Population age structure only partially explains the large number of COVID-19 deaths at the oldest ages

Comparing COVID-19 fatality across countries: a synthetic demographic indicator

Background

The case fatality rate (CFR) is one of the most important measures for monitoring disease progression and evaluating appropriate policy health measures over the course of the COVID-19 pandemic. To remove biases arising from the age structure of COVID-19 cases in international comparisons of the CFR, existing studies have relied mainly on direct standardisation.

Objective

We propose and validate a synthetic indicator of COVID-19 fatality (SCFR) that improves its comparability across countries by adjusting for the age and sex structure of COVID-19 cases without relying on the arbitrary choice of a standard population.

Results

Contrary to what comparisons of the crude CFR suggest, differences in COVID-19 fatality across countries according to the proposed SCFR are not very stark. Importantly, once we adjust for the age structure of COVID-19 cases, the higher case fatality among men emerges as the main driver of international differences in COVID-19 CFR.

Conclusions

The SCFR is a simple indicator that is useful for monitoring the fatality of SARS-CoV-2 mutations and the efficacy of health policy measures for COVID-19, including vaccination.

Contributions

(1) A simple synthetic indicator of COVID-19 fatality that improves its comparability across countries by adjusting for the age and sex structure of COVID-19 cases; (2) Evidence that sex differences in COVID-19 fatality drive international differences in the overall CFR.

Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02646-3.

Mobility patterns and COVID growth: Moderating role of country culture

The COVID-19 pandemic has resulted in countries reacting differently to an ongoing crisis situation. Latent to this reaction mechanism is the inherent cultural characteristics of each society resulting in differential responses to epidemic spread. Epidemiological studies have confirmed the positive effect of population mobility on the growth of infection. However, the effect of culture on indigenous mobility patterns during pandemics needs further investigation. This study aims to bridge this gap by exploring the moderating role of country culture on the relationship between population mobility and growth of CoVID-19. Hofstede's cultural factors; power distance, individualism/collectivism, masculinity/femininity, uncertainty avoidance, long-term and short-term orientation are hypothesised to moderate the effect of mobility on the reproduction number (R) of COVID-19. Panel regression model, using mobility data and number of confirmed cases across 95 countries for a period of 170 days has been preferred to test the hypotheses. The results are further substantiated using slope analysis and Johnson-Neyman technique. The findings suggest that as power distance, individualism and long-term orientation scores increase, the impact of mobility on epidemic growth decreases. However, masculinity scores in a society have an opposite moderating impact on epidemic growth rate. These Hofstede factors act as quasi moderators affecting mobility and epidemic growth. Similar conclusions could be not be confirmed for uncertainty avoidance. Cross-cultural impact, as elucidated by this study, forms a crucial element in policy formulation on epidemic control by indigenous Governing bodies.

Impact of COVID-19 on life expectancy at birth in India: a decomposition analysis

BACKGROUND

Quantifying excess deaths and their impact on life expectancy at birth (e0) provide a more comprehensive understanding of the burden of coronavirus disease of 2019 (COVID-19) on mortality. The study aims to comprehend the repercussions of the burden of COVID-19 disease on the life expectancy at birth and inequality in age at death in India.

METHODS

The mortality schedule of COVID-19 disease in the pandemic year 2020 was considered one of the causes of death in the category of other infectious diseases in addition to other 21 causes of death in the non-pandemic year 2019 in the Global Burden of Disease (GBD) data. The measures e0 and Gini coefficient at age zero (G0) and then sex differences in e0 and G0 over time were analysed by assessing the age-specific contributions based on the application of decomposition analyses in the entire period of 2010-2020.

RESULTS

The e0 for men and women decline from 69.5 and 72.0 years in 2019 to 67.5 and 69.8 years, respectively, in 2020. The e0 shows a drop of approximately 2.0 years in 2020 when compared to 2019. The sex differences in e0 and G0 are negatively skewed towards men. The trends in e0 and G0 value reveal that its value in 2020 is comparable to that in the early 2010s. The age group of 35-79 years showed a remarkable negative contribution to Δe0 and ΔG0. By causes of death, the COVID-19 disease has contributed - 1.5 and - 9.5%, respectively, whereas cardiovascular diseases contributed the largest value of was 44.6 and 45.9%, respectively, to sex differences in e0 and G0 in 2020. The outcomes reveal a significant impact of excess deaths caused by the COVID-19 disease on mortality patterns.

CONCLUSIONS

The COVID-19 pandemic has negative repercussions on e0 and G0 in the pandemic year 2020. It has severely affected the distribution of age at death in India, resulting in widening the sex differences in e0 and G0. The COVID-19 disease demonstrates its potential to cancel the gains of six to eight years in e0 and five years in G0 and has slowed the mortality transition in India.

The spreading of SARS-CoV-2: Interage contacts and networks degree distribution

Notable cross-country differences exist in the diffusion of the Covid-19 and in its lethality. Contact patterns in populations, and in particular intergenerational contacts, have been argued to be responsible for the most vulnerable, the elderly, getting infected more often and thus driving up mortality in some context, like in the southern European one. This paper asks a simple question: is it between whom contacts occur that matters or is it simply how many contacts people have? Due to the high number of confounding factors, it is extremely difficult to empirically assess the impact of single network features separately. This is why we rely on a simulation exercise in which we counterfactually manipulate single aspects of countries' age distribution and network structures. We disentangle the contributions of the kind and of the number of contacts while holding constant the age structure. More precisely, we isolate the respective effects of inter-age contact patterns, degree distribution and clustering on the virus propagation across age groups. We use survey data on face-to-face contacts for Great Britain, Italy, and Germany, to reconstruct networks that mirror empirical contact patterns in these three countries. It turns out that the number of social contacts (degree distribution) largely accounts for the higher infection rates of the elderly in the Italian context, while differences in inter-age contacts patterns are only responsible for minor differences. This suggests that policies specifically targeting inter-age contacts would be little effective.

Age-dependence of healthcare interventions for COVID-19 in Ontario, Canada

BACKGROUND

Patient age is one of the most salient clinical indicators of risk from COVID-19. Age-specific distributions of known SARS-CoV-2 infections and COVID-19-related deaths are available for many regions. Less attention has been given to the age distributions of serious medical interventions administered to COVID-19 patients, which could reveal sources of potential pressure on the healthcare system should SARS-CoV-2 prevalence increase, and could inform mass vaccination strategies. The aim of this study is to quantify the relationship between COVID-19 patient age and serious outcomes of the disease, beyond fatalities alone.

METHODS

We analysed 277,555 known SARS-CoV-2 infection records for Ontario, Canada, from 23 January 2020 to 16 February 2021 and estimated the age distributions of hospitalizations, Intensive Care Unit admissions, intubations, and ventilations. We quantified the probability of hospitalization given known SARS-CoV-2 infection, and of survival given COVID-19-related hospitalization.

RESULTS

The distribution of hospitalizations peaks with a wide plateau covering ages 60-90, whereas deaths are concentrated in ages 80+. The estimated probability of hospitalization given known infection reaches a maximum of 27.8% at age 80 (95% CI 26.0%-29.7%). The probability of survival given hospitalization is nearly 100% for adults younger than 40, but declines substantially after this age; for example, a hospitalized 54-year-old patient has a 91.7% chance of surviving COVID-19 (95% CI 88.3%-94.4%).

CONCLUSIONS

Our study demonstrates a significant need for hospitalization in middle-aged individuals and young seniors. This need is not captured by the distribution of deaths, which is heavily concentrated in very old ages. The probability of survival given hospitalization for COVID-19 is lower than is generally perceived for patients over 40. If acute care capacity is exceeded due to an increase in COVID-19 prevalence, the distribution of deaths could expand toward younger ages. These results suggest that vaccine programs should aim to prevent infection not only in old seniors, but also in young seniors and middle-aged individuals, to protect them from serious illness and to limit stress on the healthcare system.

'Not a good time': Assessing the economic impact of COVID-19 in Africa using a macro-micro simulation approach

The paper studies the effects of the coronavirus disease 2019 (COVID-19) pandemic on African economies and household welfare using a top-down sequential macro-micro simulation approach. The pandemic is modeled as a supply shock that disrupts economic activities of African countries and then affects households' consumption behavior, the level of their welfare, and businesses' investment decisions. The macroeconomic dynamic general equilibrium model is calibrated to account for informality, a key feature of African economies. We find that COVID-19 could diminish employment in the formal and informal sectors and contract consumption of non-savers and, especially, savers. These contractions would lead to an economic recession in Africa and widen both fiscal and current account deficits. Extreme poverty is expected to increase further in Africa, in particular if the welfare of the poorest households grows at lower rates. We also use the macroeconomic model to analyze the effects of different fiscal policy responses to the COVID-19 pandemic.

Spatial pattern of COVID-19 deaths and infections in small areas of Brazil

As of mid-August 2020, Brazil was the country with the second-highest number of cases and deaths by the COVID-19 pandemic, but with large regional and social differences. In this study, using data from the Brazilian Ministry of Health, we analyze the spatial patterns of infection and mortality from Covid-19 across small areas of Brazil. We apply spatial autoregressive Bayesian models and estimate the risks of infection and mortality, taking into account age, sex composition of the population and other variables that describe the health situation of the spatial units. We also perform a decomposition analysis to study how age composition impacts the differences in mortality and infection rates across regions. Our results indicate that death and infections are spatially distributed, forming clusters and hotspots, especially in the Northern Amazon, Northeast coast and Southeast of the country. The high mortality risk in the Southeast part of the country, where the major cities are located, can be explained by the high proportion of the elderly in the population. In the less developed areas of the North and Northeast, there are high rates of infection among young adults, people of lower socioeconomic status, and people without access to health care, resulting in more deaths.

Estimation of mortality and severity of the Covid-19 epidemic in Italy

This section considers mortality rate data in Italy in March 2020 with a view to estimating Covid-19 mortality across the Italian regions. Deaths are classified by age group: a key factor that official reports have failed to include The Italian Higher Health Institute included subjects who tested positive to a swab and plotted data by region and age group at a national level. However, it did not provide information relating to deaths by age group in each region. In the present study deaths and mortality rates in March 2020 are analyzed first. Subsequently, the number of deaths from causes attributable to Covid-19 is estimated by comparing the number of deaths recorded in March 2020 with the number of deaths recorded in the same month over the previous 5 years, from 2015 to 2019. Starting with this comparison and keeping as a constraint the data provided by the ISS, that is the number of deaths from Covid-19 for each region and for each age group at national level, it was possible to obtain an estimate of deaths attributable to Covid-19, divided by age group in each region. Using mapping, the estimates are shown in a spatial layout that records different ages in each region. The results show that in some regions the impact on mortality was much higher than official data, which only accounted for subjects who had tested positive to swabbing. A visual mapping of these estimates confirms that Lombardy was the most affected region, in terms of both deaths and mortality rates. Regarding age, Covid-19 mortality is higher in the oldest age groups. The study also outlines a partition of Italy into three geographical areas, in accordance with different mortality rates and Covid-19 contagion severity. Results are represented using reflective mapping to better understand the impact of Covid-19 on mortality. The results confirm that the Covid-19 epidemic had a major impact on Italy, and an even more significant impact on Lombardy, also in terms of deaths and mortality.