Temperature and Mortality

We are all going to die, nothing is surer. “Nobody knows the day or the hour”, but one thing is clear: we are more likely to die in winter than in summer.

Death by unnatural causes (suicide, accident, bushfire, disaster, even acute illness) can come to otherwise healthy people of any age. Death by natural causes is more predictable.

Those vulnerable to death are the elderly, very young babies, those with chronic illness (e.g. asthma, diabetes) and weakened immunity, and those with respiratory and circulatory illness.

Analysing mortality is made difficult because the sample population is always changing. Excess deaths in one month may be followed by further excess deaths in the following month, or because so many vulnerable people have already died, there will be fewer than expected deaths in the next month or months, or even the next couple of winters. Similarly, if fewer than expected deaths occur, there will be a larger cohort of the vulnerable in the following months, getting older and with probably poorer health. Population growth, aging, migration, improved vaccines, and public education programs all play a part as well.

In this analysis, I use mortality and population data from the Australian Bureau of Statistics (ABS), and temperature data from the Bureau of Meteorology (BOM), for Victoria, as it is a small and compact state which is subject to large temperature changes and also severe heat waves. Monthly mortality data are difficult to find, so this study is restricted to the period January 2002 to December 2011. A 10 year period is hardly sufficient for meaningful averages, however some useful insights can be found.

Mortality statistics are available by month, but population figures are by quarter, therefore I interpolated estimated monthly population figures based on three month growth.

Firstly, this plot shows the total deaths for every month from January 2002 to December 2011.

Fig. 1:

Note the seasonal spikes and dips. The apparent increase in deaths can be compared with Victoria’s population increase:

Fig.2:

By dividing the total deaths by the population in thousands we can calculate the death rate:

Fig. 3:

Note the mortality rate has decreased, and that, in spite of heatwaves, bushfires, and flu pandemics, 2009 had a lower death rate than 2008.

Because months have varying numbers of days, a better analysis can be made by calculating the Daily Death Rate for each month (by dividing each monthly rate by 31, 30, 29, or 28 days).

Fig. 4:

For the state of Victoria for the 10 years to 2011, on average more deaths occurred for each day in August than for any other month. The lowest Daily Death Rate was in February.

Now compare with monthly averages (2002 to 2011) for maximum and minimum temperatures:

Fig. 5:

The death rate peak lags July temperature by about a month. Cooler months (June to September) are deadlier than warmer (December to April).

The relationship with temperature can be shown with scatter plots:

Fig. 6:

Fig. 7:

Which merely reinforce that deaths are more likely in winter!

Now we look at the question of estimating how many deaths are likely in a given period, by multiplying the average daily death rate for each month by the number of days in each month and by the estimated total population for each month. By subtracting this figure from the actual number of deaths we get a mortality “anomaly”.  The following graph shows this anomaly for each year:

Fig. 8:

And each month:

Fig. 9:

Note the peaks in the winters of 2002 and 2003, and also in the summer of 2008-2009. Note also that both graphs show that in spite of a killer heatwave, the Black Saturday bushfire, and the swine flu pandemic, deaths in 2009 were below what could be expected.

To put the anomaly for January 2009 into context, we can compare actual daily deaths per 1,000 population for all months from 2002 to 2011:

Fig. 10:

Note that the extreme figure for January 2009, while extremely high for January, is still below those of the lowest extremes of June, July, and August.

Perhaps higher mortality in the winter months is coincidence and due to some other factor than temperature- seasonal flu incidence for example. I now look at the month of August with the highest average mortality rate:

Fig. 11:

There is fairly decent correlation showing that for every degree warmer in minima, the August death toll will be around 150 less than expected.

February, with the lowest rate:

Fig. 12:

Even in summer, warmer minima mean fewer deaths.

In summer, do higher maxima cause more deaths?

Fig. 13:

Even including the 173 deaths in the Black Saturday bushfires in the 200 extra deaths for February 2009, there is no trend.

January, whose data include the 2009 heatwave:

Fig. 14:

A very small trend, but the 2009 heatwave outlier is obvious and skews the data. (Victorian health authorities say there were 374 excess deaths in the week to 1 February 2009).

Extreme heatwaves are indeed killers. Normal hot summers up to two degrees above average are not.

Conclusion:

Improved public health measures, influenza vaccines, and improved public awareness – plus warmer winters- have led to a decrease in the Victorian mortality rate in the period 2002-2011.

Extreme heatwaves are dangerous in Victoria and cause hundreds of extra deaths especially amongst the elderly (>75 years old). However, these are rare events. Severe and Extreme Heatwaves are newsworthy precisely because they are unusual.

Normal Victorian winters are even more dangerous with on average 17.5% more deaths in winter than summer every year, but because this is normal and expected, this regular annual spike in deaths is unremarkable and not newsworthy- much less regarded as a natural disaster. While 374 excess deaths in a week in a heatwave is shocking, even with these included, the highest January’s Daily Death Rate (in 2009) is below that of the lowest of any winter month.

Warmer minimum temperatures are associated with lower death rates at all times of the year, but especially in August in Victoria, where for every degree of extra warmth, about 150 fewer deaths can be expected. I hope, for the sake of those who are sick or elderly, that we have a warm winter this year.

Advertisement

The Pause Update: April 2016

The complete UAH v6.0 data for April were released on Friday.  I could have presented this earlier, but there are some more important things in my life, like grandkids’ sleepovers and Mothers’ Day.  Back to business.  I present all the graphs for various regions, and as well summaries for easier comparison.  The Pause still refuses to go away, despite all expectations.

These graphs show the furthest back one can go to show a zero or negative trend (less than +0.1C/ 100 years) in lower tropospheric temperatures. I calculate 12 month running means to remove the small possibility of seasonal autocorrelation in the monthly anomalies. Note: The satellite record commences in December 1978- now 37 years and 5 months long- 448 months. 12 month running means commence in November 1979. The graphs below start in December 1978, so the vertical gridlines denote Decembers. The final plotted points are April 2016.

 [CLICK ON IMAGES TO ENLARGE]

Globe:

The 12 month mean to April 2016 is +0.43C.  However, the Pause is still an embarrassing reality! For how much longer we don’t know.

And, for the special benefit of those who think that I am deliberately fudging data by using 12 month running means, here is the plot of monthly anomalies, which shows that The Pause is over by my rather strict criterion:

+0.22C/100 years since December 1997- not exactly alarming.  The Pause will return sooner with monthly anomalies than 12 month means of course.

Northern Hemisphere:

The Northern Hemisphere Pause refuses to go quietly and remains at the same length. It may well disappear in the next month or two.

Southern Hemisphere:

The pause has shortened by one month.  For well over half the record the Southern Hemisphere has zero trend.

Tropics:

The Pause has shortened by 3 months.

Tropical Oceans:

The Pause has shortened by 3 months.

Northern Extra Tropics:

The Pause by this criterion has ended in this region, however note that the slope since 1998 is +0.17 +/- 0.1C per 100 years compared with +1.56C for the whole period.  That’s not much above dead flat.

Southern Extra Tropics:

The Pause has lengthened by one month.

Northern Polar:

No change.

Southern Polar:

At -0.18C/ 100 years, this region is cooling for the entire record.

USA 49 States:

No change

Australia:

One month longer.

The next graphs summarise the above plots. First, a graph of the relative length of The Pause in the various regions:

Apart from  the North Polar, whose Pause is shorter, and the Northern Extra Tropics, whose Pause has ended, all other regions have a Pause of 18 years 3 months (half the record) or longer- including the South Polar region which has been cooling for the whole record,

The variation in the linear trend for the whole record, 1978 to the present:

Note the decrease in trends from North Polar to South Polar.

And the variation in the linear trend since June 1998, which is about halfway between the global low point of December 1997 and the peak in December 1998:

The only region to show strong warming for this period is the North Polar region: the Northern Extra Tropics at +0.18C/ 100 years has very mild warming, and the Northern Hemisphere at +0.12C/ 100 years is virtually flat: all other regions are Paused or cooling.

12 month means will continue to grow for the next few months, so the Pause as  here defined may disappear shortly, and may not reappear until early 2018.  The impact of the coming La Nina will be worth watching.  Unless temperatures reset at a new, higher level and continue rising, very low trends will remain.