Determinants of the number of deaths from COVID-19: differences between low-income and high-income countries in the initial stages of the pandemic

Abstract

PurposeThe purpose of this study is to understand the factors that contribute to the number of reported coronavirus (COVID-19) deaths among low-income and high-income countries, and to understand the sources of differences between these two groups of countries.Design/methodology/approachMultiple linear regression models evaluate the socio-economic factors that determine COVID-19 deaths in the two groups of countries. The Oaxaca–Blinder decomposition is used to examine sources of differences between these two groups.FindingsLow-income countries report a significantly lower average number of COVID-19 deaths compared to high-income countries. Community mobility and the easiness of carrying the virus from one place to another are significant factors affecting the number of deaths, while life expectancy is only significant in high-income countries. Higher health expenditure is associated with more reported deaths in both high- and low-income countries. Factors such as the transport infrastructure system, life expectancy and the percent of expenditure on health lead to the differences in the number of deaths between high- and low-income countries.Social implicationsOur study shows that mobility measures taken by individuals to limit the spread of the virus are important to prevent deaths in both high- and low-income countries. Additionally, our results suggest that countries with weak health institutions underestimate the number of deaths from COVID-19, especially low-income countries. The underestimation of COVID-19 deaths could be affecting a great number of people in poverty in low-income economies.Originality/valueThis paper contributes to the emerging literature on COVID-19 and its relation to socio-economic factors by examining the differences in reported between deaths between rates in low-income and high-income countries.

The impact of access to financial services on mitigating COVID-19 mortality globally

“…Our results suggest a smaller relation, roughly half that, but still positive. Reasons for this counteractive yet widely duplicated result are not well understood and have been discussed at length elsewhere (e.g., [28,[37][38][39]56]).…”

“…Though our main interest here is not these previously studied population health and socioeconomic variables, we include some key national-level public health risk characteristics as control variables. These control variables were selected based on previous evidence in the literature as covariates with COVID-19 mortality, including population health and respiratory issues (levels of hypertension, lung cancer, tuberculosis, diabetes, obesity, and air pollution); healthcare system availability and effectiveness; and socio-economic characteristics, including age distribution, urbanization, population density, and socioeconomic inequalities (e.g., [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]).…”

Discriminating Between the Side Effects of the Firm Behavior: Did We Import COVID-19?

“…Similarly, Ehlert (2020) investigates socio-economic determinants of the pandemic in German counties. Valero & Valero-Gil (2020) investigates the determinants of COVID-19 related deaths among low-and highincome countries during the early period of the pandemic. Birenbaum-Carmeli & Chassida (2020) presents findings on the effects of socio-demographic variables on COVID-19 morbidity in Israel.…”

Estimating the case fatality ratio for COVID-19 using a time-shifted distribution analysis

“…Therefore, while differences in testing and reporting between different countries undoubtedly account for much of the variation in IFR between countries, we neither expect nor find that IFR is the same for all COVID-19 outbreaks. Country-specific factors that influence IFR and differ between countries include testing and reporting, age demographics [53], health-care systems and treatments [12], mask-wearing and other behaviours, climate and culture, transport infrastructure and community mobility [54], genetic factors or prevalence of particular antibodies that affect immune response [55].…”