Australian Institute of Health and Welfare (2022) Australia's mothers and babies, AIHW, Australian Government, accessed 25 September 2022.
Australian Institute of Health and Welfare. (2022). Australia's mothers and babies. Retrieved from https://www.aihw.gov.au/reports/mothers-babies/australias-mothers-babies
Australia's mothers and babies. Australian Institute of Health and Welfare, 22 July 2022, https://www.aihw.gov.au/reports/mothers-babies/australias-mothers-babies
Australian Institute of Health and Welfare. Australia's mothers and babies [Internet]. Canberra: Australian Institute of Health and Welfare, 2022 [cited 2022 Sep. 25]. Available from: https://www.aihw.gov.au/reports/mothers-babies/australias-mothers-babies
Australian Institute of Health and Welfare (AIHW) 2022, Australia's mothers and babies, viewed 25 September 2022, https://www.aihw.gov.au/reports/mothers-babies/australias-mothers-babies
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An age-specific rate is defined as the number of events for a specified age group over a specified period (for example, a year) divided by the total population exposed to the event in that age group.
Age-standardised rates enable comparisons to be made between populations that have different age structures. Direct standardisation, in which the age-specific rates are multiplied by a constant population, was used in this report. This effectively removes the influence of the age structure on the summary rate. The report states where age-standardised rates have been used.
All age-standardised rates in this report have used the June 2001 Australian female estimated resident population aged 15–44 years as the standard population. For more information refer to the Metadata Online Registry for age-standardised rates.
The incidence of maternal death is expressed as the maternal mortality ratio (MMR), which is calculated using direct and indirect deaths combined, and excludes coincidental deaths.
Although the most appropriate denominator for estimating maternal mortality would be the number of women at risk (the number of pregnant or recently pregnant women), this number is not available in Australia because the number of pregnancies ending before 20 weeks’ gestation is unknown. In Australia, accurate population data are available for the number of women who gave birth to at least 1 baby (either a live birth or a stillbirth) of 20 weeks’ completed gestation or more or birthweight of 400 grams or more and are held in the AIHW’s National Perinatal Data Collection; this is the denominator number used when calculating the MMR in this report.
MMR = (Number of direct and indirect maternal deaths(a)) / (Number of women who gave birth(a)) x 100,000
(a) For a defined place and time.
Calculation of stillbirth rate
The stillbirth rate is calculated as the proportion of births in a specified population which are stillbirths. This proportion is expressed in relation to all births.
Stillbirth rate = Number of stillbirths x 1,000 / Total number of births
Calculation of neonatal mortality rate
The neonatal mortality rate is calculated as the proportion of births in a specified population which are live born and subsequently die within 28 days of birth (neonatal deaths). This proportion is expressed in relation to all live births.
Neonatal mortality rate = Number of neonatal deaths x 1,000 / Number of live births
Calculation of perinatal mortality rate
The perinatal mortality rate is calculated as the proportion of births in a specified population which are stillbirths or neonatal deaths (perinatal deaths). This proportion is expressed in relation to all births.
Perinatal mortality rate = Number of perinatal deaths x 1,000 / Total number of births
A crude rate is defined as the number of events over a specified period (for example, a year) divided by the total population exposed to the event.
Rate ratios presented in the National Perinatal Data Collection annual update data tables are calculated by dividing the proportion of the study population (for example, Indigenous Australians) with a particular characteristic by the proportion of the standard population (for example, non-Indigenous Australians) with the same characteristic.
A rate ratio of 1 indicates that the prevalence of the characteristic is the same in the study and standard populations. Rate ratios of greater than 1 indicate higher prevalence in the study population; rate ratios of less than 1 indicate higher prevalence in the standard population.
Geographic data are based on the usual residence of the mother. In 2018, the usual residence of the mother is based on Statistical Area Level 2 (SA2) of the Australian Bureau of Statistics Australian Statistical Geography Standard Edition 2016 for all states and territories.
Primary Health Networks (PHNs) have been established by the Department of Health to increase the efficiency and effectiveness of medical services and improve the coordination of care for patients.
Perinatal data at Statistical Area Level 2 (SA2) were linked to 2017 PHNs using Australian Bureau of Statistics correspondence files.
The relevant proportion for each PHN was then calculated, and categories were developed based on the median, interquartile ranges and 10th and 90th percentiles for the proportions at the PHN level. The categories were then adjusted to account for natural breaks in the distribution of the data and for easier interpretation (for example, a range with a maximum of 52.1% of mothers receiving antenatal care in the first trimester would be revised to a maximum of 50%). PHNs were allocated to categories based on unrounded proportions.
This report uses the Australian Statistical Geography Standard Remoteness Structure, which groups geographic areas into six classes of Remoteness Area based on their relative access to services using the Accessibility/Remoteness Index of Australia.
The six classes are: Major cities, Inner regional, Outer regional, Remote, Very remote and Migratory, see the Australian Statistical Geography Standard (ASGS): Volume 5—Remoteness Structure, July 2016 (ABS 2018a).
The Socio-Economic Indexes for Areas (SEIFA) are measures of socioeconomic status (SES) that summarise a range of socioeconomic variables associated with disadvantage. Socioeconomic disadvantage is typically associated with low income, high unemployment and low levels of education.
The SEIFA index used in this report is the 2016 SEIFA Index of Relative Socioeconomic Disadvantage (IRSD) developed by the Australian Bureau of Statistics for use at Statistical Area Level 2.
Since the IRSD summarises only variables that indicate disadvantage, a low score indicates that an area has many low-income families, many people with little training and many people working in unskilled occupations; hence, this area may be considered disadvantaged relative to other areas. A high score implies that the area has few families with low incomes and few people with little or no training and working in unskilled occupations. These areas with high index scores may be considered less disadvantaged relative to other areas. It is important to understand that a high score reflects a relative lack of disadvantage rather than advantage and that the IRSD relates to the average disadvantage of all people living in a geographic area. It cannot be presumed to apply to all individuals living within the area.
Population-based Australian cut-offs for SEIFA quintiles have been used in this report. This method ranks the SEIFA scores for a particular geography (for example, Statistical Area Level 2) from lowest to highest, and the geographical areas are divided into 5 groups, such that approximately 20% of the population are in each group.
The most disadvantaged group is referred to as the Lowest socioeconomic status (SES) areas and the least disadvantaged group is referred to as the Highest SES areas.
See the Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2016 (ABS 2018b) for further information on SEIFA.
Perinatal data at Statistical Area Level 2 (SA2) were linked to Statistical Area Level 3 (SA3) using Australian Bureau of Statistics correspondence files.
The average annual change (slope estimate) is calculated using the ordinary least squares method of linear regression. The method calculates a straight line that best fits the data
(the fitted linear regression line) and returns an equation that best describes the line.
The form of the straight-line equation is:
Y = a + bX
b is the average annual change or ‘slope’ over the period
X is the independent or predictor variable (in the case of time trend analysis, this is the year)
a is the y-intercept
Y is the predicted value of the rate based on the fitted linear regression line.
Per cent change is determined by multiplying the average annual change (slope estimate) over the period by the number of data points less 1. This is then divided by the Y value calculated for the first year in the series (based on the fitted linear regression line) and multiplied by 100.
For trend analyses, the 95% confidence intervals (CIs) for the standard error of the slope estimate (average annual change) were used to determine whether the apparent increases or decreases in the data are statistically significant at the p <0.05 level. The formula used to calculate the CIs for the standard error of the slope estimate is:
95% CI(x) = x ± 1.96 x SE(x)
x is the average annual change (slope estimate).
If the upper and lower 95% CIs do not include zero, it can be concluded that there is statistical evidence of an increasing or decreasing trend in the data over the study period.
Significant changes are denoted with a ‘*’ against the per cent change statistics included in relevant tables.
Linear regression has been used to determine changes in the observed rates over specified time periods. Regression modelling analyses the series of rates jointly rather than individually, thus accounting for volatility in observed rates over time and resulting in narrower confidence intervals around the set of predicted values than if the confidence limits were calculated around the rates separately.
To maintain privacy and confidentiality of individuals, cells in the data tables are suppressed if there is a risk of disclosure of an attribute of an individual that was not already known. A cell in a table is considered identifiable if, as well as being able to identify the entity, other details are also revealed. It is AIHW policy that these cells need to be confidentialised, unless the attribute that would be disclosed is deemed to be non-sensitive in the context of the data being published.
Numbers of less than 5 have not been published (n.p.), in line with guidelines for protecting the privacy of individuals. Exceptions to this are small numbers in ‘Other’ and ‘Not stated’ categories. Consequential suppression of numbers has also been applied where required to prevent back-calculation of small numbers. However, all suppressed numbers have been included in the totals.
Per cents based on denominators of less than 100 have also been suppressed (n.p.) for reliability reasons.
Birthweight percentiles were calculated from data on all liveborn singleton babies born in Australia between 2004 and 2013 with a gestational age of 20–44 weeks.
Records with indeterminate sex were excluded from analysis. Records with missing or not stated data for sex, birthweight or gestational age were also excluded. Birthweight outliers were calculated and excluded using a method based on Tukey’s box and whisker plots.
Gestational age is reported in completed weeks of gestation, calculated from the first day of the last menstrual period (LMP) or estimated by prenatal and/or postnatal assessment if the LMP date was missing. Birthweight is reported to the nearest 5 grams.
Small for gestational age is defined as babies with birthweight below the 10th percentile according to the national birthweight percentiles for 2004 to 2013.
For more information on data used to assign percentile see National Perinatal Data Collection annual update data table 6.1.
The Robson 10 group classification system (Robson classification) categorises women who gave birth into 10 mutually exclusive groups (Table 3). In addition, groups 2 and 4 can be further broken down into subgroups. These subgroups are used to differentiate between women who were induced and who had a caesarean section before labour onset.
The Robson classification groups and subgroups were calculated from data on all women who gave birth in Australia for 2020. Data elements used for calculation of the groups and subgroups were parity, previous caesarean sections, onset of labour, birth plurality, gestational age, presentation at birth and method of birth.
Records for whom one or more of the following variables were not stated: parity, previous caesarean sections, onset of labour, birth plurality, gestational age and presentation at birth; were grouped into the ‘Not applicable’ category. The denominator of ‘Number of women who gave birth’ includes women with a ‘not stated’ method of birth.
The figure describes the process of categorising all women who gave birth into the 10 groups and the additional subgroups.
ABS (Australian Bureau of Statistics) 2018a. Australian Statistical Geography Standard (ASGS): Volume 5—Remoteness Structure, July 2016. ABS cat. no. 1270.0.55.005. Canberra: ABS.
ABS 2018b. Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2016. ABS cat. no. 2033.0.55.001. Canberra: ABS.
AIHW (Australian Institute of Health and Welfare) 2019. Australia’s mothers and babies 2016—in brief. Perinatal statistics series no. 34. Cat. no. PER 97. Canberra: AIHW.
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