Archive for October, 2020

First Wave Covid19 Mortality in Context

October 22, 2020

Key takeaway points:

  • It is likely that the real Covid19 death toll was at least double the official tally, and possibly hundreds more.
  • Despite this, there were 1,457 fewer deaths in the first six months of this year than last year.
  • The first lockdown worked- until the Victorian fiasco.

In this post I use the most recent Mortality data (released 1 October 2020) from the Australian Bureau of Statistics (ABS), up to 30 June 2020, and the most recent ABS Population data, to examine the effect of the Covid19 pandemic on Australian deaths.  This period covers the whole of the first wave of the pandemic and gives interesting insights.  Future data releases covering the second wave (with another 800 Covid19 deaths) will provide further illumination.

The ABS advises that the data are provisional and not complete as deaths subject to coroners’ inquests are not included, but with completeness percentages in the high 90s “meaningful comparison with historic counts” may be made.

Key statistics from the ABS:

  • 68,986 doctor certified deaths occurred between 1 January 2020 and 30 June 2020.
  • Numbers of deaths have been below historical averages since mid May and below baseline minimums since the week ending 9 June.
  • Deaths from respiratory diseases and heart diseases were below historical minimum counts throughout June.

Figure 1:  ABS chart of deaths and Covid19 infections

The peak of new coronavirus infections was in the week ending 31 March, with 2,428 new infections in that week (Week 13), and the peak in all mortality also occurred in that week.  The following plot shows official Covid19 mortality (from Worldometers) peaking in Week 14.

Figure 2:  Covid19 first wave deaths

The ABS says that the World Health Organisation (WHO) early in 2020 “directed that the new coronavirus strain be recorded as the underlying cause of death, i.e. the disease or condition that initiated the train of morbid events, when it is recorded as having caused death……..

……. Deaths due to COVID-19 are included in the total for all deaths certified by a doctor. They are not included in deaths due to respiratory diseases or any of the other specified causes.”

The first reported Covid19 death was on 1 March, (Week 9).  In Week 14, one week after the peak in new infections, the peak in the first wave deaths occurred.  In this post I define the first wave of the pandemic as Weeks 9 to 21.  (The second wave commenced in Week 24.)  Figure 3 shows Covid19 deaths in context.  The duration of first wave deaths is indicated by the horizontal red line.

Figure 3: Covid19 and total deaths

Note the increase in total deaths in Weeks 12 to 15, and the insignificance of official Covid19 mortality by comparison.  (Australia closed borders on 16 March- Week 11- and began restricting movement in the days following.)

The next graph compares 2020 mortality so far with the five previous years.

Figure 4: Total Australian deaths 2015 – 2020

This year’s peak in deaths also occurred in Weeks 12 to 15, at the height of the first wave infections.

You will also note Australia’s 2020 mortality levelled off well below previous years’ figures, which usually continue rising to peak in Winter and early Spring.  Mortality figures for Weeks 27 to 52 will be very interesting.  There was an unusual early surge in 2019, and a very large increase in deaths in Winter and Spring of 2017.

I now look at excess deaths.  The ABS says:

Measuring ‘excess’ deaths

Excess mortality is an epidemiological concept typically defined as the difference between the observed number of deaths in a specified time period and the expected numbers of deaths in that same time period. Estimates of excess deaths can provide information about the burden of mortality potentially related to the COVID-19 pandemic, including deaths that are directly or indirectly attributed to COVID-19.

… counts of deaths for 2020 are compared to an average number of deaths recorded over the previous 5 years (2015-2019). These average or baseline counts serve as a proxy for the expected number of deaths, so comparisons against baseline counts can provide an indication of excess mortality. “

However, Australia’s population has increased by nearly two million from March quarter 2015 to March quarter 2020 (from 23,745,629 to 25,649,985).  This has a large impact on calculations.  Mortality rate per 1,000 head of population is a better measure. Figure 5 shows mortality rates for recent years.

Figure 5:  Australian mortality rates, 1st 26 weeks, 2015 – 2020

The method I have used is different from the ABS methodology because of the population increase and is based on mortality rates rather than absolute numbers of deaths. 

I have calculated the mortality rate per 1,000 people for each of the 2015-2019 years (using the population for the March quarters of those years), and similarly for the 2020 data.  I then multiply the average of the 2015-2019 mortality rates by the 2020 March quarter population to obtain an estimate of predicted deaths for 2020.  Subtracting this from the actual 2020 number gives an estimate of excess deaths.  An excess death figure of zero indicates the mortality rate is no different from previous years.  The next figure shows plots of actual and expected deaths for the first half of 2020.

Figure 6:  Predicted and actual deaths

Figure 7 is my plot of excess deaths to 30 June.

Figure 7: Estimated Excess Mortality

Excess and actual deaths peaked in Weeks 12 to 15, with weeks 13 and 14 nearly 200 above the expected level- but there were only 56 official Covid19 deaths in those weeks.  Officially, Covid19 was involved in 29 deaths in Week 14, 12 each in Weeks 13 and 15 and only 3 in Week 12.  It is possible that Covid19 deaths were being vastly under-reported in March. 

By the end of June estimated excess deaths were at minus 349, 11.5% below the expected number for Week 26.  Actual deaths in the first half of the year were 1,457 fewer than for the same period in 2019.

States and Territories:

Figure 8 shows actual numbers of deaths for all states and territories.

Figure 8:  2020 mortality numbers for each state

Mortality figures are dominated by New South Wales, followed by Victoria and Queensland.  Figure 9 shows excess deaths.

Figure 9:  Excess mortality by states

Smaller states had smaller changes in excess mortality, although Western Australia had a peak of 54 excess deaths in Week 13.   Figure 10 shows excess deaths for the larger states only.

Figure 10:  Excess deaths in the large eastern states

Peaks in excess deaths occurred between Weeks 9 to 17, but note earlier peaks in New South Wales and Queensland 7 or 8 weeks before the pandemic peak, with Queensland much higher than New South Wales, largely counteracted by Victoria, and a peak in Victoria in Weak 11, counteracted by New South Wales.  There was a third peak in Weeks 17 to 19, coinciding with another peak in Covid19 deaths.  Remember these numbers are additional to Covid19 deaths.  And officially Queensland had only seven Covid19 deaths, almost certainly due to under-reporting.

Age at death

Figure 11 shows the ages at which excess deaths occurred.

Figure 11:  Excess mortality by age

People aged from 0 to 44 years were not affected by the large changes in death rates in older age groups, but there was an increase in excess deaths in the 45 to 64 age bracket in Week 13, at the height of Covid19 infections, as Figure 12 shows.  That looks suspicious, but may be chance.

Figure 12:  Excess deaths for younger cohorts

The majority of excess deaths were in older age groups, as Figure 13 shows.

Figure 13:  Excess deaths for older Australians

There was a peak of 132 excess deaths in those 85 years and over in Week 14, but in Week 13 there were 146 excess deaths in those aged 65 to 84.  There were additional substantial peaks in earlier weeks as well.  It was not a good first half of the year for senior citizens, but excess deaths for all age groups were well below expected numbers by June.

Cause of death

  A death certificate lists all causes of death, and with elderly people these can be three or more.  It is very likely that a person over 85 may die of pneumonia (classified as a respiratory illness), but may also have any or all of dementia, diabetes, cerebrovascular disease, ischaemic heart disease, and cancer.  However, the ABS asks doctors to report the (one) underlying cause of death, and since earlier this year, Covid19 as the underlying cause “when it is recorded as having caused death.

 Figure 14 compares all respiratory deaths with Covid19.

Figure 14:  Covid19 and respiratory deaths

Influenza and pneumonia are subsets of respiratory illness, and the next figure shows interesting excess mortality data for 2020.

Figure15:  Excess deaths due to respiratory causes

Note the peak in respiratory deaths at the height of pandemic infections, but an earlier peak some four weeks previously.  It is likely that Covid19 was not correctly reported to the ABS by all doctors until Week 14 or 15- doctors are human too.  Since the first wave and the increase in personal hygiene, social distancing and little travel, deaths have remained well below previous years.

Figure 16:  Ischaemic heart and cerebrovascular disease excess deaths

This plot illustrates the advances in medicine:  ischaemic heart disease in 2020 had fewer deaths than expected for all of the first six months apart from a peak in Week 7.  Cerebrovascular disease (chiefly strokes) also had fewer deaths than expected except for Week 14 (so was potentially related to Covid19), and another peak in Week 24. 

Figure 17 plots excess deaths caused by the common co-morbidities of Covid19, dementia and diabetes.

Figure 17:  Excess deaths caused by dementia, diabetes, and Covid19

Diabetes and Dementia excess deaths were also higher than expected during the first wave, but there was another large surge in excess deaths with dementia as a cause weeks earlier.

Conclusions:

With the caveat that the ABS mortality figures are provisional, and putting together figures for various states, ages, and causes of death, some conclusions may be drawn:-

Either a mystery respiratory illness or undiagnosed Covid19 was widespread in the eastern states amongst elderly people weeks before the peak of first wave deaths, possibly arriving from cruise ships.

There were probably many more Covid19 deaths and infections than reported.  It is likely that the real Covid19 death toll was at least double the official tally, and possibly hundreds more.

Social distancing, good hygiene, and travel restrictions have caused a large decrease in mortality in May and June by restricting the spread of many common illnesses.  The first lockdown worked- until the Victorian fiasco.

The net effect of the first wave of the Covid19 pandemic on Australian mortality was negative.  Covid19, and public health responses to it, resulted in a lower death toll in the first half of 2020.  This lower death toll was not just in relative (mortality rate) terms but also in absolute terms: there were 1,457 fewer deaths in the first six months of this year than last year.

ABS data for the second half of the year will be released around April 2021 and will provide much better information about excess mortality for all states (and Victoria in particular), for all age groups, and for all causes.

I include an appendix with raw mortality data for 2015 -2020.

Appendix:  Raw mortality data for all causes 2015 – 2020.

Figure 18:  Respiratory mortality

Note the typical winter and spring surge in respiratory deaths, mainly due to influenza outbreaks in cold months.  There was an early surge in 2019 and a very large surge in 2017 which will skew means for those weeks.  Median mortality rate may be more appropriate than means.

Figure 19:  Ischaemic heart disease mortality

Heart disease mortality has been below previous years for most of the first 26 weeks.

Figure 20:  Cerebrovascular disease mortality

Cerebrovascular disease (stroke) deaths peaked during the first wave of Covid19 but have been mostly near the bottom of the range of previous years, with a second peak in June.

Figure 21:  Dementia mortality

Deaths with dementia as a cause have increased over the years.  A peak in dementia deaths coincided with Covid19 but deaths have been in the normal range since then.

Figure 22:  Diabetes mortality

A peak in diabetes deaths coincided with the peak in Covid19 infections and deaths, and was much higher than expected.  At the end of June deaths were in the range of previous years.

Figure 23:  Cancer mortality

Cancer deaths have increased over the years and 2020 remains within the expected range.  You may note there is no winter increase in cancer mortality.

Distance Records for Temperature Adjustments

October 6, 2020

Trigger Warning:  ridicule of the Australian Bureau of Meteorology below!

The official Australian climate record is developed from ACORN-SAT– the Australian Climate Observation Reporting Network- Surface Air Temperatures.  This is relied on by governments and industry and so should be completely trustworthy and free from any problems that might lead to lack of confidence.

The Acorn stations have had their temperature records adjusted to account for any discontinuities or irregularities.  This is done by comparing Acorn stations’ data with those from a selection of comparative stations. 

The Bureau says:

The process of homogenisation seeks to answer a very simple question: what would Australia’s long-term temperature trend look like if all observations were recorded at the current sites with the current available technology? Homogenisation means we can compare apples with apples when it comes to temperature trends.

One might expect that, with the aim being to “compare apples with apples”, stations used for comparison and making adjustments would be physically not too distant- ideally, neighbouring.  

Not so.

For many stations, not even remotely so.

Australia is a very big country with vast areas of sparsely inhabited desert.  There are very large distances between towns in the outback, so it is not surprising that it is often difficult to find suitable comparative stations.

But the Acorn Station Catalogue, which has helpful lists of comparative stations used for adjustments, has some absolute doozies.  Here are some for your amusement.  (Obviously most stations have many comparative sites.)

Carnarvon, in Western Australia, has been adjusted by reference to a number of stations hundreds of kilometers away, including Southern Cross, only 897km away.

Camooweal, Queensland, “ “ “  Thargomindah, 1,067km away.

Boulia, Qld, “ “ “  Walgett, New South Wales, 1,130km

Halls Creek, WA, “ “ “  Boulia, Qld, 1,370km

Tennant Creek, Northern Territory, “ “ “   Charleville, Qld,  1,443km

Mount Gambier, in South Australia, has been adjusted with the help of Lismore in northern New South Wales, 1,526km away.  (And it’s not as if there is a shortage of sites in this well populated part of South Australia.)

But the gong, the gold medal, the record breaking achievement for the Bureau, goes to…….

Alice Springs, in the Northern Territory, which has been adjusted using data from Collarenebri in New South Wales,  1,590 kilometres away.

And they want the public to trust them.

More Questionable Adjustments- Cape Moreton

October 5, 2020

Here’s another Acorn station with interesting adjustments- Cape Moreton (40043) minimum temperatures.

Cape Moreton Lighthouse is on the north-eastern tip of Moreton Island, 65 km north-east of Brisbane.  It is not compliant with siting specifications. 

Figure 1 is the adjustment summary shown by the Bureau in its Station Catalogue.

Figure 1:  Adjustment summary for Cape Moreton

Two points to note:  The Bureau has TWO adjustments applied to the same date- 1/01/1946; and there are four comparative stations used to make these adjustments at this Acorn station.

Figure 2 shows the neighbours the BOM used for comparison. 

Figure 2:  Google Maps image showing Cape Moreton and its neighbours

There are many neighbouring stations the Bureau could have used for comparison, but the Bureau chose those with the “best correlation” during the comparison period (the late 1940s):  Brisbane Regional Office 65km away, and probably affected by Urban Heat Island effect; Yamba, also coastal but 267km south; Dalby Post Office on the Darling Downs 220km west; and Miles Post Office 330km west.

Figure 3 shows the annual average minima for these weather stations.

Figure 3:  Annual minima, Cape Moreton and neighbours

UHI at Brisbane is visible as the plot line rises faster than the others after 1950.

The next figure shows Acorn’s adjustments have increased the rate of warming from +1.2 degrees Celsius per 100 years to more than +1.5 C.

Figure 4:  Cape Moreton Minima

Figure 5 shows the difference between the original raw record and Acorn.

Figure 5:  Cape Moreton adjustments

It is plainly obvious that the Acorn adjustment summary shown in Figure 1 is wrong.  The first adjustment was applied from 01/01/1948 (not 1946) and decreased the annual minima for 1946 and 1947 by -1.2C.  The second adjustment was applied from 01/01/1946 and increased previous annual minima by +0.8C or +0.9C. The raw minima were decreased by -0.3C or -0.4C, but that is not how the Bureau describes the adjustment process:

Date applied: data prior to this date was adjusted for the reason (cause) cited. Adjustments are superimposed on each other; for example, if two adjustments are shown, one for 1/1/2000 and one for 1/1/1988, data prior to 1/1/1988 has both adjustments applied to it, data between 1/1/1988 and 1/1/2000 only has the first adjustment applied, and data after 1/1/2000 is not adjusted at all.”

The documentation of Acorn is a mess.

In order to compare data from stations with varying temperatures we need to calculate their anomalies from their means for the same period.  Figure 6 shows Cape Moreton’s and comparison stations’ anomalies from their 1931-1960 means.

Figure 6:  Minima anomalies, Cape Moreton and “neighbours”

Hard to follow, there is too much variability.  You may note that by comparison with the periods before 1948 and after 1960, the 1950s show much agreement.  The next figure shows the period from 1930 to 1960.

Figure 7:  Minima anomalies, Cape Moreton and “neighbours” 1930-1960

Notice that in 1946 and 1947 (indicated by the arrow) Cape Moreton is far too warm- the reason for the adjustment; however Yamba’s record is just as erratic or more so, being too low in 1933, 1934, 1940-1944,  and 1947; and too high in 1950.  This suggests firstly that the 1946 and 1947 adjustments were justifiable for those two years, and secondly that Yamba is not a good comparison station.  The next figure, with Yamba excluded, clearly illustrates this point.

Figure 8:  Cape Moreton and comparison stations, excluding Yamba

Apart from 1946 and 1947 Cape Moreton’s record is not greatly dissimilar from the three remaining stations. 

The object of adjusting temperatures using neighbours for comparison is to endeavour to produce a record that more truly reflects climate trends of the area.  The resulting record should be more like the neighbours than the original raw record.  We can test this by plotting the differences between Acorn and the raw record and the average of the neighbours.  If the comparison is good, while individual years’ differences may vary, the trend should be close to zero: the station should not be warming or cooling more than the neighbours.  Figure 9 shows the results for Cape Moreton minima for the period before the 1946 adjustment, excluding Yamba from the average.

Figure 9:  Differences between Cape Moreton and Qld neighbours

You will note that the blue trend line, showing the trend of the difference between Cape Moreton’s annual data and the average of Brisbane, Dalby, and Miles, has a trend of about +0.3 degrees per 100 years, indicating Cape Moreton is already warming more than the others.  The “raw” record already compares fairly well with the neighbours, considering that they are inland stations, unlike Cape Moreton.  In contrast the red trend line shows the adjusted data is warming more than three times faster, indicating a poor reflection of the climate of the area.

Conclusion

The Bureau has not followed its own methodology in its adjustment summary.

Documentation of adjustments is incorrect.

Three comparison stations are hundreds of kilometres away and another is subject to Urban Heat Island effect.

One comparison station (Yamba) has a record more erratic than Cape Moreton’s and should not have been used.

The adjustments have increased the difference between Cape Moreton and its neighbours, and has increased the warming trend by 30%.

Garbage in, garbage out.

Sources for annual minima data:

Acorn: http://www.bom.gov.au/climate/change/hqsites/data/temp/minT.040043.annual.txt

Raw:

Cape Moreton:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=40043

Brisbane Regional Office: http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=40214

Dalby Post Office:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=41023

Miles Post Office:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=42023

Yamba Pilot Station:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=58012