Title: COVID-19 Mortality Rate Variable Construction for
Country-level Analysis
Authors: Mazbahul G. Ahamad, PhD1(Corresponding author), Monir U. Ahmed, MSS2
Affiliations: 1University of
Nebraska–Lincoln, 140 Keim Hall, 1825 N 38th St, Lincoln, NE 68583,
USA. Phone: +1 (402) 318-9740. E-mail:
mahamad2@unl.edu
2Department of Economics, Shahjalal University of
Science and Technology, Sylhet 3114, Bangladesh. E-mail:
monir-eco@sust.edu
The coronavirus disease 2019 (COVID-19) mortality rate has been widely
discussed and is considered the primary variable of interest for
existing country-level studies, using data from secondary sources (Di
Gennaro et al., 2020; Hopman, Allegranzi, & Mehtar, 2020). A broad
range of COVID-19-related literature has attempted to estimate
associations between the COVID-19 mortality rate and various
country-level factors (e.g., hospital beds and the population aged 65 or
older) (Chaudhry, Dranitsaris, Mubashir, Bartoszko, & Riazi, 2020;
Lawal, 2020; Liang, Tseng, Ho, & Wu, 2020). COVID-19 death counts per
100,000 population is a straightforward and commonly used mortality rate
variable in statistical analyses. Although country-level variable is
critical for understanding the overall mortality rate, various country-,
area-, and patient-level characteristics can be used to provide
additional clarity regarding the various dimensions of COVID-19
mortality rates. In this brief note, we discuss the use of alternative
methods for exploring the COVID-19 mortality rate when estimating
associations with country-level factors.
We conducted a brief narrative review of published empirical articles on
the “mortality rate” of COVID-19. Then, we reviewed the “mortality
rate” variables used in different country-level analysis. Finally, we
explained three additional approaches to define the COVID-19 mortality
rate variable.
COVID-19 mortality rate variable construction: In the section, we
discuss a few potential approaches for the construction of a COVID-19
mortality rate variable. This discussion is intended to present
potential techniques for integrating existing country-, area-, and
patient-specific characteristics into these analyses to improve the
overall understanding.
Country characteristics: Country-level characteristics, such as per
capita health expenditures or hospital beds per 100,000 population, are
important and commonly used variables when analyzing country-level
factors that affect the COVID-19 mortality rate. In most cases, each
country is treated as an independent data point. Although this analysis
technique is appropriate, it fails to distinguish various preexisting
characteristics (e.g., income or development levels) that are likely to
affect mortality levels. To understand how mortality rates vary
according to income or other features, we can use country-level
contextual characteristics (Sornette, Mearns, Schatz, Wu, & Darcet,
2020). For example, we can use the classifications provided by the World
Bank income group, human development index, and the World Health
Organization (WHO) region. The use of interaction dummies, such as the
interaction between the World Bank-defined income group and the
WHO-defined regional classification, would also allow the exploration of
associations that exist among various income-grouped counties in
different regions. Additionally, the mortality rate can be categorized
ordinally to explore country-level characteristics. For example, five
consecutive quintiles (e.g., 1 for 0%–20%, 2 for 21%–40%, 3 for
41%–60%, 4 for 61%–80%, and 5 for 81%–100%) could be used to
indicate differences in mortality rates of affected countries. Countries
could be analyzed for quintile-specific regression analyses.
Area characteristics: In many countries, the mortality rate is higher in
urban areas than in rural areas (Dorn, Cooney, & Sabin, 2020). Using
separate mortality rates for urban and rural areas, rather than overall
country rates, would produce area-based insights. Area characteristics
can be included as a binary dummy (e.g., 1 for urban and 0 for rural),
and a country-area interaction dummy could also be examined. For
example, a dummy variable that represents urban areas in high-income
countries could be used to explore the factors associated with
respective mortality rates in these areas. In a regression analysis, two
different models examining urban and rural mortality rates, together
with an overall (country-level or pooled data) mortality rate, would
generate area-specific estimates, which could be used as robustness
checks and the identification of urban-rural variabilities.
Patient characteristics: Similar to country- and area-level
characteristics, general patient-level characteristics (e.g., income,
education, sex) should be considered when estimating the relationships
between the COVID-19 mortality rate and associated factors (Brandén et
al., 2020). Lower-income individuals residing in the urban areas of
high-income countries might be more vulnerable to COVID-19 than their
rural counterparts. To examine these relationships, the use of patient
characteristics would be necessary, although identifying the income
groups associated with all deceased COVID-19 patients may be practically
impossible to perform. However, preexisting area characteristics (e.g.,
postal code-based income group) can be used as an estimator. The use of
area-based country data (like the panel data) could also be an option.
If more localized data is unavailable, broad area-based (e.g., county)
country-level data could also be applied.