Determinants of the Number of Deaths from COVID-19: Differences between Low-Income and High-Income Countries in the Initial Stages of the Pandemic

Valero, Magali; Valero-Gil, Jorge N.

doi:10.2139/ssrn.3617049

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…For example, sub-regional countries (e.g., Southern-South Africa; Northern-Egypt, Morocco; Western-Ghana, Nigeria; Eastern-Kenya, Ethiopia) with more socially open economies for more international arrivals might have acted as a proxy for the spread of the COVID-19 disease right from the onset of the virus outbreak. Again, countries (e.g., Egypt, South Africa) with more advanced transport infrastructure might have facilitated the widespread of the disease because the mobility of persons within and between cities is quite easier [27]. Hence, higher mobility increases the spread of the disease, with more persons infected more likely to observe a…”

Section: Discussionmentioning

confidence: 99%

“…Although the GHSI metrics could be a useful measure in predicting the preparedness of countries toward outbreaks such as the current pandemic, identified ratings could either overestimate or underestimate the rigorousness of some national health care systems and their level of preparedness [48]. Some other parameters (e.g., life expectancy, death rate, health expenditure) also influence the likelihood of recoveries and deaths after COVID-19 infection [27]. According to Valero and partner, for developed (i.e., high-income) countries, more deaths occur because of higher life expectancy.…”

Section: Plos Onementioning

confidence: 99%

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Assessing Sub-Regional-Specific Strengths of Healthcare Systems Associated with COVID-19 Prevalence, Deaths and Recoveries in Africa

et al. 2020

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Our results indicate that total number of COVID-19 cases (per million) has strong correlations (r s >0.5) with the median age; aged 65 older; aged 70 older; GDP per capita; number of hospital beds per thousand; Human Development Index (HDI); recoveries (per million); and the overall risk environment of a country. All these factors including the country's commitments to improving national capacity were related to the total number of deaths (per million). Also, strong correlations existed between the total recoveries (per million) and the total number of positive cases; total deaths (per million); median age; aged 70 older; GDP per capita; the number of hospital beds (per thousand); and HDI. The fitted regression models showed strong predictive powers (R-squared>99%) of the variances in the total number of COVID-19 cases (per million); total number of deaths (per million); and the total recoveries (per million). ConclusionsThe findings from this study suggest that patient-level characteristics such as ageing population (i.e., 65 + ), poverty, underlying co-morbidities-cardiovascular disease (e.g., hypertension), and diabetes through unhealthy behaviours like smoking as well as hospital care (i.e., beds per thousand) can help explain COVID-19 confirmed cases and mortality rates in Africa. Aside from these, other determinants (e.g., population density, the ability of detection, prevention and control) also affect COVID-19 prevalence, deaths and recoveries within African countries and sub-regions. PLOS ONEStrengths of healthcare systems associated with COVID-19 prevalence, deaths and recoveries

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Section: Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Assessing Sub-Regional-Specific Strengths of Healthcare Systems Associated with COVID-19 Prevalence, Deaths and Recoveries in Africa

et al. 2020

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“…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 [49], health-care systems and treatments [32], mask-wearing and other behaviours, climate and culture, transport infrastructure and community mobility [50], genetic factors or prevalence of particular antibodies that affect immune response [51].…”

Section: Discussionmentioning

confidence: 99%

Estimating the Case Fatality Ratio for COVID-19 using a Time-Shifted Distribution Analysis

Thomas

Marks

2020

Preprint

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Estimating the case fatality ratio (CFR) for COVID-19 is an important aspect of public health. However, calculating CFR accurately is problematic early in a novel disease outbreak, due to uncertainties regarding the time course of disease and difficulties in diagnosis and reporting of cases. In this work, we present a simple method for calculating the case fatality ratio using only public case and death data over time by exploiting the correspondence between the time distributions of cases and deaths. The time-shifted distribution (TSD) analysis generates two parameters of interest: the delay time between reporting of cases and deaths and the case fatality ratio. These parameters converge reliably over time once the exponential growth phase has finished. Analysis is performed for early COVID 19 outbreaks in many countries, and we discuss corrections to CFR values using excess-death and seroprevalence data to estimate the infection fatality ratio (IFR). While CFR values range from 0.2-20% in different countries, estimates for IFR are mostly around 0.5-0.8% for countries that experienced moderate outbreaks and 1-3% for severe outbreaks. The simplicity and transparency of TSD analysis enhance its usefulness in characterizing a new disease as well as the state of the health and reporting systems.

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“…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].…”

Section: Estimating the Infection Fatality Ratio From The Cfrmentioning

confidence: 99%

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

Thomas

Marks

2021

Epidemiol. Infect.

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Estimating the case fatality ratio (CFR) for COVID-19 is an important aspect of public health. However, calculating CFR accurately is problematic early in a novel disease outbreak, due to uncertainties regarding the time course of disease and difficulties in diagnosis and reporting of cases. In this work, we present a simple method for calculating the case fatality ratio using only public case and death data over time by exploiting the correspondence between the time distributions of cases and deaths. The time-shifted distribution (TSD) analysis generates two parameters of interest: the delay time between reporting of cases and deaths and the case fatality ratio. These parameters converge reliably over time once the exponential growth phase has finished. Analysis is performed for early COVID-19 outbreaks in many countries, and we discuss corrections to CFR values using excess-death and seroprevalence data to estimate the infection fatality ratio (IFR). While CFR values range from 0.2-20% in different countries, estimates for IFR are mostly around 0.5-0.8% for countries that experienced moderate outbreaks and 1-3% for severe outbreaks. The simplicity and transparency of TSD analysis enhance its usefulness in characterizing a new disease as well as the state of the health and reporting systems.

show abstract

Determinants of the Number of Deaths from COVID-19: Differences between Low-Income and High-Income Countries in the Initial Stages of the Pandemic

Cited by 6 publications

References 10 publications

Assessing Sub-Regional-Specific Strengths of Healthcare Systems Associated with COVID-19 Prevalence, Deaths and Recoveries in Africa

Assessing Sub-Regional-Specific Strengths of Healthcare Systems Associated with COVID-19 Prevalence, Deaths and Recoveries in Africa

Estimating the Case Fatality Ratio for COVID-19 using a Time-Shifted Distribution Analysis

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

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