IHME report: Estimation of total mortality due to COVID-19 (it's double)

[Nevilledog]

http://www.healthdata.org/special-analysis/estimation-excess-mortality-due-covid-19-and-scalars-reported-covid-19-deaths

In the IHME estimation of COVID-19 infections, hospitalizations, and deaths to date, we have used officially reported COVID-19 deaths for nearly all locations. As of today, we are switching to a new approach that relies on the estimation of total mortality due to COVID-19. There are several reasons that have led us to adopt this new approach. These reasons include the fact that testing capacity varies markedly across countries and within countries over time, which means that the reported COVID-19 deaths as a proportion of all deaths due to COVID-19 also vary markedly across countries and within countries over time. In addition, in many high-income countries, deaths from COVID-19 in older individuals, especially in long-term care facilities, went unrecorded in the first few months of the pandemic. In other countries, such as Ecuador, Peru, and the Russian Federation, the discrepancy between reported deaths and analyses of death rates compared to expected death rates, sometimes referred to as “excess mortality,” suggests that the total COVID-19 death rate is many multiples larger than official reports. Estimating the total COVID-19 death rate is important both for modeling the transmission dynamics of the disease to make better forecasts, and also for understanding the drivers of larger and smaller epidemics across different countries.

Our approach to estimating the total COVID-19 death rate is based on measurement of the excess death rate during the pandemic week by week compared to what would have been expected based on past trends and seasonality. However, the excess death rate does not equal the total COVID-19 death rate. Excess mortality is influenced by six drivers of all-cause mortality that relate to the pandemic and the social distancing mandates that came with the pandemic. These six drivers are: a) the total COVID-19 death rate, that is, all deaths directly related to COVID-19 infection; b) the increase in mortality due to needed health care being delayed or deferred during the pandemic; c) the increase in mortality due to increases in mental health disorders including depression, increased alcohol use, and increased opioid use; d) the reduction in mortality due to decreases in injuries because of general reductions in mobility associated with social distancing mandates; e) the reductions in mortality due to reduced transmission of other viruses, most notably influenza, respiratory syncytial virus, and measles; and f) the reductions in mortality due to some chronic conditions, such as cardiovascular disease and chronic respiratory disease, that occur when frail individuals who would have died from these conditions died earlier from COVID-19 instead. To correctly estimate the total COVID-19 mortality, we need to take into account all six of these drivers of change in mortality that have happened since the onset of the pandemic.

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. In other words, we estimate excess mortality in all locations with sufficient data. 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. More details on each of these analytical steps are presented below.

1. Estimating excess mortality compared to expected mortality for locations where all-cause mortality data have been reported during the pandemic

56 countries and 198 subnational units have reported either weekly or monthly deaths from all causes for parts of 2020 and for prior years. Our analysis of excess mortality follows three steps. First, we estimate expected mortality in the absence of COVID-19 based on the patterns of all-cause mortality reported in prior years; second, we subtract observed all-cause mortality from March 2020 onward from expected mortality; and third, we remove from the analysis known periods of excess mortality due to causes other than COVID-19, such as the August 2020 heat wave in many European countries. For locations where vital registration systems are not complete, we apply the adjustment to the reported death counts based on our estimated completeness from the Global Burden of Disease study.1

*snip*

IHME
The Institute for Health Metrics and Evaluation (IHME) is an independent global health research center at the University of Washington.

[Deminpenn]

1. Really, why does anyone take this organization seriously?

Of course models should be updated if there is enough data to show the original theorized input parameters were wrong, but IHME is constantly changing inputs for reasons that seem to be more designed to achieve a desired result as anything else.

[Nevilledog]

2. I'd be interested in knowing your objections to this organization.

https://mediabiasfactcheck.com/institute-for-health-metrics-and-evaluation-healthdata-org/

[Deminpenn]

3. Their model is constantly shifting it's parameters

It was either poorly constructed from the start or it's being adjusted to show projections based on an agenda, which for this change is to try and reconcile "excess mortality". They are trying to "prove" that deaths above an average of deaths over x number of years are due to covid19. That may or may not be true as cause of death is nebulous as we all learned by watching the Chauvin trial.

Here is the actuarial fact about LTC residents, the average stay is 3 years before death occurs from one cause or another. We know during the fall and winter seasons, deaths increase, too. There is really no reason to suspect that deaths not citing covid19 directly or as a contributing cause are wrong. Perhaps in the early stages of the epidemic, there were deaths attributed to pneumonia that were actually covid19, but I doubt that's the case now.

[ProfessorGAC]

4. How About This, ND?

They revised their model weekly because it was consistently wrong, in the first 6 months of COVID.
Also, the actuaries at the big insurance companies are saying COVID is underreported by 20-25%.
They've been consistent & adamant about it since last summer.
Those that I know work for 3 very big insurance companies, & they all same the same thing.
This model has the actual rate at nearly 80% above reported.
Insurance companies are revising their cost & investment models based on 25%, but IHME says 80%?
I read their methodology. I question it now like I questioned it last May.

[Nevilledog]

5. TY

Appreciate your thoughts.

Too bad we'll never know the true death numbers.....for so many reasons.

[ProfessorGAC]

6. Think About India & Russia!

Talk about never knowing the real numbers!
I think this model is greatly underestimating deaths in those 2 countries, as well as Brazil.

[Pobeka]

7. I spent over 30 years as a career biometrician, building biological models.

True, it's not epidemiology what I did, but similar kinds of problems, low correlations (relative to engineering relationships), several factors at play, dynamic "feedback" loops (to put it in simple terms) must be properly constructed, analyzed, reviewed, refined, ...

I looked at IMHE "models" -- the actual source program code and documents. It was clear these folks are not in any way skilled at building a model for a dynamicly modelled system of equations. That's just one reason why they were constantly changing the parameters of their model, there was literally no epidemiological basis to their model, it was just a curve shape that could only extrapolate the future correctly if a miracle occurred. I would have expected to be fired from my position if I had done such a poor job. Really.

I don't know what they are attempting to do now, but their credibility is near zero for me at least.