The Study in a Nutshell
The World Health Organization declared COVID-19 a global pandemic on 11 March 2020. Almost no country has avoided the virus and even fewer have escaped the economic fallout. In total, there are now over 18 million confirmed cases and 700,000 deaths worldwide. In June, the IMF projected a -4.9% fall in global growth in 2020, the largest outside of wartime.
Despite the far-reaching damage to both health and economic wellbeing, the pandemic’s impacts are significantly heterogeneous. There are variations in the scale of the outbreak, the magnitude of government responses, the timing of lockdowns and the management of healthcare systems. A research paper published in The Lancet on 21 July conducts a much-needed country-level analysis. It assesses the impact of timing and the type of national health policy/actions undertaken towards COVID-19 mortality and related health outcomes. It finds:
• Increased COVID-19 caseloads were significantly associated with:
1. Higher obesity
2. Higher median population age
3. Longer time to border closures from the first reported case
4. Fewer days to any full or partial lockdown
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The Study in a Nutshell
The World Health Organization declared COVID-19 a global pandemic on 11 March 2020. Almost no country has avoided the virus and even fewer have escaped the economic fallout. In total, there are now over 18.5mn confirmed cases and 700,500 deaths worldwide. In June, the IMF projected a -4.9% fall in global growth in 2020, the largest outside of wartime.
Despite the far-reaching damage to both health and economic wellbeing, the pandemic’s impacts are significantly heterogeneous. There are variations in the scale of the outbreak, the magnitude of government responses, the timing of lockdowns and the management of healthcare systems. A research paper published in The Lancet on 21 July conducts a much-needed country-level analysis. It assesses the impact of timing and the type of national health policy/actions undertaken towards COVID-19 mortality and related health outcomes. It finds:
- Increased COVID-19 caseloads were significantly associated with:
- Higher obesity
- Higher median population age
- Longer time to border closures from the first reported case
- Fewer days to any full or partial lockdown
- Increased mortality was significantly associated with:
- Higher obesity prevalence
- Higher per capita GDP
- Higher median population age (Chart 1)
- Fewer nurses (Chart 2)
- Increased patient recovery rates were significantly associated with:
- Full lockdowns
- Higher scores on the global health security scale for risk environment
- Increased critical cases were significantly associated with:
- Higher unemployment rates
- Higher per capita GDP
The authors also find that reduced income dispersion and a higher prevalence of smoking were associated with lower mortality and a lower number of critical cases. Furthermore, border closures, full lockdowns and a high rate of COVID-19 testing were not associated with statistically significant reductions in the number of critical cases or overall mortality.
Overall, the analysis indicates that low levels of national preparedness, scale of testing and population characteristics were associated with increased national caseloads and mortality.
Charts 1 & 2: Having Older Populations and Fewer Nurses Increases Overall Mortality
Source: Page 6 of ‘A Country Level Analysis Measuring the Impact of Government Actions, Country Preparedness and Socioeconomic Factors on COVID-19 Mortality and Related Health Outcome’
The Key Questions
What needs to be understood?
Public health policies have varied widely across countries. Yet, measures such as the detection and isolation of infected individuals, contact tracing, quarantine measures, physical distancing, and closure of nonessential businesses have become major components of public health guidance, aiming to reduce the spread of further infection and prevent health system strain. The outcomes of such policies have, however, varied enormously: countries like China, South Korea and Taiwan have reduced new cases by more than 90%, but the UK, Italy and the US have struggled to curtail infections and overall mortality.
What may explain cross-country variation?
Compliance, timing, pre-existing socioeconomic characteristics, baseline healthcare capacity and other health-related population features may explain differences in the effectiveness of public health policies. The paper explores each.
Why is it important to understand?
Knowing the most effective interventions can assist health policymakers with resource allocation decisions, provide evidence regarding the effectiveness of population health measures, and help countries with internal geographic disparities mitigate risk with more informed resource planning.
What do the results in the report say?
Higher caseloads and overall mortality were associated with comorbidities such as obesity and aging populations. In contrast, lower income dispersion lowered mortality and critical cases. Furthermore, countries that are least vulnerable to biological threats had the highest number of recoveries.
What about lockdowns?
The government policy of full lockdowns (vs. partial or curfews only) was strongly associated with recovery rates. The number of days to any border closure was associated with the number of cases per million. The associations suggest that fuller lockdowns (more restrictive public health measures) and earlier border closures may lessen the peak of transmission and facilitate increased recovery rates. These results may explain why certain countries have suffered more during the pandemic.
Why does a higher per capita GDP translate into more deaths?
This may reflect more widespread testing in those countries, greater transparency with reporting and better national surveillance systems. Other possible reasons for the association include increased accessibility to air travel and international holidays in wealthier countries, since the paper identified travel to be an important factor contributing to international viral spread.
Does it make sense that countries with a higher smoking prevalence have fewer deaths?
The authors recommend further investigation into this association; however, it corroborates findings reported in another study looking at patients in New York City. The finding of relatively lower smoking rates among critically ill COVID-19 patients is due in part to their increased age distribution, since countries with a lower median age have higher smoking rates. A recent evaluation of 17 million adult patients within the UK’s NHS, with 5683 COVID-related deaths, identified a potential protective effect of smoking. In their analysis, current smokers were associated with a reduced risk of COVID-19 related mortality.
How important is testing?
The authors find that full lockdowns and widespread COVID-19 testing were not associated with reductions in the number of critical cases or overall mortality.
Are there other interesting results?
Among the top 50 countries, ranked by the total number of COVID-19 cases as of 1 April 2020, the paper finds:
Characteristics of countries
- The median age and unemployment rate was 40 years and 5.2%, respectively.
- The median GDP per capita and health care spend was $23,122 and $1,914, respectively.
- The overall Gini coefficient and corruption index score was 35.4 and 58.5, respectively.
- The median number of hospital and ICU beds per million population was 3,092 and 87, respectively.
- The number of physicians and nurses was 2,866 and 6,235, respectively.
COVID-19 infection characteristics
- Overall reported rates for mortality and critical cases were 4.2% and 2.5%, respectively.
- In total, 38 (10) countries had a complete (partial) border closure by 1 April 2020.
- The median time to any border closure from the first reported case in each country was 23 days.
- In total, 40 (5) countries had implemented a full (partial or curfew) lockdown by 1 May 2020.
- The median time to any lockdown from the first reported case in each country was 23 days.
How can I find more information on the paper and the variables they used?
Below we have listed the variables the authors test in the paper and which estimation method they used. For more information, visit the paper here.
A Comprehensive Set of Variables Were Tested
The paper gathers data for 50 countries until 1 May 2020. The variables were identified using a Likelihood Ratio test in a process of backwards elimination to remove any weak predictors. The final set of included:
- COVID-19 data:
1. Total number of cases
2. Total number of recovered cases
3. Total number of critical cases
4. Total number of deaths
5. Total number of tests
- Country-specific data:
6. GDP per capita
7. Total population
8. Median population age
9. Gender distribution of population
10. Population density
11. Unemployment rate
12. Corruption Perceptions Index
13. Income dispersion (Gini)
14. Global Health Security index (GHS)
15. Smoking prevalence
16. Diabetes prevalence
17. Adult mortality risk
18. Bloomberg Global Health Index (GHI)
- Health care capacity data:
19. Number of hospital beds
20. Number of ICU beds
21. Number of Physicians
22. Number of Nurses
23. Current health expenditure
To proxy country-specific public health policies, the researchers look for data on:
- Types of travel restrictions
1. No measure implemented
2. Partial border closures
3. Complete border closure
- Containment measures (measures as the time from first reported case to implementation in days)
1. No measure implemented
2. Partial lockdown
3. Complete lockdown
4. Curfew implemented
The unit of account was each country and the outcome variables of interest were those numbered 1-4 above. The researchers then conducted country-level analysis using a series of main effects multivariable negative binomial regression models.
Sam van de Schootbrugge is a macro research economist taking a one year industrial break from his Ph.D. in Economics. He has 2 years of experience working in government and has an MPhil degree in Economic Research from the University of Cambridge. His research expertise are in international finance, macroeconomics and fiscal policy.
(The commentary contained in the above article does not constitute an offer or a solicitation, or a recommendation to implement or liquidate an investment or to carry out any other transaction. It should not be used as a basis for any investment decision or other decision. Any investment decision should be based on appropriate professional advice specific to your needs.)