Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality

Mortality Comparisons ‘At a Glance’: A Mortality Concentration Curve and Decomposition Analysis for India

This paper uses the concept of the Mortality Concentration Curve (M-Curve), which plots the cumulative proportion of deaths against the corresponding cumulative proportion of the population (arranged in ascending order of age), and associated measures, to examine mortality experience in India. A feature of the M-curve is that it can be combined with an explicit value judgement (an aversion to early deaths) in order to make welfare-loss comparisons. Empirical comparisons over time, and between regions and genders, are made. Furthermore, in order to provide additional perspective, selective results for the UK and New Zealand are reported. It is also shown how the M-curve concept can be used to separate the contributions to overall mortality of changes over time (or differences between population groups) to the population age distribution and age-specific mortality rates.

How health capabilities and government restrictions affect the COVID-19 pandemic: Cross-country differences in Europe

The COVID-19 pandemic in the first months of 2020 posed an unprecedented threat to the health of the world's population. In this longitudinal design study, we elaborated the typology of 27 European countries based on the complete beginnings of the ongoing COVID-19 pandemic based on health indicators and contextual variables. Two-step analysis using factor scores to run a cluster analysis identifying 5 consistent groups of countries. We then analyze the relationship between the GHS predictive index, the restrictions and health care expenditures within countries categorized into 5 clusters. An analysis of the early stages of a pandemic confirmed that in countries where anti-pandemic measures were rapidly and consistently in place, the spread of the virus was suppressed more rapidly and the first wave of pandemics in these countries was incomparably more benign than in countries with later responses and milder restrictive measures.

Demographic and territorial characteristics of COVID-19 cases and excess mortality in the European Union during the first wave

This article explores for a large number of countries in the European Union (plus the United Kingdom) the main demographic differentials in positive tested COVID-19 cases and excess mortality during the first wave in 2020, accounting for differences at territorial level, where population density and size play a main role in the diffusion and effects of the disease in terms of morbidity and mortality. This knowledge complements and refines the epidemiological information about the spread and impact of the virus. For this analysis, we rely on the descriptive exploration of (1) data from The European Surveillance System (TESSy) database developed at the European Centre for Disease Prevention and Control (ECDC) on the number of cases and fatality rates and (2) of weekly mortality data collected by Eurostat. The analysis at territorial level studies the changes in R0-the basic reproduction number-and median excess mortality, across territories with different levels of urbanization. The unique findings of this study encompassing most European Union Member States confirm and define the demographic and territorial differential impacts in terms of infections and fatalities during the first wave of the pandemic in 2020. The information is important for stakeholders at European Union, national and sub-national levels in charge of designing containment measures for COVID-19 and adaptation policies for the future by anticipating the rebound for certain segments of the population with differential medical and economic needs.

Decomposing Differences in Coronavirus disease 2019-related Case-Fatality Rates across Seventeen Nations

As of 1 November 2020, estimated case-fatality rates associated with coronavirus disease 2019 are not uniformly patterned across the world and differ substantially in magnitude. Given the global spatial heterogeneity in case-fatality rates, we applied the Blinder-Oaxaca regression decomposition technique to identify how putative sociodemographic, structural, and environmental sources influence variation in case-fatality rates. We show that compositional and associational differences in country-level risk factors explain a substantial proportion of the coronavirus disease 2019-related case-fatality rate gap across nations. Asian countries fair better vis-à-vis case-fatality rate differences mainly due to variation in returns to sociodemographic, structural, and environmental sources among their citizens, relative to those who share similar attributes but live in Europe or North America. The variation in case-fatality rate is driven by Asian populations being better able to buffer the harmful effects of the very risk factors purported to exacerbate the risk of coronavirus disease 2019-related death. The dire circumstances in which we find ourselves demand better understanding of how preexisting conditions across countries contribute to observed disparities in case-fatality rates.

[Response capacity to the COVID-19 pandemic in Latin America and the Caribbean]

OBJECTIVE

To analyze the evolution of the COVID-19 pandemic in Latin American and Caribbean countries in its first 90 days and its association with variables related to public health measures, and demographic, health and social characteristics.

METHODS

he trend in new daily cases and the crude mortality rate (CMR) from COVID-19 were analyzed through the Joinpoint regression analysis methodology, using the Joinpoint Regression Program 4.8.0.1. Data was obtained from the Our World in Data registry. A multiple correspondence analysis was performed between the public health measures adopted in each country to face the COVID-19 pandemic (measured through the stringency index, Oxford University) and sanitary, demographic and social conditions, and the results of the evolution of the pandemic. SPSS software was used.

RESULTS

The Joinpoint regression analysis showed that the highest increase in the number of cases was observed in Brazil (11.3%) and the highest increase in CMR in Mexico (16.2%). The multiple correspondence analysis showed that CMR was associated with the total population, the stringency index, the level of urbanization, the proportion of the population living on less than one dollar a day, the prevalence of diabetes and the number of hospital beds.

CONCLUSIONS

The countries of the region show a heterogeneous evolution in the incidence of COVID-19. This heterogeneity is associated with both the public health measures adopted, as well as with the population size, poverty levels and pre-existing health systems.