This isn't a "study", it's a statistical model. It reflects the assumptions put into it. To wit:
> Our analysis follows four key steps. First, for all locations where weekly or monthly all-cause mortality has been reported since the start of the pandemic, we estimate how much mortality increased compared to the expected death rate....Second, based on a range of studies and consideration of other evidence, we estimate the fraction of excess mortality that is from total COVID-19 deaths as opposed to the five other drivers that influence excess mortality. Third, we build a statistical model that predicts the weekly ratio of total COVID-19 deaths to reported COVID-19 deaths based on covariates and spatial effects. Fourth, we use this statistical relationship to predict the ratio of total to reported COVID-19 deaths in places without data on total COVID-19 deaths and then multiply the reported COVID-19 deaths by this ratio to generate estimates of total COVID-19 deaths for all locations.
And, what, you may ask, are the biases of the models' authors with regards to the percentage of excess mortality due to Covid itself? Excellent question:
> Deaths that are directly due to COVID-19 are likely underreported in many locations, particularly in settings where COVID-19 testing is in short supply. Most excess mortality is likely misclassified COVID-19 deaths.
> Given that there is insufficient evidence to estimate these contributions to excess mortality, for now we assume that total COVID-19 deaths equal excess mortality. For the reasons presented in this section, we believe that this is likely an underestimate.
As someone who has now spent the better part of his life creating statistical models, when you assume something is true, your model is likely to confirm your assumptions.
This is why statistical models are worthless without prospective validation. If you haven't shown that your assumptions are correct for the future, you're just making castles in the sky.
> Our analysis follows four key steps. First, for all locations where weekly or monthly all-cause mortality has been reported since the start of the pandemic, we estimate how much mortality increased compared to the expected death rate....Second, based on a range of studies and consideration of other evidence, we estimate the fraction of excess mortality that is from total COVID-19 deaths as opposed to the five other drivers that influence excess mortality. Third, we build a statistical model that predicts the weekly ratio of total COVID-19 deaths to reported COVID-19 deaths based on covariates and spatial effects. Fourth, we use this statistical relationship to predict the ratio of total to reported COVID-19 deaths in places without data on total COVID-19 deaths and then multiply the reported COVID-19 deaths by this ratio to generate estimates of total COVID-19 deaths for all locations.
And, what, you may ask, are the biases of the models' authors with regards to the percentage of excess mortality due to Covid itself? Excellent question:
> Deaths that are directly due to COVID-19 are likely underreported in many locations, particularly in settings where COVID-19 testing is in short supply. Most excess mortality is likely misclassified COVID-19 deaths.
> Given that there is insufficient evidence to estimate these contributions to excess mortality, for now we assume that total COVID-19 deaths equal excess mortality. For the reasons presented in this section, we believe that this is likely an underestimate.
As someone who has now spent the better part of his life creating statistical models, when you assume something is true, your model is likely to confirm your assumptions.
This is why statistical models are worthless without prospective validation. If you haven't shown that your assumptions are correct for the future, you're just making castles in the sky.