scholarly journals Geographic Distribution of Excess Mortality Rate duo to COVID-19 in Iranian Population: An Ecological Study

2021 ◽  
Keyword(s):  
Geographic Distribution ◽  
Excess Mortality ◽  
Suitable Model ◽  
Historical Trends ◽  
Civil Registration ◽  
The World ◽  
All Cause Mortality ◽  
Total Death ◽  
The Difference ◽  
Very High

Introduction: COVID-19 has raised world concern since it emerged in Wuhan, China in December 2019. The direct and indirect death rates in the world and in Iran have increased significantly after the occurrence of this pandemic in the world. Objective: In this study EMR estimated by Multilevel Poison Regression then this estimation compared to the historical trends, to obtain total death related to the COVID-19 in addtion the geographic distribution of EMR has been presented for Iran country. Materials and Methods: All-cause mortality count of each province of Iran from March 21, 2013 to June 20, 2020 downloaded from National Organizationfor Civil Registration (NOCR). The data from spring of 2020 (March 20, 2020 to June 20, 2020) remove from data and then the multilevel poison model has been used to estimate all-cause mortality in spring 2020 then excess mortality attributable to COVID-19 (the difference between the numberof registered and expected deaths) has been calculated. Results: The results of this study showed that Iran’s EMR in spring 2020 was 23% (Male=25%, Female=21%). More result also showed that four category low (EMR≤5%, n=3), moderate (5 %< EMR<20%, n=10), high (20 %< EMR<40%, n=16) and very high (40≤EMR, n=2) EMR. Conclusion: Due to the diverse EMR in different provinces of Iran, the type of management of provinces with low and moderate EMR can be used as a suitable model to control EMR in provinces with high and very high EMR.

scholarly journals Geographic Distribution of Excess Mortality Rate Duo to COVID-19 in Iranian Population: An Ecological Study

2020 ◽  
Author(s):  
Mohammad Gholami Fesharaki ◽  
Alireza Molaei
Keyword(s):  
Geographic Distribution ◽  
Ecological Study ◽  
Excess Mortality ◽  
Suitable Model ◽  
Historical Trends ◽  
Civil Registration ◽  
All Cause Mortality ◽  
Total Death ◽  
The Difference ◽  
Very High

Abstract Introduction: COVID-19 has raised world concern since it emerged in Wuhan, China in December 2019. The direct and indirect death rates in the world and in Iran have increased significantly after the occurrence of this pandemic in the world.Objective: In this study EMR estimated by Multilevel Poison Regression then this estimation compared to the historical trends, to obtain total death related to the COVID-19 in addtion the geographic distribution of EMR has been presented for Iran country.Materials and Methods: All-cause mortality count of each province of Iran from March 21, 2013 to June 20, 2020 downloaded from National Organizationfor Civil Registration (NOCR). The data from spring of 2020 (March 20, 2020 to June 20, 2020) remove from data and then the multilevel poison model has been used to estimate all-cause mortality in spring 2020 then excess mortality attributable to COVID-19 (the difference between the numberof registered and expected deaths) has been calculated.Results: The results of this study showed that Iran’s EMR in spring 2020 was 23% (Male=25%, Female=21%). More result also showed that four category low (EMR≤5%, n=3), moderate (5 %< EMR<20%, n=10), high (20 %< EMR<40%, n=16) and very high (40≤EMR, n=2) EMR.Conclusion: Due to the diverse EMR in different provinces of Iran, the type of management of provinces with low and moderate EMR can be used as a suitable model to control EMR in provinces with high and very high EMR.

scholarly journals Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ariel Karlinsky ◽  
Dmitry Kobak
Keyword(s):  
New Zealand ◽  
Excess Mortality ◽  
Mortality Data ◽  
Objective Indicator ◽  
Death Toll ◽  
The World ◽  
All Cause Mortality ◽  
Expected Mortality ◽  
The Impact ◽  
Annual Mortality

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.

scholarly journals EXCESS MORTALITY FROM COVID-19. WEEKLY EXCESS DEATH RATES BY AGE AND SEX FOR SWEDEN.

2020 ◽  
Author(s):  
Karin Modig ◽  
Marcus Ebeling
Keyword(s):  
Life Expectancy ◽  
Excess Mortality ◽  
Total Population ◽  
Total Mortality ◽  
Death Rates ◽  
Excess Death ◽  
Relative Risks ◽  
All Cause Mortality ◽  
The Difference ◽  
Age And Sex

Objectives: Mortality from Covid-19 is monitored in detail both within as well as between countries with different strategies against the virus. However, death counts and relative risks based on crude numbers can be misleading. Instead, age specific death rates should be used for comparability. Given the difficulty of ascertainment of Covid-19 specific deaths, excess all-cause mortality is currently more appropriate for comparisons. By estimating age- and sex-specific death rates we aim to get more accurate estimates of the excess mortality attributed to Covid-19, as well as the difference between men and women in Sweden. Design: We make use of Swedish register data about total weekly deaths, total population at risk, and estimate age- and sex-specific weekly death rates for 2020 and the 5 previous years. The data is provided by Statistics Sweden. Results: From the first week of April and onwards, the death rates at all ages above 60 are higher than those in previous years in Sweden. Persons above age 80 are dis-proportionally more affected, and men suffer higher levels of excess mortality than women at all ages with 75% higher death rates for males and 50% higher for females. Current excess mortality corresponds to a decline in remaining life expectancy of 3 years for men and 2 years for women. Conclusion: The Covid-19 pandemic has so far had a clear and consistent effect on total mortality in Sweden, with male death rates being comparably more affected. What consequences the pandemic will eventually have on mortality and life expectancy will depend on the progression of the pandemic, the extent that some of the deaths would have occurred in the absence of the pandemic, only somewhat later, the consequences for other health conditions, as well as the health care sector at large.

Estimating influenza-related excess mortality and reproduction numbers for seasonal influenza in Norway, 1975–2004

2010 ◽  
Vol 138 (11) ◽  
pp. 1559-1568 ◽  
Author(s):  
J. M. GRAN ◽  
B. IVERSEN ◽  
O. HUNGNES ◽  
O. O. AALEN
Keyword(s):  
Significant Contribution ◽  
Excess Mortality ◽  
Reproduction Number ◽  
Severe Disease ◽  
Risk Groups ◽  
The Elderly ◽  
Age Group ◽  
Reproduction Numbers ◽  
All Cause Mortality ◽  
The Difference

SUMMARYInfluenza can be a serious, sometimes deadly, disease, especially for people in high-risk groups such as the elderly and patients with underlying, severe disease. In this paper we estimated the influenza-related excess mortality in Norway for 1975–2004, comparing it with dominant virus types and estimates of the reproduction number. Analysis was done using Poisson regression, explaining the weekly all-cause mortality by rates of reported influenza-like illness, together with markers for seasonal and year-to-year variation. The estimated excess mortality was the difference between the observed and predicted mortality, removing the influenza contribution from the prediction. We estimated the overall influenza-related excess mortality as 910 deaths per season, or 2·08% of the overall deaths. Age-grouped analyses indicated that the major part of the excess mortality occurred in the ⩾65 years age group, but that there was also a significant contribution to mortality in the 0–4 years age group. Estimates of the reproduction number R, ranged from about 1 to 1·69.

scholarly journals A Poorly Understood Disease? The Unequal Distribution of Excess Mortality Due to COVID-19 Across French Municipalities

2020 ◽  
Author(s):  
Paul Brandily ◽  
Clement Brebion ◽  
Simon Briole ◽  
Laura Khoury
Keyword(s):  
Excess Mortality ◽  
Local Level ◽  
Data Sources ◽  
Housing Conditions ◽  
Unequal Distribution ◽  
The World ◽  
The Difference ◽  
The Impact ◽  
Income Gradient ◽  
The Relationship

While COVID-19 was already responsible for more than 500,000 deaths worldwide as of July 3, 2020, very little is known on the socio-economic heterogeneity of its impact on mortality. In this paper, we combine several administrative data sources to estimate the relationship between mortality due to COVID-19 and poverty at a very local level (i.e. the municipality level) in France, one of the most severely hit country in the world. We find strong evidence of an income gradient in the impact of the pandemic on mortality: it is twice as large in the poorest municipalities compared to other municipalities. We then show that both poor housing conditions and higher occupational exposure are likely mechanisms. Overall, these mechanisms accounts for up to 60% of the difference observed between rich and poor municipalities.

scholarly journals The World Mortality Dataset: Tracking excess mortality across countries during the COVID-19 pandemic

2021 ◽  
Author(s):  
Ariel Karlinsky ◽  
Dmitry Kobak
Keyword(s):  
Excess Mortality ◽  
Mortality Data ◽  
Objective Indicator ◽  
Death Toll ◽  
The World ◽  
All Cause Mortality ◽  
Order Of Magnitude ◽  
The Impact ◽  
Entire Dataset ◽  
Annual Mortality

AbstractComparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the recent average, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no central, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 77 countries, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in the worst-affected countries the annual mortality increased by over 50%, while in several other countries it decreased by over 5%, presumably due to lockdown measures decreasing the non-COVID mortality. Moreover, we found that while some countries have been reporting the COVID-19 deaths very accurately, many countries have been underreporting their COVID-19 deaths by an order of magnitude or more. Averaging across the entire dataset suggests that the world’s COVID-19 death toll may be at least 1.6 times higher than the reported number of confirmed deaths.

scholarly journals Estimates of pandemic excess mortality in India based on civil registration data

2021 ◽  
Author(s):  
Murad Banaji ◽  
Aashish Gupta
Keyword(s):  
Excess Mortality ◽  
Strong Association ◽  
Mortality Data ◽  
Indian States ◽  
Middle Income ◽  
Civil Registration ◽  
Registration Data ◽  
Seroprevalence Data ◽  
All Cause Mortality ◽  
Excess Deaths

Background: The COVID-19 pandemic has had large impacts on population health. These impacts are less well understood in low-and middle-income countries, where mortality surveillance before the pandemic was patchy. Although limited all-cause mortality data are available in India, interpreting this data remains a challenge. Objective: We use existing data on all-cause mortality from civil registration systems of twelve Indian states comprising around 60% of the national population to understand the scale and timing of excess deaths in India during the COVID-19 pandemic. Methods: We characterize the available data, discuss the various reasons why these data are incomplete, and estimate the extent of coverage in the data. Comparing the pandemic period to 2019, we estimate excess mortality in twelve Indian states, and extrapolate our estimates to the rest of India. We explore sensitivity of the estimates to various assumptions, and present optimistic and pessimistic scenarios along with our central estimates. Results: For the 12 states with available all-cause mortality data, we document an increase of 28% in deaths during April 2020-May 2021 relative to expectations from 2019. This level of increase in mortality, if it applies nationally, would imply 2.8-2.9 million excess deaths. More limited data from June 2021 increases national estimates of excess deaths during April 2020-June 2021 to 3.8 million. With more optimistic or pessimistic assumptions, excess deaths during this period could credibly lie between 2.8 million and 5.2 million. We find that the scale of estimated excess deaths is broadly consistent with expectations based on seroprevalence data and international data on COVID-19 fatality rates. Moreover, there is a strong association between the timing of excess deaths, and of recorded COVID-19 deaths. Contribution: We show that the surveillance of pandemic mortality in India has been extremely poor, with around 8-10 times as many excess deaths as officially recorded COVID-19 deaths. Our findings highlight the utility of all-cause mortality data, as well as the significant challenges in interpreting such data from LMICs. These data reveal that India is among the countries most severely impacted by the pandemic. It is likely that in absolute terms India has seen the highest number of pandemic excess deaths of any country in the world.

Inter-country inequality as a dynamic process and the problem of post-industrial development

2018 ◽  
pp. 5-29 ◽  
Author(s):  
L. M. Grigoryev ◽  
V. A. Pavlyushina
Keyword(s):  
Economic Growth ◽  
Political Institutions ◽  
Industrial Development ◽  
Modern World ◽  
New Approach ◽  
Development Level ◽  
The World ◽  
Long Time ◽  
The Difference ◽  
Post Industrial

The phenomenon of economic growth is studied by economists and statisticians in various aspects for a long time. Economic theory is devoted to assessing factors of growth in the tradition of R. Solow, R. Barrow, W. Easterly and others. During the last quarter of the century, however, the institutionalists, namely D. North, D. Wallis, B. Weingast as well as D. Acemoglu and J. Robinson, have shown the complexity of the problem of development on the part of socioeconomic and political institutions. As a result, solving the problem of how economic growth affects inequality between countries has proved extremely difficult. The modern world is very diverse in terms of development level, and the article offers a new approach to the formation of the idea of stylized facts using cluster analysis. The existing statistics allows to estimate on a unified basis the level of GDP production by 174 countries of the world for 1992—2016. The article presents a structured picture of the world: the distribution of countries in seven clusters, different in levels of development. During the period under review, there was a strong per capita GDP growth in PPP in the middle of the distribution, poverty in various countries declined markedly. At the same time, in 1992—2016, the difference increased not only between rich and poor groups of countries, but also between clusters.

Speculative Realism and Science Fiction

2017 ◽  
Author(s):  
Brian Willems
Keyword(s):  
Science Fiction ◽  
Identity Formation ◽  
Cormac Mccarthy ◽  
Doris Lessing ◽  
Speculative Realism ◽  
Neil Gaiman ◽  
The World ◽  
The Difference ◽  
Quentin Meillassoux

A human-centred approach to the environment is leading to ecological collapse. One of the ways that speculative realism challenges anthropomorphism is by taking non-human things to be as valid objects of investivation as humans, allowing a more responsible and truthful view of the world to take place. Brian Willems uses a range of science fiction literature that questions anthropomorphism both to develop and challenge this philosophical position. He looks at how nonsense and sense exist together in science fiction, the way in which language is not a guarantee of personhood, the role of vision in relation to identity formation, the difference between metamorphosis and modulation, representations of non-human deaths and the function of plasticity within the Anthropocene. Willems considers the works of Cormac McCarthy, Paolo Bacigalupi, Neil Gaiman, China Miéville, Doris Lessing and Kim Stanley Robinson are considered alongside some of the main figures of speculative materialism including Graham Harman, Quentin Meillassoux and Jane Bennett.

Between High Culture and Low Culture; and also The Difference Between Popular Culture and Folk Culture

2019 ◽  
Vol 7 (8) ◽  
pp. 12
Author(s):  
Kunal Debnath
Keyword(s):  
Popular Culture ◽  
Folk Culture ◽  
Common People ◽  
High Culture ◽  
The World ◽  
The Common ◽  
The Difference ◽  
Low Culture

High culture is a collection of ideologies, beliefs, thoughts, trends, practices and works-- intellectual or creative-- that is intended for refined, cultured and educated elite people. Low culture is the culture of the common people and the mass. Popular culture is something that is always, most importantly, related to everyday average people and their experiences of the world; it is urban, changing and consumeristic in nature. Folk culture is the culture of preindustrial (premarket, precommodity) communities.

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