Australia Probability of Dying at Age 5-9 Years: per 1000 data was reported at 0.400 Ratio in 2019. This stayed constant from the previous number of 0.400 Ratio for 2018. Australia Probability of Dying at Age 5-9 Years: per 1000 data is updated yearly, averaging 0.500 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 0.900 Ratio in 1991 and a record low of 0.400 Ratio in 2019. Australia Probability of Dying at Age 5-9 Years: per 1000 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: Health Statistics. Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Australia Probability of Dying at Age 15-19 Years: per 1000 data was reported at 1.500 Ratio in 2019. This stayed constant from the previous number of 1.500 Ratio for 2018. Australia Probability of Dying at Age 15-19 Years: per 1000 data is updated yearly, averaging 2.000 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 3.300 Ratio in 1990 and a record low of 1.500 Ratio in 2019. Australia Probability of Dying at Age 15-19 Years: per 1000 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: Health Statistics. Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Length of residence and the odds of stillbirth in migrants from specific ethnic backgrounds compared to the Australian-born population.

Absolute numbers, rates, and unadjusted odds ratios of stillbirth for migrants, stratified by acculturative factors, compared with the Australian-born population.

Australia Probability of Dying at Age 20-24 Years: per 1000 data was reported at 2.000 Ratio in 2019. This stayed constant from the previous number of 2.000 Ratio for 2018. Australia Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 2.850 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 4.500 Ratio in 1990 and a record low of 2.000 Ratio in 2019. Australia Probability of Dying at Age 20-24 Years: per 1000 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: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Abstract Background The burden of food insecurity remains a public health challenge even in high income countries, such as Australia, and especially among culturally and linguistically diverse (CALD) communities. While research has been undertaken among several migrant communities in Australia, there is a knowledge gap about food security within some ethnic minorities such as migrants from the Middle East and North Africa (MENA). This study aims to determine the prevalence and correlates of food insecurity among Libyan migrant families in Australia. Methods A cross-sectional design utilising an online survey and convenience sampling was used to recruit 271 participants, each representing a family, who had migrated from Libya to Australia. Food security was measured using the single-item measure taken from the Australian Health Survey (AHS) and the 18-item measure from the United States Department of Agriculture Household Food Security Survey Module (USDA HFSSM). Multivariable logistic regression was used to identify independent correlates associated with food insecurity. Results Using the single-item measure, the prevalence of food insecurity was 13.7% whereas when the 18-item questionnaire was used, more than three out of five families (72.3%) reported being food insecure. In the multivariable logistic regression analysis for the single-item measure, those living alone or with others reported higher odds of being food insecure (AOR = 2.55, 95% CI 1.05, 6.21) compared to those living with their spouse, whereas higher annual income (≥AUD 40,000) was associated with lower odds of food insecurity (AOR = 0.30, 95% CI 0.11, 0.84). Higher annual income was also associated with lower odds of food insecurity (AOR = 0.49, 95% CI 0.25, 0.94) on the 18-item measure. On both single and 18-item measures, larger family size (AOR = 1.27, 95% CI 1.07, 1.49 and AOR = 1.21, 95% CI 1.01, 1.47 respectively) was associated with increased odds of food insecurity. Conclusion This study provides evidence that food insecurity amongst Libyan migrants in Australia is a widespread problem and is associated with a number of sociodemographic and socio-economic factors. The findings of this study serve to contribute to the depth and breadth of food security research among vulnerable communities, in this instance Libyan migrant families.

BackgroundThe risk of preterm birth (PTB) and low birthweight (LBW) may change over time the longer that immigrants reside in their adopted countries. We aimed to study the influence of acculturation on the risk of these outcomes in Australia.MethodsA retrospective cohort study using linked health data for all non-Indigenous births from 2005–2013 in Western Australia was undertaken. Acculturation was assessed through age on arrival, length of residence, interpreter use and having an Australian-born partner. Adjusted odds ratios (aOR) for term-LBW and PTB (all, spontaneous, medically-indicated) were calculated using multivariable logistic regression in migrants from six ethnicities (white, Asian, Indian, African, Māori, and ‘other’) for different levels of acculturation, compared to the Australian-born population as the reference.ResultsThe least acculturated migrant women, those from non-white non-Māori ethnic backgrounds who immigrated at age ≥18 years, had an overseas-born partner, lived in Australia for < 5 years and used a paid interpreter, had 58% (aOR 1.58, 95% CI 1.15–2.18) higher the risk of term-LBW and 40% (aOR 0.60, 95% CI 0.45–0.80) lower risk of spontaneous PTB compared to the Australian-born women. The most acculturated migrant women, those from non-white non-Māori ethnic backgrounds who immigrated at age 10 years and did not use an interpreter, had similar risk of term-LBW but 43% (aOR 1.43, 95% CI 1.14–1.78) higher risk of spontaneous PTB than the Australian-born women.ConclusionAcculturation is an important factor to consider when providing antenatal care to prevent PTB and LBW in migrants. Acculturation may reduce the risk of term-LBW but, conversely, may increase the risk of spontaneous PTB in migrant women residing in Western Australia. However, the effect may vary by ethnicity and warrants further investigation to fully understand the processes involved.

According to a survey conducted by Ipsos on predictions for global issues in 2020, 62 percent of Australian respondents believed it unlikely that self-driving cars will become a usual sight on the streets of their town or city in 2020. This was slightly more than the global average.

The Department of Water and Environmental Regulation produces floodplain mapping of rivers and major watercourses and provides floodplain development advice to ensure that proposed floodplain development has adequate flood protection and does not impact on the existing flood regime of the area. This advice is related to major river flooding only. Other issues, such as stormwater drainage and environmental and ecological considerations are not addressed. Both the floodway and flood fringe make up the 1 in 100 (1%) annual exceedance probability (AEP) floodplain, however there are areas where the floodplain exists, but the floodway and flood fringe does not. In such situations, a different floodplain management strategy applies (for example, the Swan River between the Narrows and Canning Bridges). This dataset contains four lines - Floodway limit, Extent of 1 in 100 (1%) AEP flooding, Extent of floodway & Extent of study. The Floodway limit, Extent of floodway and Extent of 1 in 100 (1%) AEP Flooding and Extent of study line types are boundaries which appropriately overlay over the "FPM 1 in 100 (1%) AEP Floodway and Flood Fringe Areas" (polygon) dataset. Note: To see the full scope of the floodplain mapping, 12 dataset layers are required to be loaded in the following order: FLOODPLAIN DATASET LAYERS: FPM Flood Level Points (m AHD) FPM Flood Level Contours (m AHD) FPM 1 in 100 (1%) AEP Floodway and Flood Fringe Line FPM Extent of Flooding FPM Levee Banks FPM Location of Cross Sections FPM 1 in 100 (1%) AEP Floodplain Development Control Area FPM Map Index FPM Bridges FPM Special Development Condition Area FPM 1 in 100 (1%) AEP Floodway and Flood Fringe Area FPM Floodplain Area The dataset covers the following areas: Avon River – Toodyay, Northam, York and Beverley Townsites. Blackwood River – Augusta, Bridgetown, Nannup and Boyup Brook Townsites. Brunswick River - Greater Bunbury Coblinine River & Dorderyemunning Creek - Wagin Townsite. Collie River - Collie Townsite. Chapman River – Geraldton Townsite. Denmark River – Denmark Townsite. Gascoyne River - Carnarvon Townsite and the Lower Gascoyne. Gribble Creek - Kalgoorlie Townsite. Harding River - Roebourne Townsite. Irwin River - Dongara Townsite. Lower Collie River - Greater Bunbury. Preston River - Donnybrook Townsite. Serpentine River, Peel, Birrega & Oaklands Drains, Murray River and the Peel Inlet / Harvey Estuary - Peel Inlet / Harvey Estuary to Pinjarra and south to the Darling Scarp (Murray River) and Peel Inlet / Harvey Estuary to Wellard (Peel Main Drain), east to South Western Highway (Serpentine River) and north to Wungong Brook (Birrega Drain). Swan River, Canning River and Tributaries: Perth - Fremantle to Walyunga National Park (Swan River) and Canning Bridge to Brookton Highway (Canning River). Tributaries include Bennett Brook, Blackadder Creek, Ellen Brook, Helena River, Henley Brook, Jane Brook, St Leonards Creek, Susannah Brook (Swan River) and Southern River/ Wungong Brook (Canning River). Toby Inlet – Quindalup Townsite. Vasse-Wonnerup Estuaries, Broadwater and New River - Busselton Townsite. For further information on flooding and floodplain management in Western Australia, please refer to our Water Facts publications: Flooding in Western Australia (Water facts 13) and Floodplain Management (Water facts 14). This information is available at the following addresses: https://www.wa.gov.au/system/files/2022-11/Water-facts-13-Flooding-in-Western-Australia.pdf https://www.wa.gov.au/system/files/2022-11/Water-facts-14-Floodplain-management.pdf Glossary: Annual exceedance probability (AEP) - the likelihood of occurrence of a flood of a given size or larger in any one year; usually expressed as a percentage. 1 in 100 AEP flood - this means that there is a 1 in 100 (or 1%) chance of a flow of this size or larger occurring in any one year. This flood has a 50% chance of being experienced at least once in a person's lifetime. The 1 in 100 AEP flood has been generally adopted in Australia and overseas as the basis for floodplain management planning. Flood fringe - the area of the floodplain, outside of the floodway where development could be permitted provided it is compatible with flood hazard and building conditions provide an adequate level of flood protection. These areas are generally covered by still or very slowly moving waters during a 1 in 100 (1%) AEP flood. Floodplain - the portion of a river valley next to the river channel which is covered with water when the river overflows its banks during major river flows. The term also applies to land adjacent to estuaries which is subject to flooding. Floodway - the river channel and a portion of the floodplain where a significant flow or storage of water occurs during floods. If the floodway is even partially blocked, then the natural flooding regime of the area may be detrimentally impacted with flood levels being raised and affecting areas which may not have been previously affected. Development in floodways is to be avoided wherever possible. Australian Height Datum (AHD) - is a geodetic datum for altitude measurement in Australia. It was adopted in 1971 by the National Mapping Council as the datum to which all vertical control for mapping is to be referred. The datum is based on the mean sea level (1966-1968) being assigned the value 0.000m on the Australian Height Datum (AHD) at 30 tide gauges around the coast of the Australian continent.

The Department of Water and Environmental Regulation produces floodplain mapping of rivers and major watercourses and provides floodplain development advice to ensure that proposed floodplain development has adequate flood protection and does not impact on the existing flood regime of the area. This advice is related to major river flooding only and other issues, such as stormwater drainage and environment and ecological considerations are not addressed. This dataset comprises bridge polygons that are indicated as being either ‘Wet’ (overtopped), ‘Dry’ (not overtopped) or ‘No Data’ to describe their status during a particular flood event. The status indicates whether the bridge deck itself is overtopped and is not related to the road levels approaching the bridge. They are listed as such in the following attribute tables: STATUS_10 (1 in 10 (10%) AEP flood event) STATUS_20 (1 in 20 (5%) AEP flood event) STATUS_25 (1 in 25 (4%) AEP flood event) STATUS_50 (1 in 50 (2%) AEP flood event) STATUS_100 (1 in 100 (1%) AEP flood event) STATUS_DFE (Designated flood event) STATUS_200 (1 in 200 (0.5%) AEP flood event) STATUS_500 (1 in 500 (0.2%) AEP flood event) STATUS_MCC (Maximum channel capacity) STATUS_PMF (Probable maximum flood event) Note: To see the full scope of the floodplain mapping, 12 dataset layers are required to be loaded in the following order: FLOODPLAIN DATASET LAYERS: FPM Flood Level Points (m AHD) FPM Flood Level Contours (m AHD) FPM 1 in 100 (1%) AEP Floodway and Flood Fringe Line FPM Extent of Flooding FPM Levee Banks FPM Location of Cross Sections FPM 1 in 100 (1%) AEP Floodplain Development Control Area FPM Map Index FPM Bridges FPM Special Development Condition Area FPM 1 in 100 (1%) AEP Floodway and Flood Fringe Area FPM Floodplain Area The dataset covers the following areas: Avon River - Toodyay to Beverley and Brookton Townsites. Bandy Creek - Esperance Townsite. Blackwood River – Augusta, Bridgetown, Nannup and Boyup Brook Townsites. Bow River - Bow Bridge Townsite. Capel River - Capel Townsite. Cemetery Creek - Lake Grace Townsite. Chapman River – Geraldton Townsite. Cohn Creek - Merredin Townsite. Collie River - Collie Townsite. Corrigin Townsite. Denmark River – Denmark Townsite. Fitzroy River – Fitzroy Crossing Townsite. Five Mile Brook - Bunbury Townsite. Gascoyne River - Carnarvon Townsite and the Lower Gascoyne. Gordon River – Tambellup Townsite. Greenough River – Indian Ocean to Walkaway. Gribble Creek - Kalgoorlie Townsite. Harding River - Roebourne Townsite. Irwin River - Dongara Townsite. Katanning Townsite. Lower Collie River, Brunswick River and Wellesley River - Greater Bunbury. Margaret River – Margaret River Townsite. Preston River - Boyanup and Donnybrook Townsites. Serpentine River, Peel, Birrega & Oaklands Drains, Murray River and the Peel Inlet / Harvey Estuary - Peel Inlet / Harvey Estuary to Pinjarra and south to the Darling Scarp (Murray River) and Peel Inlet / Harvey Estuary to Wellard (Peel Main Drain), east to South Western Highway (Serpentine River) and north to Wungong Brook (Birrega Drain). Swan River, Canning River and Tributaries: Perth - Fremantle to Walyunga National Park (Swan River) and Canning Bridge to Brookton Highway (Canning River). Tributaries include Bennett Brook, Blackadder Creek, Ellen Brook, Helena River, Henley Brook, Jane Brook, St Leonards Creek, Susannah Brook (Swan River) and Southern River/ Wungong Brook (Canning River). Toby Inlet – Quindalup Townsite. Turkey Creek - Warmun Aboriginal Community. Vasse-Wonnerup Estuaries, Broadwater and New River, Abba River, Buayanup Drain, Ludlow River, Sabina River, Vasse River and Vasse Diversion Drain - Busselton Townsite. Williams River and Tributaries - Williams Townsite. Willyung Creek - Albany Townsite. Yakamia Creek – Albany Townsite. For further information on flooding and floodplain management in Western Australia please refer to our Water Facts publications: Flooding in Western Australia (Water facts 13) and Floodplain Management (Water facts 14). This information is available at the following addresses: https://www.wa.gov.au/system/files/2022-11/Water-facts-14-Floodplain-management.pdf https://www.wa.gov.au/system/files/2022-11/Water-facts-14-Floodplain-management.pdf Glossary: Annual exceedance probability (AEP) - the likelihood of occurrence of a flood of a given size or larger in any one year; usually expressed as a percentage. 1 in 100 AEP flood - this means that there is a 1 in 100 (or 1%) chance of a flow of this size or larger occurring in any one year. This flood has a 50% chance of being experienced at least once in a person's lifetime. The 1 in 100 AEP flood has been generally adopted in Australia and overseas as the basis for floodplain management planning. Floodplain - the portion of a river valley next to the river channel which is covered with water when the river overflows its banks during major river flows. The term also applies to land adjacent to estuaries which is subject to flooding. Designated flood event (DFE) - used for planning purposes and is generally the 1 in 100 (1%) AEP "designed" flood event. However, a designated flood event could be based on an "actual" flood event (e.g. Moora 1999 flood event) or an alternative scenario (i.e. Yakamia Creek - 1 in 100 (1%) AEP flood event plus sea level rise). Maximum channel capacity (MCC) - the maximum flow that a waterway can contain before breaking out across the floodplain during a flood event. Probable maximum flood (PMF) - the largest flood that could conceivably occur at a particular location, resulting from probable maximum precipitation. The PMF defines the extent of flood-prone land. Generally, it is not physically or financially possible to provide general protection against this event. Australian Height Datum (AHD) - is a geodetic datum for altitude measurement in Australia. It was adopted in 1971 by the National Mapping Council as the datum to which all vertical control for mapping is to be referred. The datum is based on the mean sea level (1966-1968) being assigned the value 0.000m on the Australian Height Datum (AHD) at 30 tide gauges around the coast of the Australian continent.

Characteristics of the population of the study.

Abstract

Previous surveys on labor migration from Pacific Island countries are often cross-sectional, not readily available, and focusing on one migration scheme, country, or issue and hence incompatible. Such limitation of existing data restricts analysis of a range of policy-relevant issues that present themselves over the migrants' life cycle such as those on migration pathways, long-term changes in household livelihood, and trajectory of migrants’ labor market outcomes, despite the significant impacts of labor migration on the economy of the Pacific Island countries. To address these shortfalls in the Pacific migration data landscape, the PLMS is designed to be longitudinal, spanning multiple labor sending and receiving countries and collecting omnibus information on both migrants, their households and non-migrant households. The survey allows for disaggregation and reliable comparative analysis both within and across countries and labor mobility schemes. This open-access and high-quality data will facilitate more research about the Pacific migration, help inform and improve Pacific migration policy deliberations, and engender broader positive change in the Pacific data ecosystem.

Geographic coverage

Tonga: Tongatapu, ‘Eua, Vava’u, Ha’apai, Ongo Niua. Vanuatu: Malampa, Penama, Sanma, Shefa, Tafea, Torba. Kiribati: Abaiang, Abemama, Aranuka, Arorae, Banaba, Beru, Butaritari, Kiritimati, Maiana, Makin, Marakei, Nikunau, Nonouti, North Tabiteuea, North Tarawa, Onotoa, South Tabiteuea, South Tarawa, Tabuaeran, Tamana, Teraina.

Analysis unit

• Households in Kiribati, Tonga, and Vanuatu.
• Temporary migrant workers from Kiribati, Tonga and Vanuatu who participated in the Pacific Australia Labour Mobility scheme in Australia and the Recognised Seasonal Employers scheme in New Zealand

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling frame: The PLMS sample was designed based on a Total Survey Error framework, seeking to minimize errors and bias at every stage of the process throughout preparation and implementation.

The worker sample frame is an extensive list of approximately 11,600 migrant workers from Kiribati, Tonga and Vanuatu who had participated in the RSE and PALM schemes. Due to the different modes of interviews, sampling strategies for the face-to-face segment of the household survey in Tonga was different from the rest of the surveys implemented via phone interviews. The face-to-face segment of the household survey selected households using Probability Proportional to Size sampling based on the latest population census listing and our worker sample frame, with technical inputs from the Tonga Statistics Department. The phone-based segment of the household survey used a combination of Probability Proportional to Size sampling based on the existing sample frame and random digit dialing. The design of the sample benefited from technical inputs from the Tonga Statistics Departments and the Vanuatu National Statistics Office, as well as World Bank staff from Kiribati.

As participation in the survey is voluntary, a worker might agree to participate while their household did not, and vice versa. Because of this, the survey did not achieve a complete one-to-one match between interviewed workers and sending households. Of all interviewed respondents, 418 workers in the worker survey are linked to their households in the household survey. However, after removing incomplete interviews, 341 worker-household pairs remain. They are matched by either pre-assigned serial ID numbers or contact details collected in the household and worker surveys during the post-fieldwork data cleaning process.

Sampling deviation

The survey was originally planned to be conducted face-to-face and was so for most of the collection of household data in Tonga. However, due to COVID-19, it was switched to phone-based mode and the survey instruments were adjusted accordingly to better suit the phone-based data collection while ensuring data quality. In particular, the household questionnaire was shortened, and sampling strategy changed to a combination of Probability Proportional to Size sampling based on the existing household listing and random digit dialing.

Compared to in-person data collection, the usual caveats of potential biases in phone-based survey related to disproportional phone ownership and connectivity apply here. The random digit dialing approach provides data representative of the phone-owning population. Yet due to lack of information, it is difficult to judge whether sending households in Kiribati, Tonga, and Vanuatu are more or less likely to own a phone and/or respond positively to survey request than non-sending households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• The questionnaires were jointly designed in English by the World Bank and researchers at the Development Policy Centre, Australian National University. They were translated into Bislama, Gilbertese and Tongan, scripted into CAPI/CATI programs, tested and piloted before being finalized. The design of the questionnaires and the samples benefited from technical inputs from the Tonga Statistics Departments, Pacific consultants, and academic experts specialized in Pacific labor mobility and remittances.
• Enumerators are native speakers from the labor-sending countries covered in the survey and were trained to elicit information asked in the questionnaire in local languages.
• The phone-based household questionnaire is moderately shorter than the in-person version.

Cleaning operations

The published data have been cleaned and anonymized. All incomplete interview records have been removed from the final datasets. The anonymization process followed the theory of Statistical Disclosure Control for microdata, aiming to minimize re-identification risk, i.e. the risk that the identity of an individual (or a household) described by a specific record could be determined with a high level of confidence. The anonymization process employs the k-anonymity method to calculate the re-identification risk. Risk measurement, anonymization and utility measurement for the PLMS were done using sdcMicro, an add-on package for the statistical software R for Statistical Disclosure Control (SDC) of microdata.

Since the household questionnaire was shortened when the survey switched from face-to-face to phone-based data collection, there face-to-face datasets and phone-based datasets are not identical, but they are consistent and can be harmonized. The mapping guide enclosed in this publication provides a guide to data users to wish to harmonize them.

Household expenditure variables in the household dataset and individual wage variable in the household member dataset are in USD. Local currencies were converted into USD based on the following exchange rates: 1 Tongan Pa'anga= 0.42201412 USD; 1 Vanuatu Vatu= 0.0083905322 USD; 1 Kiribati dollar= 0.66942499 USD.

Response rate

Face-to-face segment of the PLMS household survey: not applicable. Phone-based segment of the PLMS household survey: 26%. The PLMS Worker survey: 31%

Odds ratios showing the likelihood of isolates being methicillin-resistant in Staphylococcus pseudintermedius isolates from dogs in Australia for different combinations of site of infection in the host and exposure of the host to prior antimicrobial treatment.

Australia Probability of Dying at Age 10-14 Years: per 1000 data was reported at 0.400 Ratio in 2019. This stayed constant from the previous number of 0.400 Ratio for 2018. Australia Probability of Dying at Age 10-14 Years: per 1000 data is updated yearly, averaging 0.600 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 1.000 Ratio in 1991 and a record low of 0.400 Ratio in 2019. Australia Probability of Dying at Age 10-14 Years: per 1000 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: Health Statistics. Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Banking arrangements among heterosexual couples in Australia, odds ratios.

In 2022, the proportion of people in Australia who were obese by age group peaked at **** percent for people 55 to 64 years old. Children had a much lower likelihood of being obese, but there is a clear increase in obesity rates with age. Recommended serves of food groups The Australian Dietary Guidelines recommend the number of serves from each of the five food groups; vegetables, fruit, grains, meat and protein, and dairy. Around half of Australian adults eat the recommended daily serves of fruit, however, the vast majority are not consuming the recommended * to * servings of vegetables a day. Furthermore, Australian’s appear to consume significantly less dairy than is recommended, with less than * estimated average serves per capita, compared to the recommended *** serves for adult men and over *** for adolescents and older women.  Diet related illness Chronic illnesses such as diabetes and heart disease have been linked to poor diet and obesity. Over *********** Australians are diabetic and type 2 diabetes accounts for around *** in **** men over the age of **. Affecting around **** percent of the population, heart disease is only slightly less prevalent than diabetes. While a poor diet is one significant risk factor for these illnesses, smoking, being overweight, and lack of exercise can also contribute to increasing the risk of developing a chronic disease.

In 2023, Australia's fertility rate reached its lowest ever figure, at fewer than 1.5 children born per women of childbearing age. In general, Australia’s fertility rate has been fairly consistent throughout the past four decades, fluctuating between 1.7 and two births per woman, however the recent drop in fertility may be a result of the Covid-19 pandemic - it remains to be seen what the full extent of the pandemic will be on demographic trends. Population aging in Australia Like most other developed nations, Australia has been experiencing population ageing, driven by declining fertility rate and increased longevity, with an average life expectancy at birth of 83 years in 2020. Amid the pandemic, Australia also witnessed a noticeable decrease in the number of births to approximately 294.4 thousand, the lowest value since 2011. “No kids attached” Childfree couples could become the norm in Australia, as couples living without children are expected to become Australia’s most common family type in a few years’ time. While many families may suffer from involuntary childlessness, other couples would opt for a childfree life for various reasons. Especially in times of COVID-19, couples might not want to risk having children with increasing job insecurity.