6 ms·
> [Edited to note: the linked article discusses this with further detail and evidence] Here's the sum total of evidence provided: > An analysis by the Netherl
by timr 6y ago
> [Edited to note: the linked article discusses this with further detail and evidence]
Here's the sum total of evidence provided:
> An analysis by the Netherlands statistical agency suggested that all excess deaths in the Netherlands were directly due to COVID-19
So they use a single study out of Europe to extrapolate here.
> The second driver of excess mortality is reduced health care utilization for many causes;3 however, the impact of reduced health care use on health outcomes is harder to prove. Many mechanisms have been proposed, including reduced vaccination rates and reduced births in hospital.4 Demonstrated increases in cause-specific mortality related to these causes, however, have not yet been verified. The impact of changes in health care utilization on excess mortality may be observed in later years, rather than in 2020 or the first quarter of 2021.
So they dismiss this.
> Third, convincing evidence has been found that rates of anxiety and depression have increased, which might in turn lead to increases in deaths from suicide.5 To date, the evidence on increased suicide is very limited.6 Opioid deaths, on the other hand, have clearly increased7 in the United States. Compared to past trends, opioid deaths increased by around 15,000 since March 2020. Evidence on whether this has also occurred in other countries awaits further study.
So again, they dismiss this.
> Fourth, we reviewed the evidence on decreases in injuries as a result of reductions in mobility. We analyzed data from 12 countries that provide cause of death data by week or month, which allows us to test whether some causes decreased significantly during 2020 and whether that decrease was related to the decreases in mobility that have been reported. This analysis suggests that globally, injury mortality decreased by 5% in 2020 due to reductions in mobility. At the global level, this translates into a reduction of approximately 215,000 deaths.
Conveniently, when then assumption works out in their favor, they include it in the model.
> Fifth, some infectious causes of death may have declined during the pandemic due to the behavioral changes associated with control of the pandemic, including mask use and reduced contact with others. Causes that have clearly declined are influenza,8,9 respiratory syncytial virus,10 measles,11 and possibly other respiratory viruses and viral diarrheas. For example, influenza cases in the United States declined 99.3% from the winter season of 2019–2020 to the winter season of 2020–2021. Combining the reductions reported in different countries in influenza, respiratory syncytial virus, and measles, the global reduction in mortality from these causes may be larger than 400,000 deaths.
Again, when the assumption works in their favor, they include it.
> Sixth, deaths from some chronic conditions such as ischemic heart disease or chronic respiratory disease declined in some months of 2020, most notably in May and June in Europe. These declines were most likely due to the fact that frail individuals who died from COVID-19 earlier in the year would otherwise likely have died from these chronic conditions. The strongest evidence for this effect is that excess mortality was negative in some countries in Europe in June when the reported COVID-19 death rate was very low. In aggregate, this effect likely reduced mortality by only 2% based on our analysis.
This is so egregious as to be laughable...they find evidence for something, and then "based on their analysis", limit the effects.
> The main potential increases in excess mortality due to deferred care and increases in drug overdose and depression are hard to quantify at this point or are of a much smaller magnitude. 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.
So they didn't even try.
Again...I don't know what the right answer is here, but this is, at best, a biased reading of the available data. All you can do is throw up your hands and go back to first principles: without prospective validation, statistical models are fiction.
- teachingassist 6y agoOn the other hand: "in many European countries there was a spike of excess deaths in weeks 31–35 during a period when COVID-19 reported deaths were extremely low. This period coincided with a heat wave and national reports of deaths due to the heat wave. We excluded these weeks of data from subsequent analyses." I personally wouldn't have excluded these weeks - unless you're going to exclude historical (near-annual) heatwaves from the figures, this should balance out. > All you can do is throw up your hands and go back to first principles: without prospective validation, statistical models are fiction. No estimation is perfect, nor expected to be, and so your criticism is unreasonable. You're doing exactly the things that you are criticising the study for, and more so. You provide zero evidence to counter their sourced evidence, and ignore and dismiss points that don't match what you are trying to say.
- timr 6y ago> No estimation is perfect, nor expected to be, and so your criticism is unreasonable. You're doing exactly the things that you are criticising the study for, and more so. You provide zero evidence to counter their sourced evidence, and ignore and dismiss points that don't match what you are trying to say. They have provided no "evidence" -- they made a model. They have provided no validation of that model, so it could be completely incorrect. Nobody knows. Furthermore, I just showed you that of the sourced evidence they did consider, they dismissed nearly everything that reduced the death rate attributable to Covid, and said it was "too dificult" to quantify. Mathematical models without prospective validation are not evidence. They are not science. They are bedtime stories.
- WalterBright 6y ago> without prospective validation, statistical models are fiction. I wouldn't be completely dismissive. The first attempts at using statistical data to drive medical practice were during the Napoleonic wars, and dramatically improved medicine. I would expect the statistical methods used were pretty crude, as it was a very young discipline.
- timr 6y ago