Measuring Australia's excess mortality during the COVID-19 pandemic until August 2023

Measuring Australia's excess mortality during the COVID-19 pandemic until August 2023

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Released
18/12/2023
Source
Provisional Mortality Statistics, Jan - Aug 2023

Key statistics

Overview

This article provides excess mortality estimates for deaths occurring in Australia until the end of August 2023. It updates the article on excess mortality in Australia that was published in July which provided estimates until the end of March 2023. Excess mortality estimates produced in these articles are answering the research question: 'How does the number of deaths which has occurred during the COVID-19 pandemic (2020-2023) compare to the number of deaths expected had the pandemic not occurred?'. See Interpreting Results section in the previous article for more information.

Excess mortality is 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. Excess mortality measures can account for deaths due to COVID-19, potentially misclassified or undiagnosed COVID-19 deaths, and other mortality that may be indirectly related to the pandemic (e.g. relating to social isolation or changed access to health care). 

This article presents weekly and annual excess mortality estimates for Australia during the COVID-19 pandemic until the end of August 2023 for Australia and states and territories. For the purposes of this article, the end of August 2023 refers to the first 34 weeks of 2023, up until 27 August 2023. Expected mortality in this article is modelled as being in the absence of the COVID-19 pandemic.

Key points:

  • Mortality is 6.1% higher than expected for the first eight months of 2023. 
  • Mortality in July and August of 2023 is closer to expected levels. 
  • Mortality in the first eight months of 2022 was 14.1% higher than expected. 

COVID-19 pandemic in Australia

Australia has had multiple waves of COVID-19 infections since the start of the pandemic in March 2020.

The first wave was recorded from mid-March to mid-April, with most states in Australia recording active infections and associated mortality. The predominant variant during Wave 1 was the original virus strain. Nationally, public health measures were implemented which included social distancing and hygiene recommendations, stay at home measures and shut down of national borders. 

The second wave started in June 2020, with the largest number of active infections and deaths occurring in Victoria. The variant during Wave 2 was the original virus strain. ‘Stay at Home’ orders were implemented in Victoria from July 2020 until the end of October 2020. While stay at home measures were lifted across other jurisdictions, there were still strict travel restrictions in place nationally. 

Between the second and third wave, many jurisdictions managed smaller outbreaks with localised lockdowns.

A third wave began in June 2021 with the spread of the Delta variant. Most jurisdictions recorded COVID-19 infections during this period. New South Wales and Victoria recorded the highest number of infections and mortality due to COVID-19 during the Delta variant wave and both states implemented public health measures to manage this, including vaccinations, social distancing and stay at home measures. 

In November 2021, a staged re-opening of the international borders began. The Western Australia border was the last to re-open on 3 March 2022. The re-opening of borders coincided with high vaccination rates in Australia. 

The Omicron variant was first identified in Australia in November 2021 and began to spread throughout the country at the end of 2021, continuing into 2023. There have been multiple waves associated with the Omicron variant with waves often being associated with a new strain. 

The following table shows excess mortality for each jurisdiction expressed as the percentage above expected for that year.

Excess mortality as a percentage above expected by jurisdiction, 2020-23
 2020202120222023
Australia-3.11.611.76.1
New South Wales-4.10.311.54.8
Victoria-0.93.714.07.6
Queensland-4.31.010.65.9
South Australia-3.10.59.96.2
Western Australia-3.90.97.15.5
Tasmania-3.65.613.212.6
Northern Territory1.66.618.4-2.9
Australian Capital Territory-4.3-4.010.4-1.0

a. Data is provisional and subject to change. 
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023 (21 weeks for the Northern Territory). 
c. Deaths in 2023 are deaths that occurred by 27 August (28 May for Northern Territory) and were registered and received by the ABS by 31 October 2023.

Introduction

Excess mortality measures have been a common statistical tool used around the world to understand the full impact of the COVID-19 pandemic on mortality. Excess mortality is defined as the difference between the total number of deaths from all causes in a specified period and the expected numbers of deaths from all causes in that same period. Excess mortality measures can account for deaths due to COVID-19, potentially misclassified or undiagnosed COVID-19 deaths, and other mortality that may be indirectly related to the pandemic (e.g. relating to social isolation or changed access to health care). For these reasons, excess mortality is generally considered a more comprehensive measure for assessing the impact of the pandemic rather than considering only the number of deaths due to COVID-19. 

As the pandemic progresses, the use of excess mortality has become even more pertinent as a measure. Attributing COVID-19 as a direct or contributing cause of death is dependent on a number of factors including rates and types of testing (e.g. Rapid Antigen Test versus Polymerase Chain Reaction testing), diagnostic ability (e.g. having the ability and resources to diagnose the train of morbid conditions leading to death), and infection prevalence. Even in Australia, a country with a strong health care system and robust certification of deaths, the attribution of COVID-19 to a death can be difficult to assess. Reasons for this include a patient having multiple co-morbidities, doctors being unaware of a previous COVID-19 infection or difficulties in assessing the impact of an acute infection compared to a chronic infection (e.g. long COVID). Changes in the attribution of COVID-19 to death are evident in the data. In 2023, 73.7% of COVID-19 associated deaths were "from COVID-19", where the deceased has been certified as dying as a direct consequence of the virus. In 2020 this percentage was 99.0%. There have also been increases in the number of incidental mentions of COVID-19 as part of the death certification process. These deaths (coded to ICD-10 code U08.9, Personal history of COVID-19) are not included in official COVID-19 reporting. See article, COVID-19 Mortality in Australia for more information. 

Taking the above into account, this article provides excess mortality estimates for Australia and states and territories until August 2023. Information on how to interpret and use the excess mortality estimates is also provided. 

Measuring excess mortality

Across the world, health and statistical authorities have sought to measure excess mortality during the COVID-19 pandemic. Different methodologies have been applied, with the goal to predict an expected number of deaths for a given year. Choice of model and baseline can markedly affect estimates of expected (and therefore excess) mortality. Estimating the expected future seasonality for deaths can be a challenge in many models. The suitability of a model can depend on factors such as country context, data quality and collection methods.

The ABS has adopted aspects of a methodology used by New South Wales (NSW) Health, applying a cyclical linear regression with a robust estimation procedure to produce both an expected number of deaths and a range of expected deaths. The ABS has applied this model to estimate age specific death rates for certain age groups, and converted the expected death rates into an expected number of deaths for each age group. These are then added across age groups to obtain an expected count for the total population. Using ASDRs allows for changes in population size and age composition. Further information is provided in the Methodology section. 

Interpreting results

Considerations for interpreting information in this report

Outputs from excess mortality estimates will differ depending on the calculation applied and the scope of the input data. When interpreting the results in this report the following factors must be taken into consideration. 

  • This report highlights weeks where excess deaths are statistically significant. In any given period, even if no temporary health hazards influence the number of deaths (such as community transmission of influenza or COVID-19) there is some natural variation in patterns of mortality. While the actual number of deaths may be different from the expected number of deaths, it should fall within an expected range (i.e. there is a 95% chance that the expected number of deaths would lie between the upper and lower bounds of the confidence intervals). When actual observations (counts of death) exceed the upper threshold or drop below the lower threshold this indicates a statistically significant change in the pattern of mortality. This should be used in conjunction with the percentage excess mortality.
  • A single week above threshold does not necessarily suggest statistically significant excess mortality. Prolonged periods (2 or more weeks) where counts exceed thresholds suggest more strongly that the numbers of deaths are above or below normal. 
  • Excess mortality estimates for the Northern Territory are presented to the end of May 2023. The Northern Territory has longer delays between date of death and date of registration. Reasons for this include a higher number of deaths referred to the coroner and geographical challenges including a high number of remote locations which can delay funeral times and access to services (and therefore delay the reporting of deaths).
  • Data is reported on the basis of occurrence, and is subject to revision as late registrations are received by the ABS. Only deaths that have been registered by 31 October 2023 and received by the ABS have been included in this publication. Please refer to the Timeliness and completeness of the data section of Provisional Mortality Statistics for further information. 
  • Reported deaths from or with COVID-19 are identified from death certificates or coroner reports as part of the death registration process. There may be some deaths where COVID-19 was a contributing factor but it was not recorded on the death certificate (for example, the medical practitioner may be unaware of a present or past infection). If COVID-19 is not recorded on the death certificate it is not included in COVID-19 death tabulations presented. 

Weekly all-cause mortality: Australia

  • Excess mortality for the first eight months of 2023 (until the end of winter) for Australia is estimated at 6.1% above expected. 
  • This compares to 14.1% for the first eight months of 2022. 
  • Sustained statistically significant excess mortality was recorded in Australia in the first half of 2023. Since mid July, mortality has returned more closely to expected levels.
  • Excess mortality has been consistently recorded over 2022 and the first six months of 2023. Deaths exceeded the upper limit of variation for the majority of deaths recorded during this period.
  • The first six months of 2023 recorded excess mortality of 8.4%. 
  • COVID-19 associated deaths were still a key contributor to excess mortality in Australia in 2023. The number of deaths excluding deaths "from" or "with" COVID-19 for 2023 has been largely within normal bounds. 
  • Deaths due to COVID-19 (as identified on death certificates) were the main contributor to excess mortality during 2022. Excess mortality during this period corresponded with COVID-19 waves. 
  • Deaths were significantly lower than expected from the week beginning 1 June to mid-July 2020, dropping below lower thresholds. Winter months are typically associated with higher mortality. These decreases provide insights into how public health measures put in place to manage the COVID-19 pandemic impacted mortality. 
  • Australia has recorded excess mortality in previous years, including 2014, 2015 and 2017. While the first graph demonstrates the fit of the 2013-2019 baseline used to produce excess mortality estimates rather than official excess mortality estimates for this time period, it does show an actual pattern of mortality where deaths were higher than expected. In past years excess mortality has typically occurred during the winter months and has been associated with virulent influenza seasons. 
Excess mortality by year, Australia, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
2020170,045164,795-5,250-3.1916
2021169,048171,7992,7511.61,448
2022170,911190,85619,94511.713,287
2023112,714119,6196,9056.14,444

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 27 August and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

Weekly all-cause mortality: New South Wales

  • Excess mortality for the first eight months of 2023 (until the end of winter) for New South Wales is estimated at 4.8% above expected. 
  • This compares to 15.0% for the first eight months of 2022. 
  • From early November 2022 to the end of July 2023 New South Wales recorded higher than expected mortality.  
  • COVID-19 associated mortality as recorded on death certificates has been a significant contributor to excess mortality in New South Wales during 2022 and 2023. 
  • Mortality in August was lower than expected. The number of deaths recorded in New South Wales during this period may increase as additional registrations are received by the ABS. 
  • Sustained excess mortality started being recorded in New South Wales from the end of 2021 until the end of August 2022. Other periods of excess mortality in New South Wales were December 2022 to February 2023 and May to June 2023.
  • Excess mortality was 11.5% above expected for 2022.
  • New South Wales did not record excess mortality during the Delta wave in the second half of 2021. 
  • Although most jurisdictions recorded lower than expected mortality across the winter and spring months of 2020. New South Wales was the only jurisdiction where this decrease was below the lower bounds for the entire winter period. 
Excess mortality by year, New South Wales, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
202055,87853,562-2,316-4.164
202155,43555,6071720.3668
202255,80962,2226,41311.54,824
202336,76738,5341,7674.81,618

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 27 August and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

Weekly all-cause mortality: Victoria

  • Excess mortality for the first eight months of 2023 (until the end of winter) for Victoria is estimated at 7.6% above expected. 
  • This compares to 16.9% for the first eight months of 2022.  
  • Victoria recorded excess mortality through 2022 and 2023. Most weeks in 2022 exceeded the upper limits of expectation. 
  • Deaths due to COVID-19 (as identified on death certificates) were a significant contributor to increased mortality in Victoria across 2022 and until the end of May 2023. 
  • From June 2023, mortality falls into the expected range of variation and is close to expected for much of this period. 
  • Victoria had the second highest excess mortality recorded in 2022 at 14.0% above expected. 
  • The highest peak in January 2022 (1,045 deaths) was similar to the highest peak in July 2022 (1,055 deaths). Typically winter has much higher rates of mortality than summer months. 
  • Victoria was the only jurisdiction to record statistically significant excess mortality during the second wave (between June and September 2020). Excess mortality during this period was driven by COVID-19 mortality. All other jurisdictions had low rates of infections. 
Excess mortality by year, Victoria, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
202041,98741,612-375-0.9814
202141,42642,9581,5323.7757
202241,64347,4795,83614.03,460
202327,30129,3662,0657.6900

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 27 August and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

Weekly all-cause mortality: Queensland

  • Excess mortality for the first eight months of 2023 (until the end of winter) for Queensland is estimated at 5.9% above expected.
  • This compares to 13.1% for the first eight months of 2022. 
  • Between February 2023 and May 2023 mortality in Queensland was mostly in the expected bounds of variation. 
  • During July and August 2023 mortality in Queensland is close to expected levels. 
  • Queensland has some distinct peaks of excess mortality since 2022 - one in the first quarters of both 2022 and 2023, a more severe wave during winter 2022, and a small peak in June 2023.
  • Deaths associated with COVID-19 (as identified on death certificates) were a key contributor to excess mortality in 2022 and have remained so for periods during 2023. 
  • Excess mortality in Queensland for 2021 was 1.0% and fell within the expected range of variation.
  • Queensland was one of three jurisdictions (alongside New South Wales and the Australian Capital Territory) to record negative excess mortality in 2020 of more than 4%. 
Excess mortality by year, Queensland, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
202033,79332,329-1,464-4.34
202133,96434,3183541.04
202234,78638,4773,69110.62,342
202323,18924,5651,3765.9880

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 27 August and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

Weekly all-cause mortality: South Australia

  • Excess mortality for the first eight months of 2023 (until the end of winter) for South Australia is estimated at 6.2% above expected.
  • This compares to 11.4% for the first eight months of 2022.
  • The number of deaths was within the expected range of variation for all weeks except one in the first eight months of 2023. 
  • In July and August of 2023, deaths in South Australia were close to expected. 
  • Except for two weeks in March, excess mortality was sustained in South Australia from mid-January 2022 until the end of August 2022. COVID-19 (as identified on a death certificates) was a key contributor to increased mortality during this time.  
  • Mortality was generally in the expected range of variation during the first two years of the pandemic. From June 2020 to the start of 2021, mortality was mostly lower than expected. 
Excess mortality by year, South Australia, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
202014,41713,963-454-3.14
202114,33814,405670.54
202214,48215,9121,4309.91,059
20239,46310,0465836.2363

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 27 August and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

Weekly all-cause mortality: Western Australia

  • Excess mortality for the first eight months of 2023 (until the end of winter) for Western Australia is estimated at 5.5% above expected. 
  • This compares to 6.5% for the first eight months of 2022. 
  • Mortality in Western Australia from the end of June 2023 was close to expected. 
  • Between April and May 2023, deaths due to COVID-19 (as identified on death certificates) were a key contributor to excess mortality. 
  • The first peak of excess mortality in WA occurred in the week ending 8 May 2022. This differed from most other jurisdictions where the first peak of excess mortality in 2022 occurred in January or February.
  • During the first two years of the pandemic, mortality in WA was generally within the expected range of variation. There was less than 1% excess mortality in 2021. In 2020, lower than expected mortality was recorded, with a period of statistically significant lower than expected mortality between August and September. 
Excess mortality by year, Western Australia, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
202015,89115,275-616-3.911
202115,92516,0611360.90
202216,25917,4181,1597.1963
202310,77511,3665915.5458

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 27 August and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

Weekly all-cause mortality: Tasmania

  • Excess mortality for the first eight months of 2023 (until the end of winter) for Tasmania is estimated at 12.6% above expected. Tasmania has the highest recorded excess mortality rate of all jurisdictions. 
  • This compares to 14.7% for the first eight months of 2022.
  • From the end of July to the end of August 2023 mortality was close to expected.  
  • Deaths due to COVID-19 (as identified on death certificates) were a key contributor to excess mortality in 2022. While COVID-19 has been recorded as a factor on 138 death certificates in 2023, its contribution to increased mortality is not as clear compared to 2022.
  • Prior to September 2021, mortality in Tasmania was generally in the expected range. Similar to other states, there was a period of lower than expected mortality in 2020.
Excess mortality by year, Tasmania, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
20204,6314,462-169-3.617
20214,5454,8012565.60
20224,5315,12759613.2342
20232,9353,30436912.6138

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 27 August and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

Weekly all-cause mortality: Northern Territory

  • Excess mortality estimates for the Northern Territory are only available until the end of May 2023. There is a longer time between the date of death and date of registration in the NT, meaning there is a delay in reporting more complete figures. This is due to the dispersed geography, with the wet season and remote communities sometimes leading to a delay in burial and funerals (and therefore death registrations). Additionally, a higher proportion of deaths are referred to a coroner in the NT. 
  • Excess mortality for the first five months of 2023 (until the end of autumn) for the Northern Territory is estimated at 2.9% below expected.
  • This compares to 14.1% above expected for the five months of 2022. 
  • Excess mortality estimates for the Northern Territory should be treated with caution. There are a small number of deaths occurring in the NT each year, and often there are less than 20 deaths per week. The small number of deaths in the NT is due to a combination of factors, including the Territory's smaller population size and younger population (Darwin has the youngest median age of all capital cities). 
  • Due to the low number of weekly deaths, calibration by age was not possible. Predicting an expected number of deaths from a small base can lead to volatility. This is evident from the range of the expected number - the upper and lower bounds are wider to account for this high variation. 
  • For most weeks in 2023 the number of deaths in the Norther Territory have been lower than expected. For most of the pandemic, mortality in the NT has been in the expected range of variation. 
  • COVID-19 (as identified on death certificates) contributed to increased mortality in 2022. While numbers are small, COVID-19 mortality peaks occurred at similar times to other jurisdictions, with an increase in February and March and then again over the winter months.
  • There have been 13 COVID-19 associated deaths recorded on death certificates in the Northern Territory for the first five months of 2023. This compares to 46 for the first five months of 2022. 
Excess mortality by year, Northern Territory, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
20201,1431,161181.60
20211,1241,198746.62
20221,1281,33520718.490
2023454441-13-2.913

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 21 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 28 May and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 28 May 2023 and registered and received by the ABS by 31 October 2023. 

Weekly all-cause mortality: Australian Capital Territory

  • Excess mortality for the first eight months of 2023 (until the end of winter) for the Australian Capital Territory is estimated at 1.0% below expected.
  • This compares to 10.2% above expected for the first eight months of 2022.
  • Since the end of April 2023, mortality in the Australian Capital Territory has generally been below expected.  
  • Mortality in all weeks except three in the first eight months of 2023 was within the expected bounds of variation. 
  • Over the winter months of 2022, deaths due to COVID-19 (as recorded on death certificates) was a key contributor to excess mortality.  
  • Mortality was lower than expected across the first two years of the pandemic in the Australian Capital Territory. 
  • Similar to the Northern Territory, the population of the ACT is small, with Canberra having the second youngest population in Australia. This results in a small number of deaths by week and the predicted count can be subject to volatility. 
Excess mortality by year, Australian Capital Territory, 2020-23
 ExpectedObservedExcess% ExcessReported deaths from or with COVID-19
20202,5412,431-110-4.32
20212,5522,451-101-4.013
20222,6142,88627210.4207
20231,7641,747-17-1.071

a. Data is provisional and subject to change.
b. Years are based on a sum of ISO weeks derived from the weekly modelling. There are 53 weeks in 2020. There are 52 weeks in 2021 and 2022. Excess mortality has been estimated for the first 34 weeks of 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Deaths in 2023 are deaths that occurred by 27 August and were registered and received by the ABS by 31 October 2023.

a. Dates for key events are indicative only and may differ to other sources. 

b. Data is provisional and will change as additional death registrations are received.

c. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 

d. Data includes all deaths occurring by 27 August 2023 and registered and received by the ABS by 31 October 2023. 

Excess mortality by age: Australia

The excess mortality profile in Australia differs by age. This is for a number of reasons but includes increased vulnerability to COVID-19 due to health status. The following two graphs shows the number and proportion of excess deaths by selected age groups modelled for each year of the pandemic.

  • In 2023 people aged over 55 years recorded some excess mortality. No excess mortality was recorded for those aged under 55 years in the presented age groupings. 
  • The highest number of excess deaths was recorded in those aged between 75 and 84 years (2,756 excess deaths). 
  • Those aged over 95 years recorded the highest proportion of excess deaths at 12.1% higher than expected. This age group has recorded the highest proportion of excess deaths each year since 2021. 
  • In 2022 all age groups recorded excess mortality (both number and proportion). 
  • In 2020, all age groups except those aged under 34 years recorded lower than expected excess mortality. In this age group 30 deaths above expected were recorded (0.7%). This number and proportion is considered to be within the expected range rather than statistically significant excess mortality. 

Methodology

The analysis of 2020-2023 mortality data is based on a model developed by Serfling¹⁰ and later adapted by the US Center for Disease Control (CDC) and New South Wales Health (NSW Health). This section provides an overview of how the model has been developed over time, key aspects of the model, and how the model has been adapted and applied by the ABS in this analysis.

Historical development of model

Historically this type of model has been used to estimate excess mortality caused by influenza.

As early as 1932, Collins determined that excess mortality during winter months in the United States of America (USA) was a consequence of epidemic influenza and therefore could be used as an indicator for the recognition of influenza outbreaks.²

In 1963, Serfling described a cyclic regression model, based on the seasonal pattern of pneumonia and influenza (P&I) deaths, to infer excess deaths due to influenza.¹⁰

Since then, models based on Serfling's have been applied in a number of temperate countries (USA,³,⁴ France,⁴ Australia,⁴ and Italy⁵) to demonstrate that excess mortality occurring during winter months is associated with the pandemic and seasonal epidemics of influenza.

The CDC took the approach further and established the 122 Cities Surveillance System to provide timely, prospective information on excess mortality due to influenza. ⁶,⁷

The New South Wales mortality surveillance was included as part of the routine Influenza Surveillance Program prior to COVID-19 and used an adaptation of the Serfling model to monitor for influenza epidemics. This system primarily monitored the proportion of influenza positive specimens among all respiratory specimens from the major public health laboratories, the proportion of emergency department visits diagnosed with influenza, and outbreaks of influenza reported to Public Health Units by residential care facilities.⁹ 

Aspects of the Serfling model and its implementations

The Serfling model uses historical data to predict current patterns of mortality. This enables the identification of any deviation from that prediction that may signal an influenza epidemic. In developing this model, Serfling recognised that past influenza epidemics could overly influence the regression, limiting the ability of the model to identify any new epidemic. For this reason, Serfling excluded past epidemics when fitting the model. 

An important feature of this methodology is the identification of a threshold that can be used as the upper and/or lower limit for the data. Observations that lie between these upper and lower limits are considered to be normal. The chosen threshold will vary depending on the desired specificity of the analysis and the methods used for calculation. In statistics there are default or familiar thresholds used in measuring these boundaries such as 1.96 standard errors either side of the mid-point.

An upper threshold was chosen to define the upper limit of the expected number of deaths in the absence of an influenza epidemic. This upper threshold was calculated as the predicted number of deaths in a given week plus a constant multiple of the standard error of the time series (the differences between each value predicted by the model and the actual observed values, or the 'model residuals'). Serfling chose the constant multiple to be 1.64 standard errors, and considered 2 consecutive weeks above the threshold to indicate epidemic behaviour.¹⁰

The NSW Health model used for the Influenza Surveillance Program is based on the Serfling model. This variant of the model fits a cyclic linear regression to data for the previous 5 calendar years and then forecasts the current year's time series from data up to the end of the previous year.

Data source

Refer to the methodology section of Provisional Mortality Statistics for a description of the scope of data used in this model.

ABS adaptation of the model

The ABS has applied key characteristics of the model used in the New South Wales Influenza Surveillance Program. The ABS model is also based on the application of a cyclic linear regression model to the time series of weekly age-specific death rates. The model has been applied to both doctor certified and coroner referred deaths from all causes. 

The approach used by the ABS involves use of a 'robust' estimation procedure for fitting the model. Epidemics can cause increased mortality which leads to outliers in time series data. By down-weighting outliers it aims to fit the model to patterns of mortality that are expected to occur in the absence of an epidemic. This robust regression down-weights the influence of extreme observations (outliers) and is applied to a seven-year baseline time series (weeks commencing on a Monday). Some additional adjustments were made to extreme outliers, for example the 2017 influenza season. This adjustment involved applying a missing value to some age groups across some weeks where the actual rate was unusually high. This was done separately for each jurisdiction. 

The cyclical regression model includes: a linear time term, t, with values 1, 2, 3, ... for each week of the time series. Also included are annual seasonal harmonic variables to describe the cyclical seasonal background pattern. The harmonic variables are functions of the week number, t, and the periodicity in the same units – in this case, yearly (52 weeks). The 2 harmonic variables in this case are: sine(2π t/52) and cosine(2π t/52). For years with 53 weeks these harmonic terms have been divided by 53 instead of 52.

The final model was:

\(Expected(proportion) =A + Bt + Csin(2\pi t/52) + Dcos(2\pi t/52)\)

where A, B, C and D, are the coefficients calculated from the regression. Previous versions of this model also include a time squared term. This term was removed as it resulted in increasingly unrealistic output as the prediction window extended further from the modelling window.

To evaluate the approach, the model was fitted using PROC ROBUSTREG in SAS Software with the simplest, default 'M estimation' method.¹² The PROC SCORE procedure was then used to forecast values for 2020-2023. The standard error and threshold is derived from the stdi option in PROC ROBUSTREG which is run a second time with the 2020-2023 results attached.

Threshold identification

Identifying the threshold at which to signal excess mortality will vary depending on the goals of the research, application of the method, and seasonality of diseases of interest. Several national statistical organisations are using different models including Farrington surveillance algorithms, z -scores and prediction intervals to inform such thresholds.

A method for calculating the threshold for this analysis was selected which had a broader application and was more easily interpreted across a range of conditions, namely 1.96 standard errors or a 95% confidence interval. Use of the 95% confidence interval does introduce the expectation that random weekly variation could lead to counts that exceed thresholds 5% of the time. As such, individual weeks that exceed thresholds should be interpreted with caution. However, any prolonged period of weeks, 2 or more, exceeding the expected range can suggest excess mortality has been signalled.

The SAS function PROC ROBUSTREG offers the STDI parameter to calculate the 'standard error of prediction' of the model¹² which is used to define 1 standard error. The standard error of prediction is a more logical choice for assigning the threshold of excess mortality than the root mean square error (standard error of the model residuals) because it incorporates not only the variance of the residuals but also the variance of the model parameter estimates. This provides an estimate of the expected variability of the observed values in the absence of an epidemic.

Determining the baseline

Choosing the reference period (i.e. the number of years in the baseline) is important as it can change the expected number of deaths. When selecting the reference period key attributes were required:

  • There needed to be enough input available to predict the number of deaths from 2020-2023. As the paper is looking at excess mortality in the absence of the pandemic, no data from 2020-2023 is part of the reference period when predicting the expected number of deaths. Estimates of excess mortality are more accurate for years that are closer to the reference period.
  • A stable and clear mortality trend needed to be identified. This was particularly important as the model used is a harmonic with trend, meaning the pattern of death should be stable over a period to estimate an expected trend accurately.
  • The baseline period needed to be applied consistently across jurisdictions.

To make the decision, a sensitivity analysis was conducted. This analysis tested three reference periods: 2010-2019, 2013-2019 and 2015-2019. Standardised death rates were used to determine which years to test (these are presented below). Each baseline produced slightly different expected counts of deaths, which altered the % excess figures. The ABS decided to implement 2013-2019 as the predictor reference period. This baseline was chosen because:

  • There was a large decline in mortality between 2017 and 2018. This is likely due to the severe influenza season in 2017 causing some mortality displacement in 2018. Mortality displacement is an epidemiological concept which describes the phenomenon of a period of very high mortality being followed by a period of low mortality. Even controlling for 2017, the model was overcompensating for the rate of decline during 2015-2019, resulting in a very low number of expected deaths in 2022 and 2023.
  • Not all jurisdictions experienced a severe influenza season in 2017. Western Australia for example, had higher mortality rates in 2015 and 2016. Variability across jurisdictions meant that 2015-2019 had different outcomes across jurisdictions.
  • Western Australia had steeper declines in mortality rates between 2015-2016 and 2017 compared to other jurisdictions. Starting the baseline at the highest mortality points was overstating the rate of decline in WA and resulting in a very low number of expected deaths in 2022 and 2023. This was also affected by the model – as a harmonic with trend model is being used, volatility in the input trend affected the expected number of deaths.
  • For smaller jurisdictions with low numbers of weekly deaths, adding additional years onto the baseline provided a more stable trend.
  • There was some excess mortality in 2014, 2015 and 2017. Adjustments were made to outliers to control for these. 2013 was a year of stable mortality where no adjustments had to be made across any jurisdictions.
  • 2010-2019 and 2013-2019 produced similar results. However, there is less population change to account for from 2013 onwards. This was especially important for smaller jurisdictions where age adjustment was not as precise due to small numbers.
  • This model will also be used for analysis of diseases. There were a number of coding changes in 2013. Starting the reference period ensures continuity of time series for this analysis.

Modelling rates

Weekly mortality rates were calculated for 2013-2019 to predict expected weekly rates of death for 2020-2023. Rates were then converted to numbers for ease of interpretation.

Age groups selected for modelling ASDRs went through a number of assessments including:

  • A consistent number of deaths each week, ideally at least 20 deaths per week in the age group.
  • Population to be used as denominators should have at least 30 people in each age group.

Final age groups vary by jurisdiction based on the above criteria. Where possible, age groups were modelled separately up to 95 years and over (Australia, New South Wales, Victoria and Queensland). For South Australia and Western Australia age groups were modelled up to 90 years and over. In Tasmania age groups were modelled to 85 years and over. Weekly numbers of deaths for the Northern Territory and the Australian Capital Territory were too small to allow robust age-specific rates to be calculated, and crude rates (total population) were used instead.

Consideration was given to whether age-specific rates or age-standardised rates should be modelled. Both age-specific rates and age-standardised rates control for the age composition of a population and take into account growth rates within a population over time. Age-specific rates show the intensity of deaths within a population and express real-life mortality and population loss. Age-standardised rates are a modelled rate, standardised against a hypothetical population to enable comparison across cohorts. As comparison was not the main focus, age-specific rates were chosen as the model input.

References

1. CDC. Excess Deaths Associated with COVID-19 https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm#techNotes

2. Collins SD. Excess mortality from causes other than influenza and pneumonia during influenza epidemics. Public Health Rep 1932;47:2159–2180.

3. Reichert T, Simonsen L, Sharma A, Pardo S, Fedson D, Miller M. Influenza and the winter increase in mortality in the United States, 1959–1999. Am J Epidemiol 2004;160:492–502.

4. Viboud C, Boelle P, Pakdaman K, Carrat F, Valleron A, Flahault A. Influenza epidemics in the United States, France, and Australia, 1972–1997. Emerg Infect Dis 2004;10:32–39.

5. Rizzo C, Bella A, Viboud C, Simonsen L, Miller M, Rota M, et al. Trends for influenza-related deaths during pandemic and epidemic seasons, Italy, 1969–2001. Emerg Infect Dis 2007;5:694–699.

6. Centers for Disease Control and Prevention. 122 Cities Mortality Reporting System, Manual of Procedures. Atlanta (Georgia, United States): US Department of Health and Human Services; 2004.

7. Simonsen L, Clarke MJ, Stroup DF, Williamson GD, Arden NH, Cox NJ. A method for timely assessment of influenza-associated mortality in the United States. Epidemiology 1997;8:90–395.

8. O'Brien K, Barr IG. Annual Report of the National Influenza Surveillance Scheme, 2006. Commun Dis Intell 2007;31:167–179.

9. NSW Health, Communicable Diseases Branch. New South Wales Influenza Surveillance Report. NSW Health.

10. Serfling RE. Methods for current statistical analysis of excess pneumonia-influenza deaths. Public Health Rep 1963;78:494–506.

11. The ROBUSTREG procedure. SAS/STAT(R) 12.3 User's Guide. Cary (USA): SAS Institute, 2013. Available from: https://support.sas.com/documentation/onlinedoc/stat/123/rreg.pdf

12. Model Fit and Diagnostic Statistics. SAS/STAT(R) 9.2 User's Guide. Cary (USA): SAS Institute, 2008.

Data downloads

Excess mortality, Australia and by state, Jan 2013 - Aug 2023

Data files
Download xlsx [580 KB]