PeterMcCluskey comments on Covid 7/​9: Lies, Damn Lies and Death Rates

[PeterMcCluskey]

For suspicions about bad reporting of deaths, this CDC page (somewhat tricky to read) has relevant data in the column ‘Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)’.

New York state showed an unusual surge that peaked in late February.

Many states have shown unusual increases in that category starting around mid April.

It looks like the deaths in that category for the week ending June 13 were about 2491 above 2019 levels (more recent weeks have less complete data).

The states with the biggest recent increases in that death category (using the difference from the comparable week in 2019, as a fraction of all deaths): Hawaii, New Mexico, Colorado, Nevada, Utah, and Ohio. I’m unsure what to make of this pattern.

[CellBioGuy]

Oh my god… in the Florida data, the numbers of that ‘not elsewhere classified’ category are now twice the number of official COVID-coded deaths, and 4x baseline!

This would mean that they are mislabeling two thirds of the deaths. If you add up the excess deaths of that code and the covid deaths, the death rate did not actually decline in that state at all and instead has been in a plateau since April.

[Zvi]

That’s it! Thanks. How did you do that calculation?

I see ways to do it but none that aren’t annoying to implement. If I was a better coder I’d be tempted to write a scraper.

[PeterMcCluskey]

I downloaded it in their csv format and whipped up this python program to process it (I hope someone improves on it):

#!/usr/bin/python
# -*- coding: utf-8 -*-

import re, sys, io, getopt, csv

def parse(filename):
    column_dict = {}
    list_states = []
    val_dict = {}
    week_dicts = {'2019' : {}, '2020': {}}
    with open(filename, 'r') as fd:
        reader = csv.reader(fd, delimiter=',', quotechar='"')
        for row in reader:
            if row[0] == 'Jurisdiction of Occurrence':
                for i, fld in enumerate(row):
                    column_dict[fld] = i
                unknown_col = column_dict['Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)']
                continue
            yyyymmdd = row[0][:10].replace('-', '')
            yyyy = row[1]
            week_num = int(row[2])
            tokens = row[3].split('/')
            yymmdd = "%s%02d%02d" % (tokens[2][-2:], int(tokens[0]), int(tokens[1]))
            state = row[0]
            mystery_deaths = row[unknown_col].strip(' ')
            all_deaths = row[column_dict['All Cause']].strip(' ')
            if not (state in list_states):
                list_states.append(state)
            #print("%s %6s %6s %-s" % (yyyymmdd, mystery_deaths, all_deaths, state))
            week_dicts[yyyy][week_num] = yymmdd
            val_dict[yyyy + str(week_num) + state] = (mystery_deaths, all_deaths)
            #break
    num_weeks = len(week_dicts['2020'].keys())
    start_week = 4
    s = "%15s" % ''
    s2 = "%15s" % ''
    for i in range(start_week, num_weeks + 1):
        s += "%6d " % i
        s2 += "%s " % week_dicts['2020'][i]
    print(s)
    print(s2)
    tot_w24 = 0
    for state in list_states:
        s = "%-15.15s " % state
        for week_num in range(start_week, num_weeks + 1):
            try:
                (mystery_deaths2020, all_deaths) = val_dict['2020' + str(week_num) + state]
                (mystery_deaths2019, all_deaths2019) = val_dict['2019' + str(week_num) + state]
            except KeyError:
                s += '       '
                continue
            try:
                delta = (float(mystery_deaths2020 or 0) - float(mystery_deaths2019 or 0)) / float(all_deaths)
            except ValueError:
                print("skip %s %s %s %s %s." % (state, week_num, mystery_deaths2019, mystery_deaths2020, all_deaths))
                s += '       '
                continue
            if week_num == 24:
                tot_w24 += float(mystery_deaths2020 or 0) - float(mystery_deaths2019 or 0)
            s += "%5.2f  " % (100*delta)
        print(s)
    print("tot_w24 %s" % tot_w24)

if __name__ == '__main__':
    import getopt
    avail_opts = ['acct=', 
    ]
    (options, args) = getopt.getopt(sys.argv[1:], '', avail_opts)
    option_dict = {}
    for opt in options:
    option_dict[opt[0][2:]] = opt[1]
    parse(args[0])

[spkoc]

This website seems to do the trick and have charts: https://​​episphere.github.io/​​mortalitytracker/​​#cause=symptoms_signs_and_abnormal&state=Florida

The spike in Unclassified… is huge. They’re fudging the data, ffs.

[Joshua Goings]

It’s incredible. I tried to compare with the COVID-19 deaths, but the chart provided did not seem to be working and only showed unclassified. I worked up a few states like Florida, AZ, TX, NY, and the US based on the link in a blog post here, maybe it will be helpful to look at https://​​joshuagoings.com/​​2020/​​07/​​11/​​coronavirus-deaths/​​

[Joshua Goings]

You might find the method of mortality coding interesting, which the CDC and WHO compiles and relies on: http://​​library.ahima.org/​​doc?oid=91930#.Xwsi6yQiclQ

In particular: “For every death that occurs in the US, a physician, coroner, or medical examiner completes a death certificate. The certificate lists details about the cause of death as well as any contributing conditions or injuries.

All death certificates are sent to a state agency, which varies by state but can include the department of health...

… ‘We don’t necessarily take what the certifier or physician writes as the underlying cause of death, because what they may write down may not necessarily be the best underlying cause,’ Anderson says. ‘Even if they do it exactly correctly, since we are looking for something that is most useful from a public health standpoint, we may reselect another cause on the certificate.’”

Wow. So ultimately the mortality statistics are determined by the state (usually heath department) and not the original examiner.