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Excess mortality by selected causes of death, 2020-23

Australia's excess mortality by cause of death during the COVID-19 pandemic 2020-23

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Released
29/09/2025
Source
Provisional Mortality Statistics, Jan - May 2025
Released
29/09/2025 11:30am AEST

Key statistics

Overview

This article provides weekly and annual excess mortality estimates for selected causes of death occurring in Australia until the end of December 2023.

The ABS has produced several reports on excess mortality during the COVID-19 pandemic. The most recent two reports provided estimates by state/territory and by remoteness areas (i.e. for major cities, inner regional, outer regional, remote, and very remote parts of Australia), respectively.  This article complements national excess mortality reporting by providing estimates for selected causes of death. 

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). 

As this data is designed to provide further insights to excess mortality estimates previously produced by the ABS it uses the same methods as those earlier reports. It’s designed to answer the research question: 'How does the number of deaths which have occurred since the beginning of the COVID-19 pandemic (2020-2023) compare to the number of deaths expected had the pandemic not occurred?'. Please see the Interpreting Results section in the previous article for more information.

Key points:

  • All causes recorded their highest excess mortality over the four-year period in 2022, except for blood and lymph cancers (2021) and colorectal and anal cancers (2020).
  • Excess mortality in 2022 and 2023 was the highest for alcohol-induced conditions, liver diseases, kidney diseases, diabetes, and ischaemic heart disease.
  • Dementia, including Alzheimer’s disease, respiratory diseases, and external causes excluding accidental falls experienced negative excess mortality every year from 2020 through 2023 (meaning there were fewer deaths than expected).

The following table provides a summary of excess mortality estimates by cause of death expressed as the percentage above expected mortality for the years 2020-2023. Excess mortality estimates for Australia (i.e., all-cause excess mortality) are included as a reference point.

Excess mortality as a percentage above expected by cause of death, 2020-23
 2020202120222023
Australia-3.11.611.75.1
Cancer-1.30.30.60.4
Lung cancer-2.20.63.21.2
Blood and lymph cancer-0.71.4-1.7-6.7
Colorectal and anal cancers3.92.30.72.1
Dementia, including Alzheimer’s disease-6.3-3.2-0.3-7.6
Ischaemic heart disease-4.02.314.16.7
Cerebrovascular diseases-5.4-1.01.0-2.2
Other cardiac conditions-4.53.88.95.9
Diabetes4.54.714.99.4
Kidney diseases8.312.221.016.8
Respiratory diseases-21.6-16.9-8.7-11.9
External causes (excluding accidental falls)-5.5-8.2-3.2-7.5
Accidental falls-0.53.55.3-1.7
Liver diseases6.411.121.313.3
Alcohol-induced conditions9.516.427.818.8

Causes of death

Producing all-cause mortality is considered to be best practice for understanding excess mortality as it provides an overall understanding of changed mortality patterns. However, cause-specific mortality does not always follow the same patterns as all-cause mortality and there is value in examining this separately to gain additional insights on changes to diseases and external causes. Changes to cause-specific mortality during the pandemic may have been directly linked to COVID-19, for example the virus may not have directly caused death but exacerbated a pre-existing condition in an individual. Indirectly, the pandemic may also have altered mortality patterns, for example through changes in how health care was accessed and conditions managed, or through public health measures such as stay at home measures. This report presents excess mortality estimates for 2020-2023 for selected causes of death. Causes of death for both doctor and coroner certified deaths are included. This differs from Provisional Mortality Statistics reporting where only causes of death for doctor certified deaths are included. 

National causes of death data are compiled by the ABS with information provided by doctors and coroners as part of the death registration process. Conditions on the medical certificate of cause of death and from the coronial investigation are assigned codes from the International Classification of Diseases, 10th revision, and an underlying cause of death is selected. This enables causes of death to be categorised and tabulated for statistical purposes. 

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 (ASDRs) for certain age groups for each cause, and converted the expected death rates into an expected number of deaths for each age group. The ABS then added these across age groups to obtain an expected count for each cause of death. Using ASDRs accounts for changes in population size and age composition. For more information, please see 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 (for the statistics in this article, 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. The expected range should be used in conjunction with the percentage of excess mortality.
  • A single week above threshold does not necessarily suggest statistically significant excess mortality. Prolonged periods (2 or more weeks) where counts are outside thresholds suggest more strongly that the numbers of deaths are above or below normal.
  • Data is reported by date of death occurrence. Cause of death analysis includes all deaths that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025. Data may change as additional registrations are received by the ABS. For the Australia level data, the scope is deaths that occurred by 31 December 2023 and were registered and received by the ABS by 31 March 2024. This is because Australia level data is used as a reference point only and matches previously published counts. It is expected that Australia level data would be close to complete and any additional deaths received would make a limited difference to high level counts and estimates. Please refer to the ‘Timeliness and completeness of the data’ section of Provisional Mortality Statistics for more information.
  • Data is presented for selected causes of death only. While there were many areas of interest, some causes have small numbers of deaths occurring weekly, affecting the ability to model robust excess mortality estimates.
  • Some cause of death groups presented are not mutually exclusive. For example, alcoholic liver disease is presented in both the tabulation for liver diseases and alcohol-induced conditions.
  • Data is presented for the underlying cause of death only. While multi morbidity at death is increasing with an ageing population, there have been changes to the coding over time for associated causes which would introduce bias into the modelled results. 

Weekly all-cause mortality: Australia

The ABS has previously published excess mortality at the national level in Measuring Australia's excess mortality during the COVID-19 pandemic until December 2023). It is included again in this article as a reference point for excess mortality estimates for the causes of death. 

  • Excess mortality for Australia in 2023 was estimated at 5.1% above expected. This is a decrease compared to 2022, when excess mortality was 11.7%.  
  • COVID-19 associated deaths (as identified on death certificates) were a key contributor to excess mortality in Australia from late 2021 through to 2023. Excess mortality during this period corresponded with peaks in 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.
Excess mortality by year, Australia, 2020-23
 2020202120222023
Expected170,046169,048170,933173,121
Observed164,805171,823190,955181,865
Excess-5,2412,77520,0228,744
% Excess-3.11.611.75.1
Reported deaths from or with COVID-199161,44813,3175,952

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. Reported deaths 'from' or 'with' COVID-19 are as recorded on the death certificate. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 March 2024.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 March 2024. 

Weekly mortality: Cancer

Cancer is a group of conditions which are caused by abnormal cells in the body growing uncontrollably and damaging tissue. Cancers are also referred to as "malignant neoplasms" and are a significant cause of mortality in Australia. 

  • There was relatively little excess mortality for cancer between 2020 and 2023. The highest level was in 2022, when mortality was 0.6% higher than expected. This is much lower than the all-cause excess mortality estimate (11.7%).
  • Excess mortality for cancer in 2021 (0.3%) and 2023 (0.4%) was slightly lower than in 2022.
  • Mortality for cancer in 2020 was 1.3% lower than expected, with 666 less deaths than expected. 
Excess mortality by year, Cancer, 2020-23
 2020202120222023
Expected50,29150,09650,81351,632
Observed49,62550,23151,09651,843
Excess-666135283211
% Excess-1.30.30.60.4

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for cancer: C00-C97, D45, D46, D47.1 and D47.3-D47.5. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025. 

Weekly mortality: Lung cancer

Lung cancer is a condition where abnormal cells that originate in the lung grow uncontrollably and spread (metastasise) to other parts of the body. Lung cancer is the leading cause of cancer death in Australia.  

  • Excess mortality for lung cancer between 2020 and 2023 was highest in 2022, at 3.2% higher than expected. This is lower than the all-cause excess mortality estimate (11.7%).
  • Excess mortality for lung cancer more than halved in 2023 (1.2%) compared to 2022.
  • In 2020, mortality for lung cancer was 2.2% lower than expected.
  • The pattern of excess mortality for lung cancer was similar to that for all cancer, with negative excess mortality in 2020, relatively low excess mortality in 2021 and 2023, and slightly higher excess mortality in 2022.  
Excess mortality by year, Lung cancer, 2020-23
 2020202120222023
Expected8,8468,7338,7898,892
Observed8,6528,7839,0699,001
Excess-19450280109
% Excess-2.20.63.21.2

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for lung cancer: C33 and C34. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025. 

Weekly mortality: Blood and lymph cancer

Blood and lymph cancers are a group of conditions where blood cells do not form properly and new blood cell production is affected by the growth of abnormal blood cells. Leukaemia, myeloma and lymphoma are the three most common types of blood and lymph cancers. 

  • Between 2020 and 2023, the only year with excess mortality for blood and lymph cancer was 2021, when mortality was 1.4% higher than expected. This is comparable to the all-cause excess mortality estimate in 2021 (1.6%).
  • Mortality for blood and lymph cancer was slightly lower than expected in 2020 and 2022, and 6.7% below expected in 2023.
  • While excess mortality for blood and lymph cancer was similar to all cancer in 2020 and 2021, this was not the case in 2022 and 2023. In 2022 and 2023 there was a low level of positive excess mortality for all cancer, but negative excess mortality for blood and lymph cancer, particularly in 2023.
Excess mortality by year, Blood and lymph cancer, 2020-23
 2020202120222023
Expected5,7775,8346,0336,255
Observed5,7345,9185,9305,834
Excess-4384-103-421
% Excess-0.71.4-1.7-6.7

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for blood and lymph cancer: C81-C88, C90-C96, D45, D46, D47.1, and D47.3-D47.5. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Colorectal and anal cancers

Colorectal and anal cancers are a group of conditions where abnormal cells that originate in the colon, rectum or anus grow uncontrollably and spread (metastasise) to other parts of the body. 

  • Between 2020 and 2023, excess mortality for colorectal and anal cancers was highest in 2020 (3.9%) and lowest in 2022 (0.7%). This is opposite to the pattern observed in all-cause excess mortality, which was negative in 2020 and highest in 2022.
  • Among the selected three cancers where excess mortality estimates were calculated (lung, blood and lymph, and colorectal and anal), colorectal and anal cancers were the only cancer to record excess mortality in 2020.
Excess mortality by year, Colorectal and anal cancers, 2020-23
 2020202120222023
Expected5,4465,3315,3465,365
Observed5,6585,4515,3815,477
Excess21212035112
% Excess3.92.30.72.1

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for colorectal and anal cancers: C18-C21, and C26.0. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Dementia, including Alzheimer’s disease

Dementia refers to a group of diseases where memory, thinking and ability to perform social and daily activities is affected. Included in this category are Alzheimer's disease, vascular dementia, fronto-temporal dementia, lewy body dementia and dementia where the type has not been specified. 

  • Mortality for dementia, including Alzheimer's disease, was lower than expected every year from 2020 through 2023.
  • In 2020, mortality for dementia, including Alzheimer's disease, was 6.3% lower than expected, and consistently below expected from May to December.
  • In 2023, mortality for dementia, including Alzheimer's disease, was 7.6% lower than expected, with most weeks below the expected range, particularly from July to October. 
Excess mortality by year, Dementia, including Alzheimer's disease, 2020-23
 2020202120222023
Expected16,73717,20417,88918,689
Observed15,67916,65217,83917,269
Excess-1,058-552-50-1,420
% Excess-6.3-3.2-0.3-7.6

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for dementia, including Alzheimer's disease: F01, F03, G30, G31.0, and G31.8. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Ischaemic heart disease

Ischaemic heart disease is a condition where the heart does not receive enough oxygen-rich blood due to narrowed arteries. Example of conditions included in this category are "acute myocardial infarction" and "coronary atherosclerosis". 

  • In 2022, mortality for ischaemic heart disease was 14.1% higher than expected.
  • Excess mortality for ischaemic heart disease more than halved in 2023 (6.7%) compared to 2022.
  • From December 2021 to July 2023, mortality for ischaemic heart disease has been largely above the expected value, and was largely above the expected range from December 2021 to April 2022, and again from November 2022 to March 2023.
  • In 2020, mortality for ischaemic heart disease was 4.0% lower than expected, with 725 less deaths than expected. From May to December mortality was largely lower than expected.
Excess mortality by year, Ischaemic heart disease, 2020-23
 2020202120222023
Expected17,99717,11116,47815,864
Observed17,27217,50118,80216,921
Excess-7253902,3241,057
% Excess-4.02.314.16.7

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for ischaemic heart disease: I20-I25. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

 

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Cerebrovascular diseases

Cerebrovascular diseases are conditions affecting blood supply and blood vessels of the brain. Examples of conditions included in this category are "strokes" and "cerebral atherosclerosis". 

  • Except for 2022, mortality for cerebrovascular diseases was lower than expected every year from 2020 to 2023.
  • In 2020, mortality for cerebrovascular diseases was 5.4% lower than expected, with 556 less deaths than expected.
  • In 2022 mortality for cerebrovascular diseases was 1.0% higher than expected and in 2023 it was 2.2% lower than expected. 
Excess mortality by year, Cerebrovascular diseases, 2020-23
 2020202120222023
Expected10,2739,9159,7069,550
Observed9,7179,8129,8039,339
Excess-556-10397-211
% Excess-5.4-1.01.0-2.2

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for cerebrovascular diseases: I60-I69. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Other cardiac conditions

The term "other cardiac conditions" are conditions affecting the heart other than ischaemic heart disease. Examples of conditions that are commonly coded to this category include non-rheumatic valvular heart diseases, cardiomyopathy, congestive heart failure and arrhythmia. 

  • In 2020, mortality for other cardiac conditions was 4.5% lower than expected, with 453 less deaths than expected.
  • From 2021 to 2023, mortality for other cardiac conditions was higher than expected every year, with excess mortality in 2022 being the highest (8.9%).
  • Since 2021 there have been substantially more weeks where mortality for other cardiac conditions has been higher than expected than there were weeks with mortality below expected. 
Excess mortality by year, Other cardiac conditions, 2020-23
 2020202120222023
Expected10,13310,09510,22210,387
Observed9,68010,47511,13511,001
Excess-453380913614
% Excess-4.53.88.95.9

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for other cardiac conditions: I26-I45, and I47-I51. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Diabetes

Diabetes is an endocrine disorder where there is too much glucose in the blood and the body is unable to make, make enough or use insulin to regulate these high glucose levels. Both type one and type two diabetes are included in this category. 

  • There was excess mortality for diabetes every year from 2020 to 2023.
  • Excess mortality for diabetes in 2022 was 14.9%, which was higher than the all-cause excess mortality estimate in 2022 (11.7%).
  • Excess mortality for diabetes was 9.4% in 2023, a decrease from 2022. Excess mortality remained higher than the all-cause excess mortality in 2023 (5.1%).
  • Since March 2021 there have been few weeks where the number of deaths from diabetes have been below expected. There have been prolonged periods when mortality for diabetes has been higher than the expected range. These include January to February 2022, June to August 2022, and January to February 2023. 
Excess mortality by year, Diabetes, 2020-23
 2020202120222023
Expected5,1695,1705,3035,453
Observed5,4025,4116,0915,967
Excess233241788514
% Excess4.54.714.99.4

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for diabetes: E10-E14. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Kidney diseases

Kidney diseases are conditions where kidney function is affected and they are unable to filter waste and toxins and clean your blood. Examples of conditions included in this category include "glumeronephritis", "pyelonephritis" and "chronic kidney disease" where no cause has been stated on the death certificate. Diabetes is one of the most common causes of kidney diseases. Deaths where it is known from the death certificate that the kidney disease was due to diabetes have not been included in this category, but have been included in the section on diabetes above. 

  • There was excess mortality for kidney diseases every year from 2020 to 2023, and the magnitude of excess mortality for kidney diseases was higher than all-cause excess mortality every year.
  • Excess mortality for kidney diseases was highest in 2022 when it was 21.0% higher than expected, and this figure decreased to 16.8% in 2023.
  • Since July 2021 there have been few weeks when the number of deaths from kidney diseases was lower than expected, and more frequently weeks when the number of deaths exceeded the expected range. 
Excess mortality by year, Kidney diseases, 2020-23
 2020202120222023
Expected2,7392,7492,8102,890
Observed2,9653,0853,3993,375
Excess226336589485
% Excess8.312.221.016.8

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for kidney diseases: N00-N08, N10-N19, and N20-N28. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Respiratory diseases

Respiratory diseases are conditions that affect the lungs and airways impacting breathing. Both chronic respiratory diseases (e.g. emphysema, asthma, asbestosis) and acute respiratory diseases (e.g. influenza, pneumonia) are included in this category. Deaths due to COVID-19 are not included. 

  • Mortality for respiratory diseases was lower than expected every year from 2020 to 2023.
  • In 2020, mortality for respiratory diseases was 21.6% lower than expected, with 3,517 less deaths than expected.
  • There have been several prolonged periods when mortality for respiratory diseases was lower than the expected range. These include April to November 2020, June to November 2021, August to October 2022, and June to October 2023.
  • Deaths due to influenza were particularly low in the first two years of the pandemic, with only 50 deaths recorded. 
Excess mortality by year, Respiratory diseases, 2020-23
 2020202120222023
Expected16,27516,42116,83017,315
Observed12,75813,65315,36515,253
Excess-3,517-2,768-1,465-2,062
% Excess-21.6-16.9-8.7-11.9

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for respiratory diseases: J00-J99. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

 

 

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: External causes (excluding accidental falls)

An external cause is where an injury caused by an external mechanism has led to death rather than a disease or condition. Included in this category are deaths due to suicide and assault, as well as accidents such as motor vehicle crashes, drowning, drug-induced deaths and other misadventure. Accidental falls have not been included in this category as most occur in older populations and are associated with chronic disease such as dementia, cardiovascular diseases and disorders affecting gait and mobility. For this reason accidental falls have been modelled separately. 

Most external causes of death are certified by a coroner in Australia. Investigations for coroner certified deaths can take some time to finalise. To account for this, the ABS revises the cause of death for cases which have taken some time to finalise. In this analysis, deaths for 2020 and 2021 are final with all revisions applied, but 2022 and 2023 are still likely to undergo some change. As cases finalise it is likely that the number of deaths due to external causes in 2022 and 2023 will increase, bringing the observed number closer to the expected number of deaths. This will reduce the magnitude of negative excess mortality in 2022 and 2023. 

  • Mortality for external causes (excluding accidental falls) was lower than expected every year from 2020 to 2023.
  • The largest variances from expected were in 2021 (8.2% lower than expected) and 2023 (7.5% below expected). The excess mortality estimate may change for 2023 as causes of death update as part of the coronial investigation.
  • For all of 2021 and since September 2022 there were few weeks where the number of deaths from external causes excluding accidental falls exceeded the expected values.
Excess mortality by year, External causes (excluding accidental falls), 2020-23
 2020202120222023
Expected8,1748,1188,2938,534
Observed7,7267,4558,0277,892
Excess-448-663-266-642
% Excess-5.5-8.2-3.2-7.5

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for external causes excluding accidental falls: V01-V99, W20-W99, X00-X99, Y00-Y36, Y85-Y89, and Y90-Y98, excluding the deaths with 'X59.0' as the underlying cause of death and 'S72' as a contributing cause of death. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Accidental falls

Accidental falls occur when a person unintentionally comes to rest on the ground or lower level. Accidental falls causing death most commonly occur in people aged over 75 years with pre-existing chronic diseases or conditions, result in a hip fracture or brain injury and are a result of tripping or slipping. 

  • Excess mortality for accidental falls has not been consistent between 2020 and 2023, with positive excess mortality in 2021 and 2022, and negative excess mortality in 2020 and 2023.
  • Excess mortality was highest in 2022. For most weeks between January and September 2022 the number of deaths due to accidental falls was higher than expected.
  • For most weeks between September to December 2023 the number of deaths from accidental falls was lower than expected. 
Excess mortality by year, Accidental falls, 2020-23
 2020202120222023
Expected4,0824,2414,4794,769
Observed4,0614,3884,7174,686
Excess-21147238-83
% Excess-0.53.55.3-1.7

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for accidental falls: W00-W19, and X59.0 (X59.0 as the underlying cause of death and S72 as a contributing cause of death). 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Liver diseases

Liver diseases are conditions where the liver has been damaged and unable to perform its usual function. Common conditions included this category include "alcoholic liver diseases", "liver cirrhosis with no cause specified" and "non-alcoholic fatty liver disease". Liver disease caused by viral hepatitis is not included in this category. 

  • There was excess mortality for liver diseases every year from 2020 to 2023, and the magnitude of excess mortality for liver diseases was higher than all-cause excess mortality every year.
  • Excess mortality for liver diseases was highest in 2022 (21.3%) and decreased in 2023 (13.3%).
  • Since October 2021 there have been few weeks when the numbers of deaths from liver diseases was lower than expected, and more frequently weeks when the number of deaths exceeded the expected range. 
Excess mortality by year, Liver diseases, 2020-23
 2020202120222023
Expected2,1292,1192,1732,256
Observed2,2662,3552,6362,557
Excess137236463301
% Excess6.411.121.313.3

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for liver diseases: K70-K76. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Weekly mortality: Alcohol-induced conditions

Alcohol-induced conditions are where the toxic effects of alcohol have directly caused death. This includes conditions caused by the chronic use of alcohol including alcoholic liver cirrhosis, alcoholic cardiomyopathy and alcoholic ketosis. It also includes deaths caused by the acute effects of alcohol such as poisoning. Most alcohol-induced deaths are due to chronic conditions. 

  • Of the selected causes presented in this excess mortality report, alcohol-induced conditions experienced the highest excess mortality for every year from 2020 to 2023.
  • For all years, the excess mortality estimates for alcohol-induced deaths are significantly higher than the all-cause estimates.
  • Mortality for alcohol-induced conditions was 27.8% higher than expected in 2022, and 18.8% higher in 2023.
  • Alcohol-induced mortality has been largely above the expected levels, particularly since 2022, and was frequently above the expected range in 2022.
Excess mortality by year, Alcohol-induced conditions, 2020-23
 2020202120222023
Expected1,4171,3921,4111,441
Observed1,5521,6201,8031,712
Excess135228392271
% Excess9.516.427.818.8

a. Data is provisional and subject to change.
b. Years are based on a sum of International Organization for Standardisation (ISO) weeks. Weeks start on Monday, and week 1 always contains the 4th of January. There are 53 weeks in 2020. There are 52 weeks in 2021, 2022 and 2023. 
c. ICD-10 codes for alcohol-induced conditions: E24.4, F10, G31.2, G62.1, G72.1, I42.6, K29.2, K70, K85.2, K86.0, X45, X65, and Y15. 
d. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

a. Data is provisional and will change as additional death registrations are received.
b. The baseline period generating the regression model is from 2013 to 2019. The prediction window begins in 2020. 
c. Includes all deaths (both doctor and coroner certified) that occurred by 31 December 2023 and were registered and received by the ABS by 31 July 2025.

Methodology

The analysis of 2020-2023 mortality data is based on a model developed by Serfling¹⁰ and later adapted by the US Centers for Disease Control and Prevention (CDC) and the Centre for Epidemiology and Evidence at New South Wales Ministry of 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

Please see the methodology section of Provisional Mortality Statistics for a description of the scope of data used in this model. This article included causes of death for coroner certified deaths, while cause of death information in Provisional Mortality Statistics is limited to doctor certified deaths only due to timeliness constraints.

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). 

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 harmonic terms reflect the fact that the number of deaths is usually higher in winter than in summer.

The final model was:

\(Expected(proportion)=A+Bt+CSin(2πt/52)+DCos(2πt/52)\)

 

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, testing 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 main predictor reference period. Reasons for this decision were outlined in previous articles. This same baseline was retained in this cause of death analysis, with the exception of ischaemic heart disease (IHD) and cerebrovascular diseases, where using 2013–2019 as the predictor reference period overestimated the declining mortality trend from 2020 to 2023. A sensitivity analysis showed that using 2013–2017 as the reference period provided the best estimate of the mortality trend for these causes during 2020–2023.

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 cause of death based on the above criteria. Where possible, age groups were modelled separately up to 95 years and over. Weekly numbers of deaths for some causes 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

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.

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

Data downloads

Excess mortality by cause of death, Australia, Jan 2013 - Dec 2023

Data files
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