Crude birth rates, age-specific fertility rates and total fertility rates (live births), 2000 to most recent year.

This dataset contains California’s adolescent birth rate (ABR) by county, age group and race/ethnicity using aggregated years 2014-2016. The ABR is calculated as the number of live births to females aged 15-19 divided by the female population aged 15-19, multiplied by 1,000. Births to females under age 15 are uncommon and thus added to the numerator (total number of births aged 15-19) in calculating the ABR for aged 15-19. The categories by age group are aged 18-19 and aged 15-17; births occurring to females under aged 15 are added to the numerator for aged 15-17 in calculating the ABR for this age group. The race and ethnic groups in this table utilized five mutually exclusive race and ethnicity categories. These categories are Hispanic and the following Non-Hispanic categories of Multi-Race, Black, American Indian (includes Eskimo and Aleut), Asian and Pacific Islander (includes Hawaiian) combined, and White. Note that there are birth records with missing race/ethnicity or categorized as “Other” and not shown in the dataset but included in the ABR calculation overall.

Teenage birth rate is number of live births among females ages 15 to 19 years per 1,000 females in that age group in a year. Data are for Santa Clara County residents. The measure is summarized for total county population by mother's zip code of residence at the time of birth. Data are presented for pooled years combined and are available for three time periods. Source: Santa Clara County Public Health Department, 2000-2015 Birth Statistical Master File; U.S. Census Bureau, 2010 Census.METADATA:Notes (String): Lists table title, notes, sourcesTime_period (String): Year of birth. Pooled data years are presented to meet the minimum data requirements.Zip_code (Numeric): Lists the mother's zip code of residence.Age_group (String): Lists the age of mother at the time of birth: 15 to 19 years.Birth_count females 15-19 (Numeric): Number of live births to mothers ages 15 to 19 years at the time of birth in a year. Birth count less than 6 in a year in the area are not presented.Rate per 1,000 females ages 15-19 (Numeric): Teen birth rate is number of live births to mothers ages 15 to 19 years at the time of birth per 1,000 females in that age group in a year. Rate based on birth count less than 6 in a year in the area are not presented.

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex.

Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).

Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm.

SOURCES

CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.

2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

3. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf.

4. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Background: Mortality rate rapidly decreases with age after birth, and, simultaneously, the spectrum of death causes show remarkable changes with age. This study analyzed age-associated decreases in mortality rate from diseases of all main chapters of the 10th revision of the International Classification of Diseases.Methods: The number of deaths was extracted from the mortality database of the World Health Organization. As zero cases could be ascertained for a specific age category, the Halley method was used to calculate the mortality rates in all possible calendar years and in all countries combined.Results: All causes mortality from the 1st day of life to the age of 10 years can be represented by an inverse proportion model with a single parameter. High coefficients of determination were observed for total mortality in all populations (arithmetic mean = 0.9942 and standard deviation = 0.0039).Slower or no mortality decrease with age was detected in the 1st year of life, while the inverse proportion method was valid for the age range [1, 10) years in most of all main chapters with three exceptions. The decrease was faster for the chapter “Certain conditions originating in the perinatal period” (XVI).The inverse proportion was valid already from the 1st day for the chapter “Congenital malformations, deformations and chromosomal abnormalities” (XVII).The shape of the mortality decrease was very different for the chapter “Neoplasms” (II) and the rates of mortality from neoplasms were age-independent in the age range [1, 10) years in all populations.Conclusion: The theory of congenital individual risks of death is presented and can explain the results. If it is valid, latent congenital impairments may be present among all cases of death that are not related to congenital impairments. All results are based on published data, and the data are presented as a supplement.

Background: In humans, the mortality rate dramatically decreases with age after birth, and the causes of death change significantly during childhood. In the present study, we attempted to explain age-associated decreases in mortality for congenital anomalies of the central nervous system (CACNS), as well as decreases in total mortality with age. We further investigated the age trajectory of mortality in the biologically related category “diseases of the nervous system” (DNS).Methods: The numbers of deaths were extracted from the mortality database of the World Health Organization (WHO) for the following nine countries: Denmark, Finland, Norway, Sweden, Austria, the Czech Republic, Hungary, Poland, and Slovakia. Because zero cases could be ascertained over the age of 30 years in a specific age category, the Halley method was used to calculate the mortality rates in all possible calendar years and in all countries combined.Results: Total mortality from the first day of life up to the age of 10 years and mortality due to CACNS within the age interval of [0, 90) years can be represented by an inverse proportion with a single parameter. High coefficients of determination were observed for both total mortality (R2 = 0.996) and CACNS mortality (R2 = 0.990). Our findings indicated that mortality rates for DNS slowly decrease with age during the first 2 years of life, following which they decrease in accordance with an inverse proportion up to the age of 10 years. The theory of congenital individual risk (TCIR) may explain these observations based on the extinction of individuals with more severe impairments, as well as the bent curve of DNS, which exhibited an adjusted coefficient of determination of R¯2 = 0.966.Conclusion: The coincidence between the age trajectories of all-cause and CACNS-related mortality may indicate that the overall decrease in mortality after birth is due to the extinction of individuals with more severe impairments. More deaths unrelated to congenital anomalies may be caused by the manifestation of latent congenital impairments during childhood.

This table contains forecast figures on the number of live births by age of the mother (on 31 December) and the age-specific fertility rate of women, both by female rank. The figures relate to the population of the Netherlands. Data available: 2017-2059 Status of the figures: The figures in this table are calculated forecast figures. Changes as of December 16, 2020: This table has been discontinued. See section 3 for the successor to this table. Changes as of January 17, 2018: For the calculation of the number of live births, the number of women of childbearing age from wrong years has been used. The figures on live births have therefore been corrected. Fertility numbers were correct. Changes as of December 19, 2017: None, this is a new table in which the previous forecast has been adjusted on the basis of the observations that have now become available. The forecast period now runs from 2017 to 2060. When will new figures be released? The publication frequency of this table is one-off. In December 2020, a new table will be published with the prognosis of the number of live births.

Total first marriage rates and age-specific first marriage rates per 1,000 females, all marriages, by place of occurrence, 2000 to 2004.

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2022-23, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2023. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019 the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fourth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with breakdowns including the baby's first feed type, birthweight, place of birth, and breastfeeding activity; and the mothers' ethnicity and age at booking. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. The count of Total Babies includes both live and still births, and previous changes to how Total Babies and Total Deliveries were calculated means that comparisons between 2019-20 MSDS data and later years should be made with care. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

Indicator Overview: Conception rate per 1,000 women under age 18 is sourced from the ONS Conception Statistics Tables.

Age Calculation: A woman's age at conception is calculated as the number of complete years between her date of birth and the date she conceived. For example, a woman may conceive at age 19 and give birth at age 20.

Calendar Year Considerations: Conception and birth may occur in different calendar years. Therefore, the number of conceptions to women of a given age in a given year does not match the number of maternities and abortions to women of the same age in the same year.

Population Basis: Rates are based on the population of women:

All ages: 15 to 44 Under 16: 13 to 15 Under 18: 15 to 17

Population Estimates: The population figures used to calculate rates are mid-year estimates of the resident population for England and Wales, based on the Census.

Historical Context: This indicator was formally known as NI 112.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This dataset provides a range of demographic and socio-economic variables for Registration Sub-Districts (RSDs) in England and Wales, 1851-1911. The measures have mainly been derived from the computerised individual level census enumerators' books (and household schedules for 1911) for England and Wales enhanced under the I-CeM project. I-CeM does not currently include data for 1871, although the project has been able to access a version of the data for that year it does not contain information necessary to calculate many of the variables presented here. Users should therefore beware that 1871 does not contain data for many of the variables. Additional data, for some indicators, has been derived from the tables summarising numbers of births and deaths by year and areas, which were published by the Registrar General in his quarterly, annual and decennial reports of births, deaths and marriages. More information on the data, including overviews of the geographical patterns and changes over time, can be found on the Populations Past – Atlas of Victorian and Edwardian Population website, which provides an interactive mapping facility for these data. The second half of the nineteenth century was a period of major change in the dynamics of the British population. This was a time of transformation from a relatively 'high pressure' demographic regime characterised by medium to high birth and death rates towards a 'low pressure' regime of low birth and death rates, a transformation known as the 'demographic transition'. This transition was not uniform across England and Wales: certain places and social groups appear to have led the declines while others lagged behind. Exploring these geographical patterns can provide insights into the process of change and the influence of economic and geographical factors. This project aimed to utilise the individual-level data of the Integrated Census Microdata (I-CeM) project to calculate age-specific fertility rates both for a range of fine geographical units covering England and Wales and for occupational groups and then to investigate the relationships between these rates and other socioeconomic variables. This was to provide, for the first time, widespread information of the age patterns of fertility which render insight into ‘starting’, ‘spacing’ or ‘stopping’ fertility regulating behaviour. A time series of such measures across geographical and social space is also vital when trying to identify how new forms of behaviour spread through the population. This database contains a variety of measures of fertility, marriage and infant and child mortality, and also a range of socio-economic indicators (related to households, age structure, and social class) for the 2000+ Registration Sub Districts (RSDs) in both England and Wales, for each census year between 1851 and 1871. Most of these data can be mapped using our interactive website www.populationspast.org. This data collection was derived from near complete count individual level census data, from which we have created demographic and socio-economic indicators at a Registration Sub-District level, using a variety of demographic and statistical techniques. For a few variables, birth and death summary data (at Sub-Registration District level) were also used.

This series has been discontinued. Life expectancy at birth and age 65 by sex and ward, London borough, region, 1999/03 - 2010/14. The population data used is revised 2002-2010 ONS mid year estimates (MYE) - revised post 2011 Census. Revised population estimates by single year of age for wards can also be found on the ONS website for 2002-2010, 2011, 2012, and 2013. These figures are consistent with the published revised mid-2002 to mid-2010 local authority estimates. Rolling 5-year combined life expectancies are used for wards to reduce the effects of the variability in number of deaths in each year. The same method is applied to higher geographies to enable meaningful comparisons. However, 3-year combined expectancies are published separately on the Datastore for geographical areas that are local authority and above. If the GLA publish revised 2002-2010 population data for wards then these life expectancy figures will also be revised to reflect them. The ONS vital statistics mortality data breaks deaths into 10 year age bands. 5 year age band deaths were modelled using this data. Vital Statistics: Population and Health Reference Tables are available on the ONS website here. The tool for calculating life expectancy is available from Public Health England. The highest age band in the calculator is currently 85+. If the tool is updated with a higher upper age band (ie 90+), this data will be revised to reflect this change. Healthy life expectancy and disability-free life expectancy (1999-2003) at birth have been calculated for wards in England and Wales. These can be found on the ONS website. This data is also presented in the GLA ward profiles.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

This statistical release makes available the most recent monthly data on NHS-funded maternity services in England, using data submitted to the Maternity Services Data Set (MSDS). This is the latest report from the newest version of the data set, MSDS.v.2, which has been in place since April 2019. The new data set was a significant change which added support for key policy initiatives such as continuity of carer, as well as increased flexibility through the introduction of new clinical coding. This was a major change, so data quality and coverage initially reduced from the levels seen in earlier publications. MSDS.v.2 data completeness improved over time, and we are looking at ways of supporting further improvements. This publication also includes the National Maternity Dashboard. Recently, Statistical Process Control (SPC) charts were included in the National Maternity Dashboard. These can be accessed via the CQIM+ page in the dashboard. Data derived from SNOMED codes is used in some measures such as those for smoking at booking and delivery, and birth weight, and others will follow in later publications. SNOMED data is also included in some of the published Clinical Quality Improvement Metrics (CQIMs), where rules have been applied to ensure measure rates are calculated only where data quality is high enough. System suppliers are at different stages of development and delivery to trusts. In some cases, this has limited the aspects of data that can be submitted in the MSDS. To help Trusts understand to what extent they met the Clinical Negligence Scheme for Trusts (CNST) Maternity Incentive Scheme (MIS) Data Quality Criteria for Safety Action 2, we have been producing a CNST Scorecard Dashboard showing trust performance against this criteria. This month, this dashboard has been updated following the release of CNST Y6 criteria, and can be accessed via the link below. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. More information about experimental statistics can be found on the UK Statistics Authority website. The percentages presented in this report are based on rounded figures and therefore may not total to 100%.

Abstract

Objective: Preterm birth is the primary driver of neonatal mortality worldwide, but it is defined by gestational age (GA) which is challenging to accurately assess in low-resource settings. In a commitment to reducing preterm birth while reinforcing and strengthening facility, routine data sources, the East Africa Preterm Birth Initiative (PTBi-EA) chose eligibility criteria that combined GA and birth weight. This analysis evaluated the quality of the GA data as recorded in maternity registers in PTBi-EA study facilities and the validity of the PTBi-EA eligibility criteria.

Methods: We conducted a retrospective analysis of maternity register data from March – September 2016. GA data from 23 study facilities in Migori, Kenya and the Busoga Region of Uganda were evaluated for completeness (variable present), consistency (recorded versus calculated GA), and plausibility (falling within the 3^rd and 97^th birth weight percentiles for GA of the INTERGROWTH-21^st Newborn Birth Weight Standards). Preterm birth rates were calculated using: 1) recorded GA <37 weeks, 2) recorded GA <37 weeks, excluding implausible GAs, 3) birth weight <2500g, and 4) PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks.

Results: In both countries, GA was the least recorded variable in the maternity register (77.6%). Recorded and calculated GA (Kenya only) were consistent in 29.5% of births. Implausible GAs accounted for 11.7% of births. The four preterm birth rates were 1) 14.5%, 2) 10.6%, 3) 9.6%, 4) 13.4%.

Conclusions: Maternity register GA data presented quality concerns in PTBi-EA study sites. The PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks adjusted for these concerns by using both birth weight and GA, balancing issues of accuracy and completeness with practical applicability.

Life expectancy at birth and age 65 by sex and ward, London borough, region, 1999/03 - 2008/12.

The population data used is revised 2002-2010 ONS mid year estimates (MYE) - revised post 2011 Census. Revised population estimates by single year of age for wards can also be found on the ONS website for 2002-2010, 2011, 2012, and 2013. These figures are consistent with the published revised mid-2002 to mid-2010 local authority estimates.

Rolling 5-year combined life expectancies are used for wards to reduce the effects of the variability in number of deaths in each year. The same method is applied to higher geographies to enable meaningful comparisons. However, 3-year combined expectancies are published separately on the Datastore for geographical areas that are local authority and above.

If the GLA publish revised 2002-2010 population data for wards then these life expectancy figures will also be revised to reflect them.

The ONS vital statistics mortality data breaks deaths into 10 year age bands. 5 year age band deaths were modelled using this data.

Vital Statistics: Population and Health Reference Tables are available on the ONS website http://www.ons.gov.uk/ons/rel/vsob1/vital-statistics--population-and-health-reference-tables/index.html">here.

The tool for calculating life expectancy is available from Public Health England.
The highest age band in the calculator is currently 85+. If the tool is updated with a higher upper age band (ie 90+), this data will be revised to reflect this change.

Healthy life expectancy and disability-free life expectancy (1999-2003) at birth have been calculated for wards in England and Wales. These can be found on the ONS website.

This data is also presented in the GLA ward profiles.

The DSS Payment Demographic data set is made up of:

Selected DSS payment data by

• Geography: state/territory, electorate, postcode, LGA and SA2 (for 2015 onwards)

• Demographic: age, sex and Indigenous/non-Indigenous

• Duration on Payment (Working Age & Pensions)

• Duration on Income Support (Working Age, Carer payment & Disability Support Pension)

• Rate (Working Age & Pensions)

• Earnings (Working Age & Pensions)

• Age Pension assets data

• JobSeeker Payment and Youth Allowance (other) Principal Carers

• Activity Tested Recipients by Partial Capacity to Work (NSA,PPS & YAO)

• Exits within 3, 6 and 12 months (Newstart Allowance/JobSeeker Payment, Parenting Payment, Sickness Allowance & Youth Allowance)

• Disability Support Pension by medical condition

• Care Receiver by medical conditions

• Commonwealth Rent Assistance by Payment type and Income Unit type have been added from March 2017. For further information about Commonwealth Rent Assistance and Income Units see the Data Descriptions and Glossary included in the dataset.

From December 2022, the "DSS Expanded Benefit and Payment Recipient Demographics – quarterly data" publication has introduced expanded reporting populations for income support recipients. As a result, the reporting population for Jobseeker Payment and Special Benefit has changed to include recipients who are current but on zero rate of payment and those who are suspended from payment. The reporting population for ABSTUDY, Austudy, Parenting Payment and Youth Allowance has changed to include those who are suspended from payment. The expanded report will replace the standard report after June 2023.

Additional data for DSS Expanded Benefit and Payment Recipient Demographics – quarterly data includes:

• A new contents page to assist users locate the information within the spreadsheet

• Additional data for the ‘Suspended’ population in the ‘Payment by Rate’ tab to enable users to calculate the old reporting rules.

• Additional information on the Employment Earning by ‘Income Free Area’ tab.

From December 2022, Services Australia have implemented a change in the Centrelink payment system to recognise gender other than the sex assigned at birth or during infancy, or as a gender which is not exclusively male or female. To protect the privacy of individuals and comply with confidentialisation policy, persons identifying as ‘non-binary’ will initially be grouped with ‘females’ in the period immediately following implementation of this change. The Department will monitor the implications of this change and will publish the ‘non-binary’ gender category as soon as privacy and confidentialisation considerations allow.

Local Government Area has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2022 boundaries from June 2023.

Commonwealth Electorate Division has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.

SA2 has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.

From December 2021, the following are included in the report:

• selected payments by work capacity, by various demographic breakdowns

• rental type and homeownership

• Family Tax Benefit recipients and children by payment type

• Commonwealth Rent Assistance by proportion eligible for the maximum rate

• an age breakdown for Age Pension recipients

For further information, please see the Glossary.

From June 2021, data on the Paid Parental Leave Scheme is included yearly in June releases. This includes both Parental Leave Pay and Dad and Partner Pay, across multiple breakdowns. Please see Glossary for further information.

From March 2017 the DSS demographic dataset will include top 25 countries of birth. For further information see the glossary.

From March 2016 machine readable files containing the three geographic breakdowns have also been published for use in National Map, links to these datasets are below:

• Statistical Area 2 - SA2

• Commonwealth Electoral Division - CED

• Local Government Area - LGA

Pre June 2014 Quarter Data contains:

Selected DSS payment data by

• Geography: state/territory; electorate; postcode and LGA

• Demographic: age, sex and Indigenous/non-Indigenous

Note: JobSeeker Payment replaced Newstart Allowance and other working age payments from 20 March 2020, for further details see: https://www.dss.gov.au/benefits-payments/jobseeker-payment

For data on DSS payment demographics as at June 2013 or earlier, the department has published data which was produced annually. Data is provided by payment type containing timeseries’, state, gender, age range, and various other demographics. Links to these publications are below:

• Statistical Paper series

Concession card data in the March and June 2020 quarters have been re-stated to address an over-count in reported cardholder numbers.

28/06/2024 – The March 2024 and December 2023 reports were republished with updated data in the ‘Carer Receivers by Med Condition’ section, updates are exclusive to the ‘Care Receivers of Carer Payment recipients’ table, under ‘Intellectual / Learning’ and ‘Circulatory System’ conditions only.

The current extinction and climate change crises pressure us to predict population dynamics with ever-greater accuracy. Although predictions rest on the well-advanced theory of age-structured populations, two key issues remain poorly-explored. Specifically, how the age-dependency in demographic rates and the year-to-year interactions between survival and fecundity affect stochastic population growth rates. We use inference, simulations, and mathematical derivations to explore how environmental perturbations determine population growth rates for populations with different age-specific demographic rates and when ages are reduced to stages. We find that stage- vs. age-based models can produce markedly divergent stochastic population growth rates. The differences are most pronounced when there are survival-fecundity-trade-offs, which reduce the variance in the population growth rate. Finally, the expected value and variance of the stochastic growth rates of populations with different age-specific demographic rates can diverge to the extent that, while some populations may thrive, others will inevitably go extinct.