Estimating the Early Death Toll of COVID-19 in the US

Transcript

• 00:00I would not let you juice our next Speaker.
• 00:04Doctor Daniel Weinberger.
• 00:05Doctor Weinberger joined the faculty at
• 00:08the Yale School of public health in 2013.
• 00:11He earned his PhD in Biological Sciences from
• 00:14the Harvard School of public health in 2009.
• 00:17His research is at the intersection
• 00:19of Microbiology and Epidemiology
• 00:21and focuses on understanding the
• 00:23biological and epidemiological
• 00:24drivers of respiratory infections,
• 00:26including pneumococcus RSVP
• 00:27influenza and Legionella.
• 00:28Doctor Weinberger thank you for being here.
• 00:38Thank you very much. I can share my
• 00:40screen if it's OK. If you're able to.
• 01:05OK, thank you very much for the
• 01:08invitation to share this work with you.
• 01:10So this is a collaborative project I've
• 01:13been working on since late March with a
• 01:16large group that includes plugs from the
• 01:19NIH in New York City Department of Health,
• 01:22UMass Amherst, alidade health, and Russ.
• 01:24Killed the University.
• 01:26So when we started this project.
• 01:29We were interested in trying to extend some
• 01:32work that we've been doing as a group.
• 01:35I'm trying to estimate the burden of
• 01:37influenza during previous pandemics
• 01:39and thought that it would be a
• 01:41fairly straightforward project.
• 01:42Didn't anticipate that it would get
• 01:44a lot of attention in particular,
• 01:47but it turns out that this has become a.
• 01:51Sort of radioactively hot topic.
• 01:53Since we started doing this project.
• 01:56There's been.
• 01:58A lot of controversy.
• 02:02Sort of about the official kovit death tool.
• 02:06And a lot of questions about the
• 02:08reliability of sort of the official.
• 02:11That's that are being reported by ACDC,
• 02:13which I think makes this work especially
• 02:16important because we try to sidestep
• 02:18some of the issues around testing
• 02:20which had sort of played some of the.
• 02:23The official numbers said basically
• 02:25the controversy is been around
• 02:27sort of weather deaths are being
• 02:29accurately recorded as due to Cove it,
• 02:31or whether sort of of.
• 02:34People are sort of inappropriately
• 02:36coding deaths as Judah covered
• 02:38when people are dying.
• 02:40Of other causes,
• 02:41and maybe you're infected with so good,
• 02:43but not necessarily dying due to Cove in.
• 02:47So we have been working on this
• 02:49group that I mentioned at the
• 02:51beginning and have been partnering
• 02:52with the Washington Post on trying
• 02:54to try to estimate excess death.
• 02:56So this the idea here is instead of
• 02:59looking at the number of deaths that are
• 03:01actually recorded as being due to Kobe,
• 03:04just looking at sort of the
• 03:05changes in total deaths and deaths
• 03:07due to pneumonia or influenza,
• 03:09which are less likely to be biased
• 03:11by some of these coding issues.
• 03:16So in the United States you know the
• 03:18cause of death is decided by individuals
• 03:20by individuals all around the country
• 03:23who have their own sort of criteria,
• 03:25and it's typically done by by physician
• 03:28when the death is due to natural causes
• 03:31or by metal medical examiner or coroner,
• 03:33when it's when it's an unattended death.
• 03:38And the CDC and state helps parents are
• 03:40recording typically both an underlying cause
• 03:43of death and a contributing cause of death.
• 03:45So, for instance, you could have current
• 03:48virus listed as the underlying cause,
• 03:50an ammonia listed as a contributing
• 03:52cause or or heart attack.
• 03:54Let's just contributing cause.
• 03:56Or vice versa.
• 03:57Or you might have one of these things listed,
• 04:00so you might have pneumonia listed,
• 04:02but current viruses left off,
• 04:04or any combination of those possibilities.
• 04:07So the official death registration in
• 04:08the US is done at the state level.
• 04:11Minutes reported up to the CDC
• 04:13National Center for health statistics.
• 04:15And there is some like that data.
• 04:19So we have good reason to think that the
• 04:22number of reported deaths is an undercount.
• 04:25This is sort of a typical feature of sort
• 04:27of pathogen specific deaths in general,
• 04:30so this is, you know,
• 04:32been staying in previous influenza pandemics,
• 04:34where it's it's typically assume
• 04:35that just some fraction of the
• 04:37deaths that are due to influenza
• 04:39during an influenza pandemic or even
• 04:42during sort of typical seasonal flu,
• 04:44or actually recorded as such,
• 04:45so it's much more common to look at
• 04:48pneumonia and influenza together.
• 04:50And looking at sort of increases
• 04:52above a typical seasonal baseline to
• 04:55try to estimate the full burden of.
• 04:57Of death and of course this is
• 04:59particularly important with current virus,
• 05:01especially early in the epidemic where
• 05:04the testing was really slow to ramp up,
• 05:07and in many states testing was
• 05:09really inadequate at a time when
• 05:11we think that the virus might have
• 05:14been circulating at a high level.
• 05:19Complicating things further.
• 05:20The way that CDC records the data
• 05:23and it gets reported from the states
• 05:26there is optimal lag in the data,
• 05:29so the data that we're seeing from one
• 05:31or two or three or even four or five
• 05:34weeks ago is going to be incomplete
• 05:36in this varies quite a bit by state,
• 05:39so this plot is just showing our
• 05:41estimates for the proportion of
• 05:42deaths that are reported based on
• 05:44how far we are from the death,
• 05:46so we can see sort of study on
• 05:49the left hand side.
• 05:50Is starting at 2 weeks after the death.
• 05:53There's a huge amount of variability.
• 05:55Some states are only capturing maybe
• 05:5730% of the deaths or reporting 30%
• 05:59of the deaths that will eventually
• 06:01be reported two weeks out.
• 06:03Other states,
• 06:04like New York or quite good and we're
• 06:06getting something like 95% of the
• 06:08deaths even after just two weeks.
• 06:11It tends to ramp up fairly rapidly
• 06:13in most states,
• 06:14so you know after three or four weeks,
• 06:16we're getting more than 90% of the deaths.
• 06:18It will eventually be reported
• 06:20getting reported on,
• 06:21but there are some states like
• 06:22this is Kentucky here,
• 06:24which seems to have a particularly
• 06:25slow reporting where even out to
• 06:27sort of 10 weeks after the death,
• 06:29we're sort of in the 80% range
• 06:31of death that are reported.
• 06:34So our analysis is goals were quite simple.
• 06:37It was to quantify the excess
• 06:39burden of death.
• 06:41Student ammonia,
• 06:41or influenza,
• 06:42or do the code 19 to quantify the
• 06:45excess burden of deaths due to any
• 06:47cause and then to try to compare
• 06:50the excess Destin reported deaths.
• 06:52And we're also hoping to adjust for
• 06:55reporting delays and variations in
• 06:58deaths that are related to influenza.
• 07:00We're using data that are publicly
• 07:03available that are reported by the
• 07:05National Center for health statistics.
• 07:07Were there sort of updating these data
• 07:10on a daily basis in reporting the
• 07:12number of deaths in each week that
• 07:15were due to any cause or due to pneumonia,
• 07:18influenza,
• 07:18or current virus as a as a grouping?
• 07:22We also have some information on.
• 07:26Testing another source of data on deaths,
• 07:28which tends to be a little bit
• 07:30more up to date than the NCHS data
• 07:32from kobetracking.com,
• 07:33which is the data source that Ginny
• 07:35pits are mentioned in the previous stuff.
• 07:39And essentially, we're doing a fairly
• 07:42simple regression model where we're
• 07:45trying to model the number of deaths
• 07:48that occur in a given state in each week.
• 07:51We're adjusting for seasonality.
• 07:53We're adjusting forward influenza
• 07:55activity during the previous week to take
• 07:58into account through the lag between.
• 08:00Who activity in deaths?
• 08:02We're allowing the baseline to
• 08:04vary year to year to account
• 08:07for changes in population size,
• 08:09and we're adjusting for this reporting delay,
• 08:12which is estimated separately using a
• 08:15Bayesian Nowcasting algorithm called knobs,
• 08:17which was recently described
• 08:19by Nick Menzies Group.
• 08:24So we're basically fitting this
• 08:25regression through data today
• 08:27through the beginning of February.
• 08:28So this is a period when we don't think
• 08:31there was much coronavirus there,
• 08:33so we're sort of fitting to the data
• 08:35to try to get sort of a sense for
• 08:38with the typical seasonal pattern
• 08:40looks like and then extrapolating
• 08:42that baseline forward for the
• 08:44period from February to April.
• 08:46Then we're generating uncertainty
• 08:47intervals by resampling scheme that Nick
• 08:49breaks group is previously developed.
• 08:51I try to get an estimate for sort
• 08:53of the uncertainty in those.
• 08:55Baseline estimates
• 08:59So this is just sort of a simple top
• 09:02line picture of the excess or the total
• 09:05deaths occurring in each state overtime.
• 09:08So on the upper left corner this is New York,
• 09:12including New York City, Which.
• 09:15His, as everyone knows, experienced
• 09:17the most severe epidemic in the US.
• 09:19So the typical seasonal baseline
• 09:21is shown with this red line.
• 09:23Here the variation in deaths in
• 09:25previous years is shown in Gray,
• 09:27which you can't even really see.
• 09:29Do the baseline for New York there and then.
• 09:32The observed number of deaths.
• 09:35Of or 2020 is shown with this black line.
• 09:37So really what we're looking at
• 09:39is the difference between the
• 09:40black line in the red light.
• 09:42Here for each state,
• 09:43and so you can see pretty
• 09:44sizable increases in deaths.
• 09:46Above the seasonal baseline for New York,
• 09:48New Jersey, Massachusetts,
• 09:49District of Columbia,
• 09:50the data or a bit more noise of it.
• 09:53You can see this clear increase about it.
• 09:56Above historical patterns,
• 09:57Maryland sort of across the board
• 10:00we see pretty strong increases.
• 10:03There are a number of states
• 10:05where we do not see increases and
• 10:08these tend to be sort of smaller,
• 10:11more rural states.
• 10:12Vermont a number of states in the Midwest.
• 10:16New Hampshire we got Minnesota.
• 10:17Here are the Minnesota is starting
• 10:19to increase in more recent weeks,
• 10:20so I think if we were to extend this
• 10:22at another couple of weeks we probably
• 10:24would see something for Minnesota here.
• 10:30We are also interested in looking at
• 10:33the estimates for excess deaths in
• 10:35relation to the reported cova death.
• 10:37So here we're just looking at the
• 10:39excess pneumonia and influenza deaths,
• 10:41which are shown in red.
• 10:42So the trajectory for so this is just
• 10:45basically subtracting off the baseline
• 10:47from the previous plot and the reported
• 10:49number of covad deaths for the same week.
• 10:52So you can see in New Jersey those
• 10:54two curves basically line up very
• 10:56well in the great dash line.
• 10:59Here is showing us the increase in testing.
• 11:01So this is basically.
• 11:03Saying he was increasing testing
• 11:04at about the same time we were,
• 11:07they were increasing.
• 11:09In cases and very strong agreement
• 11:12between those two curves.
• 11:14You can contrast that with Florida,
• 11:16where we see this earlier increased
• 11:18sort of in early March of pneumonia
• 11:21and influenza deaths.
• 11:23And then the reported code.
• 11:25The deaths don't actually increased until.
• 11:28Several weeks later and one possible
• 11:30explanation for that is if you
• 11:32look at the Great Dash Line here,
• 11:34they're testing levels were quite
• 11:35low and they really didn't even
• 11:37start until a couple weeks after
• 11:39the epidemic had taken off.
• 11:41So there are quite a few deaths we think.
• 11:44Sort of during early to merge that were
• 11:46missed in Florida in the official tallies.
• 11:49Louisiana pretty good concordance between
• 11:51the observed and reported deaths,
• 11:53and likewise for Washington.
• 11:55There just seems to be a relationship
• 11:58between sort of wind testing started
• 12:01relative to the epidemic and the amount of.
• 12:05Sort of unexplained cases that we see.
• 12:09We're also interested in looking
• 12:10at the increase in deaths in
• 12:13relation to influence like illness,
• 12:15so we look if we lineups for the.
• 12:18Unexplained increase in pneumonia,
• 12:20ones adepts again and explain
• 12:22increases and influence like illness.
• 12:23Basically what we see is that the
• 12:26influence like illness increases.
• 12:28Its the blue line here and then about
• 12:30a week later we see an increase in
• 12:33the pneumonia and influenza deaths,
• 12:35suggesting that what we're seeing in
• 12:38terms of excess jets is related to
• 12:40the virus and not necessarily due
• 12:42to lock down measures which would
• 12:45have sort of more diffuse effect.
• 12:47Enough necessarily have such
• 12:49a sort of temporarily.
• 12:50Related Increase similar to Iowa.
• 12:56So just looking through the
• 12:58top line estimates here,
• 13:00this is data through April 25th
• 13:03where we have an estimate for the
• 13:06entire US of about 51,000 kuva dots.
• 13:09During this period,
• 13:11if we look at the excess
• 13:13pneumonia influenza deaths,
• 13:15we have about 57,000 deaths,
• 13:17so just a little bit more pneumonia,
• 13:20influenza deaths nationally.
• 13:22Then we have reported cova deaths
• 13:25and then about 8083 thousand.
• 13:27All cause deaths,
• 13:29so overall about sort of 40 to 50% higher.
• 13:34Death toll rather than we get from
• 13:38what's reported in the data in
• 13:40the sort of official coded data.
• 13:42This does vary quite a bit
• 13:44by state and overtime,
• 13:45so if you were to look at your
• 13:47New York City in particular,
• 13:49I'm early in the epidemic.
• 13:51The reported number of Kobe
• 13:53deaths is about 3 times higher
• 13:55than sorry that the access code.
• 13:57It's about 3 times higher than
• 13:59reported number of coded deaths as
• 14:01they have sort of increased testing
• 14:03and change the reporting guidelines.
• 14:05That gap is narrowed to about so now the.
• 14:09Exodus that's about 50% higher
• 14:10than the Coca dots,
• 14:11and in other states there
• 14:13is not much of a gap at all.
• 14:17So we were also interested in just
• 14:20trying to do something very simple
• 14:23without any sort of model behind it.
• 14:27Just because you know,
• 14:28every every analysis approach has
• 14:30assumptions that we wanted to see if we
• 14:32sort of took an independent approach.
• 14:34If we get a similar answer and what we see.
• 14:37So basically what we did was we took the
• 14:39provisional data that are reported that
• 14:41were reported this year and week 19th.
• 14:44This is reported last week.
• 14:46And we know that those data
• 14:48are highly incomplete.
• 14:49Over the last month or so,
• 14:51and there's this lag in the data,
• 14:53and we can see that here you
• 14:55can see sort of the yellow line
• 14:58sort of would be trailing down.
• 15:00And So what we did was we looked
• 15:02at the data from this year.
• 15:04That were reported in Week 19 and also
• 15:07looked at the at the data reporting
• 15:09Week 19 from the previous year.
• 15:12And what we see?
• 15:14Is that?
• 15:15The data in this year were sort
• 15:18of well aligned up until.
• 15:20So in mid to late March and then
• 15:22you see this sort of very sharp
• 15:25divergences between those lines.
• 15:26So the provisional data for 2020 are much
• 15:29higher than the original data for 2019,
• 15:31and this is sort of a very crude way
• 15:33of adjusting for the reporting delays.
• 15:36If we just look at the difference
• 15:38between these curves,
• 15:39we have about 79,000 excess deaths.
• 15:41You can compare that to the 83,000 that
• 15:43we estimated with the regression model.
• 15:45We have some other approaches we've been
• 15:48using as well where we don't adjust for.
• 15:50Blue where we get a slightly smaller effect,
• 15:53but you know we're sort of in the range to.
• 15:57You know 70 to 80,000 excess deaths,
• 15:59regardless of the method that we're using.
• 16:02So just to conclude,
• 16:04the estimated death tool related
• 16:07the pandemic is about 50% higher
• 16:09than the reported number of deaths.
• 16:12This that sort of estimate for
• 16:15how much higher the excess deaths
• 16:18is has been changing overtime and
• 16:21has been narrowing so so the gap
• 16:24has been narrowing as.
• 16:26Sort of,
• 16:27the standards for reporting deaths for Cove.
• 16:29It have changed and the recommendations
• 16:31have changed so you know with the current
• 16:34data were probably closer to about 40%,
• 16:37but in any case I think this
• 16:39puts to bed the idea that were.
• 16:42Start over accounting the cova
• 16:44deaths when we're.
• 16:46Certainly can't be official data.
• 16:47There's no evidence that we see that.
• 16:50The official data are in any way inflated,
• 16:53and if anything we think that they
• 16:56are substantially under reported.
• 16:58And if you just seem to be a lot of it,
• 17:01a pretty sizable gap between states
• 17:03in how much of a difference there is
• 17:05between the reported number of Copa
• 17:07deaths in the total number of death
• 17:09suspect this has something to do with
• 17:11testing practices as well as sort of
• 17:13culture around how deaths are coded.
• 17:15And we still don't know what's
• 17:17driving that unexplained increase,
• 17:19so we know that.
• 17:21You know, pneumonia,
• 17:22influenza are sort of accounting for some.
• 17:26Get code is pneumonia,
• 17:27influenza or accounting.
• 17:29For some of that increase that we're seeing,
• 17:31but there's still a large
• 17:33unattributed increase and future
• 17:35work will try to understand.
• 17:36Sort of what's driving them with
• 17:39somebody 'cause specific factors out.
• 17:41So thank you very much.
• 17:42We have a previous version of
• 17:44this work is unmet archive.
• 17:46If you'd like to learn more or I'd be
• 17:48happy to answer any questions by email.
• 17:54Thank you very much doctor Weinberger.