This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Mali ML: Mortality Rate: Infant: per 1000 Live Births data was reported at 68.000 Ratio in 2016. This records a decrease from the previous number of 69.600 Ratio for 2015. Mali ML: Mortality Rate: Infant: per 1000 Live Births data is updated yearly, averaging 131.200 Ratio from Dec 1963 (Median) to 2016, with 54 observations. The data reached an all-time high of 213.400 Ratio in 1963 and a record low of 68.000 Ratio in 2016. Mali ML: Mortality Rate: Infant: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted Average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Series Name: Under-five mortality rate by sex (deaths per 1 000 live births)Series Code: SH_DYN_MORTRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.2.1: Under-5 mortality rateTarget 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Series Name: Neonatal mortality rate (deaths per 1 000 live births)Series Code: SH_DYN_NMRTRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.2.2: Neonatal mortality rateTarget 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country Marshall Islands. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 28.20 as of 12/31/2023, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -3.09 percent compared to the value the year prior.The 1 year change in percent is -3.09.The 3 year change in percent is -9.03.The 5 year change in percent is -14.29.The 10 year change in percent is -24.80.The Serie's long term average value is 56.21. It's latest available value, on 12/31/2023, is 49.83 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1960, to it's latest available value, on 12/31/2023, is -76.81%.

JP: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 2.700 Ratio in 2017. This records a decrease from the previous number of 3.200 Ratio for 2015. JP: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 3.400 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 6.900 Ratio in 1990 and a record low of 2.700 Ratio in 2017. JP: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Background: Under-five mortality remains concentrated in resource-poor countries. Post-discharge mortality is becoming increasingly recognized as a significant contributor to overall child mortality. With a substantial recent expansion of research and novel data synthesis methods, this study aims to update the current evidence base by providing a more nuanced understanding of the burden and associated risk factors of pediatric post-discharge mortality after acute illness. Methods: Eligible studies published between January 1, 2017 and January 31, 2023, were retrieved using MEDLINE, Embase, and CINAHL databases. Studies published before 2017 were identified in a previous review and added to the total pool of studies. Only studies from countries with low or low-middle Socio-Demographic Index with a post-discharge observation period greater than seven days were included. Risk of bias was assessed using a modified version of the Joanna Briggs Institute critical appraisal tool for prevalence studies. Studies were grouped by patient population, and 6-month post-discharge mortality rates were quantified by random-effects meta-analysis. Secondary outcomes included post-discharge mortality relative to in-hospital mortality, pooled risk factor estimates, and pooled post-discharge Kaplan–Meier survival curves. PROSPERO study registration: #CRD42022350975. Findings: Of 1963 articles screened, 42 eligible articles were identified and combined with 22 articles identified in the previous review, resulting in 64 total articles. These articles represented 46 unique patient cohorts and included a total of 105,560 children. For children admitted with a general acute illness, the pooled risk of mortality six months post-discharge was 4.4% (95% CI: 3.5%–5.4%, I2 = 94.2%, n = 11 studies, 34,457 children), and the pooled in-hospital mortality rate was 5.9% (95% CI: 4.2%–7.7%, I2 = 98.7%, n = 12 studies, 63,307 children). Among disease subgroups, severe malnutrition (12.2%, 95% CI: 6.2%–19.7%, I2 = 98.2%, n = 10 studies, 7760 children) and severe anemia (6.4%, 95% CI: 4.2%–9.1%, I2 = 93.3%, n = 9 studies, 7806 children) demonstrated the highest 6-month post-discharge mortality estimates. Diarrhea demonstrated the shortest median time to death (3.3 weeks) and anemia the longest (8.9 weeks). Most significant risk factors for post-discharge mortality included unplanned discharges, severe malnutrition, and HIV seropositivity. Interpretation: Pediatric post-discharge mortality rates remain high in resource-poor settings, especially among children admitted with malnutrition or anemia. Global health strategies must prioritize this health issue by dedicating resources to research and policy innovation. Data Processing Methods: Data were extracted using a standard data extraction form developed by the review authors. Kaplan–Meier survival curves, where provided, were extracted using a plot digitizer. The data extraction file, “PDMSR2024_DataExtraction_Dataset_SD” was generated as described above and analyzed as is. Co-ordinates were extracted from the survival curves in their original, published form, using a plot digitizer (https://automeris.io/WebPlotDigitizer/). The co-ordinates for each survival curve were then cleaned up to: 1. Re-scale the time points to weeks 2. Curves which reported % mortality were converted to % survival (1 – mortality) 3. First co-ordinate was set to (0, 1), i.e., survival is 100% at time-point 0 4. Include the numbers at risk (if reported), primary reference, and subgroup information Using these cleaned co-ordinates, individual-level patient data were extracted (see Guyot et al, 2012, doi.org/10.1186/1471-2288-12-9) and the survival curves re-constructed to obtain the survival and number at risk at specified time-points (0-52 weeks). Where possible, disease and age subgroups were combined to create all admissions curves by combining the individual-level patient data from multiple curves in the same study. Additional data from the survival curves were extracted to produce the “PDMSR2024_AdditionalDataSurvivalCurves6M_Dataset_SD” and “PDMSR2024_AdditionalDataSurvivalCurves12M_Dataset_SD” files by extracting the survival rate at 6 and 12 months. Previously unpublished hazards ratios were extracted from the dataset used in the Wiens et al (2015) study on post-discharge mortality (doi:10.1136/bmjopen-2015-009449) to produce the “PDMSR2024_Wiens2015HazardsRatios_Dataset_SD.xlsx” file. These original data are published on Dataverse at: doi.org/10.5683/SP2/VBPLRM Analyses were in R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria), and RStudio version 2023.6.1 (RStudio, Boston, MA). Additional Files: Survival curves in their original, published form, as well as survival curve coordinates files can be made available by request. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business...

Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country Sudan. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 50.10 as of 12/31/2023, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -3.28 percent compared to the value the year prior.The 1 year change in percent is -3.28.The 3 year change in percent is -9.40.The 5 year change in percent is -14.94.The 10 year change in percent is -26.65.The Serie's long term average value is 120.60. It's latest available value, on 12/31/2023, is 58.46 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1983, to it's latest available value, on 12/31/2023, is -72.50%.

Objective: This study examined cumulative excess mortality in European countries in the year of the Covid-19 pandemic and characterized the dynamics of the pandemic in different countries, focusing on Hungary and the Central and Eastern European region.Methods: Age-standardized cumulative excess mortality was calculated based on weekly mortality data from the EUROSTAT database, and was compared between 2020 and the 2016–2019 reference period in European countries.Results: Cumulate weekly excess mortality in Hungary was in the negative range until week 44. By week 52, it reached 9,998 excess deaths, corresponding to 7.73% cumulative excess mortality vs. 2016–2019 (p-value = 0.030 vs. 2016–2019). In Q1, only Spain and Italy reported excess mortality compared to the reference period. Significant increases in excess mortality were detected between weeks 13 and 26 in Spain, United Kingdom, Belgium, Netherland and Sweden. Romania and Portugal showed the largest increases in age-standardized cumulative excess mortality in the Q3. The majority of Central and Eastern European countries experienced an outstandingly high impact of the pandemic in Q4 in terms of excess deaths. Hungary ranked 11th in cumulative excess mortality based on the latest available data of from the EUROSTAT database.Conclusion: Hungary experienced a mortality deficit in the first half of 2020 compared to previous years, which was followed by an increase in mortality during the second wave of the COVID-19 pandemic, reaching 7.7% cumulative excess mortality by the end of 2020. The excess was lower than in neighboring countries with similar dynamics of the pandemic.

JP: Mortality Rate: Infant: Male: per 1000 Live Births data was reported at 2.100 Ratio in 2016. This stayed constant from the previous number of 2.100 Ratio for 2015. JP: Mortality Rate: Infant: Male: per 1000 Live Births data is updated yearly, averaging 2.500 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 4.900 Ratio in 1990 and a record low of 2.100 Ratio in 2016. JP: Mortality Rate: Infant: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Health Statistics. Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted Average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (https://childmortality.org/data, downloaded June 2019).

It includes:

Reference area: name of the country

Indicator: child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14)

Sex: sex of the child (male, female and total)

Series name: name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field]

Series year: year of survey/census/VR series

Observation value: value of indicator from survey/census/VR

Observation status: indicates whether the data point is included or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data]

Series Category: category of survey/census/VR, and can be:

DHS [Demographic and Health Survey]

MIS [Malaria Indicator Survey]

AIS [AIDS Indicator Survey]

Interim DHS

Special DHS

NDHS [National DHS]

WFS [World Fertility Survey]

MICS [Multiple Indicator Cluster Survey]

NMICS [National MICS]

RHS [Reproductive Health Survey]

PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]

LSMS [Living Standard Measurement Survey]

Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]

Census

VR [Vital Registration]

SVR [Sample Vital Registration]

Others [e.g. Life Tables]

Series type: the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records)

Standard error: sampling standard error of the observation value

Series method: data collection method, and can be:

Survey/census with Full Birth Histories

Survey/census with Summary Birth Histories

Survey/census with Household death

Vital Registration

Other

Lower and upper bound: the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data).

The dataset is used in the following paper:

Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)

Time series data for the statistic Mortality rate, infant, female (per 1,000 live births) and country Comoros. Indicator Definition:Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year.The indicator "Mortality rate, infant, female (per 1,000 live births)" stands at 33.30 as of 12/31/2023, the lowest value at least since 12/31/1974, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -3.20 percent compared to the value the year prior.The 1 year change in percent is -3.20.The 3 year change in percent is -8.01.The 5 year change in percent is -11.90.The 10 year change in percent is -19.37.The Serie's long term average value is 74.22. It's latest available value, on 12/31/2023, is 55.14 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1973, to it's latest available value, on 12/31/2023, is -76.28%.

Context

Although there have been lot of studies undertaken in the past on factors affecting life expectancy considering demographic variables, income composition and mortality rates. It was found that affect of immunization and human development index was not taken into account in the past. Also, some of the past research was done considering multiple linear regression based on data set of one year for all the countries. Hence, this gives motivation to resolve both the factors stated previously by formulating a regression model based on mixed effects model and multiple linear regression while considering data from a period of 2000 to 2015 for all the countries. Important immunization like Hepatitis B, Polio and Diphtheria will also be considered. In a nutshell, this study will focus on immunization factors, mortality factors, economic factors, social factors and other health related factors as well. Since the observations this dataset are based on different countries, it will be easier for a country to determine the predicting factor which is contributing to lower value of life expectancy. This will help in suggesting a country which area should be given importance in order to efficiently improve the life expectancy of its population.

Content

The project relies on accuracy of data. The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The data-sets are made available to public for the purpose of health data analysis. The data-set related to life expectancy, health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. Among all categories of health-related factors only those critical factors were chosen which are more representative. It has been observed that in the past 15 years , there has been a huge development in health sector resulting in improvement of human mortality rates especially in the developing nations in comparison to the past 30 years. Therefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. The individual data files have been merged together into a single data-set. On initial visual inspection of the data showed some missing values. As the data-sets were from WHO, we found no evident errors. Missing data was handled in R software by using Missmap command. The result indicated that most of the missing data was for population, Hepatitis B and GDP. The missing data were from less known countries like Vanuatu, Tonga, Togo, Cabo Verde etc. Finding all data for these countries was difficult and hence, it was decided that we exclude these countries from the final model data-set. The final merged file(final dataset) consists of 22 Columns and 2938 rows which meant 20 predicting variables. All predicting variables was then divided into several broad categories:​Immunization related factors, Mortality factors, Economical factors and Social factors.

Acknowledgements

The data was collected from WHO and United Nations website with the help of Deeksha Russell and Duan Wang.

Inspiration

The data-set aims to answer the following key questions: 1. Does various predicting factors which has been chosen initially really affect the Life expectancy? What are the predicting variables actually affecting the life expectancy? 2. Should a country having a lower life expectancy value(<65) increase its healthcare expenditure in order to improve its average lifespan? 3. How does Infant and Adult mortality rates affect life expectancy? 4. Does Life Expectancy has positive or negative correlation with eating habits, lifestyle, exercise, smoking, drinking alcohol etc. 5. What is the impact of schooling on the lifespan of humans? 6. Does Life Expectancy have positive or negative relationship with drinking alcohol? 7. Do densely populated countries tend to have lower life expectancy? 8. What is the impact of Immunization coverage on life Expectancy?

Andorra AD: Mortality Rate: Infant: Female: per 1000 Live Births data was reported at 2.200 Ratio in 2023. This records a decrease from the previous number of 2.300 Ratio for 2022. Andorra AD: Mortality Rate: Infant: Female: per 1000 Live Births data is updated yearly, averaging 4.800 Ratio from Dec 1985 (Median) to 2023, with 39 observations. The data reached an all-time high of 8.700 Ratio in 1985 and a record low of 2.200 Ratio in 2023. Andorra AD: Mortality Rate: Infant: Female: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Andorra – Table AD.World Bank.WDI: Social: Health Statistics. Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Weighted average;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Context - Covid data falsification discussion:

An active discussion about the mortality data in Moscow has erupted in the days. The Moscow Times newspaper drew attention to a significant increase in official mortality rates in April 2020: "Moscow recorded 20% more fatalities in April 2020 compared to its average April mortality total over the past decade, according to newly published preliminary data from Moscow’s civil registry office. The data comes as Russia sees the fastest growth in coronavirus infections in Europe, while its mortality rate remains much lower than in many countries. Moscow, the epicenter of Russia’s coronavirus outbreak, has continued to see daily spikes in new cases despite being under lockdown since March 30. According to the official data, 11,846 people died in Russia’s capital in April of this year, roughly a 20% increase from the 10-year average for April deaths, which is 9,866. The numbers suggest that the city’s statistics of coronavirus deaths may be higher in reality than official numbers indicate. Russia boasts a relatively low coronavirus mortality rate of 0.9%, which experts believe is linked to the way coronavirus-related deaths are counted."

After this publication have been realesed The Moscow Department of Health has denied the statement of the inaccuracy of counting.:

First, Moscow is a region that openly publishes mortality data on its websites. Moscow on an initiative basis published data for April before the federal structures did it. Secondly, the comparison of mortality rates in the monthly dynamics is incorrect and is not a clear evidence of any trends. In April 2020, indeed, according to the Civil Registry Office in Moscow, 11,846 death certificates were issued. So, the increase compared to April 2019 amounted to 1841 people, and compared to the same month of 2018 - 985 people, i.e. 2 times less. Thirdly, the diagnosis of coronavirus-infected deaths in Moscow is established after a mandatory autopsy is performed in strict accordance with the Provisional Guidelines of the Russian Ministry of Health.Of the total number of deaths in April 2020, 639 are people whose cause of death is coronavirus infection and its complications, most often pneumonia.It should be emphasized that the pathological autopsy of the dead with suspected CoV-19 in Russia and Moscow is carried out in 100% of cases, unlike most other countries.It is impossible to name the cause of death of COVID-19 in other cases. For example, over 60% of deaths occurred from obvious alternative causes, such as vascular accidents (myocardial infarction and stroke), stage 4 malignant diseases (essentially palliative patients), leukemia, systemic diseases with the development of organ failure (e.g. amyloidosis and terminal renal insufficiency) and other non-curable deadly diseases. Fourth, any seasonal increase in the incidence of SARS, not to mention the pandemic caused by the spread of the new coronavirus, is always accompanied by an increase in mortality. This is due to the appearance of the dead directly from an infectious disease, but to an even greater extent from other diseases, the exacerbation of which and the decompensation of the condition of patients suffering from these diseases also leads to death. In these cases, the infectious onset is a catalyst for the rapid progression of chronic diseases and the manifestation of new diseases. Fifthly, a similar situation with statistics is observed in other countries - mortality from COVID-19 is lower than the overall increase in mortality. According to the official sites of cities:In New York, mortality from coronavirus in April amounted to 11,861 people. At the same time, the total increase in mortality compared to the same period in 2019 is 15709.In London, in April, 3,589 people died with a diagnosis of coronavirus, while the total increase was 5531 Sixth, even if all the additional mortality for April in Moscow is attributed to coronavirus, the mortality from COVID will be slightly more than 3%, which is lower than the official mortality in New York and London (10% and 23%, respectively). Moreover, if you make such a recount in these cities, the mortality rate in them will be 13% and 32%, respectively. Seventh, Moscow is open for discussion and is ready to share experience with both Russian and foreign experts.

Content

I think community members would be interested in studying the data on mortality in the Russian capital themselves and conducting a competent statistical check.

This may be of particular interest in connection with that he [US announced a grant of $ 250 thousand to "expose the disinformation of health care" in Russia](https://www....

Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country Bahamas, The. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 12.70 as of 12/31/2023, the lowest value at least since 12/31/1969, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -1.55 percent compared to the value the year prior.The 1 year change in percent is -1.55.The 3 year change in percent is -4.51.The 5 year change in percent is -5.93.The 10 year change in percent is -11.81.The Serie's long term average value is 21.49. It's latest available value, on 12/31/2023, is 40.91 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1974, to it's latest available value, on 12/31/2023, is -59.55%.

BackgroundSocioeconomic inequalities in alcohol-related mortality have been documented in several European countries, but it is unknown whether the magnitude of these inequalities differs between countries and whether these inequalities increase or decrease over time.Methods and FindingsWe collected and harmonized data on mortality from four alcohol-related causes (alcoholic psychosis, dependence, and abuse; alcoholic cardiomyopathy; alcoholic liver cirrhosis; and accidental poisoning by alcohol) by age, sex, education level, and occupational class in 20 European populations from 17 different countries, both for a recent period and for previous points in time, using data from mortality registers. Mortality was age-standardized using the European Standard Population, and measures for both relative and absolute inequality between low and high socioeconomic groups (as measured by educational level and occupational class) were calculated.Rates of alcohol-related mortality are higher in lower educational and occupational groups in all countries. Both relative and absolute inequalities are largest in Eastern Europe, and Finland and Denmark also have very large absolute inequalities in alcohol-related mortality. For example, for educational inequality among Finnish men, the relative index of inequality is 3.6 (95% CI 3.3–4.0) and the slope index of inequality is 112.5 (95% CI 106.2–118.8) deaths per 100,000 person-years. Over time, the relative inequality in alcohol-related mortality has increased in many countries, but the main change is a strong rise of absolute inequality in several countries in Eastern Europe (Hungary, Lithuania, Estonia) and Northern Europe (Finland, Denmark) because of a rapid rise in alcohol-related mortality in lower socioeconomic groups. In some of these countries, alcohol-related causes now account for 10% or more of the socioeconomic inequality in total mortality.Because our study relies on routinely collected underlying causes of death, it is likely that our results underestimate the true extent of the problem.ConclusionsAlcohol-related conditions play an important role in generating inequalities in total mortality in many European countries. Countering increases in alcohol-related mortality in lower socioeconomic groups is essential for reducing inequalities in mortality. Studies of why such increases have not occurred in countries like France, Switzerland, Spain, and Italy can help in developing evidence-based policies in other European countries.

Background: Recent studies have described a low occurrence of Amyotrophic Lateral Sclerosis (ALS) in Latin America. Significant differences in ALS risk have been reported among ethnic populations in the region. We conducted a meta-analysis using population-based data to describe ALS mortality rates in Latin America. We explored sources of heterogeneity among key covariates. Methods: National mortality registries from Latin American countries were searched to identify ALS deaths according to the International Classification of Diseases (ICD-9: code 335.2 and ICD-10: code G12.2). Crude and standardized mortality rates were calculated. A random-effect meta-analysis was conducted to estimate pooled mortality rates. Subgroup analysis was performed as a means of investigating heterogeneity. Results: Overall, 28,548 ALS deaths and 819 million person-years of follow-up (PYFU) from ten Latin American countries were considered. Standardized mortality varied among countries. The highest mortality rates were observed in Uruguay and Costa Rica at 1.3 and 1.2 per 100,000 PYFU, respectively. The pooled crude mortality rate was 0.38 (95%CI: 0.28–0.53) and the pooled standardized mortality was 0.62 (95%CI: 0.49–0.77) per 100,000 PYFU. Heterogeneity was high (I2: 99.9%, Cochran’s Q p < 0.001). Subgroup analysis showed a higher mortality rate among countries with a higher proportion of Caucasian populations and higher income levels. Conclusion: There is a lower ALS occurrence in Latin America compared to Europe and North America. This meta-analysis supports the hypothesis of a higher ALS risk among the Caucasian population. Further studies are needed to investigate the role of ancestral origins in ALS, taking socioeconomic status into consideration.

BackgroundMaternity leave reduces neonatal and infant mortality rates in high-income countries. However, the impact of maternity leave on infant health has not been rigorously evaluated in low- and middle-income countries (LMICs). In this study, we utilized a difference-in-differences approach to evaluate whether paid maternity leave policies affect infant mortality in LMICs.Methods and FindingsWe used birth history data collected via the Demographic and Health Surveys to assemble a panel of approximately 300,000 live births in 20 countries from 2000 to 2008; these observational data were merged with longitudinal information on the duration of paid maternity leave provided by each country. We estimated the effect of an increase in maternity leave in the prior year on the probability of infant (<1 y), neonatal (<28 d), and post-neonatal (between 28 d and 1 y after birth) mortality. Fixed effects for country and year were included to control for, respectively, unobserved time-invariant confounders that varied across countries and temporal trends in mortality that were shared across countries. Average rates of infant, neonatal, and post-neonatal mortality over the study period were 55.2, 30.7, and 23.0 per 1,000 live births, respectively. Each additional month of paid maternity was associated with 7.9 fewer infant deaths per 1,000 live births (95% CI 3.7, 12.0), reflecting a 13% relative reduction. Reductions in infant mortality associated with increases in the duration of paid maternity leave were concentrated in the post-neonatal period. Estimates were robust to adjustment for individual, household, and country-level characteristics, although there may be residual confounding by unmeasured time-varying confounders, such as coincident policy changes.ConclusionsMore generous paid maternity leave policies represent a potential instrument for facilitating early-life interventions and reducing infant mortality in LMICs and warrant further discussion in the post-2015 sustainable development agenda. From a policy planning perspective, further work is needed to elucidate the mechanisms that explain the benefits of paid maternity leave for infant mortality.