This dataset presents life expectancy at birth estimates for males, females and persons. This dataset covers the reference period 2010-12 to 2017-19, and is based on Greater Capital City Statistical Areas (GCCSA), according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).

For further information please visit the Australian Bureau of Statistics.

Internationally, life tables are used to measure mortality. In its simplest form, a life table is generated from age-specific death rates and the resulting values are used to measure mortality, survivorship and life expectancy. The life table depicts the mortality experience of a hypothetical group of newborn babies throughout their entire lifetime. It is based on the assumption that this group is subject to the age-specific mortality rates of the reference period. Typically this hypothetical group is 100,000 persons in size.

AURIN has spatially enabled the original data.

This dataset presents life expectancy at birth estimates for males, females and persons. This dataset covers the reference period 2010-12 to 2017-19, and is based on Statistical Area Level 4 (SA4), according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).

For further information please visit the Australian Bureau of Statistics.

Internationally, life tables are used to measure mortality. In its simplest form, a life table is generated from age-specific death rates and the resulting values are used to measure mortality, survivorship and life expectancy. The life table depicts the mortality experience of a hypothetical group of newborn babies throughout their entire lifetime. It is based on the assumption that this group is subject to the age-specific mortality rates of the reference period. Typically this hypothetical group is 100,000 persons in size.

AURIN has spatially enabled the original data.

This dataset presents the life tables for males and females for the reference period 2008-2015. It contains life expectancy at birth estimates for males, females and persons for Statistical Area Level 4 (SA4). Boundaries are based on ABS ASGS 2011.

For further information please visit the Australian Bureau of Statistics

AURIN has spatially enabled the original data.

Life expectancy in Australia was just below 35 in the year 1870, and over the course of the next 150 years, it is expected to have increased to 83.2 by the year 2020. Although life expectancy has generally increased throughout Australia's history, there were several times where the rate deviated from its previous trajectory. the most noticeable changes were between 1890 and 1920. This period included Australia's Independence movement, the implementation of the 'White Australia' policy, the First World War and Spanish Flu epidemic, all of which impacted the demographics of Australia.

This dataset presents the footprint of the average number of years a person is expected to live at birth by sex, assuming that the current age-specific death rates are experienced throughout their life. The data spans the years of 2011-2016 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is based on the Australian Institute of Health and Welfare (AIHW) analysis of life expectancy estimates as provided by the Australian Bureau of Statistics (ABS). Life expectancies at birth were calculated with reference to state/territory and Australian life tables (where appropriate) for a three year period. The disaggregation used for reporting life expectancy at birth is PHN area. These values are provided by the ABS.

For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Life Expectancy and Potentially Avoidable Deaths 2014-2016 Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Life expectancy for 2014-2016 are based on the average number of deaths over three years, 2014-2016, and the estimated resident population (ERP) as at 30 Jun 2015.

This dataset presents the life tables for males and females for the reference period 2008-2015. It contains life expectancy at birth estimates for males, females and persons for Greater Capital City Statistical Areas (GCCSA). Boundaries are based on ABS ASGS 2011. For further information please visit the Australian Bureau of Statistics AURIN has spatially enabled the original data.

Australia Life Expectancy at Birth: Female data was reported at 85.300 Year in 2022. This records a decrease from the previous number of 85.400 Year for 2021. Australia Life Expectancy at Birth: Female data is updated yearly, averaging 80.400 Year from Dec 1960 (Median) to 2022, with 63 observations. The data reached an all-time high of 85.400 Year in 2021 and a record low of 74.000 Year in 1960. Australia Life Expectancy at Birth: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.;(1) United Nations Population Division. World Population Prospects: 2022 Revision; (2) Statistical databases and publications from national statistical offices; (3) Eurostat: Demographic Statistics.;Weighted average;

Reference categories – a. First year of follow up; b. Female. c. Non-Indigenous d. Age group 15-44 years. e. Charlson comorbidity index = 0; f. Non-severe sepsis patients; g. Non-bacteraemic patients.Multivariable analysis of predictors of excess mortality using Poisson regression.

BackgroundThe Indigenous population of Australia suffers considerable disadvantage across a wide range of socio-economic indicators, and is therefore the focus of many policy initiatives attempting to ‘close the gap’ between Indigenous and non-Indigenous Australians. Unfortunately, past population estimates have proved unreliable as denominators for these indicators. The aim of the paper is to contribute more robust estimates for the Northern Territory Indigenous population for the period 1966–2011, and hence estimate one of the most important of socio-economic indicators, life expectancy at birth.MethodA consistent time series of population estimates from 1966 to 2011, based off the more reliable 2011 official population estimates, was created by a mix of reverse and forward cohort survival. Adjustments were made to ensure sensible sex ratios and consistency with recent birth registrations. Standard life table methods were employed to estimate life expectancy. Drawing on an approach from probabilistic forecasting, confidence intervals surrounding population numbers and life expectancies were estimated.ResultsThe Northern Territory Indigenous population in 1966 numbered between 23,800 and 26,100, compared to between 66,100 and 73,200 in 2011. In 1966–71 Indigenous life expectancy at birth lay between 49.1 and 56.9 years for males and between 49.7 and 57.9 years for females, whilst by 2006–11 it had increased to between 60.5 and 66.2 years for males and between 65.4 and 70.8 for females. Over the last 40 years the gap with all-Australian life expectancy has not narrowed, fluctuating at about 17 years for both males and females. Whilst considerable progress has been made in closing the gap in under-five mortality, at most other ages the mortality rate differential has increased.ConclusionsA huge public health challenge remains. Efforts need to be redoubled to reduce the large gap in life expectancy between Indigenous and non-Indigenous Australians.

This dataset presents the footprint of the rate of potentially avoidable deaths per 100,000 people, age-standardised, by sex. Potentially avoidable deaths are deaths below the age of 75 from conditions that are potentially preventable through individualised care and/or treatable through existing primary or hospital care. The data spans the years of 2009-2016 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is based on analysis of the Australian Institute of Health and Welfare (AIHW) National Mortality Database (NMD). The database includes cause of death information which is sourced from the Registrars of Births, Deaths and Marriages in each state and territory, the National Coronial Information System, and compiled and coded by the Australian Bureau of Statistics (ABS). For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Life Expectancy and Potentially Avoidable Deaths 2014-2016 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Rates have been age-standardised to facilitate comparisons between populations with different age structures.

The Life Insurance Supplementary Statistical Tables contains aggregate level data about Sources of profit, Assets backing policy liabilities and Policy liabilities.

Interval specific relative survival by age category (severe sepsis patients).

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Australia Life Insurance: Gross Policy Revenue: by Business Class: Superannuation: Annuity without Longevity Risk data was reported at 0.000 AUD th in Jun 2023. This stayed constant from the previous number of 0.000 AUD th for Mar 2023. Australia Life Insurance: Gross Policy Revenue: by Business Class: Superannuation: Annuity without Longevity Risk data is updated quarterly, averaging 0.000 AUD th from Mar 2013 (Median) to Jun 2023, with 42 observations. The data reached an all-time high of 125,074.000 AUD th in Dec 2016 and a record low of -125,074.000 AUD th in Mar 2017. Australia Life Insurance: Gross Policy Revenue: by Business Class: Superannuation: Annuity without Longevity Risk data remains active status in CEIC and is reported by Australian Prudential Regulation Authority. The data is categorized under Global Database’s Australia – Table AU.RG017: Life Insurance: Gross Policy Revenue by Product Group (Discontinued).

Australia Life Insurance: Net Policy Revenue: by Business Class: Superannuation: Annuity with Longevity Risk data was reported at 113,465.788 AUD th in Jun 2023. This records an increase from the previous number of 82,774.444 AUD th for Mar 2023. Australia Life Insurance: Net Policy Revenue: by Business Class: Superannuation: Annuity with Longevity Risk data is updated quarterly, averaging 78,158.000 AUD th from Jun 2008 (Median) to Jun 2023, with 61 observations. The data reached an all-time high of 253,810.000 AUD th in Sep 2016 and a record low of -89,183.000 AUD th in Sep 2015. Australia Life Insurance: Net Policy Revenue: by Business Class: Superannuation: Annuity with Longevity Risk data remains active status in CEIC and is reported by Australian Prudential Regulation Authority. The data is categorized under Global Database’s Australia – Table AU.RG019: Life Insurance: Net Policy Revenue by Product Group (Discontinued).