy MS, G Reinke MA, D F Santomauro PhD, A Sholokhov MSc, E Spurlock MPH, C E Troeger MPH, A T Vo BSc, Prof T Vos PhD, A Walker MA, Y Wu MPH, S Wulf Hanson PhD, Prof P Zheng PhD, Prof S I Hay DSc, Prof C J L Murray DPhil, J L Dieleman PhD), Department of Global Health (E N Hulland MPH, R J D Sorensen MPH, S Chakrabarti MA), Department of Health Metrics Sciences, School of Medicine (D M Pigott PhD, R C Reiner Jr PhD, A Y Aravkin PhD, X Dai PhD, A D Flaxman PhD, Prof E Gakidou PhD, Prof R Lozano MD, A H Mokdad PhD, Prof T Vos PhD, Prof P Zheng PhD, Prof S I Hay DSc, Prof C J L Murray DPhil, J L Dieleman PhD), Department of Political Science (Prof C Adolph PhD), Center for Statistics and the Social Sciences (Prof C Adolph PhD), Department of Applied Mathematics (A Y Aravkin PhD), Henry M Jackson School of International Studies (S M Ostroff PhD), Evans School of Public Policy & Governance (R Walcott MPH), University of Washington, Seattle, WA, USA; Department of Juridical and Economic Studies (C Abbafati PhD), La Sapienza University, Rome, Italy; Postgraduate Program in Epidemiology (Prof B B Duncan MD, Prof M I Schmidt MD), Federal University of Rio Grande do Sul, Porto Alegre, Brazil; School of Public Health (A J Ferrari PhD, A Lindstrom MEpi, A M Mantilla Herrera PhD, D F Santomauro PhD), The University of Queensland, Brisbane, QLD, Australia; Department of Social and Behavioral Sciences (E Spurlock MPH), Yale University, New Haven, CT, USA (B Galal BS); Infectious Diseases Department (B Idrisov MD), Bashkir State Medical University, Ufa, Russia; Laboratory of Public Health Indicators Analysis and Health Digitalization (B Idrisov MD), Moscow Institute of Physics and Technology, Moscow, Russia; School of Public Health (A Lindstrom MEpi), West Moreton Hospital Health Services (A M Mantilla Herrera PhD), Policy and Epidemiology Group (D F Santomauro PhD), Queensland Centre for Mental Health Research, Wacol, QLD, Australia; Department of Medicine (Prof P A Lotufo DrPH), University of São Paulo, São Paulo, Brazil; Munk School of Global Affairs and Public Policy (B Magistro PhD), University of Toronto, Toronto, ON, Canada; Department of Maternal and Child Nursing and Public Health (Prof D C Malta PhD), Department of Public Health (F Marinho PhD), Faculty of Medical Sciences of Minas Gerais (Prof V M d Passos PhD), Federal University of Minas Gerais, Belo Horizonte, Brazil; Department of Public Health (F Marinho PhD), Vital Strategies, São Paulo, Brazil; National Data Management Center for Health (NDMC) (A H Mirkuzie PhD, N A Worku MSc), Ethiopian Public Health Institute, Addis Ababa, Ethiopia; Center for International Health (A H Mirkuzie PhD), University of Bergen, Bergen, Norway; Clinical Epidemiology and Public Health Research Unit (L Monasta DSc), Burlo Garofolo Institute for Maternal and Child Health, Trieste, Italy; Department of Health Policy and Management (S Nomura PhD), Keio University, Tokyo, Japan; Department of Global Health Policy (S Nomura PhD), University of Tokyo, Tokyo, Japan; School of Electrical and Computer Engineering (L T Olana BSc), Addis Ababa University, Addis Ababa, Ethiopia; Central Research Institute of Cytology and Genetics (E Varavikova PhD), Federal Research Institute for Health Organization and Informatics of the Ministry of Health (FRIHOI), Moscow, Russia; Department of Philosophy (S D Wigley PhD), Bilkent University, Ankara, Turkey; Cochrane South Africa (Prof C S Wiysonge MD), South African Medical Research Council, Cape Town, South Africa; School of Public Health and Family Medicine (Prof C S Wiysonge MD), University of Cape Town, Cape Town, South Africa.
Contributors
Declaration of interests
C Adolph reports support from the Beneficus Foundation as funding support for collection of data on state-level physical-distancing policies in the USA. T J Bolyky reports support from Bloomberg Philanthropies for the Council on Foreign Relations; grants or contracts from the Bill & Melinda Gates Foundation for the Council on Foreign Relations; consulting fees from the Coalition for Epidemic Preparedness Innovations and from the Bill & Melinda Gates Foundation as personal payments; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from the Varieties of Democracy Project, Stanford University, and Emory University, all outside the submitted work. J L Dieleman reports support from the Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom. A D Flaxman reports grants or contracts from the Alfred P Sloan Foundation, Bill & Melinda Gates Foundation, NORC, National Science Foundation, and the US Census Bureau; consulting fees from Kaiser Permanente; stock or stock options in Agathos; other support from Johnson & Johnson, Merck for Mothers, Sanofi, and Swiss Re, all outside the submitted work. N Fullman reports receiving funding from WHO as a consultant from June to September, 2019, and Gates Ventures since June, 2020, all outside the submitted work. S Nomura reports support from Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT), outside the submitted work. V M D Passos reports consulting fees from the Global Grant Program as personal payment through the FUNDEP, Universidade Federal de Minas Gerais, outside the submitted work. D M Pigott reports support from the Bill & Melinda Gates Foundation; and grants or contracts from the Bill & Melinda Gates Foundation, outside the submitted work. All other authors declare no competing interests.
Data sharing
Acknowledgments
T J Bolyky acknowledges support from Bloomberg Philanthropies. A J Ferrari acknowledges support from a National Health and Medical Research Council Early Career Fellowship Grant APP1121516 and is employed by the Queensland Centre for Mental Health Research, which receives core funding from the Queensland Department of Health. S Nomura acknowledges the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT). R C Reiner Jr acknowledges partial support by the National Science Foundation (FAIN: 2031096). D F Santomauro is employed by the Queensland Centre for Mental Health Research, which receives core funding from the Department of Health, Queensland Government. C S Wiysonge is supported by the South African Medical Research Council.
Supplementary Material
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Published: February 01, 2022
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© 2022 The Author(s). Published by Elsevier Ltd.