Can the current system really reduce the number of COVID–19 deaths?
As of October 2020, one after another, countries around the world have launched policies and systems to combat the novel coronavirus infection (COVID-19). Among the daily reports issued by the mass media on related policies and expert opinions from around the world, some of those reports do not inspire confidence.
For example, one television news program argued that the family doctor system was one of the factors in the suppression of the fatality rate of COVID-19 in Germany. However, a similar system was in operation at the same time in Italy, which then had the highest number of deaths in the world. That raises the question as to whether Germany’s low case fatality rate may have been due to other factors.
In order to answer such questions accurately, I am conducting extensive statistical and quantitative analysis of rather a lot of data. Most importantly, rather than conducting medical analyses, I am analyzing social issues from a political economy perspective, studying the effect of policies and institutions on social problems.
Relationships that only become clear when we see the quantitative analysis
The case of the German family doctor system led to discussion based on the assumption that Germany’s system was effective, which in turn was based on the news analysis of only a part of the German system. In that light, the co-author and I began with an analysis of a body of data from 78 countries.
For the study, we used data from reliable sources such as the WHO database and data published by Johns Hopkins University; we conducted statistical analysis of the relationship between number of COVID-19 deaths and socioeconomic variables (policy, system, and social situation—Table 1 shows the case fatality rate (CFR), the ratio of deaths to positives, and Table 2 shows the result of deaths per 100,000 population. We conducted those two types of analysis, one for CFR and one for deaths per 100,000 population, in order to identify socioeconomic and demographic variables common to the two variables and to draw verifiable conclusions.
Variables in Tables 1 and 2 whose coefficients are marked with * or + bear some correlation with the number of deaths, and when the coefficient is positive (negative), an increase (decrease) in that variable leads to an increase in number of deaths. In both Tables 1 and 2, only two variables had the same coefficient sign and were marked with * or + : “hospital bed utilization ratio” and “population ratio over 65 years old.” From that analysis, we concluded that the low COVID-19 death toll was due to the socioeconomic and demographic factors “high number of hospital beds” and “low rate of population age 65 and over.”
This analysis was completed March 31, 2020. In the future, I aim to examine the recent situation as well as analyzing the impact of number of doctors and political system (degree of democracy).*
Relationship between socio-economic and demographic variables estimated by multiple regression analysis and CFR (logarithmic scale)
|Hospital bed ratio (log)||-0.4813*||0.1870|
|Population ratio of age 65 and over (log)||0.8136**||0.2472|
|Population density (log)||0.1525*||0.0761|
|Ratio of smokers||0.0029||0.0117|
|Number of days to present since confirmation of first infected person||-0.0012||0.0070|
|Number of infected persons per 100,000 population.||0.0012||0.0018|
|Number of countries||78|
Relationship between socio-economic demographic variables (estimated by multiple regression analysis) and number of deaths per 100,000 population (logarithmic scale)
|Hospital bed ratio (log)||-0.6565+||0.2727|
|Population ratio of age 65 and over (log)||1.2342**||0.3605|
|Population density (log)||0.1339||0.1109|
|Ratio of smokers||0.0396*||0.0171|
|Number of days to present since confirmation of first infected person||-0.0092||0.0101|
|Number of infected persons per 100,000 population.||0.0101***||0.0027|
|Number of countries||78|
In Table 1, the coefficient is negative and the result can be summarized as “CFR tends to decrease as GDP increases.” In Table 2, the result can be summarized as “Number of deaths per 100,000 population increases as GDP increases.” The analyses in Tables 1 and 2 are different, so it is not possible here to confirm the correlation between GDP and number of deaths.
(The above paragraph is quoted from International Comparative Analysis of COVID-19 Mortality Factors by Mogi and Annaka, WIAS Discussion Paper No. 2020-001 https://www.waseda.jp/inst/wias/assets/uploads/2020/05/001_Mogi-and-Annaka-2020.pdf )
We conducted another analysis using data from 151 countries to determine what kind of events will trigger the issuance of policy. Figure 1 shows what kind of COVID-19 situation triggered the implementation of non-medical government intervention policies (non-pharmaceutical interventions, hereinafter NPIs). NPIs include movement restrictions, requests for store closures, and penalties.
The data points in Fig. 1 show the relationship between number of deaths and number of positives (confirmed) each day before the interventions. The y-axis value is strength of effect of number of deaths and positives on NPIs. In Fig. 1, comparison of the two variables on the same day (e.g, -5 deaths and -5 confirmed cases) reveals that NPIs tend to be launched in response to number of positives rather than number of deaths, and were immediately put into practice (when the number of lagged days was small).
Figure 2 also shows the results of an analysis of that daily data, i.e. how many days after the start of NPIs reduction of the number of deaths appears. The results show that even if NPIs are implemented, sufficient effects cannot be obtained until approximately 20 to 30 days later. In addition, the effect continues weakly for several weeks.
Objective analysis of social issues by means of experimentation and cliometrics
Statistical analysis of COVID-19 is now my major research theme, but I am interested in various other themes and the use of various research methods.
For example, in one large scale survey study, I elucidated that the kind of tax system people prefer depends on their income, political party affiliation, and ideological beliefs.
I am also pursuing cliometrics using data such as records of testimony in war trials and Ministry of Interior statistics from the Meiji and Taisho eras. I have been unraveling anecdotal reports using data and historical records. I will continue to pursue this kind of study.
I decided to pursue that domain of research because I was interested in the relationship between policies and systems in response to issues such as North Korean poverty and the Lehman shock crisis. I intend to continue my research towards objective analysis of human life and social systems.
[Note] This article is scheduled to be published in the academic journal Public Choice.
Interview and composition: OISHI, Kaori
In cooperation with: Waseda University Graduate School of Political Science J-School