Michigan Gets Results with HCQ

BREAKING: ‘Trump Drug’ Hydroxychloroquine ‘Significantly’ Reduces Death Rate From COVID-19, Henry Ford Health Study Finds. H/T Jaime Jessop.  Excerpts in italics with my bolds

A Henry Ford Health System study shows the controversial anti-malaria drug hydroxychloroquine helps lower the death rate of COVID-19 patients, the Detroit-based health system said Thursday.

Officials with the Michigan health system said the study found the drug “significantly” decreased the death rate of patients involved in the analysis.

The study analyzed 2,541 patients hospitalized among the system’s six hospitals between March 10 and May 2 and found 13% of those treated with hydroxychloroquine died while 26% of those who did not receive the drug died.

Among all the patients in the study, there was an overall in-hospital mortality rate of 18%, and many who died had underlying conditions, the hospital system said. Globally, the mortality rate for hospitalized patients is between 10% and 30%, and 58% among those in the ICU or on a ventilator.

“As doctors and scientists, we look to the data for insight,” said Steven Kalkanis, CEO of the Henry Ford Medical Group. “And the data here is clear that there was a benefit to using the drug as a treatment for sick, hospitalized patients.”

A previous study by French doctors considered the efficacy of HCQ along with other widely available drugs during the several stages of Covid19.  The Michigan study shows benefits for people hospitalized in phases II and III.  The French study emphasized early use during phase I at onset of testing positive for SARS CV2.

See Pandemic Update: Virus Weaker, HCQ Stronger

HCQ Hit Job by Big Pharma Data Miners

 

 

 

Cooling June for Land and Ocean Air Temps

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With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  UAH has updated their tlt (temperatures in lower troposphere) dataset for June 2020.  Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HADSST3. This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

HadSST3 results were delayed with February and March updates only appearing together end of April.  For comparison we can look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for June. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI). In 2015 there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the latest and current dataset, Version 6.0.

The graph above shows monthly anomalies for ocean temps since January 2015. After all regions peaked with the El Nino in early 2016, the ocean air temps dropped back down with all regions showing the same low anomaly August 2018.  Then a warming phase ensued with NH and Tropics spikes in February and May 2020. As was the case in 2015-16, the warming was driven by the Tropics and NH, with SH lagging behind. After the up and down fluxes, oceans temps in June are back to a neutral point, close to the 0.4C average for the period.

Land Air Temperatures Showing a Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for June 2020 is below.

Here we see evidence of the greater volatility of the Land temperatures, along with extraordinary departures, first by NH land with SH often offsetting.   The overall pattern is similar to the ocean air temps, but obviously driven by NH with its greater amount of land surface. The Tropics synchronized with NH for the 2016 event, but otherwise follow a contrary rhythm.  SH seems to vary wildly, especially in recent months.  Note the extremely high anomaly last November, cold in March 2020, and then again a spike in April. Now in June 2020, all land regions have converged, erasing the earlier spikes in NH and SH, and showing anomalies comparable to the 0.4C anomaly prior to the 2015-16 El Nino.

The longer term picture from UAH is a return to the mean for the period starting with 1995.  2019 average rose but currently lacks any El Nino to sustain it.

These charts demonstrate that underneath the averages, warming and cooling is diverse and constantly changing, contrary to the notion of a global climate that can be fixed at some favorable temperature.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, more than 1C lower than the 2016 peak, prior to these last several months. TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

Arctic Ice Usual June Swoon

 

The image above shows melting of Arctic sea ice extent over the month of June 2020.  As usual the process of declining ice extent follows a LIFO pattern:  Last In First Out.  That is, the marginal seas are the last to freeze and the first to melt.  Thus at the top center and right of the image, the Pacific basins of Bering and Okohtsk seas lost what little ice they had.  Meanwhile at extreme left, Hudson Bay ice retreats 300k km2 from north to south.  Note center left Baffin Bay loses 320k km2 of ice during the month.  The most dramatic melting is in the Russian shelf seas at the center right.  Laptev and Kara Seas combined to lose 600k km2 of ice extent. The central mass of Arctic ice is intact with some fluctuations back and forth, and as well Greenland Sea and CAA (Canadian Arctic Archipelago) were slow to melt in June

The graph below shows the ice extent retreating during June compared to some other years and the 13 year average (2007 to 2019 inclusive).

Note that the  MASIE NH ice extent 13 year average loses about 2M km2 during June, down to 9.6M km2. MASIE 2019 started nearly 500k km2 lower and lost ice at a similar rate, ending 476 km2 below average.  The most interesting thing was the wide divergence between SII and MASIE reports during June, SII starting the month about 500k km2 higher before narrowing at the end to exceed MASIE by 133k km2.  I inquired whether NIC had experienced any measurement issues, but their response indicated nothing remarkable.  It is unusual for MASIE to be the lower estimate of the two.

The table shows where the ice is distributed compared to average.  Bering and Okhotsk are open water at this point and will be dropped from future monthly updates. The deficit of 476k km2 represents 5% of the total, or an ice extent melting 5 days ahead of average.

Region 2020183 Day 183 Average 2020-Ave. 2007183 2020-2007
 (0) Northern_Hemisphere 9128615 9604642  -476028  9269301 -140686 
 (1) Beaufort_Sea 982475 882878  99597  891858 90617 
 (2) Chukchi_Sea 730000 703162  26838  637536 92464 
 (3) East_Siberian_Sea 885090 1014587  -129497  855267 29823 
 (4) Laptev_Sea 469839 704231  -234392  646683 -176844 
 (5) Kara_Sea 274007 535421  -261414  596916 -322909 
 (6) Barents_Sea 111016 106522  4494  97267 13749 
 (7) Greenland_Sea 474331 498794  -24463  548566 -74236 
 (8) Baffin_Bay_Gulf_of_St._Lawrence 438007 479675  -41668  414283 23724 
 (9) Canadian_Archipelago 780765 774360  6405  759177 21589 
 (10) Hudson_Bay 739422 686381  53041  613940 125482 
 (11) Central_Arctic 3235174 3202495  32679  3202330 32844 
 (12) Bering_Sea 315 3673  -3357  981 -665 
 (13) Baltic_Sea 0 -4  0
 (14) Sea_of_Okhotsk 7051 11237  -4185  2983 4068 

Note that all of the deficit to average is accounted for by the Russian shelf seas of East Siberian, Laptev and Kara. Beaufort and Hudson Bay are slightly surplus.

Illustration by Eleanor Lutz shows Earth’s seasonal climate changes. If played in full screen, the four corners present views from top, bottom and sides. It is a visual representation of scientific datasets measuring Arctic ice extents.

Canada Covid Wrap Up

The map shows that in Canada 8591 deaths have been attributed to Covid19, meaning people who died having tested positive for SARS CV2 virus.  This number accumulated over a period of 132 days starting January 31. The daily death rate reached a peak of 177 on May 6, 2020, and is down to 20 as of yesterday.  More details on this below, but first the summary picture. (Note: 2019 is the latest demographic report)

Canada Pop Ann Deaths Daily Deaths Risk per
Person
2019 37589262 330786 906 0.8800%
Covid 2020 37589262 8591 65 0.0228%

Over the epidemic months, the average Covid daily death rate amounted to 7% of the All Causes death rate. During this time a Canadian had an average risk of 1 in 5000 of dying with SARS CV2 versus a 1 in 114 chance of dying regardless of that infection. As shown later below the risk varied greatly with age, much lower for younger, healthier people.

Background Updated from Previous Post

In reporting on Covid19 pandemic, governments have provided information intended to frighten the public into compliance with orders constraining freedom of movement and activity. For example, the above map of the Canadian experience is all cumulative, and the curve will continue upward as long as cases can be found and deaths attributed.  As shown below, we can work around this myopia by calculating the daily differentials, and then averaging newly reported cases and deaths by seven days to smooth out lumps in the data processing by institutions.

A second major deficiency is lack of reporting of recoveries, including people infected and not requiring hospitalization or, in many cases, without professional diagnosis or treatment. The only recoveries presently to be found are limited statistics on patients released from hospital. The only way to get at the scale of recoveries is to subtract deaths from cases, considering survivors to be in recovery or cured. Comparing such numbers involves the delay between infection, symptoms and death. Herein lies another issue of terminology: a positive test for the SARS CV2 virus is reported as a case of the disease COVID19. In fact, an unknown number of people have been infected without symptoms, and many with very mild discomfort.

For this discussion let’s assume that anyone reported as dying from COVD19 tested positive for the virus at some point prior. A recent article by Nic Lewis at Climate Etc. referred to evidence that the average time from infection to symptoms is 5.1 days, and from symptoms to death 18.8 days. A separate issue, of course, is that 95+% of those dying had one or more co-morbidities contributing to the patient’s demise. Setting aside the issue of dying with/from Covid19, it is reasonable to assume that 24 days after testing positive for the virus, survivors can be considered recoveries.

Recoveries are calculated as cases minus deaths with a lag of 24 days. Daily cases and deaths are averages of the seven days ending on the stated date. Recoveries are # of cases from 24 days earlier minus # of daily deaths on the stated date. Since both testing and reports of Covid deaths were sketchy in the beginning, this graph begins with daily deaths as of April 24, 2020 compared to cases reported on March 31, 2020. Another view of the data is shown below.

The scale of testing has increased and has now reached 40,000 a day, while positive tests (cases) are dwindling.  The shape of the recovery curve resembles the case curve lagged by 24 days, since death rates are a small portion of cases.  The recovery rate has grown from 83% to 98% steady over the last 5 days.  This approximation surely understates the number of those infected with SAR CV2 who are healthy afterwards, since antibody studies show infection rates multiples higher than confirmed positive tests. In absolute terms, cases are now down to 320 a day and deaths 20 a day, while estimates of recoveries are 804 a day.

Summary of Canada Covid Epidemic

It took a lot of work, but I was able to produce something akin to the Dutch advice to their citizens.

The media and governmental reports focus on total accumulated numbers which are big enough to scare people to do as they are told.  In the absence of contextual comparisons, citizens have difficulty answering the main (perhaps only) question on their minds:  What are my chances of catching Covid19 and dying from it?  The map shows a lot of cases, and the chart looks like an hockey stick, going upward on a straight line. So why do I say canadians are safer than it looks like from such images?

First let’s look at daily numbers to see where we are in this process.  All the statistics come from Canada Public Health Coronavirus disease (COVID-19): Outbreak update.

By showing daily tests, new cases and reported deaths, we can see how the outbreak has built up, peaked and declined over the last 3 months.  The green line shows how testing steadily grew up to a daily rate of 40,000 (all numbers are smoothed with 7 day averages ending with the stated date.) Note that the curve is now descending after peaking at 1800 on April 22, now down to 320 new cases per day.  This lower rate of infections is despite the highest rate of testing since the outbreak began. Deaths have also peaked at 177 on May 6, down to 20 on June 30. The percentage of people testing positive is down to 3.8%, and deaths are 0.31% of the tests administered.

But it matters greatly where in Canada you live.  In the map at the top, Quebec is the dark blue province leading the nation in both cases and deaths.  Quebec has always celebrated being a distinct society, but not in this way. Below is the same chart for the Quebec epidemic from the same dataset. The province has about 23% of the national population and does about 26% of the tests.  But Quebec contributed 56% of the cases and 64% of the deaths, as of yesterday.  Here how the outbreak has gone in La Belle Province.

The Quebec graph is more lumpy showing cases peaking May 1-9, including several days inflated by data catchups. Cases have dropped off recently, from 1100 May 7 down to 82 yesterday.  Deaths are also slowing, declining from 110 on May 7 to 11 on June 30. The animation below shows the epidemic in Canada with and without Quebec statistics.

But clearly everywhere else in Canada, people are much safer than those living in Quebec.  So what is going on?

To enlarge image, open in new tab.

The graph shows that people in Quebec are dying in group homes, the majority in CHSLD (long term medical care facilities) and also in PSR (private seniors’ residences).  The huge majority of Quebecers in other, more typical living arrangements have very little chance of dying from this disease. Not even prisoners are much at risk.

Of course the other dimension is years of age, since this disease has punished mostly people suffering from end-of-life frailties.  A previous post reported that the Netherlands parliament was provided with the type of guidance everyone wants to see.

For canadians, the most similar analysis is this one from the Daily Epidemiology Update: :

The table presents only those cases with a full clinical documentation, which included some 2194 deaths compared to the 5842 total reported.  The numbers show that under 60 years old, few adults and almost no children have anything to fear.

Update May 20, 2020

It is really quite difficult to find cases and deaths broken down by age groups.  For Canadian national statistics, I resorted to a report from Ontario to get the age distributions, since that province provides 69% of the cases outside of Quebec and 87% of the deaths.  Applying those proportions across Canada results in this table. For Canada as a whole nation:

Age  Risk of Test +  Risk of Death Population
per 1 CV death
<20 0.05% None NA
20-39 0.20% 0.000% 431817
40-59 0.25% 0.002% 42273
60-79 0.20% 0.020% 4984
80+ 0.76% 0.251% 398

In the worst case, if you are a Canadian aged more than 80 years, you have a 1 in 400 chance of dying from Covid19.  If you are 60 to 80 years old, your odds are 1 in 5000.  Younger than that, it’s only slightly higher than winning (or in this case, losing the lottery).

As noted above Quebec provides the bulk of cases and deaths in Canada, and also reports age distribution more precisely,  The numbers in the table below show risks for Quebecers.

Age  Risk of Test +  Risk of Death Population
per 1 CV death
0-9 yrs 0.13% 0 NA
10-19 yrs 0.21% 0 NA
20-29 yrs 0.50% 0.000% 289,647
30-39 0.51% 0.001% 152,009
40-49 years 0.63% 0.001% 73,342
50-59 years 0.53% 0.005% 21,087
60-69 years 0.37% 0.021% 4,778
70-79 years 0.52% 0.094% 1,069
80-89 1.78% 0.469% 213
90  + 5.19% 1.608% 62

While some of the risk factors are higher in the viral hotspot of Quebec, it is still the case that under 80 years of age, your chances of dying from Covid 19 are better than 1 in 1000, and much better the younger you are.