Excess mortality - increase of the all-cause mortality expected mortality based on historic trends, has long been used to estimate the death toll of pandemics and other extreme events. From the 17th century at least up to more recent events like Hurricane Maria in 2017.
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No matter how estimate are made, they require data on all cause mortality. In #WorldMortality Dataset we have collected data from over 100 countries and territories from all corners of the world.
Our findings show significant excess mortality in many countries from all over the world, both in absolute numbers, per capita terms and increase from expected mortality.
Even if countries had not tested or reported COVID - it's easily detectable in excess mortality.
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In many countries, COVID deaths are likely heavily undercounted, with excess deaths being 2, 3, 5, 15, 30 and even 50 to 100 times higher.
So while Mexico reported 235K COVID deaths, excess > 468K. Russia reported 136K, excess > 550K. Nicaragua reported 137. excess > 6900.
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In other, mostly advanced countries, undercounting is not as high and has been decreasing through time, most likely due to ILI suppression in the winter months.
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We break down excess deaths into its' factors and discuss them one by one.
Evidence tend to show that (C) and (D) are small, with deaths from traffic accidents, homicides, suicides ~same or even lower (except in the US which seems to be an outlier).
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As for lockdown-caused-deaths, we find very little evidence for it.
Excess mortality is exactly the measure to test this hypothesis - it's a catch-all-measure that would have shown it if it was true.
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To get excess deaths estimates, we need to obtain "expected deaths" estimates first. We do this with a simple and efficient model for each country that accounts both for annual trend (e.g. aging pop) and seasonality (winter deaths higher).
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Summing up excess mortality estimates across all countries in our dataset = 4.0M excess deaths. while total offical COVID-19 deaths = 2.9M deaths,
corresponding to the global undercount ratio of 1.4.
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We posit that this is a lower-bound, as there is ample evidence that among the countries for which the all-cause mortality data are not available the undercount ratio is high - India, Indonesia, ME, Africa etc.
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Just as countries collect and regularly report estimates of economic output such as the gross domestic product and COVID-19 mortality, they should be reporting all-cause mortality. Importance of CRVS improvements such as @VitalStrat cannot be understated.
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I would like to thank many colleagues and organizations from around the world who helped, commented, shared and provided data - some even under threat of repression.
@OrsolaTorrisi_ and I estimate the war led to ~6,500 excess deaths among people aged 15–49:
~2,800 in Armenia
~3,400 in Azerbaijan
and 310 in de facto Artsakh.
Deaths were highly concentrated among late adolescent and young adult males, suggesting that most excess mortality was directly related to combat.
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For small countries like Armenia (pop ~3 million) and Azerbaijan (~10 million), such loss of young men represents a considerable long-term cost for future demographic, economic, and social development.
It's often argued that in the absence of all-cause-mortality data to directly estimate excess deaths, we should rely on the officially reported covid-19 data. This is false, as we explain here
It is high time that countries, regions and the world report all-cause-mortality in (as close to) real time as possible.
This will help prevent future outbreaks from evolving to an epidemic and then a pandemic.
Vital Registration is VITAL.
I have officially submitted a request to retract Motallebi et al (2022) from @AmJPrevMed.
There are many conceptual and practical issues with the analysis, but the most important one is miscoding of countries into "masking yes/no" groups.
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The paper codes Belgium, France, Italy, Spain, UK into the "no masking policy" group during the 1st COVID wave.
This is plainly false, not just by examining data on masking policies but by anyone who lives in or was in Italy for example.
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Another issue is that the study essentially compares countries that had COVID during the 1st wave to countries without, wrongly attributing difference in deaths to policy...
This is a terrible study.
The sample period is only February 15 to May 31st 2020.
Many countries in the "yes mask policy" are countries with literally NO COVID SPREAD (as evident from both reported cases/deaths and lack of excess mortality)...
PM contains data which doesn't fit into World Mortality or Local Mortality for various reasons such as time format (not weekly, monthly or quarterly), un-official status, projections, early releases of more detailed data forthcoming, etc.
Each datum source, properties and limitations is listed below. As the time format varies substantially, each source is provided as a separate csv file.
אני רואה ששוב ושוב מפיצים את התרשים מהמחקר שלנו בנוגע להתחלקות הוצאות מדינה ומיסים על פני משקי בית בישראל.
זה מאוד מחמם את הלב אבל אשמח אם תזכרו שהוא טרם פורסם ועד ייתכנו בו שינויים, אם כי התמונה הגדולה לא תשתנה הרבה.
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אישית אני חושב שזה אחד המחקרים החשובים ביותר שעבדתי עליהם ואני שמח לראות שיש לו תהודה גדולה עוד לפני שפורסם.
מאז שעזבתי את קהלת לכבוד הדוקטורט לקחו את המושכות שלו ביתר חבריי לפורום ולגבי השאלה "מתי תפרסמו" התשובה היא "when it's ready".
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הסבר קטן לגבי מה רואים פה:
המחקר אומד על בסיס סקר הוצ' משק הבית את תשלומי משקי הבית על מס הכנסה, דמי ביטוח לאומי ובריאות (גם "חלק מעסיק", שאינו מופיע ישירות בסקר ונאמד בעזרת ההכנסה ברוטו מעבודה ולוחות הניכוי של המוסד לבט"ל)...