Chapter 5 Health of population groups

5.0 Introduction

While some health concerns span all age groups, others tend to emerge at specific life stages. This chapter focuses on some of the issues and challenges that can arise at different times during our lives, starting at the very beginning—mothers and babies.

Almost all pregnant women access antenatal care at some time during their pregnancies, and the vast majority of mothers give birth in hospital. Most of their babies are born at full term and are in the normal weight range.

The majority of primary school children are in the normal weight range and are fully immunised. However, many do not get enough exercise or eat the recommended serves of fruit and vegetables each day.

Many important health risk factors for later life either emerge or increase during adolescence and young adulthood, including smoking, risky drinking, illicit drug use, physical inactivity and poor nutrition. This is also a time when mental disorders may arise, particularly anxiety and depression.

Today there are nearly 487,000 people aged 85 and over in Australia, and this number is projected to more than double by 2036, to 1.0 million. The majority of older Australians consider themselves to be in good, very good, or excellent health. The most common health conditions reported by this age group—long-sightedness, deafness and arthritis—have moderate yet long-term effects on quality of life.

This chapter also examines the health of specific groups with higher rates of illness, health risk factors and death: people in lower socioeconomic groups, Aboriginal and Torres Strait Islanders, Australians living with disability, prisoners, and people living in rural and remote areas.

Despite improvements in Indigenous health in recent years (such as the decline in infant and child mortality and in mortality related to circulatory and kidney diseases), Indigenous Australians have a lower life expectancy, higher rates of chronic and preventable illnesses, and poorer self-reported health than non-Indigenous Australians.

Similarly, Australians living in rural and remote areas tend to have shorter lives and higher rates of disease and injury than their Major cities counterparts. They are also more likely to engage in health behaviours that can lead to adverse health outcomes, such as smoking, risky drinking and being insufficiently active.

5.1 Health across socioeconomic groups

Socioeconomic factors are important determinants of health and wellbeing in Australia. The higher a person's income, education or occupation level, the healthier they tend to be—a phenomenon often termed the 'social gradient of health. In general, people from lower socioeconomic groups are at greater risk of poor health, have higher rates of illness, disability and death, and live shorter lives than those from higher groups (Mackenbach 2015). In 2001–2007, for example, men and women aged 20 in the lowest socioeconomic group could expect to live 2.6 years less than those in the highest group (Clarke & Leigh 2011).

Socioeconomic position can be measured using a single characteristic, such as income, education, or occupation, or a composite measure such as the Index of Relative Socio-Economic Disadvantage (IRSD). This snapshot uses the IRSD, which is compiled by the Australian Bureau of Statistics after each Census of Population and Housing using area-based population attributes such as low income, low educational attainment, high unemployment and jobs in relatively unskilled occupations (see 'Chapter 4.1 Social determinants of health'). The IRSD reflects the overall or average level of socioeconomic disadvantage of the population of an area; it does not show how individuals living in the same area differ from each other in these socioeconomic factors. In this snapshot, people living in the lowest socioeconomic areas are also referred to as the 'lowest socioeconomic group', and those living in the highest socioeconomic areas as the 'highest socioeconomic group'.

People in the lowest socioeconomic group are compared with the highest group on selected health measures, including health risk factors, chronic diseases and causes of death. On almost all of these measures, people in the lowest socioeconomic group fared worse.

  • On average, those in the lowest socioeconomic group were far more likely to smoke daily. In 2013, 20% of those aged 14 and over in this group smoked daily, a rate 3 times that of people in the highest socioeconomic group (6.7%) (Table 5.1.1).
  • For lifetime risky drinking of alcohol, people in the lowest socioeconomic group had a lower rate. In 2013, these adults were less likely to exceed alcohol consumption guidelines than adults in the highest socioeconomic group (16.4% compared with 18.5% respectively).
  • On other health risk factors—inadequate fruit and vegetable consumption, dyslipidaemia (abnormal amounts of lipids such as cholesterol in the blood), and impaired fasting glucose levels—there were no significant differences between people in the lowest and highest socioeconomic groups.

Table 5.1.1: Inequalities in selected health risk factors

Risk factors Year Lowest
socio-economic
group
(%)
Highest
socio-economic
group
(%)
Rate ratio:
lowest / highest
socio-economic
group
Low birthweight 2013 7.5 5.6 1.3  
Daily smoking 2013 20 6.7 3.0  
Inactive or insufficiently active 2014–15 76 56 1.4  
Lifetime risky drinking 2013 16.4 18.5 0.9  
Overweight or obese 2014–15 66 58 1.1  
High blood pressure 2014–15 26 21 1.2  
Participation of women aged 20–69 in cervical screening 2012–13 52 64 0.8  

Sources: ABS 2015; AIHW 2014a, 2015a, 2015b.
 

  • The prevalence of some chronic diseases was substantially higher among adults in the lowest socioeconomic group. Diabetes, for example, was 2.6 times as high, and coronary heart disease and stroke 2.2 times as high, as for those in the highest socioeconomic group (Table 5.1.2).
  • Lung cancer incidence was 1.6 times as high in the lowest socioeconomic group as for the highest group in 2006–2009 (rates of 52 and 33 per 100,000 population respectively), reflecting the higher rates of smoking in the lowest socioeconomic group.
  • Adults from lowest income households were far more likely to rate their oral health status as 'fair' or 'poor', compared with adults from the highest income households (31% compared with 12%, respectively).

Table 5.1.2: Inequalities in selected chronic diseases

Chronic diseases Year Lowest
socio-economic
group
(%)
Highest
socio-economic
group
(%)
Rate ratio:
lowest / highest
socio-economic
group
Arthritis 2014–15 19.7 12.1 1.6  
Asthma 2014–15 12.8 9.8 1.3  
Back problems 2014–15 18.9 15.9 1.2  
Chronic kidney disease 2011–12 13.5 8.3 1.6  
Coronary heart disease 2011–12 5.0 2.3 2.2  
Diabetes 2014–15 8.2 3.1 2.6  
Lung cancer incidence 2006–2009 52 per 100,000 33 per 100,000 1.6  
Mental and behavioural problems 2014–15 21.5 15.0 1.4  
Oral health rated as fair or poor(a)   2010 31.2 12.2 2.6  
Stroke 2014–15 1.1 0.5 2.2  

(a) Classified by household income groups.

Sources: ABS 2015; AIHW 2013, 2014b, 2014c.
 

  • Mortality from all causes in the lowest socioeconomic group was 29% higher than in the highest socioeconomic group in 2009–2011 (639 and 495 per 100,000 population, respectively) (Table 5.1.3).
  • Lung cancer death rates were 1.6 times as high in the lowest socioeconomic group.
  • Rates of potentially avoidable deaths—premature deaths that could have been avoided in the presence of timely and effective health care—were 1.8 times as high in the lowest socioeconomic group, compared with the highest (194 compared with 105 per 100,000 population).
  • In terms of excess deaths and population impact, if all Australians had the same death rates as the 20% of Australians in the highest socioeconomic group, there would have been 54,214 fewer deaths in 2009–2011.

Table 5.1.3: Inequalities in leading causes of death

Causes of death Year Lowest
socio-economic
group
(per 100,000)
Highest
socio-economic
group
(per 100,000)
Rate ratio:
lowest / highest
socio-economic
group
Coronary heart disease 2009–2011 98 71 1.4  
Cerebrovascular diseases 2009–2011 46 42 1.1  
Dementia and Alzheimer disease 2009–2011 32 34 0.9  
Lung cancer 2009–2011 40 25 1.6  
Chronic obstructive pulmonary disease 2009–2011 27 16 1.7  
All causes 2009–2011 639 495 1.3  
Potentially avoidable deaths 2009–2011 194 105 1.8  

Source: AIHW 2014d.

What is missing from the picture?

Ongoing work is needed to monitor progress in closing health gaps between socioeconomic groups. Most health data collections in Australia do not include information to measure an individual's socioeconomic position. The use of an area-based measure such as the IRSD limits the extent of analysis regarding the relationship between socioeconomic position and health.

Statistical linkage of health and welfare data sets to provide added information on wealth, education, employment and other social determinants will assist in better understanding pathways through the health system and the relationships between risk factors, disease, service use and outcomes for all socioeconomic groups.

Where do I go for more information?

Many reports from the AIHW include analysis of health indicators based on socioeconomic position (for example, Mortality inequalities in Australia 2009–2011).

For more information about disadvantage and social inequalities, see the AIHW report Australia's welfare 2015.

References

ABS (Australian Bureau of Statistics) 2015. National Health Survey: first results, 2014–15. Cat. no. 4364.0.55.001. Canberra: ABS.

AIHW (Australian Institute of Health and Welfare): Harford JE & Islam S 2013. Adult oral health and dental visiting in Australia: results from the National Dental Telephone Interview Survey 2010. Dental statistics and research series no. 65. Cat. no. DEN 227. Canberra: AIHW.

AIHW 2014a. National Drug Strategy Household Survey detailed report 2013. Drug statistics series no. 28. Cat. no. PHE 183. Canberra: AIHW.

AIHW 2014b. Cardiovascular disease, diabetes and chronic kidney disease—Australian facts: prevalence and incidence. Cardiovascular, diabetes and chronic kidney disease series no. 2. Cat. no. CDK 2. Canberra: AIHW.

AIHW 2014c. Cancer in Australia: an overview 2014. Cancer series no. 90. Cat. no. CAN 88. Canberra: AIHW.

AIHW 2014d. Mortality inequalities in Australia 2009–2011. AIHW bulletin no. 124. Cat. no. AUS 184. Canberra: AIHW.

AIHW 2015a. Australia's mothers and babies 2013—in brief. Perinatal statistics series no. 31. Cat no. PER 72. Canberra: AIHW.

AIHW 2015b. BreastScreen Australia monitoring report 2012–2013. Cancer series no. 95. Cat. no. CAN 93. Canberra: AIHW.

Clarke P & Leigh A 2011. Death, dollars and degrees: socioeconomic status and longevity in Australia. Economic Papers 30(3):348–55.

Mackenbach JP 2015. Socioeconomic inequalities in health in high-income countries: the facts and the options. In: Detels R, Gulliford M, Karim QA & Tan CC (eds). Oxford textbook of global public health. Vol. 1. 6th edn. Oxford: Oxford University Press.

5.2 Trends and patterns in maternal and perinatal health

A newborn baby's health can be a key determinant of their health and wellbeing throughout life. Factors such as a baby's gestational age and birthweight can influence their chance of survival and health outcomes. Mothers' attributes, such as age, whether they smoke or drink during pregnancy, and where they live, can also affect obstetric and perinatal outcomes (Bywood et al. 2015; WHO 2015a).

Australia's maternity services are among the best in the world—with one of the lowest maternal mortality ratios (MMR) reported globally in 2015 (6 maternal deaths per 100,000 women who gave birth, compared with 9 in the United Kingdom, and 11 in New Zealand and the United States of America) (WHO et al. 2015). However, in 2008–2012, the ratio in Australia for Aboriginal and Torres Strait Islander mothers was double that of other Australian women who gave birth (14 per 100,000 compared with 6.6 per 100,000) (AIHW: Humphrey et al. 2015). Monitoring MMR alongside other indicators designed to assess the safety and quality of maternity care (such as the type of delivery a mother has) is important to ensure that there is continual improvement in the quality of maternity services (see the National Core Maternity Indicators, or NCMI) (AIHW National Perinatal Epidemiology and Statistics Unit & AIHW 2013).

Data on almost every birth in Australia are collected by health professionals and included in the National Perinatal Data Collection at the AIHW. This article uses these data to explore aspects of pregnancy and childbirth, including differences that occur due to characteristics such as a mother's age, where she lives, the socioeconomic conditions in which she lives, her Indigenous status and her country of birth; as well as characteristics such as a baby's gestational age at birth and their birthweight.

Mothers and babies at a glance

In 2013, about 305,000 women gave birth to around 309,000 babies. Slightly fewer women gave birth in 2013 than in 2012 (approximately 307,500). In 2013:

  • most mothers lived in Major cities (71%) and most (69%) were born in Australia—similar to the proportions of all women of reproductive age in the population (Figure 5.2.1)
  • of all mothers, just over one-fifth (21%) were from the lowest socioeconomic areas and 18% were from the highest socioeconomic areas
  • 4.1% of all Australian women who gave birth were of Aboriginal and Torres Strait Islander origin, slightly higher than the proportion of Aboriginal and Torres Strait Islander women of reproductive age in the population (which was 3.3%, or about 160,700 women)
  • 5.2% of babies (or approximately 16,100 babies) born in Australia were reported to be of Aboriginal and Torres Strait Islander origin in 2013, based on the Indigenous status of the baby.

Figure 5.2.1: Selected characteristics of women giving birth in 2013

Bar chart showing characteristics of women giving birth in 2013. Most were aged 30-34, lived in major cities, were in the lowest socioeconomic quintile, were non-Indigenous, and were born in Australia.

Note: The 'Other main English-speaking countries' category comprises New Zealand, the United Kingdom, Ireland, South Africa, the United States of America and Canada.

Source: National Perinatal Data Collection (AIHW).

Summary of selected maternal characteristics by population group

Many factors contribute to inequalities in maternal and perinatal health outcomes. For example, in 2013:

  • older mothers (40 and over) were more likely to attend antenatal care in the first trimester and were significantly more likely to give birth via caesarean section, than teenage mothers (Tables 5.2.1, 5.2.2)
  • pregnant women living in Major cities were less likely to smoke or to be overweight or obese—with a body mass index (BMI) of 25.0 and over—than those living in Very remote areas. They were also less likely to have a pre-term or low birthweight baby
  • women from the lowest socioeconomic areas were more likely to begin antenatal care later in pregnancy, to smoke in pregnancy and to be overweight or obese in pregnancy than women from the highest socioeconomic areas. They were also more likely to give birth early (or pre-term), to babies of low birthweight, than women from the highest socioeconomic areas
  • Aboriginal and Torres Strait Islander pregnant women were more likely to smoke than non-Indigenous pregnant women and to be overweight or obese. They were also more likely to give birth early (pre-term) and twice as likely to have a baby of low birthweight
  • women born overseas were less likely to attend antenatal care early in pregnancy, to smoke during pregnancy or be overweight in pregnancy, than women born in Australia. They were equally likely, compared with women born in Australia, to have a baby pre-term.

Table 5.2.1: Selected maternal characteristics by population group, 2013

Characteristic Attended antenatal care in first trimester
(%)
Rate ratio Did not smoke during first 20 weeks of pregnancy
(%)
Rate ratio BMI under 25 at first antenatal visit
(%)
Rate ratio
Maternal age: less than 20 46.0 66.4 34.9
Maternal age: 40 and over 65.6 0.7 92.2 0.7 19.2 1.8
Major city   60.7 91.2 59.5
Very remote   64.3 0.9 62.3 1.5 50.3 1.2
Lowest socioeconomic area 54.8 80.4 50.6
Highest socioeconomic area 68.4 0.8 96.3 0.8 65.2 0.8
Indigenous 51.9 53.1 52.3
Non-Indigenous 62.2 0.8 90.2 0.6 57.3 0.9
Born in Australia 64.3 85.5 54.2
Born in 'other main English-speaking countries' 59.7 1.1 89.8 1.0 55.3 1.0
Born in country other than main English-speaking countries 55.5 1.2 97.7 0.9 67.0 0.8
All mothers 61.9   88.3   49.3   —  

— Denotes the reference point used to calculate the rate ratio for each characteristic listed (age, remoteness, socioeconomic area, Indigenous status, maternal country of birth).

Note: The 'Other main English-speaking countries' category comprises New Zealand, the United Kingdom, Ireland, South Africa, the United States of America and Canada.

Source: National Perinatal Data Collection (AIHW).

Table 5.2.2: Selected labour and birth outcomes by population group, 2013

Characteristic Caesarean section(a) 
(%)
Rate ratio Pre-term baby
(%)
Rate ratio Low birthweight
(%)
Rate ratio
Maternal age: less than 20 18.3 9.8 8.1
Maternal age: 40 and over 50.6 0.4 12.0 0.8 8.5 1.0
Major city   33.6 8.3 6.3
Very remote   32.6 1.0 13.3 0.6 10.6 0.6
Lowest socioeconomic area 31.9 9.5 7.5
Highest socioeconomic area 34.3 0.9 7.8 1.2 5.6 1.3
Indigenous 29.2 14.4 12.2
Non-Indigenous 33.3 0.9 8.3 1.7 6.1 2.0
Born in Australia 33.6 8.5 6.4
Born in 'other main English-speaking countries' 31.6 1.1 7.3 1.2 5.3 1.2
Born in country other than main English-speaking countries 32.7 1.0 7.0 1.2 6.6 1.0
All mothers 33.3   8.6   —  6.4  

(a) Age-standardised rate.

— Denotes the reference point used to calculate the rate ratio for each characteristic listed (age, remoteness, socioeconomic area, Indigenous status, maternal country of birth).

Source: National Perinatal Data Collection (AIHW).

Antenatal risk factors

Maternal age

The age at which a woman gives birth can be a risk factor for obstetric and perinatal outcomes, with adverse outcomes more likely to occur in women aged under 20 and over 40. Consistent with recent trends in Australia, women are continuing to give birth later in life. Although just over 3 in 5 (61%) women who gave birth were aged between 25 and 34 in 2013 (Figure 5.2.1), the proportion of mothers aged 35 and over increased from 19% in 2003 to 22% in 2013, and the proportion of teenage mothers (aged under 20) decreased from 4.6% to 3.3% over the same period. In 2013, the average age of all women who gave birth was 30.1, compared with 29.5 in 2003.

The average age of Aboriginal and Torres Strait Islander mothers also increased, from 24.7 in 2003 to 25.3 in 2013. Even though the proportion of Aboriginal and Torres Strait Islander mothers who were teenagers decreased over the same time period (from 23% to 18%), Aboriginal and Torres Strait Islander mothers were 6 times as likely to be teenage mothers as were non-Indigenous mothers (18% compared with 2.7%, respectively). Conversely, 9.6% of Aboriginal and Torres Strait Islander mothers were aged 35 and over, compared with 23% of non-Indigenous mothers.

Antenatal care

Accessing routine antenatal care, beginning in the first trimester (before 14 weeks gestational age), is known to contribute to better maternal health in pregnancy, fewer interventions in late pregnancy, and positive child health outcomes (AHMAC 2012; WHO 2015a). The Australian Antenatal Guidelines recommend that the first antenatal visit occur within the first 10 weeks of pregnancy and that first-time mothers with an uncomplicated pregnancy have 10 antenatal visits (seven visits for subsequent uncomplicated pregnancies) (AHMAC 2012).

In recent times, the options available for Australian women for antenatal care and birthing have expanded significantly, including the provision of more midwifery-led care. Women can now choose to receive antenatal care from a range of practitioners (including general practitioners, midwives and obstetricians) and to give birth in a range of different settings (such as in hospital, in a birthing centre or at home). Aspects that differ across types of care include:

  • the practitioners supporting the women (community or independent midwives, general practitioners, obstetricians)
  • aspects of how the care is provided (shared care, continuity of care, place of care, place of birth, private versus public settings)
  • the target of specific types of care for the woman (low or high risk, specific cultural background).

In 2013, nearly 100% of women accessed antenatal care at some point in their pregnancies, with just over three-fifths (62%) attending in the first trimester. Fewer women (87%) met the recommended standard in the Australian Antenatal Guidelines by making seven or more antenatal visits, and the number of antenatal visits increased with gestational age (Figure 5.2.2).

Figure 5.2.2: Number of antenatal visits, by baby's gestational age, 2013

Stacked column graph showing the number of antenatal visits made, by baby’s gestational age, in 2013. By post term 42+ weeks old, well over 80%25 of people had made 7 or more antenatal visits.

Note: 'Pre-term' is classed as 20–36 weeks gestation, 'term' is 37–41 weeks gestation and 'post-term' is gestation of 42 weeks and over.

Source: National Perinatal Data Collection (AIHW).
 

The number of antenatal visits accessed in the first trimester varied by remoteness, socioeconomic position, Indigenous status and country of birth of the mother (Figure 5.2.3). Knowledge of access to services, availability of culturally appropriate services, and language barriers are likely to influence access to antenatal visits.

Women born overseas in non-English speaking countries were less likely to access an antenatal visit in the first trimester (56%) than Australia-born women (64%), but they were almost as likely as other mothers to access seven or more antenatal visits (86% of mothers born in other countries compared with 87% of Australian-born mothers).

Aboriginal and Torres Strait Islander women were less likely to access either an antenatal visit in the first trimester (52%, compared with 62% of non-Indigenous women) or to access seven or more visits (71%, compared with 88% of non-Indigenous women).

Women living in the lowest socioeconomic areas tend to begin antenatal care later in pregnancy—just over half (55%) of these women accessed antenatal care in the first trimester, compared with 68% of women living in the highest socioeconomic areas.

Figure 5.2.3: Antenatal visits in the first trimester and seven or more antenatal visits, by selected maternal characteristics, 2013

Bar chart showing the number of antenatal visits made according to different population characteristics. Indigenous w and women in lower socioeconomic areas attended slightly fewer antenatal visits.

Notes

  1. The 'Other main English-speaking countries' category comprises New Zealand, the United Kingdom, Ireland, South Africa, the United States of America and Canada.
  2. Indigenous and non-Indigenous data is age-standardised.

Source: National Perinatal Data Collection (AIHW).

Smoking during pregnancy

Tobacco smoking at any time during pregnancy is the most common modifiable risk factor for pregnancy complications, and is associated with poorer perinatal outcomes, including a baby being of low birthweight or small for gestational age, a pre-term birth or perinatal death. Women who smoke while pregnant are also at increased risk of a wide range of problems, including ectopic pregnancy, miscarriage and premature labour (DHHS 2014).

Around 1 in 8 women (about 35,000 or 12%) smoked at some time during their pregnancy in 2013, a decrease from 15% in 2009. About one-fifth (22%) of women who reported smoking during the first 20 weeks of pregnancy reported not smoking during the second 20 weeks.

On average, women who smoked during pregnancy accessed their first antenatal visit later in pregnancy and also had fewer overall antenatal visits (15 weeks and 9 visits) than women who did not smoke (13 weeks and 10 visits).

Aboriginal and Torres Strait Islander mothers accounted for 17% of mothers who smoked at any time during pregnancy in 2013, despite accounting for only 4.1% of mothers. However, the rate of smoking during pregnancy decreased between 2009 and 2013 for Aboriginal and Torres Strait Islander women (50% to 47%, respectively).

Smoking during pregnancy is strongly associated with socioeconomic factors. Teenage mothers (34%); mothers living in Remote (21%) and Very remote (38%) areas; those living in the lowest socioeconomic areas (20%); Aboriginal and Torres Strait Islander women (47%); and mothers born in Australia (15%) were most likely to smoke in the first 20 weeks of pregnancy. It should be noted that these categories are not mutually exclusive and it is likely that many of these influencing factors overlap (Figure 5.2.4).

Figure 5.2.4: Smoking during the first 20 weeks of pregnancy, by selected maternal characteristics, 2013

Bar chart showing rates of smoking during the first 20 weeks of pregnancy. Rates were significantly higher for women younger than 20, women living in very remote areas, and Indigenous women.

Note: The 'Other main English-speaking countries' category comprises New Zealand, the United Kingdom, Ireland, South Africa, the United States of America and Canada.

Source: National Perinatal Data Collection (AIHW).

Alcohol consumption during pregnancy

Alcohol use during pregnancy can disturb the development of the fetus and lead to problems later in life. Fetal Alcohol Spectrum Disorder (FASD) is a term that describes the range of effects that can occur in a baby who has been exposed to alcohol in its mother's womb (Burns et al. 2013; NHMRC 2009). It is not yet known how much alcohol is safe to drink during pregnancy; however, it is known that the risk of damage to the baby increases the more women drink and that binge drinking is especially harmful. Therefore, the National Health and Medical Research Council advises that the safest option for women is to abstain from drinking if they are pregnant, planning a pregnancy or breastfeeding.

In recent years the Australian Government has funded research to improve the understanding of FASD. A FASD Action Plan has also been developed to inform the future directions in the area. The plan outlines five priority areas for action, including the provision of better diagnosis and management; development of best practice interventions; and services to support high-risk women (Department of Health 2016a, 2016b).

The main source of data on alcohol use in pregnancy in 2013 is the National Drug Strategy Household Survey. This survey contains a number of questions on alcohol consumption in pregnancy, with additional questions added in 2013. It should be noted that the number of pregnant women answering the survey was low and thus further disaggregations (based on age, remoteness, socioeconomic position or any other demographics) are not available.

The proportion of pregnant women abstaining from alcohol rose slightly between 2010 and 2013 (from 49% to 53%), but this increase was not statistically significant. More than half (56%) of pregnant women consumed alcohol before they knew they were pregnant and about 1 in 4 (26%) of these women continued to drink, even after they knew they were pregnant. About 3 in 4 (78%) pregnant women who consumed alcohol while pregnant drank monthly or less, and 17% drank 2–4 times a month. Most (96%) usually consumed 1–2 standard drinks.

Obesity in pregnancy

In line with trends relating to overweight and obesity generally in adults in Australia (see 'Chapter 4.4 Overweight and obesity'), obesity in pregnancy is becoming more of an issue in maternity services. Data on obesity in pregnant women was collected in the National Perinatal Data Collection for the first time in 2013, though it should be noted that data are not available for all women who gave birth. Data are available for BMI for around two-thirds of women who gave birth in 2013 (Victoria, Queensland, South Australia, Tasmania, Western Australia and the Australian Capital Territory).

Being overweight or obese during pregnancy contributes to an increased risk of complications during pregnancy and delivery, including increased morbidity and mortality for both mother and baby. A normal BMI for a non-pregnant woman is 18.5–24.9. While increases in BMI in pregnancy are expected, a BMI of 25–29 at the first antenatal visit has been defined as 'overweight' and a BMI of 30 and over as 'obese' in pregnancy.

In 2013, 46% of women who gave birth in these jurisdictions were classified as of normal weight and 3.5% as underweight. At the same time, 24% were classified as overweight and 19% as obese, with the remainder either morbidly obese (2.7%) or not reported (7.8%). Overweight or obesity tended to increase with lower socioeconomic position, with around half (49%) of pregnant women living in the lowest socioeconomic areas being overweight or obese.

Aboriginal and Torres Strait Islander mothers were slightly more likely to be overweight or obese than non-Indigenous mothers (48% compared with 43%). Women born in Australia were more likely to be overweight or obese than those born in countries other than Australia or the other 'main English-speaking' countries (46% compared with 33% and 45% respectively) (Figure 5.2.5).

Figure 5.2.5: Women who gave birth, and were classified as overweight or obese (BMI 25+), by selected maternal characteristics, 2013

Bar chart showing rates of women who were classified as overweight or obese at giving birth. Most population groups had around a 30-50%25 rate of being overweight or obese.

Notes

  1. BMI data available for Victoria, Queensland, South Australia and Tasmania and partially collected from Western Australia and the Australian Capital Territory. BMI source data and methods used for collection in states and territories is not uniform.
  2. The 'Other main English-speaking countries' category comprises New Zealand, the United Kingdom, Ireland, South Africa, the United States of America and Canada.

Source: National Perinatal Data Collection (AIHW).

Labour and birth

Place of birth

Hospitals were still the most common place to give birth in Australia (97% of all births). Place of birth is influenced by a range of factors: for example, birth centres are generally located in Major cities and thus are not always an option for women living in regional or remote areas. Publicly funded home birth programs do exist in Australia but they are limited in number with no programs in the Australian Capital Territory or in Queensland. Birth centres and home birthing programs also tend to limit their care/services to low-risk mothers. Mothers who have given birth previously are more likely to give birth in other settings—including births occurring before arrival at hospital (in the 'other' category)—as their labour time tends to decrease with the number of births experienced.

309,000 births in 2013

Image of a hospital. 97% were in a hospital: Public 72%, Private 28%

Image of a home. 0.3% at home

Image of a birthing centre. 2.0% were in a birthing centre

Image of a person leaning into a car. 0.3% occurred before arrival at hospital

Mothers living in Major cities accounted for:

Image of a city. 72% of hospital births, 84% of birthing centre births, 70% of home births.

First-time mothers accounted for:

Image of a baby. 44% of hospital births, 35% of birthing centre births, 25% of home births.

Average age of mothers was:

Image of a birthday cake. 30.1 years in hospitals, 29.9 years in birthing centres, 32.0 years at home births.

Mothers living in the lowest socioeconomic areas accounted for:

Image of a house containing symbols for education, income, work, and transport. 21% of hospital births, 16% of birthing centre births, 12% of home births.

Method of birth

In 2013, about two-thirds (67%) of women had a vaginal birth, while the remaining third (33%) had a caesarean section. Most vaginal births (82%) were unassisted, or 'non-instrumental' (see Glossary). The prevalence of unassisted vaginal deliveries decreased with age and increased marginally with each category of remoteness and socioeconomic position (Figure 5.2.6).

The vaginal unassisted delivery rate has fallen from 61% in 2003 to 55% in 2013, while the caesarean section rate has increased from 29% to 33% over the same time period. Assisted or 'instrumental' vaginal delivery (see Glossary) has remained relatively stable at around 12% throughout the same period.

Figure 5.2.6: Method of birth by selected maternal characteristics, 2013

Stacked bar chart showing rates of different methods of birth according to maternal age, remoteness, and socioeconomic group in 2013. In each group except for women aged 40 and over, the most common birth was non-instrumental vaginal. Caesarean section was the most common for women with a higher maternal age.

(a) Age-standardised rates.

Source: National Perinatal Data Collection (AIHW).

Aboriginal and Torres Strait Islander mothers were more likely to have a vaginal birth (71%) than non-Indigenous mothers (67%), and less likely to have an assisted vaginal delivery (6.0%) or a caesarean section delivery (29%) than non-Indigenous women (12% and 33%, respectively).

In 2013, 33 in 100 births in Australia were by caesarean section delivery. Internationally, the caesarean section delivery rate has been increasing in most Organisation for Economic Co-operation and Development (OECD) countries in recent years. The OECD average increased from a rate of 20 per 100 live births in 2000 to 28 per 100 in 2013. Australia's rate has remained higher than the OECD average over this time and ranked 22nd out of 32 OECD countries in 2013 (when caesarean section rates are ranked from lowest to highest) (OECD 2015).

Since 1985, the international health care community has considered the ideal rate for caesarean section to be between 10% and 15% (WHO 2015c). However, caesarean sections have become increasingly common both in developed and developing nations (WHO 2015b). In recent years, governments and clinicians have expressed concern about the rise in the numbers of caesarean section births and the potential negative consequences for maternal and infant health. There is growing evidence of increasing maternal mortality and morbidity associated with multiple caesarean operations, such as more difficult surgery, increased blood loss, abdominal organ injury, hysterectomy and longer hospital stay, as well as the risk of multiple exposures to anaesthesia (Hager et al. 2004; Macdorman et al. 2008).

The World Health Organization (WHO) has recently released a WHO statement on caesarean section rates which states that every effort should be made to provide caesarean sections to women in need, rather than striving for a specific rate (WHO 2015b).

Caesarean sections were more common among older mothers, first-time mothers and women who had given birth by caesarean section before. In 2013, 85% of mothers with a history of caesarean section had a further caesarean section delivery, while the remainder had a vaginal birth (12% had an unassisted vaginal birth and 3.6% had an assisted vaginal birth).

Caesarean section rates differed by hospital sector and age, but not by remoteness and socioeconomic position (Figure 5.2.6). Rates of caesarean section were 1.5 times as high in private hospitals (44%) as in public hospitals (30%) and women older than 40 were almost 3 times as likely to deliver by caesarean section as teenagers were (51% compared with 18%). Rates of caesarean section delivery were similar for mothers in Major cities (34%) and Very remote areas (33%) and for those living in the highest socioeconomic areas (34%) compared with women living in the lowest socioeconomic areas (32%).

Babies

Key indicators of babies' health include gestational age and birthweight. This section of the article focuses on how maternal characteristics such as place of residence, socioeconomic position and Indigenous status affect babies' outcomes.

Gestational age

In 2013, the average gestational age for all babies was 38.7 weeks, with the vast majority (91%) born at term (37–41 weeks). Pre-term births were more common for mothers who smoked or who did not receive antenatal care during pregnancy.

Overall, 8.6% of babies were born pre-term in 2013, with most of these births occurring at gestational ages of between 32 and 36 completed weeks. The average gestational age for all pre-term babies was 33.3 weeks.

Mother's smoking status was associated with the baby's gestational age. Babies whose mothers smoked during pregnancy were 1.5 times as likely to be born pre-term (12%) as those whose mothers did not smoke during pregnancy (7.9%) (Figure 5.2.7).

Babies were also more likely to be pre-term according to other selected maternal characteristics, including:

  • the age of their mother—10% of babies born to teenage mothers and 12% of babies born to older mothers (those aged 40 and over) were pre-term compared with 8.4% of babies with mothers aged 20–39
  • where their mother lives—13% of babies with mothers who lived in Very remote areas were born pre-term compared with 8.3% of babies whose mothers lived in Major cities, and 9.5% of babies with mothers who lived in the lowest socioeconomic areas were born pre-term compared with 7.8% of babies born to mothers in the highest socioeconomic areas
  • their mother's Indigenous status—14% of babies born to Aboriginal and Torres Strait Islander mothers were born pre-term compared with 8.3% of babies born to non-Indigenous mothers.

Figure 5.2.7: Pre-term births, by selected maternal characteristics, 2013

Bar chart showing rates of pre-term births in 2013 according to different maternal characteristics. The highest proportion of pre-term births occurred in those who only made 0-4 antenatal visits.

Note: The 'Other main English-speaking countries' category comprises New Zealand, the United Kingdom, Ireland, South Africa, the United States of America and Canada.

Source: National Perinatal Data Collection (AIHW).

Birthweight

A baby's birthweight is a key indicator of infant health and a determinant of a baby's chance of survival and health later in life. A baby may be small due to being born early (pre-term) or be small for gestational age, which indicates a possible growth restriction within the uterus.

Birthweight ranges

Normal: 2,500 to 4,499 grams

Low: less than 2,500 grams

Extremely low: less than 1,000 grams

(WHO 1992)

In 2013, the vast majority of liveborn babies (92%) were in the normal weight range, with the mean birthweight of liveborn babies at 3,355 grams. One in 16 (6.4%) babies were of low birthweight, and a small proportion (1.6%) were of high birthweight (4,500 grams and over). Despite a fall in rates of smoking in pregnancy between 2009 and 2013 (from 15% to 12%), there has been little change in the proportion of low birthweight babies, with the proportion remaining between 6.1% and 6.4% over this period. This may be due to other factors, such as advances in medical care, that mean that small babies are more likely to be liveborn than in the past.

The proportion of low birthweight babies was higher among:

Image of a female baby. female babies (6.9%) compared with male babies (5.9%)

Image of a hospital. babies born in public hospitals (7.0%) compared with babies born in private hospitals (4.8%)

Image of a pair of twins. twins (56%) and other multiples (98%) compared with singletons (4.8%)

Image of a pregnant woman smoking. babies whose mothers smoked during pregnancy (12%) compared with babies whose mothers did not smoke (5.7%)

Aboriginal Australian flag and Torres Strait Islander flag. babies of Aboriginal and Torres Strait Islander mothers (12.2%) compared with babies of non-Indigenous mothers (6.1%).

The proportion of low birthweight babies increased with remoteness and socioeconomic position.

In 2012, the proportion of low birthweight babies in Australia (6.2%) was lower than the OECD average (6.6%), with Australia ranked 15th out of 34 OECD countries (OECD 2015). (See 'Chapter 4.1 Social determinants of health'.)

What is the AIHW doing?

The AIHW is continually striving to improve the quality, timeliness and reporting of maternal and perinatal information in Australia—identifying information needs for maternity and perinatal data, enhancing reporting by making data more accessible online, and developing a range of nationally agreed indicators. Expanding the range of indicators will enable better monitoring and evaluation of safety and quality of maternity care in Australia.

What is missing from the picture?

Though Australia's maternal and perinatal mortality is among the lowest in the world, there are a range of areas where collecting new data or improving current data would allow specific areas of concern to be better monitored and targeted by health services.

There are no routine data available on the prevalence of pre- or postnatal depression, on domestic violence during pregnancy, or on FASD in Australia. Current data on alcohol use in pregnancy are from the National Drug Strategy Household Survey and thus are based only on a small number of pregnant women. Due to the sample size, further disaggregation (for example, by Indigenous status, remoteness or socioeconomic position) is not possible. BMI is currently only available from the National Perinatal Data Collection for a limited number of states and territories. Having the full picture across Australia for these issues would allow health services to target subpopulations more effectively.

There are also limited data on caesarean sections—especially when caesarean sections are deemed to be urgent and the reason(s) for, and health condition(s) associated with, the procedure. This is essential information for evaluating the outcomes of caesarean sections. National data development is being pursued with the states and territories to improve data used to generate information on caesarean sections. Data being developed on maternal risk factors and the clinical indications for caesarean section will provide a more complete picture of an individual woman's risk profile, and further highlight potentially preventable variations in health care practice across Australia. Understanding variations across hospitals in performing caesarean sections and implementing evidence-based practices may result in improved maternity care (Lee et al. 2013). This should better inform policy and care aimed at ensuring that caesarean sections are targeted appropriately to those women who need them.

Where do I go for more information?

More information on Australia's mothers and babies, the perinatal data portal, the Maternity Information Matrix (MIM), the National Maternity Data Development Project (NMDDP) and the NCMI is available at Maternal and perinatal data. The latest edition (and previous editions) of the annual publication Australia's mothers and babies and reports associated with the MIM, the NMDDP and the NCMI's are available for free download.

References

AHMAC (Australian Health Ministers' Advisory Council) 2012. Clinical Practice Guidelines: antenatal care—module 1. Canberra: Department of Health and Ageing.

AIHW (Australian Institute of Health and Welfare): Humphrey MD, Bonello MR, Chughtai A, Macaldowie A, Harris K & Chambers GM 2015. Maternal deaths in Australia 2008–2012. Maternal deaths series no. 5. Cat. no. PER 70. Canberra: AIHW.

AIHW National Perinatal Epidemiology and Statistics Unit (NPESU) and AIHW 2013. National core maternity indicators. Cat. no. PER 58. Canberra: AIHW.

Burns L, Breen C, Bower C, O' Leary C, & Elliot EJ 2013. Counting fetal alcohol spectrum disorder in Australia: the evidence and the challenges. Drug Alcohol Review 32(5):461–67.

Bywood PT, Raven M & Erny-Albrecht K 2015. Improving health in Aboriginal and Torres Strait Islander mothers, babies and young children: a literature review. Adelaide: Primary Health Care Research & Information Service.

Department of Health 2016a. Alcohol issues. Canberra: Department of Health. Viewed 21 January 2016.

Department of Health 2016b. Fetal alcohol spectrum disorders. Viewed 21 January 2016.

DHHS (Department of Health & Human Services, Victoria) 2014. Pregnancy and smoking. Melbourne: DHHS. Viewed 15 May 2014.

Hager RM, Daltveit AK, Hofoss D, Nilsen ST, Kolaas T, Oian P et al. 2004. Complications of cesarean deliveries: rates and risk factors. American Journal of Obstetrics and Gynecology 190(2):428–34.

Lee YY, Roberts CL, Patterson JA, Simpson JM, Nicholl MC, Morris JM et al. 2013. Unexplained variation in hospital caesarean section rates. Medical Journal of Australia 199(5):348–53.

MacDorman MF, Menacker F & Declercq E 2008. Cesarean birth in the United States: epidemiology, trends and outcomes. Clinics in Perinatology 35(2):293–307.

NHMRC (National Health and Medical Research Council) 2009. Australian Guidelines to Reduce Health Risks from Drinking Alcohol. Canberra: NHMRC.

OECD (Organisation for Economic Co-operation and Development) 2015. Health at a glance 2015: OECD indicators. Paris: OECD Publishing. Viewed 7 December 2015.

WHO (World Health Organization) 1992. International Statistical Classification of Diseases and Related Health Problems. 10th Revision. Geneva: WHO.

WHO 2015a. State of inequality: reproductive, maternal, newborn and child health. Geneva: WHO. Viewed 3 November 2015.

WHO 2015b. WHO statement on caesarean section rates. Geneva: WHO. Viewed 7 December 2015.

WHO, UNICEF (The United Nations Children's Emergency Fund), UNFPA (United Nations Population Fund), World Bank Group and the United Nations Population Division 2015. Trends in maternal mortality: 1990 to 2015. Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. Geneva: WHO.

5.3 How healthy are Australia's children?

Good health is a critical element in a child's quality of life and can influence participation in many aspects of life, including learning, recreation and relationships, not only in childhood but throughout life.

Children's health and developmental outcomes are closely related to the social environment in which they live—differences in socioeconomic position translate into inequalities in child development. These social determinants of health include socioeconomic, political, cultural context and daily living conditions. Discrepancies between children based on differences in social and economic circumstances can be evident from birth, and grow larger as children get older (CSDH 2008; Moore et al. 2015; Woolfenden et al. 2013). See also 'Chapter 4.1 Social determinants of health' and 'Chapter 4.2 Social determinants of Indigenous health'.

In recognition of the importance of ensuring that children have the best possible start in life, the Council of Australian Governments Health Council recently endorsed Healthy, Safe and Thriving: National Strategic Framework for Child and Youth Health. The framework identifies the five strategic priorities in Australia for the next 10 years (COAG 2015):

  • equip children and young people with the foundations for a healthy life
  • support children and young people to become strong and resilient
  • support children and young people to live in healthy and safe homes, communities and environments
  • ensure that children and young people have equitable access to health care services and equitable health outcomes
  • improve systems to optimise the health outcomes of children and young people.

These strategic priorities comprise 27 objectives and 64 actions. Four of the 27 objectives have actions relating to child health which are measurable using existing data:

  1. Children and young people are active, healthy and thriving.
  2. Children and young people are immunised against preventable illnesses.
  3. Children and young people have lower rates of preventable injury and mortality.
  4. Children and young people experience lower rates and impact of chronic disease.

This snapshot examines how Australia's 4.4 million children aged 0–14 (19% of the Australian population) are faring against these four objectives.

How children are faring

Objective 1: Children are active, healthy and thriving

An apple and a carrot. In 2014–15, only 2.5% of children aged 5–14 ate enough fruit and vegetables as recommended in the Australian Dietary Guidelines (NHRMC 2013): 70% ate the recommended 2 daily serves of fruit (or 1.5 serves for 4–8 year olds), but only 2.9% ate the recommended serves of vegetables daily (4.5 serves for 4–8 year olds; 5 serves for 9–11 year olds and for girls aged 12–14; and 5.5 for boys aged 12–14) (ABS 2015a).

A set of scales. At the same time, just over one-quarter (26%) of children aged 5–14 were classified as overweight or obese (19% as overweight and 7.1% as obese). Two-thirds (68%) were in the normal weight range with a body mass index of 18.50–24.99 (ABS 2015b).

A man running. In 2011–12, less than one-quarter (23%) of Australian children aged 5–14 met the national physical activity recommendations every day. About half collectively met the recommendations on 5–6 days (32%) or on 3–4 days (22%) a week, while the remainder met the guidelines even less frequently (ABS 2013; Department of Health 2014).

Objective 2: Children are immunised against preventable illnesses

As at December 2015, 92.6% of Australian children were fully immunised by the time they started school. Rates among Indigenous children were slightly higher at 93.9% (Department of Health 2016a, 2016b).

While vaccination rates have increased since the Australian Childhood Immunisation Register was established in 1996, vaccine objection rates for children under the age of 7 have also increased steadily, especially under the 'conscientious objector' category.

However, between 2014 and 2015, for the first time since 1999, national vaccine objection rates have decreased (from 1.8% to 1.3%) (Department of Health 2016c).

In 2015, more than 1.3% (equivalent to 30,000) children aged under 7 were not vaccinated because their parents were vaccine objectors (Figure 5.3.1). This equates to an increase of more than 13,000 children over 10 years. In order to protect children and the community from preventable diseases, the Australian Government will remove 'conscientious objection' as an exemption category for child care payments from 1 January 2016. See 'Chapter 6.1 Prevention and health promotion' and 'Chapter 7.1 Indicators of Australia's health'.

Figure 5.3.1: Proportion of Australian children with conscientious objection recorded, 1999–2015

Line chart showing the steady increase (then sharp decline in the last year) in the proportion of Australian children with a recorded conscientious objection to vaccinations from 1999 to 2015.

Source: Department of Health 2016c.

Objective 3: Children have lower rates of preventable injury and mortality

In 2013–14, there were over 74,000 hospitalisations (1,686 per 100,000) due to injury and poisoning for children aged 0–14. Rates have changed little since 1993–94.

Hospitalisations tend to increase with age—in 2013–14, 80,000 (2,572 per 100,000) young people aged 15–24 were hospitalised with a principal diagnosis of injury and poisoning. Boys aged 0–14 were almost 1.5 times as likely to be hospitalised for injury and poisoning as girls (2,006 compared with 1,347 per 100,000).

Falls (700 per 100,000) were the most common cause of injury/poisoning hospitalisations for children in 2013–14, with rates 4.4 times as high as for transport accidents (160 per 100,000). Boys were more likely to be hospitalised than girls across all causes of injury and poisoning (Figure 5.3.2).

Figure 5.3.2: Hospitalisations for the top five principal diagnoses of injury and poisoning, by cause and sex, children aged 0–14, 2013–14

Column graph showing the number of hospitalisations per 100000 children for different causes of injury or poisoning, for both boys and girls in 2013-14. The leading cause of injury or poisoning is falls (around 800 hospitalisations for boys and 550 for girls).

(a) This category comprises accidental drowning and submersion; accidental threats to breathing; overexertion; travel and privation; and accidental exposure to other and unspecified factors.

Source: AIHW Morbidity Database.

During 2011–2013, there were 3,265 infant deaths, a rate of 361 deaths per 100,000 infants aged under 1. During the same period, there were 1,441 child deaths, a rate of 12 deaths per 100,000 children (aged 1–14).

The leading causes of death for infants were conditions originating in the perinatal period and congenital conditions (76% of all infant deaths, or 272 per 100,000); sudden infant death syndrome (6.0% of all infant deaths, or 22 per 100,000); other ill-defined causes (5.0% of all infant deaths, or 18 per 100,000); and accidental threats to breathing (1.2% of all infant deaths, or 4 per 100,000).

The leading causes of death for children aged 1–14 were land transport accidents (14%, or 1.7 per 100,000), conditions originating in the perinatal period (8.9%, or 1.1 per 100,000), brain cancer (6.5%, or 0.8 per 100,000) and accidental drowning (6.0%, or 0.7 per 100,000).

On average, 7 pedestrians aged 0–14 were killed each year (in the 10-year period 2001–02 to 2009–2010) and 60 were seriously injured (in the 8-year period 2002–2003 to 2009–2010) due to being hit by a four-wheeled motor vehicle moving around a home (commonly known as 'driveway run-overs') (BITRE 2012).

Objective 4: Children experience lower rates and impact of chronic disease

The most common reported long-term conditions in children were asthma, and hayfever and allergic rhinitis.

A man with his lungs highlighted. In 2014–15, just over 1 in 10 (11%) children were diagnosed with asthma. See 'Chapter 3.10 Chronic respiratory conditions'.

A person coughing. In 2014–15, the prevalence of allergic rhinitis (hay fever) was also 11%. Food allergies were reported for the first time in the 2014–15 National Health Survey, with 6.3% of children having a food allergy (ABS 2015b).

A finger with a drop of blood coming from it. In 2014, 1,088 new cases of type 1 diabetes were diagnosed in children (equivalent to rate of 25 cases per 100,000). Rates for this age group have remained relatively stable since 2000, and were similar for males and females.

What is missing from the picture?

Data are not collected for the majority of the priorities in the Strategic Framework. Data development work to scope and benchmark these priorities, objectives and actions needs to be progressed so that the success of targeted policies, resources and initiatives can be measured.

Key topics of public interest relating to child health and wellbeing include obesity, sleep disorders and the effects of screen-time use. Recent and regularly updated data on physical activity and screen-time use for children would be useful to help understand the factors affecting levels of childhood obesity.

Where do I go for more information?

Further information on children's health is available on the AIHW website at Child health, development and wellbeing or the AIHW's Children's Headline Indicators online data portal. More detailed information on diabetes in children is available in the AIHW report Prevalence of type 1 diabetes among children aged 014 in Australia 2013 which can be downloaded for free.

References

ABS (Australian Bureau of Statistics) 2009. National Health Survey: summary of results, 2007–08. ABS cat. no. 4364.0. Canberra: ABS.

ABS 2013. National Nutrition and Physical Activity Survey: 2011–12, customised report. Canberra: ABS.

ABS 2015a. National Health Survey: 2014–15, customised report. Canberra: ABS.

ABS 2015b. National Health Survey: first results, 2014–15. ABS cat. no. 4364.0.55.001. Canberra: ABS.

BITRE (Bureau of Infrastructure, Transport and Regional Economics) 2015. Australian Road Deaths Database. Canberra: BITRE. Viewed 13 January 2016.

BITRE 2012. Child pedestrian safety: 'driveway deaths' and 'low-speed vehicle run-overs', Australia, 2001-10. Information sheet 43. Canberra: BITRE. Viewed 8 February 2016.

COAG Health Council (Council of Australian Governments Health Council) 2015. Healthy, Safe and Thriving: National Strategic Framework for Child and Youth Health. Adelaide: COAG Health Council.

CSDH (Commission on Social Determinants of Health) 2008. Closing the gap in a generation: health equity through action on the social determinants of health. Final report of the Commission on Social Determinants of Health. Geneva: WHO.

Department of Health 2014. Australia's Physical Activity and Sedentary Behaviour Guidelines. Canberra: Department of Health. Viewed 3 February 2016.

Department of Health 2016a. Immunise Australia Program. ACIR: current data. Canberra: Department of Health. Viewed 7 March 2016.

Department of Health 2016b. Immunise Australia Program. ACIR: current data: Aboriginal and Torres Strait Islander children. Canberra: Department of Health. Viewed 7 March 2016.

Department of Health 2016c. Immunise Australia Program. ACIR current data: national vaccine objection (conscientious objection) data. Canberra: Department of Health. Viewed 7 March 2016.

NHMRC (National Health and Medical Research Council) 2013. Australian dietary guidelines (2013). Canberra: NHMRC.

Moore TG, McDonald M, Carlon L & O'Rourke K. 2015. Early childhood development and the social determinants of health inequities. Health Promotion International 2015 Volume 30(S2):ii102–ii105.

Woolfenden S, Goldfeld S, Raman S, Eapen V, Kemp L & Williams K 2013. Inequity in child health: The importance of early childhood development. Journal of Paediatrics and Child Health 49(9):E365–E369.

5.4 Health of young Australians

Adolescence and young adulthood is a significant period of transition in a person's life. Many modifiable behavioural risk factors that can affect current and future health and wellbeing either emerge or accelerate during this time. Addressing health concerns and choices early can improve the immediate quality of life for young people and is socially and economically more effective than dealing with enduring problems in adulthood.

Recognising the importance of youth health, the Council of Australian Governments Health Council recently endorsed Healthy, Safe and Thriving: National Strategic Framework for Child and Youth Health. The framework identifies five strategic priorities in Australia for the next 10 years (COAG Health Council 2015). (See 'Chapter 5.3 How healthy are Australia's children?') These strategic priorities comprise 27 objectives and 64 actions. Six of these objectives have actions relating to youth health which are measurable using existing data:

  1. Children and young people are active, healthy and thriving.
  2. Children and young people have lower rates of preventable injury and mortality.
  3. Children and young people experience lower rates and impact of chronic disease.
  4. Children and young people are supported in their mental health needs.
  5. Young people reduce risk behaviours such as smoking, drug and alcohol use.
  6. Young people make sound choices about their sexual and reproductive health.

This snapshot examines how Australia's 3.1 million young people aged 15–24 (13% of the Australian population) are faring against these six objectives.

How young people are faring

Objective 1: Young people are active, healthy and thriving

According to the 2014–15 National Health Survey (ABS 2015c):

A set of scales. 57% of young people were in the normal weight range (with a body mass index of 18.50–24.99), but 22% were classified as overweight and 15% as obese.

A person running. Just over half (52%) of young people aged 18–24 were sufficiently active (more than 150 minutes of physical activity over 5 or more sessions in the previous week).

An apple and a carrot. Only 3.3% of young people ate enough fruit and vegetables according to the Australian Dietary Guidelines (NHRMC 2013)—46% ate the recommended 2 daily serves of fruit (3 serves for 15–17 year olds) but only 4.0% had 5 serves of vegetables a day (or 4 serves for 15–17 year olds).

Objective 2: Young people have lower rates of preventable injury and mortality

In 2013–14, there were over 80,000 hospitalisations (2,572 per 100,000) of young people due to injury and poisoning. Rates have changed little since 1993–94 (which were then 2,514 per 100,000). Males aged 15–24 were 1.9 times as likely as females to be hospitalised for injury/poisoning, but causes of hospitalised injuries by sex were very different for this age group (Figure 5.4.1).

The most common cause of injury/poisoning hospitalisation for females was intentional self-harm (410 per 100,000), which ranked 8th for males (147 per 100,000)—females were 2.8 times as likely to be hospitalised for self-harm as males aged 15–24. Males were most likely to be hospitalised for transport accidents (613 per 100,000), and 2.2 times as likely to be hospitalised for this reason as females. Transport accidents ranked as the 3rd most common reason for hospitalisations for females (276 per 100,000). See 'Chapter 3.13 Injury'.

Figure 5.4.1: Hospitalisations for the most common principal diagnoses of injury and poisoning, by cause and sex, young people aged 15–24, 2013–14

Column graph showing the number of hospitalisations per 100000 young people aged 15-24 for different causes of injury or poisoning, for both males and females in 2013-14. The leading cause for males was transport accidents (around 600 hospitalisations) while the leading cause for females were falls (around 400 hospitalisations).

(a) This category comprises accidental drowning and submersion; accidental threats to breathing; overexertion; travel and privation; and accidental exposure to other and unspecified factors.

Source: AIHW Morbidity Database.

In 2014, there were 989 deaths (32 per 100,000 young people) among young people aged 15–24. Males were 2.2 times as likely as females to die between the ages of 15 and 24 (22 per 100,000 young people compared with 10 per 100,000 young people, respectively) (ABS 2015b).

A person with depression. The leading causes of death for young people in 2011-2013 were suicide (11 per 100,000), land transport accidents (9 per 100,000), accidental poisoning (2 per 100,000) and assault (1 per 100,000).

A damaged car. In 2015, there were 225 deaths due to road traffic accidents (7 per 100,000 young people), significantly less than in 2010 (when there were 332 deaths or 11 per 100,000 young people) (BITRE 2015).

Objective 3: Young people experience lower rates and impact of chronic disease

A man with his lungs highlighted. In 2014–15, just over 1 in 10 (11%) young people were diagnosed with asthma. Rates have remained stable since 2011–12 (ABS 2015b). See 'Chapter 3.10 Chronic respiratory conditions'.

A person coughing. The most common reported long-term conditions in young people were allergic rhinitis (hay fever) (24%, up from 19% in 2011–12) and short-sightedness/myopia (20%). Food allergies were reported for the first time, with 8.2% of young people having a food allergy (ABS 2015d).

A finger with blood coming from it. In 2014, 484 new cases of type 1 diabetes were diagnosed in young people (equivalent to a rate of 16 cases per 100,000). Rates for this age group have remained relatively stable since 2000. Males were 1.8 times as likely to be newly diagnosed as females.

Objective 4: Young people are supported in their mental health needs

According to the Young Minds Matter Survey, in 2013–14 (Lawrence et al. 2015):

A person with depression. 14% (245,000) of young people aged 12–17 had a mental health disorder in the last 12 months—anxiety was most common (7.0%), followed by Attention Deficit Hyperactivity Disorder (6.3%) and major depressive disorders (5.0%).

Two people talking. Just over one-fifth (21%) of all 12–17 year olds accessed services for emotional or behavioural problems and almost two-thirds (65%) of 12–17 year olds with mental disorders had used these services in the previous 12 months. See 'Chapter 5.5 Mental health of Australia's young people and adolescents'.

Objective 5: Young people reduce risk behaviours such as smoking, drug and alcohol use

According to the 2013 National Drug Strategy Household Survey:

Two alcoholic drinks. 39% of young people aged 15–24 drank alcohol at levels that put them at risk of harm (more than four standard drinks on one occasion, at least once a month)—down from 49% in 2001. Over one-quarter (27%) had never drunk alcohol—an increase from 16% in 2001.

A cigarette. Just over 1 in 10 (11%) young people were current, daily smokers in 2013. This has almost halved since 2001 when 21% were daily smokers. The majority of young people have never smoked (81%) (see Figure 5.4.2). See also 'Chapter 4.7 Tobacco smoking'.

A marijuana leaf. In 2013, one-quarter of young people (25%) had used illicit drugs in the previous 12 months—significantly less than in 2001 (33%). See also 'Chapter 4.5 Illicit drug use'.

Figure 5.4.2: Tobacco smoking status, young people aged 15–24, 2001–2013

Line chart showing rates of tobacco smoking among young people aged 15-24 from 2001 to 2013. There is a slowly increasing proportion (around 80%25 in 2013) of people who have never smoked and a slowly decreasing proportion (around 10%25 in 2013) of people who smoke daily.

Source: 2013 National Drug Strategy Household Survey, unpublished data.

Objective 6: Young people make sound choices about their sexual and reproductive health

A condom. In 2013, 43% of sexually active young people (in Years 10–12) reported 'always' using condoms when they had sex in the previous year. A further 39% used condoms only 'sometimes' and 13% 'never' used condoms (Mitchell et al. 2014).

Graphic saying ‘STI’. In 2014, there were more than 50,000 notifications of chlamydia, gonorrhoea and syphilis, a rate of 1,812 per 100,000, with chlamydia accounting for 89% of notifications among young people. Rates have increased since 2005 when 1,040 per 100,000 notifications were reported (Department of Health 2015).

A pregnant woman. There were around 9,200 births to teenage mothers in 2014. The corresponding age-specific fertility rate for 15–19 year olds was 13 births per 100,000, which has decreased from 16 births per 100,000 in 2004 (ABS 2015a).

What is missing from the picture?

A significant number of the new priorities outlined in the Healthy, Safe and Thriving: National Strategic Framework for Child and Youth Health do not have any associated data sources or measures for success. Work to develop measures of how children and young people are faring against all objectives in the framework should be progressed so that the success of the framework can be measured.

Where do I go for more information?

More information on youth health is available at Youth health and wellbeing or the AIHWs National Youth Information Framework data portal.

References

ABS (Australian Bureau of Statistics) 2015a. Births, Australia, 2014. ABS cat. no. 3301.0. Canberra: ABS.

ABS 2015b. Deaths, Australia, 2014. ABS cat. no. 3302.0. Canberra: ABS.

ABS 2015c. National Health Survey: 2014–15, customised report. Canberra: ABS.

ABS 2015d. National Health Survey: first results, 2014–15. ABS cat. no. 4364.0.55.001. Canberra: ABS.

BITRE 2015 (Bureau of Infrastructure, Transport and Regional Economics). Australian Road Deaths Database. Canberra: BITRE. Viewed 24 November 2015.

COAG Health Council (Council of Australian Governments Health Council) 2015. Healthy, Safe and Thriving: National Strategic Framework for Child and Youth Health. Adelaide: COAG Health Council.

Department of Health 2015. National Notifiable Disease Surveillance System, customised reports. Canberra: Department of Health. Viewed 23 October 2015.

Lawrence D, Johnson S, Hafekost J, Boterhoven De Haan K, Sawyer M, Ainley J et al. 2015 The mental health of children and adolescents: report on the second Australian Child and Adolescent Survey of Mental Health and Wellbeing. Canberra: Department of Health.

Mitchell A, Patrick K, Heywood W, Blackman P & Pitts M 2014. 5th National Survey of Australian Secondary Students and Sexual Health 2013, ARCSHS monograph series no. 97. Melbourne: Australian Research Centre in Sex, Health and Society, La Trobe University.

NHMRC (National Health and Medical Research Council) 2013. Australian Dietary Guidelines 2013. Canberra: NHMRC. Viewed 24 November.

5.5 Mental health of Australia's young people and adolescents

Mental health disorders that emerge during the formative years of childhood can have a lasting impact on the health and wellbeing of the individual and on the lives of those around them (Erskine et al. 2015). The development of programs and initiatives for young people who need mental health care and support requires a sound understanding of the scope of the problem, which is best achieved through population-based prevalence studies.

Australia has an epidemiological mental health program, known as the National Survey of Mental Health and Wellbeing, which comprises three surveys: a survey of the adult population, a survey of people living with psychotic mental illness, and a survey of children and adolescents. Together, these surveys provide a detailed national view of the prevalence of mental health issues in Australia and of their impact.

The Australian Child and Adolescent Survey of Mental Health and Wellbeing (known as the Young Minds Matter Survey), conducted in 2013–14, is the second survey to be conducted on the prevalence of mental health disorders in children and adolescents (Telethon Kids Institute 2015), which surveyed households with 4–17 year olds (the methodology is briefly summarised in Box 5.5.1). The survey also examined the health behaviours of young people and their use of the available support services and provided the opportunity to make comparisons against the first survey, conducted in 1998.

Box 5.5.1: The Young Minds Matter Survey methodology

Responses were obtained from over 6,000 households in Australia using two components.

1. A component for parents and carers

Parents and carers were questioned by a trained interviewer on a range of topics, including:

  • family structure and sociodemographics
  • health of the child and any disabilities
  • the child's mental health service usage in the 12 months prior to the survey
  • school attendance
  • family characteristics.

Survey instrument examples included:

  • strengths and difficulties questionnaire in relation to one selected child
  • the Diagnostic Interview Schedule for Children Version IV.

2. A component for young people

A total of 3,000 people aged 11–17 from the participating households completed a self-report questionnaire that included:

  • a strengths and difficulties questionnaire
  • the Diagnostic Interview Schedule for Children Version IV major depressive disorder module
  • the Kessler Psychological Distress Scale.

Questions included information about:

  • self-harm and suicidal behaviours
  • mental health service usage in the 12 months prior to the survey
  • experience of bullying and health risk behaviours
  • use of the internet and informal support mechanisms.

Source: Lawrence et al. 2015.

Prevalence of mental health disorders in young people

Results from the 2013–14 Young Minds Matter Survey indicate that the majority of children and adolescents in Australia have good mental health. However, the results also indicate that 1 in 7 (14%, or 560,000) children and adolescents aged 4–17 had a mental disorder in the previous 12 months. Common mental disorders covered in the Young Minds Matter Survey are briefly described in Box 5.5.2. Prevalence rates were higher overall among males (16%) than females (12%) across all disorders except Major depressive disorder (Figure 5.5.1). Attention deficit hyperactivity disorder (ADHD) was the most prevalent disorder for males, and more common in the 4–11 years age group than in the 12–17 years age group. Anxiety disorders was the most prevalent disorder group among females, and more common in the 12–17 years age group. The prevalence of Major depressive disorder was higher when young people aged 11–17 provided the information themselves (7.7%) than when the information was provided by their parent/carer (4.7%).

Box 5.5.2: Common mental disorders covered in the Young Minds Matter Survey

Major depressive disorder—the key feature is the presence of either depressed mood, loss of interest or pleasure or being grouchy, irritable and in a bad mood. Symptoms of major depressive disorder may include significant weight change, loss of appetite, difficulty sleeping, restlessness, fatigue and loss of energy, feeling of worthlessness and inability to concentrate. The diagnostic criteria for this disorder specify that at least five symptoms of depression must be present for a minimum of a 2-week period; that these symptoms cause clinically significant distress; and that they interfere with normal functions at school, at home or in social settings.

Anxiety disorders—a class of mental disorders defined by the experience of intense and debilitating anxiety. The type of anxiety disorders covered in the survey were social phobia, separation anxiety disorder, generalised anxiety disorder, and obsessive-compulsive disorder.

Attention deficit hyperactivity disorder (ADHD)—a persistent pattern of inattention and/or hyperactivity-impulsivity. Children and adolescents with this condition may find it difficult to pay attention and to see tasks or activities through to the end, or may make careless mistakes with school work or other tasks. Children and adolescents with problems in the area of hyperactivity may talk excessively; have trouble staying still when it is appropriate or expected; and act like they are 'always on the go'.

Conduct disorder—repetitive and persistent behaviour to a degree that violates the basic rights of others, major societal norms or rules—in terms of aggression towards people or animals, destruction of property, deceitfulness or theft, and serious violation of rules.

Source: Lawrence et al. 2015.

Figure 5.5.1: 12-month prevalence of mental health disorders, by disorder type, by age and sex, 2013–14

Bar chart showing the proportion of males and females of different age groups who suffered different kinds of mental health disorders in 2013-14. More males suffered any mental disorder than females in all age groups (around 15%25 compared to around 10%25 of females.).  

Comparison of the 2013–14 data with the results of the first survey of young people, conducted in 1998, was limited due to changes in the survey design—most notably differences in the types of disorders that were assessed—and was limited to the 6–17 year old age group. Overall prevalence of any mental health disorder was similar to that indicated in the earlier 1998 survey; however, there were changes in the prevalence of specific disorders between the two surveys. Prevalence rates for ADHD declined over the 15 years between the surveys. By contrast, the rate of Major depressive disorder increased. A comparison for Anxiety disorders could not be made due to survey design changes.

Social and demographics characteristics

The Young Minds Matter Survey identified associations between household demographics and the prevalence of mental health disorders (Table 5.5.1).

Table 5.5.1: Associations between household demographics and 12-month prevalence of mental disorders, 2013–14
Household demographics Lower prevalence Higher prevalence
Family composition 10.4%
Original family
18.3–23.7%
Step families, blended families and one parent families
Income bracket ($ per year) 10.5%
Highest ($130,000+)
20.5%
Lowest ($52,000 or less)
Parent/carer employment 10.8%
Both parents/carers employed
21.3-29.6%
Sole parent/carer; neither parent/carer in employment
Family functioning 10.9%
Very good
35.3%
Poor
Location 12.6%
Greater capital city
16.2%
Rest of state

Severity of conditions

Unlike many other health conditions, the experience of a mental health disorder is unique to the individual, meaning that the impact of the disorder on daily life and activities is very different among individuals with the same diagnosis. Four domains relating to impact were assessed by the Young Minds Matter Survey: at school or work; with friends and social activities; on the family; and on the children themselves. These factors were combined to determine the severity profile for each disorder. However, the most severe forms of mental illness (for example, psychotic disorders) were out of scope for the survey.

The prevalence of mental health disorders for 4–17 year olds decreased with increasing severity, with 8.3% having 'mild' disorders, 3.5% 'moderate' and 2.1% 'severe' disorders (Figure 5.5.2). Importantly, the higher prevalence conditions, such as ADHD and Anxiety disorders, were more likely to be rated as having 'mild' and 'moderate' than 'severe' impact. Major depressive disorder was the only condition in which 'mild' impact was less common than 'moderate' and 'severe' impact.

Figure 5.5.2: 12-month prevalence of mental disorders among 4–17 year olds, by disorder type and severity, 2013–14

Stacked bar chart showing the proportion of 4-17 year olds with various mental disorders in 2013-14, either mild, moderate or severe. Around 8%25 suffered some kind of mild mental disorder, around 4%25 suffered some kind of moderate mental disorder, and around 2%25 suffered some kind of severe mental disorder.

Self-harm, suicidal ideation and attempted suicide

Suicide, self-harm behaviours, suicidal ideation and attempted suicide have long-lasting impacts on individuals, families and communities. The relationship between suicide and previous self-harm behaviours is strong, with around half of young people who die by suicide having previously engaged in self-harm behaviours (Hawton & James 2005). While suicide is uncommon among young people aged 0–14, it is the leading cause of death for young Australians aged 15–24. In 2013, there was fewer than one suicide death per 100,000 population in young people aged 0–14, increasing to 10 deaths per 100,000 population for the 15–19 age group and 12 deaths per 100,000 population for the 20–24 age group (ABS 2015). Rates have been relatively stable over the last 15 years. (Note: suicide data for children aged under 15 years should be treated with caution because there are difficulties determining a suitable age at which self-inflicted acts can be interpreted as an intentional act of self-harm.)

The Young Minds Matter Survey asked participants questions about self-harm, suicidal ideation and attempted suicide.

Self-harm

The survey showed that 11% of young people aged 12–17 had ever self-harmed, which equates to around 186,000 adolescents. However, these figures are likely to be an underestimate, with around 7.5% of survey respondents preferring not to answer questions about self-harm. Females aged 16–17 had the highest prevalence of ever having harmed themselves (23%), over 3 times the rate of males the same age. Self-harm was shown to be most commonly associated with Major depressive disorder, with nearly half of all females with the disorder having ever self-harmed. Around half of 12–17 year olds who self-harmed in the 12 months prior to the survey had used support services such as health or school services; however, it is not known if service use was before or after the self-harm event.

Suicidal ideation

One in 20 young people (5.6%) aged 12–15 had thoughts of suicide in the 12 months prior to the survey. The suicidal ideation rate for 16–17 year olds was greater than the 12–15 age group: 1 in 10 (11%) had suicidal thoughts, and 7.8% had made a suicide plan in the 12 months prior to the survey. Rates were higher in females (15%) than males (6.8%) and, similar to self-harm, the strongest association between thoughts of suicide and mental disorders occurred for those with a Major depressive disorder.

Suicide attempt

Suicide attempt in the 12 months prior to the survey was highest in females aged 16–17 (4.7%), followed by males aged 16–17 (2.9%); however, 5.3% of 16–17 year olds reported having ever attempted suicide. Suicide attempts in the 12 months prior to the survey in children aged 12–15 (1.7%) were half that of young people aged 16–17 (3.8%). One in 5 females with Major depressive disorder, as determined by their own survey response, had attempted suicide. Males (14%) with Major depressive disorder were less likely to have attempted suicide than females (23%). The majority of 13–17 year olds who reported a suicide attempt had used support services in the previous 12 months, although it is not possible to establish whether the service use was before or after the suicide attempt.

Services available for people at risk of suicide are:

Lifeline 13 11 14

Kids Help Line 1800 55 1800

Suicide Call Back Service 1300 659 467

Health risk behaviours

Behaviours that impact on the overall health and wellbeing of an individual are termed 'health risk behaviours'—that is, they increase a person's risk of developing ill health. For example, smoking, alcohol consumption and drugs use are all considered to be health risk behaviours. Some health risk behaviours are also known risk factors for the development of mental disorders. For example, in young people, risk factors associated with depression include alcohol consumption, drug use, unhealthy diet and negative coping strategies; conversely, maintaining a healthy weight, adequate diet and appropriate levels of sleep have been shown to reduce the risk of depression, and are also known as 'protective health factors' (Cairns et al. 2014). The Young Minds Matter Survey results provide an insight into the prevalence of young people engaging in health risk behaviours.

Alcohol consumption was the most prevalent health-risk behaviour identified in young people by the survey. Nearly 4 in 10 (38%) of all 13–17 year olds reported having ever consumed alcohol, with consumption rates higher for those with a mental disorder, particularly those with Major depressive disorder (65%). The self-reported rate for consuming four drinks of alcohol in a row in the last 30 days, by young people with a Major depressive disorder (28%), was more than double the rate for young people without a disorder (10%). Three in 10 females (31%) with Major depressive disorder (based on self-report) engaged in risky alcohol consumption, compared with 2 in 10 males (19%).

Around 1 in 10 (9.9%) 13–17 year old survey respondents reported having 'ever smoked at least once a week', with 7.2% having smoked in the 30 days prior to the survey. Smoking rates were 4–5 times higher in young people with a mental health disorder than in people without a disorder. Females (8.2%) were more likely than males (6.2%) to report having smoked in the 30 days prior to the survey. The highest smoking rate was in females with a Major depressive disorder (27%): 1 in 4 smoked in the 30 days prior to the survey.

Similar usage patterns to alcohol consumption and smoking were observed for cannabis and other drug use. That is, higher usage rates were observed in young people with mental disorders—in particular for young people with Major depressive disorder—compared with those who did not have a disorder. Overall, 12% of 13–17 year olds reported having ever used cannabis and 4.5% reported using other drugs. Rates of ever using cannabis by those with Major depressive disorder were over 3 times the rate for those without a disorder, and 6–8 times greater for other drugs (based on parent/carer and self-report respectively).

These results suggest that while Major depressive disorder was less prevalent across the total youth population than other disorders, it was more prevalent in 16–17 year olds and more often associated with risky health-related behaviours that may impact on the overall health and wellbeing of the individual. However, it is important to note that these data only illustrate the association between risk behaviours and mental health conditions. They cannot identify cause and effect—that is, whether the health behaviours occur before or after the development of a mental health condition.

Emotional and behavioural support for young people

Young people often need support for emotional and behavioural issues in their formative years that may not be due to a diagnosable mental disorder. That is, support is often required to help young people negotiate 'normal' childhood/adolescent issues that are part of the transition to adulthood (Zimmerman et al. 2013). The lives of young people are dominated by family relationships, peer relationships and the school environment. Each of these elements provides a critical gateway through which support for young people can be delivered. Support at critical times may ease the transition to adulthood and prevent the onset and/or severity of mental health issues.

Family, friends and school staff—informal support

Young people most often rely on those close to them for informal support. Informal support for emotional or behavioural issues is often provided by relatives, friends and school staff. The Young Minds Matter Survey found that, in 2013–14, nearly two-thirds (63%) of young people aged 13–17 received informal help from their family members, friends or school staff in the 12 months prior to the survey. While males (52%) were less likely to receive informal support, they were equally likely to receive support from a parent (38%) or a friend (35%). By contrast, females (74%) reported receiving more informal support than males but were more likely to receive support from a friend (62%) than from a parent (55%). Four in 5 (80%) young people aged 13–17 with a mental health disorder received informal support (based on the parent report), compared with 3 in 5 of those without a disorder (58%). These data suggest that the majority of young people receive support from a range of sources, regardless of their mental health status.

Support services

Support services and clinical care options specifically designed for young people are provided by governments through various portfolios, including education and health. Support services are also provided by the non-government sector. The first Australian Child and Adolescent Survey of Mental Health and Wellbeing, conducted in 1998, estimated that only around one-third of 6–17 year olds with mental health issues sought and received care in the 6 months prior to the survey, suggesting that more needed to be done for young people requiring care (Sawyer et al. 2000). The 2013–14 Young Minds Matter Survey results provide a timely update on the use of services by all children and adolescents, as well as those with mental health disorders.

Service use by all 417 year olds

Health and school services were the most common services used by 4–17 year olds in 2013–14.

  • One in 10 young people who received support for their emotional and behavioural issues did not have a diagnosable mental disorder, as measured by the survey.
  • A further 40% of service users had symptoms of a mental disorder but did not meet the threshold for a 'mental disorder.
  • The remaining 50% of service users were assessed as having a mental disorder.

Data imply that many young people with emotional and behavioural issues are seeking and receiving support regardless of whether they have a diagnosable mental health illness.

Service use by 417 year olds with a mental health disorder

Over half (56%) of 4–17 year olds with a mental health disorder had used services for emotional or behavioural issues in 2013–14 (Figure 5.5.3). Service use comparisons with the 1998 estimates can only be made for those aged 6–17 years with either Major depressive disorder, ADHD or Conduct disorder. Service use in this group was 68% in the 12 months prior to the 2013–14 survey compared with 31% in the six months prior to the 1998 survey.

Service use for all disorders in 2013–14 was greater for the 12–17 years age group than for the corresponding 4–11 years age group. The service-use profiles for each of the mental health disorders (Figure 5.5.3) largely reflect the severity profile of each disorder: that is, disorders with a greater proportion of severe impact (see Figure 5.5.2) were associated with greater service usage rates. When severity is considered, regardless of disorder, 88% of young people with a mental disorder that severely affected their daily lives accessed services, compared with 73% of those with moderate disorders and 41% with mild disorders.

Figure 5.5.3: Service use in the 12-months prior to the survey among 4–17 year olds with mental disorders, by disorder type and age, 2013–14

Column graph comparing the rate of service use by 4-11 year olds and 12-17 year olds with mental disorders in the 12 months prior to the survey, by disorder type in 2013-14. A higher proportion of 12-17 year olds used services: around 60%25 with any mental disorder compared to 50%25 of 4-11 year olds.Column graph comparing the rate of service use by 4-11 year olds and 12-17 year olds with mental disorders in the 12 months prior to the survey, by disorder type in 2013-14. A higher proportion of 12-17 year olds used services: around 60%25 with any mental disorder compared to 50%25 of 4-11 year olds.  

Types of support services

School-based services

School-based services—such as counsellors, welfare officers and support resources— provide support for young people as they negotiate challenges in their school-based social environment, since social connectedness has been shown to be a predictive factor for substance abuse, mental health disorders and school outcomes (Bond 2007).

Almost all (96%) of survey respondents aged 4–17 reported attending school or another educational institution. Of these, 1 in 9 students had used a school service for emotional or behavioural problems in the 12 months prior to the survey. Students with a mental health disorder used school services at a higher rate, with 40% receiving school-based services for their emotional and behavioural issues. The most common school service received was individual counselling followed by special class or school; group counselling or support program; and school nurse services. Similar to the overall usage profile, severity of the impact of the disorder was associated with much higher service-usage rates.

Headspace

Headspace is an early intervention service model aimed at providing mental health services to 12–25-year-olds (National Youth Mental Health Foundation 2015). More than one-third (37%) of all Young Minds Matter Survey participants had heard of headspace, and 7.4% had accessed one or more of headspace's services—for example, accessed online information, spoken to a headspace professional, or visited a headspace site.

The online environment

Online services were accessed by nearly 3 in 10 (30%) of young people aged 13–17 with any mental disorder. The most common service accessed by this group was information about mental health issues, followed by assessment tools. One in 5 young people without any disorder also accessed online services, mostly seeking information about mental health issues.

Health services

Health services are most commonly clinical in nature: that is, they treat patients. Available mental health services are diverse and include:

  • primary care services—that is, the first point of contact with the health system (for example, general practitioners)
  • community-based care, including psychologists and psychiatrists and community-based specialised mental health teams operated and managed by state and territory health departments
  • specialised mental health care facilities in hospitals.

The Young Minds Matter Survey results showed that mental health-related services were used by around half of all 4–17 year olds with a mental health disorder. As would be expected, given the predominantly clinical nature of health services, usage was greater for young people with disorders that had a severe impact on the individual, regardless of the disorder type (Figure 5.5.4).

Figure 5.5.4: Mental health-related health service use in past 12 months among 4–17 year olds with mental disorders, by disorder type and severity of impact, 2013–14

Column graph showing the rate of service use by 4-17 year olds with mental disorders in the last 12 months, by disorder type and the severity of the disorder in 2013-14. Over 80%25 of those with any severe mental disorder used services, around 70%25 of those with any moderate mental disorder used services, and around 40%25 of those with any mild mental disorder used services.  

Barriers to seeking and receiving care

The survey results indicate that around one-third (31%) of parents of 4–17 year olds with mental health disorders reported accessibility issues as the main reason for not seeking help or not having their needs met. Accessibility issues, such as inability to afford a service, are a continuing challenge for the mental health care system.

There are many reasons why children and adolescents with mental health disorders might not seek or receive the care they need. The Young Minds Matter Survey provides some insight into this issue.

Parents and carers of 4–11 year olds with mental health disorders who reported they did not seek help for their child, or that their child's needs were not met by services, most commonly reported that they could not afford it (41%), were not sure where to get help (40%), and that they would prefer to handle the issues by themselves or with family/friends (37%) (note that more than one response was allowed). Parents of 12–17 year olds were most likely to report that their child/adolescent refused help (48%), that they were not sure where to get help (39%), or that they could not afford it (33%). Three out of 10 (29%) parents of 4–17 year olds with mental disorders reported that they could not get an appointment.

What is the AIHW doing?

The program of population surveys and the National Survey of Mental Health and Wellbeing, is supplemented by AIHW's administrative mental health data sets. These latter data provide detailed information on the response of governments to the mental health needs of Australians, including children and adolescents. These data—published on the AIHW's Mental health services in Australia website—monitor the support services provided by Australia's specialised mental health care services.

What is missing from the picture?

National mental health prevalence studies require rigorous survey design and data analysis and hence are costly exercises. However, the surveys afford the opportunity to obtain additional valuable information about other aspects of children's and adolescents' mental health and wellbeing, including service usage (of both health and non-health services); self-reported problems, behaviours and risk factors in young people; and self-harm and suicidal behaviours. Although a range of health administrative data sets are available to provide information about mental health service usage by children and adolescents, there is a paucity of national data on the support provided by other sectors, for example by education and welfare. Development of alternative data sources/methodologies to supplement the cycle of prevalence surveys, providing more regular information about these other aspects of children and adolescents' mental health and wellbeing, would be valuable.

The Young Minds Matter Survey's design did not enable representative data to be collected on Indigenous status; therefore, no comparisons can be made between the prevalence of mental health disorders for Aboriginal and Torres Strait Islander people and non-Indigenous Australians (see 'Chapter 5.7 How healthy are Indigenous Australians?').

Where do I go for more information?

The Young Minds Matter website (Telethon Kids Institute 2015) provides a range of information about the study, including an online data portal for interrogation of some aspects of the data.

There are a number of Australian & New Zealand Journal of Psychiatry articles either in press or published which supplement the survey report and supplementary tables.

More information about mental health is available at the Mental health services in Australia website, which provides a comprehensive picture of the national response of the health and welfare service system to the mental health care needs of Australians.

References

ABS (Australian Bureau of Statistics) 2015. Causes of death, Australia, 2013. ABS Cat. no. 3303.0. Canberra: ABS.

Bond L, Butler H, Thomas L, Carlin J, Glover S, Bowes G et al. 2007. Social and school connectedness in early secondary school as predictors of late teenage substance use, mental health, and academic outcomes. Journal of Adolescent Health 40(4):357e.9–18.

Cairns KE, Yap MB, Pilkington PD & Jorm AF 2014. Risk and protective factors for depression that adolescents can modify: a systematic review and meta-analysis of longitudinal studies. Journal of Affective Disorders 169:61–75.

Erskine HE, Moffitt TE, Copeland WE, Costello EJ, Ferrari AJ, Patton G et al. 2015. A heavy burden on young minds: the global burden of mental and substance use disorders in children and youth. Psychological Medicine 45(7):1551–63.

Hawton K, James A 2005. Suicide and deliberate self harm in young people. BMJ 330(7496):891–94.

Lawrence D, Johnson S, Hafekost J, Boterhoven De Haan K, Sawyer M, Ainley J et al. 2015. The mental health of children and adolescents. Report on the second Australian Child and Adolescent Survey of Mental Health and Wellbeing. Canberra: Department of Health.

National Youth Mental Health Foundation 2015. Headspace. Melbourne: National Youth Mental Health Foundation. Viewed 16 October 2015.

Sawyer MG, Arney FM, Baghurst PA, Clark JJ, Graetz BW, Kosky RJ et al. 2000. Mental health of young people in Australia. Canberra: Department of Health and Aged Care.

Telethon Kids Institute 2015. Young Minds Matter 2015. Perth: Telethon Kids Institute. Viewed October 2015.

Zimmerman MA, Stoddard SA, Eisman AB, Caldwell CH, Aiyer SM & Miller A 2013. Adolescent resilience: promotive factors that inform prevention. Child Development Perspectives 7(4):215–20.

5.6 Health of the very old

The proportion of the very old in the Australian population is increasing: in 2016, there are 486,700 people aged 85 and over, representing 2.0% of the population. This number is projected to more than double by 2036, to 1.0 million (3.2% of the population) (ABS 2013).

The health of people aged 85 and over

Life expectancy is increasing both at birth, and over the course of a person's life, as most Australians enjoy better health, greater standards of living, and improved access to high-quality health care. Other topics within this publication present more information on this (see 'Chapter 1.3 How healthy are we' and 'Chapter 6.17 Health care use by older Australians'). Selected health characteristics of Australia's older people are shown here.

A birthday cake for a person turning 85. A man turning 85 in 2013 could expect to live another 6.1 years, and a woman the same age could expect another 7.1 years.

Graphic indicating two people out of three. Two-thirds (65%) of people aged 85 and over rated their health as 'good', 'very good' or 'excellent' in 2014–15. Only 9.0% of people aged 85 and over reported a 'high' or 'very high' level of psychological distress, the lowest rate in any age group (ABS 2015b).

A health professional. Most common health conditions reported by people aged 85 and over in 2014–15 included long-sightedness (61%), deafness (57%), and arthritis (49%) (ABS 2015b).

A hospital. The three most common reasons for hospitalisation for people aged 85 and over in 2013–14 were for care involving dialysis (11%), rehabilitation (8.6%), and heart failure (3.0%).

Graphic indicating two people out of five. Nearly 2 in 5 people (39%) who died in 2013 were aged 85 and over.

A person suffering from depression. Between 2009 and 2013, 34 deaths per 100,000 men aged 85 and over were caused by suicide—the highest rate of suicide of any age group, although people aged 85 and over account for only a small number of deaths by suicide (ABS 2015a).

A man with his heart highlighted. The most common cause of death in 2013 for people aged 85 and over was coronary heart disease (17%), followed by dementia (12%).

The risks to health for people aged 85 and over

Ageing may be accompanied by physiological changes, such as increased frailty, reduced mobility, and progressive loss of vision and hearing. Common risk factors can exacerbate the impact of these changes, some of which are described here.

A set of scales. Nearly 4 in 10 (39%) people aged 85 and over were overweight but not obese in 2014–15. A further 18% were obese (ABS 2015b).

A slightly overweight person. In 2014–15, 74% of men and 85% of women aged 85 and over had a waist circumference that placed them at an increased risk of chronic disease (ABS 2015b).

An apple and a carrot. Only 6.2% of people aged 85 and over had an 'adequate' daily fruit and vegetable consumption in 2014–15. While 65% ate the recommended 2 or more serves of fruit, only 6.9% had the recommended 5 or more serves of vegetables a day. Just 5.1% of all adults consumed an adequate amount of fruit and vegetables (ABS 2015b).

A cigarette. Fewer than 4.0% of people aged 85 and over were daily smokers in 2013, compared with 13% of all adults. Over one-quarter (28%) of people aged 85 and over were ex-smokers.

Two alcoholic drinks. Almost two-thirds (63%) of people 85 and over drank alcohol, with 19% of them having at least one alcoholic drink every day in 2013. While the proportion of people who drank daily increased with age, over 20% of people aged 85 and over had never drunk alcohol.

A person running. Only 29% of people aged 85 and over were 'sufficiently active for health' in 2014–15, undertaking 150 minutes of physical activity over five or more sessions in a week—and 45% undertook no physical activity. Overall, 45% of adults met the threshold for 'sufficient' activity (ABS 2015b).

A blood pressure cuff. More than half (52%) of people aged 85 and over had high blood pressure in 2014-15 (the highest of any age group).

What is missing from the picture?

The very old are under-represented in many health surveys, particularly frail or ill people who are cared for in settings such as hospitals and permanent residential aged care. As a result, the proportion of frail or ill people in the population may be under-estimated, and there is limited information on their experience of, and outcomes from, interactions with the health system. In addition, data regarding the very old is not consistently collected or reported—instead, broader 'old' age groups, such as people aged 65 and over, are often used instead.

Where do I go for more information?

More information is available through the AIHW website on topics such as ageing, deaths, hospitals, and risk factors to health.

References

ABS (Australian Bureau of Statistics) 2013. Population projections, Australia, 2012 (base) to 2101. ABS cat. no. 3222.0. Canberra: ABS.

ABS 2015a. Causes of death, Australia, 2013. ABS cat. no. 3303.0. Canberra: ABS.

ABS 2015b. National Health Survey: first results, 2014–15. ABS cat. no. 4364.0.55.001. Canberra: ABS.

5.7 How healthy are Indigenous Australians?

The health of Aboriginal and Torres Strait Islander Australians is improving on a number of measures, including significant declines in infant and child mortality and decreases in avoidable mortality related to cardiovascular and kidney diseases. Despite these improvements, significant disparities persist between Indigenous and non-Indigenous Australians. Indigenous Australians continue to have lower life expectancy, higher rates of chronic and preventable illnesses, poorer self-reported health, and a higher likelihood of being hospitalised than non-Indigenous Australians (AIHW 2015a, 2015b).

There are many dimensions to the poorer health status of Indigenous Australians compared with other Australians and a complex range of factors are behind these differences. These include:

  • differences in the social determinants of health, including lower levels of education, employment, income and poorer quality housing, on average, compared with non-Indigenous Australians
  • differences in behavioural and biomedical risk factors such as higher rates of smoking and risky alcohol consumption, lack of exercise, and higher rates of high blood pressure for Indigenous Australians
  • the greater difficulty that Indigenous people have in accessing affordable and culturally appropriate health services that are in close proximity.

Each of these three aspects contributing to the Indigenous health gap are reviewed in separate snapshots ('Chapter 4.2 Social determinants of Indigenous health', 'Chapter 4.8 Health behaviours and biomedical risks of Indigenous Australians' and 'Chapter 6.6 Indigenous Australians' access to health services').

This snapshot focuses on two selected topics:

  • progress on the two measures of Indigenous health in the Council of Australian Governments (COAG) Closing the Gap targets: life expectancy and child mortality
  • summaries of three commonly used measures of how healthy Indigenous Australians are: self-assessed health rating; disability and prevalence of major long-term conditions; and potentially avoidable deaths.

Life expectancy

Life expectancy at birth is a measure of how long a newborn person is expected to live, on average, given the currently observed pattern of mortality in the population. The COAG target is to fully close the gap in life expectancy between Indigenous and non-Indigenous Australians by 2031.

The latest available estimates of Indigenous life expectancy were released in 2013 and they show that Indigenous Australians have a life expectancy of around 10 years less than non-Indigenous Australians.

  • For the 3-year period 2010–2012, estimated Indigenous life expectancy at birth was 69.1 years for males and 73.7 years for females.
  • Life expectancy at birth has increased by 1.6 years for Indigenous males and 0.6 years for Indigenous females since 2005–2007 (corresponding to annual increases of 0.3 and 0.1 years of life, respectively).
  • Between 2005–2007 and 2010–2012, the life expectancy gap between Indigenous and non-Indigenous Australians decreased by 0.8 years for males and by 0.1 years for females (taking into consideration that life expectancy also increased for non-Indigenous Australians over this period).
  • To meet the Closing the Gap target by 2031, an annual increase of 0.6 to 0.8 years in Indigenous life expectancy at birth will be required (AHMAC 2015).

See also 'Chapter 5.8 Main contributors to the Indigenous life expectancy gap'.

Child mortality

The mortality rate for young children is also a key indicator of the general health of a population. Indigenous child mortality has been declining steadily over time (Figure 5.7.1).

Between 1998 and 2014, there was a significant:

  • decline in Indigenous child mortality rates (by 33%)
  • narrowing of the gap (by 34%) with non-Indigenous child mortality.

Figure 5.7.1: Child mortality rates for children aged under 5, by Indigenous status, 1998 to 2014

Line chart comparing the Indigenous and non-Indigenous child mortality rates for children under 5, from 1998 to 2014. Both rates have a slightly trending decrease, but the Indigenous rate remains higher than the non-Indigenous rate (around 220 deaths per 100000 compared to around 120 per 100000).

Note: Based on combined data for NSW, QLD, WA, SA and the NT.

Source: ABS and AIHW analysis of National Mortality Database.

The Closing the Gap target is to halve the gap in mortality rates between Indigenous and non-Indigenous children aged under 5 within the decade between 2008 and 2018.

Progress on this target is assessed to be 'on track' (PM&C 2016). Progress is assessed by comparing the annual outcome of the Indigenous child mortality rate to a range of values that indicate whether the required trajectory for that year has been met. The latest (2014) Indigenous child mortality rate was within the specified range for 2014 and so was on track towards the 2018 target (PM&C 2016).

Self-assessed health

Self-assessed rating of health is a widely used measure of overall health status. The most recent data are from the 2012–13 Australian Aboriginal and Torres Strait Islander Health Survey (AATSIHS).

  • Nearly 4 in 10 (39%) Indigenous Australians aged 15 and over reported their health status as 'excellent' or 'very good' in 2012–13—a decrease from 44% in 2008 and 43% in 2004–05 (SCRGSP 2014).
  • A further 37% reported their health as 'good, and 24% as 'fair' or 'poor' in 2012–13.
  • Adjusting for differences in age structure, 29% of Indigenous Australians rated their health as 'fair' or 'poor', which was more than double the non-Indigenous rate of 14%.
  • The proportion of Indigenous Australians reporting their health status as 'fair' or 'poor' was lowest in Very remote areas (16%).

Disability status and long-term health conditions

  • According to the 2012–13 AATSIHS, 36% of Indigenous Australians (an estimated 228,000 people) had some form of disability (AIHW 2015b). Based on age-standardised rates of 44% and 29%, this is 1.5 times the rate experienced by non-Indigenous Australians. Indigenous Australians were twice as likely to have a severe or profound form of disability (with age-standardised rates of 7.9% and 3.9%, respectively).
  • In 2012–13, two-thirds (67%) of Indigenous people reported at least one chronic health condition, with 33% reporting three or more. The proportion of Indigenous people reporting at least one health condition was similar to that of non-Indigenous people.
  • The prevalence of the leading long-term health conditions (excluding mental health) for Indigenous Australians, by specific age groups and in total for all ages, is shown in Figure 5.7.2. The relative importance of specific conditions varies considerably by age.
  • Overall, the most common conditions reported by Indigenous Australians (excluding mental health) were eye diseases and vision problems (33%), respiratory diseases (31%) and musculoskeletal diseases (20%).
  • Data on the overall prevalence of mental health conditions are not available from the most recent AATSIHS. Some related mental health indicators showed that in 2012–13:
    • 12% of Indigenous Australians reported feeling depressed or having depression as a long-term condition
    • 30% of Indigenous adults had high or very high levels of psychological distress in the 4 weeks prior to the survey (AIHW 2015b).

(See also 'Chapter 5.9 Health of Australians with disability'.)

Figure 5.7.2: Age-specific prevalence of leading long-term conditions for Indigenous Australians, 2012–13

Table showing the leading long-term health conditions for Indigenous Australians, by age group, in 2012-13. The leading condition for people aged 0-34 is respiratory diseases, and the leading condition for people aged 35+ is eye diseases and vision problems.

See this figure as a larger image.

Notes

  1. The top 5 disease categories for each age group excluding 'Symptoms, signs and conditions not elsewhere classified'.
  2. Data on the overall prevalence of mental health conditions are not available from the 2012–13 AATSIHS.

Source: AIHW 2015b based on analyses of 2012–13 AATSIHS data.

Potentially avoidable deaths

'Potentially avoidable deaths' refer to deaths from conditions that could have been avoided, given timely and effective health care. Rates of potentially avoidable deaths in a population represent the underlying population health, as well as health-service utilisation and the accessibility and effectiveness of the health system. Total counts and rates of potentially avoidable deaths of Indigenous Australians are based on data from the five jurisdictions (New South Wales, Queensland, Western Australia, South Australia and the Northern Territory) where the quality of Indigenous identification is considered to be of acceptable quality in the recording of deaths.

  • In the 5-year period 2009 to 2013, approximately 6,000 deaths (or 61% of all deaths) of Indigenous Australians aged 0–74 were classified as potentially avoidable deaths (compared with 51% of all deaths of non-Indigenous Australians in that age group).
  • After adjusting for differences in age structure, in the 2009–2013 period the mortality rate for Indigenous Australians who died from all potentially avoidable causes was more than 3 times the rate for non-Indigenous Australians (351 and 110 deaths per 100,000 population, respectively).
  • There was a 10% decline in the potentially avoidable death rate for Indigenous Australians in the 2009–2013 period compared with the previous 5-year period of 2003–2007. However, in the same period the potentially avoidable death rate also declined for the non-Indigenous population (SCRGSP 2016). Accordingly, the gap between the rates for the Indigenous and non-Indigenous population did not narrow.

Note that these rates are based on a new standard adopted in the National Healthcare Agreement 2015 by which specific causes of death are classified as 'potentially avoidable' in the context of the current Australian health system. The new classification leads to a smaller number of deaths categorised as 'potentially avoidable' than the previous classification did for both Indigenous and non-Indigenous deaths. Therefore, the counts and rates of potentially avoidable deaths presented here are different to those published in previous AIHW reports.

What is missing from the picture?

There are many complex interactions determining Indigenous health and mortality rates that still have data gaps or lack timely data. The national-level Aboriginal and Torres Strait Islander health surveys are carried out only once in 6 years, and coverage is not large enough to provide reliable small-area estimates. There is increasing use of health administrative data sets, such as hospital records or cancer registries, but the identification of the Indigenous status of all persons in these records is incomplete (though increasing).

There are also data gaps in the extent of, and reasons for, the inequalities in health status within the Indigenous population itself. These are important analyses to undertake. Better reporting of Indigenous health outcomes and analyses of causal factors can be achieved through a more coordinated effort to combine or link administrative health data from a number of sources and covering a number of years.

Where do I go for more information?

More information on the general and specific health condition of Indigenous Australians is available in the AIHW report The health and welfare of Australia's Aboriginal and Torres Strait Islander peoples 2015.

References

AHMAC (Australian Health Ministers' Advisory Council) 2015. Aboriginal and Torres Strait Islander Health Performance Framework: 2014 report. Canberra: AHMAC.

AIHW (Australian Institute of Health and Welfare) 2015a. Aboriginal and Torres Strait Islander Health Performance Framework 2014 report: detailed analyses. Cat. no. IHW 167. Canberra: AIHW.

AIHW 2015b. The health and welfare of Australia's Aboriginal and Torres Strait Islander peoples: 2015. Cat. no. IHW 147. Canberra: AIHW.

PM&C (Department of the Prime Minister and Cabinet) 2016. Closing the Gap Prime Minister's report 2016. Canberra: PM&C.

SCRGSP (Steering Committee for the Review of Government Service Provision) 2014. Overcoming Indigenous disadvantage: key indicators 2014. Canberra: Productivity Commission.

SCRGSP 2016. Report on government services 2016. Vol. E, Health. Canberra: Productivity Commission.

5.8 Main contributors to the Indigenous life expectancy gap

Life expectancy is an important measure of the health status of a population: it indicates how long a person can expect to live, based on current mortality patterns. Indigenous Australians tend to die at younger ages than non-Indigenous Australians and as such have shorter life expectancies. Life expectancy is affected by a range of factors, including disease incidence and prevalence; health behaviours such as smoking; social determinants such as education, income and employment; and access to health services (AHMAC 2015).

This article presents estimates of the contribution of different age groups and causes of death to the current gap in life expectancy between Indigenous and non-Indigenous Australians. This analysis will assist policymakers by showing where interventions are best targeted to reduce the gap. It is important to note that the gap in life expectancy is a relative measure and, as such, the size of the gap is not just influenced by changes in Indigenous life expectancy, but also by changes in the life expectancy of the non-Indigenous population.

The main analyses presented are for the 3-year period 2010–2012, to align with the most recent Indigenous life expectancy estimates available. Contextual information presented on the age profile and main causes of death among the Indigenous population is based on data for the 5-year period 2009–2013 (5 years of deaths are combined for reporting of Indigenous mortality to overcome the small number of Indigenous deaths from some conditions and age groups each year). All mortality data in this article relate to the five jurisdictions for which the quality of Indigenous identification is considered to be of acceptable quality for reporting—New South Wales, Queensland, Western Australia, South Australia and the Northern Territory (AIHW 2015a).

As this analysis refers to the life expectancy gap based on the 2010–2012 time period only, any comparisons over time (or with other time periods) should be made with caution. This is due to changes over time in the propensity of individuals to identify as Aboriginal or Torres Strait Islander, which may affect both the estimation of the size of the life expectancy gap, and comparability of associated analyses over time (see The health and welfare of Australia's Aboriginal and Torres Strait Islander peoples 2015 for more information on Indigenous identification).

What do we know?

How big is the gap?

The gap in life expectancy between Indigenous and non-Indigenous Australians in 2010-2012 was around 10 years: 10.6 years for males (Indigenous life expectancy at birth 69.1 years, non-Indigenous 79.7) and 9.5 years for females (Indigenous life expectancy at birth 73.7 years, non-Indigenous 83.2) (Figure 5.8.1).

Figure 5.8.1: Life expectancy at birth, by Indigenous status and sex, 2010–2012

Column graph comparing Indigenous and non-Indigenous life expectancy at birth, by sex, in 2010-2012. Male Indigenous life expectancy is 69.1, male non-Indigenous life expectancy is 79.7, female Indigenous life expectancy is 73.7 and female non-Indigenous life expectancy is 83.2.

Source: ABS 2013a.

Mortality age profile

Most deaths for Indigenous Australians occur in the middle age groups. In contrast, most deaths for non-Indigenous Australians occur in the older age groups. This partly reflects the younger age profile of the Indigenous population. A relatively large proportion of Indigenous deaths were premature (for example, before age 75) (Figure 5.8.2). During the 5-year period 2009–2013, around 81% of deaths among Indigenous people occurred before the age of 75, compared with 34% of deaths for non-Indigenous people (AIHW 2015a). (See 'Chapter 3.2 Premature mortality').

Figure 5.8.2: Age distribution of deaths, by Indigenous status, age and sex, 2009–2013

Bar charts comparing male and female age distribution of deaths by Indigenous status and age in 2009-2013. Both male and female non-Indigenous people have a steady increase in number of deaths as age increases, while Indigenous people experience the most death around middle age (around 10%25 of males and females die aged 55-59).

Note: Data are for NSW, Qld, WA, SA and NT.

Source: AIHW National Mortality Database.

The largest gaps in mortality between Indigenous and non-Indigenous Australians were in the 35–59 age groups, based on potential years of life lost (PYLL) due to premature mortality (Figure 5.8.3). PYLL is the number of additional years a person would have been expected to live had they not died before the age of 75.

Figure 5.8.3: Gap (rate difference) in potential years of life lost (PYLL) before age 75 between Indigenous and non-Indigenous Australians, by age and sex, 2009–2013

Column graph comparing the rate difference in potential years of life lost between Indigenous and non-Indigenous males and females in 2009-2013. The rate difference is greater for males for most of life, peaking at around 850 per 1000 for males aged 45-49.

Note: Data are for NSW, Qld, WA, SA and NT.

Source: AIHW National Mortality Database (AIHW 2015a).

Main causes of death

The main broad causes of deaths among Indigenous Australians in the 2009–2013 period were cardiovascular disease (25%); cancer (neoplasms) (20%); external causes (including suicide and transport accidents) (15%); endocrine, metabolic and nutritional disorders (including diabetes) (8.9%); and respiratory diseases (7.9%) (Figure 5.8.4). Compared with non-Indigenous Australians, cardiovascular diseases and cancer represented a smaller proportion of deaths, and external causes and endocrine, metabolic and nutritional disorders represented a larger proportion of deaths, among Indigenous Australians.

Figure 5.8.4: Causes of death, by Indigenous status, 2009–2013

Graph indicating the rates for different causes of death in 2009-2013, for Indigenous and non-Indigenous people. Most people died of circulatory disease (26%25 of Indigenous people, 32%25 of non-Indigenous people). The rate of death from cancer was 10%25 greater for non-Indigenous people.

Notes

  1. Data are for NSW, Qld, WA, SA and NT.
  2. Proportions may not sum to 100%, due to rounding.

Source: AIHW National Mortality Database.

The contribution of age group and causes of death to the life expectancy gap

New analysis undertaken by the AIHW measures the contributions of age and causes of death to the gap in life expectancy between Indigenous and non-Indigenous Australians (see Box 5.8.1).

Box 5.8.1: Estimating the contribution of age group and causes of death to the life expectancy gap

There were two steps in estimating the contribution of age groups and causes of death to the gap in life expectancy between Indigenous and non-Indigenous Australians in 2010–12:

  1. Indigenous and non-Indigenous life expectancy estimates and associated age-specific mortality rates for Australia for the period 2010–2012 (the latest available data for life expectancy) were sourced from the ABS (ABS 2013a, 2013b, 2014, 2015). Causes of death data for 2010–2012 were obtained from five jurisdictions: New South Wales, Queensland, South Australia, Western Australia and the Northern Territory (analysis of the AIHW National Mortality Database).
  2. Decomposition methods were used to estimate the contribution (in number of years) of age groups and causes of death to the life expectancy gap for 2010–2012 (Arriaga 1984; Pollard 1982; Preston et al. 2001; Wilson et al. 2007). This involved estimating the contribution of the difference in all-cause mortality between the Indigenous and non-Indigenous populations in each 5-year age group (<1, 1–4, 5–9 and so forth, to 85 years and over) to the difference in life expectancy at birth for both males and females. The sum of the contribution of age groups to the life expectancy gap between Indigenous and non-Indigenous Australians is equal the total life expectancy gap. The proportion of deaths in each age group in the Indigenous and non-Indigenous populations due to each of the six causes of death examined (cardiovascular diseases, diabetes, external causes, cancer, respiratory diseases and other causes) were then applied to the all-cause mortality differences, to estimate the relative contribution of each cause of death to the life expectancy gap.

Which age groups contribute the most to the life expectancy gap?

The analysis presented was undertaken by 5-year age group; however, results are presented in broader 20-year age groups for ease of interpretation.

Deaths in the 55–74 age group contributed the most to the life expectancy gap in 2010–2012 for both males and females, with each 5-year age group contributing around 1 year to the life expectancy gap (Table 5.8.1). This age group contributed 42% to the total life expectancy gap for males and 45% to the total life expectancy gap for females. The 35–54 age group made the second largest contribution to the life expectancy gap for both males and females (31% and 26% respectively). The very young and very old made a smaller contribution to the gap.

Table 5.8.1: Contribution of age groups to the life expectancy gap, by sex, 2010–2012

Age group Life expectancy
gap (years)
Males
Life expectancy
gap (years)
Females
Life expectancy
gap (%)
Males
Life expectancy
gap (%)
Females
0–14 0.5 0.5 4.7 5.3
15–34 1.3 0.8 12.3 8.4
35–54 3.3 2.5 31.1 26.3
55–74 4.4 4.3 41.5 45.3
75+ 1.0 1.4 9.4 14.7
Total 10.6 9.5 100.0 100.0

Sources: AIHW analysis of the AIHW National Mortality Database and ABS 2013a.

Which causes of death contribute the most to the life expectancy gap?

The contribution of different age groups to the life expectancy gap between Indigenous and non-Indigenous Australians is largely determined by patterns in the disease profiles of those age groups.

In 2010–2012, for males, the largest contributors to the life expectancy gap were cardiovascular diseases (2.9 years), external causes (or injuries) (1.9 years) and cancer (neoplasms) (1.5 years). For females, the largest contributors were also cardiovascular diseases (2.7 years), cancer (1.6 years) and external causes (1.3 years).

Different age groups had different disease profiles. For the 0–14 age group, the main causes of death contributing to the gap were conditions originating in the perinatal period (included in the 'other' category in Figure 5.8.4 and Table 5.8.2). External causes (injuries) contributed the most to the gap in life expectancy in the 15–34 age group, for both males and females. For age groups 35–54, 55–74 and 75 years and over, cardiovascular diseases contributed the most to the gap in life expectancy for both males and females (Table 5.8.2).

Table 5.8.2: Contribution of causes of death to life expectancy gap, by age and sex, 2010–2012 (years)

Males (years)
Age group Causes of
death
Cardiovascular
Causes of
death
Diabetes
Causes of
death
External
Causes of
death
Cancer
Causes of
death
Respiratory
Causes of
death
Other(a)  
Causes of
death
Total
0–14 0.0 0.0 0.1 0.0 0.0 0.4 0.5
15–34 0.2 0.0 1.0 0.0 0.0 0.1 1.3
35–54 1.1 0.3 0.5 0.3 0.2 0.9 3.3
55–74 1.3 0.6 0.2 1.0 0.5 0.8 4.4
75+ 0.3 0.2 0.0 0.1 0.2 0.2 1.0
Total 2.9   1.1   1.9   1.5   0.9   2.3   10.6  
Females (years)
Age group Causes of
death
Cardiovascular
Causes of
death
Diabetes
Causes of
death
External
Causes of
death
Cancer
Causes of
death
Respiratory
Causes of
death
Other(a)  
Causes of
death
Total
0–14 0.0 0.0 0.1 0.0 0.0 0.3 0.5
15–34 0.1 0.0 0.6 0.0 0.0 0.1 0.8
35–54 0.8 0.2 0.4 0.3 0.1 0.6 2.5
55–74 1.2 0.6 0.2 1.1 0.4 0.7 4.3
75+ 0.5 0.2 0.0 0.2 0.3 0.3 1.4
Total 2.7   1.0   1.3   1.6   0.9   2.1   9.5  

(a) Includes digestive diseases, conditions originating in the perinatal period, nervous system diseases, kidney diseases, infectious and parasitic diseases, and other causes.

Sources: AIHW analysis of the AIHW National Mortality Database and ABS 2013a.

What are the implications of these findings?

The findings suggest that chronic diseases, such as cardiovascular diseases and cancer, as well as injuries, which usually occur in the 35 to 74 year age groups in the Indigenous population, are responsible for the majority of the life expectancy gap. In comparison, the relative contribution of infant and child deaths to the gap is small. Strategies and programs to close the gap, however, should consider addressing disparities in childhood as well as the older age groups, because health conditions that become more apparent at older ages can begin in childhood or young adulthood. For example, factors such as poor diet, smoking, and unresolved mental trauma early in life can lead to heart disease or depression later in life.

What is the AIHW doing?

The AIHW's Enhanced Mortality Database project is using data linkage to improve estimates of Indigenous deaths and life expectancy. Death registrations are linked with hospital, residential aged care and perinatal data to investigate opportunities to improve the measurement of Indigenous deaths and life expectancy.

The AIHW is currently undertaking a study to measure the burden of disease experienced by the Indigenous population. The study will provide updated information on the impact of diseases and injuries on Indigenous Australians, as well as estimates of the gap in disease burden between Indigenous and non-Indigenous Australians. An initial report has been published: Australian Burden of Disease Study: fatal burden of disease in Aboriginal and Torres Strait Islander people 2010 (AIHW 2015b). A comprehensive report with 2011 and 2003 estimates of fatal and non-fatal burden, as well as the burden attributable to selected risk factors, will be released later this year.

What is missing from the picture?

Behavioural risk factors (such as smoking, diet and physical activity) as well as social determinants (such as income, education and employment) are also important factors which contribute to disparities in health outcomes between Indigenous and non-Indigenous Australians and, consequently, to the life expectancy gap. While previous studies have shown the importance of social determinants in addressing the health gap between Indigenous and non-Indigenous people (AIHW 2014; Booth & Carroll 2005; DSI Consulting 2009; Marmot 2011; Zhao et al. 2013), these were based on survey data now over a decade old. These studies did not look at the contribution of lack of access to affordable and culturally acceptable health services to the life expectancy gap, which is another important determinant of health that is difficult to measure. The evidence suggests that a complex relationship exists between health service access, social disadvantage, health behaviours, and health outcomes. Additional research, using the latest available data, on the overlap and causal links between these factors for the Indigenous population will provide a broader and more comprehensive understanding of the main drivers of the life expectancy gap, and where interventions are best targeted to reduce this gap. (For more information see 'Chapter 4.2 Social determinants of Indigenous health' and 'Chapter 6.6 Indigenous Australians' access to health services').

Where do I go for more information?

More information about Indigenous life expectancy and the gap in health status between Indigenous and non-Indigenous Australians is available at Indigenous Observatory.

Information on Indigenous burden of disease is available at Burden of disease.

The report Australian Burden of Disease Study: fatal burden of disease in Aboriginal and Torres Strait Islander people 2010 and other recent publications are available for free download.

References

ABS (Australian Bureau of Statistics) 2013a. Life tables for Aboriginal and Torres Strait Islander Australians, 2010–2012. ABS cat. no. 3302.0.55.003. Canberra: ABS.

ABS 2013b. Population projections, Australia, 2012 (base) to 2101. ABS cat. no. 3222.0. Canberra: ABS.

ABS 2014. Estimates and projections, Aboriginal and Torres Strait Islander Australians, 2001 to 2026. ABS cat. no. 3238.0. Canberra: ABS.

ABS 2015. Australian Demographic Statistics, March 2015. ABS cat. no. 3101.0. Canberra: ABS.

AHMAC 2015. Aboriginal and Torres Strait Islander Health Performance Framework 2014 report. Canberra: AHMAC.

AIHW (Australian Institute of Health and Welfare) 2014. Australia's health 2014. Australia's health series no. 14. Cat. no. AUS 178. Canberra: AIHW.

AIHW 2015a. Aboriginal and Torres Strait Islander health performance framework 2014: data tables. Canberra: AIHW.

AIHW 2015b. Australian Burden of Disease Study: fatal burden of disease in Aboriginal and Torres Strait Islander people 2010. Australian Burden of Disease Study series no. 2. Cat. no. BOD 2. Canberra: AIHW.

AIHW 2015c. The health and welfare of Australia's Aboriginal and Torres Strait Islander peoples: 2015. Cat. no. IHW 147. Canberra: AIHW.

Arriaga, E 1984. Measuring and explaining the change in life expectancies. Demography 21(1):83–96.

Booth A & Carroll N 2005. The health status of Indigenous and non-Indigenous Australians. Centre for Economic Policy research discussion paper no. 486. Canberra: Australian National University.

DSI Consulting Pty Ltd & Benham D 2009. An investigation of the effect of socio-economic factors on the Indigenous life expectancy gap. Canberra: DSI Consulting Pty Ltd.

Marmot, M 2011. Social determinants and the health of Indigenous Australians. Medical Journal of Australia 194(10):512–513.

Pollard JH 1982. The expectation of life and its relationship to mortality. Journal of the Institute of Actuaries 109(2):225–40.

Preston S, Heuveline P & Guillot M 2001. Demography: measuring and modelling population processes. Oxford: Blackwell Publishing.

Wilson T, Condon JR, and Barnes T 2007. Northern Territory Indigenous life expectancy improvements, 1967–2004. Australian and New Zealand Journal of Public Health 31(2):184–88.

Zhao Y, Wright J, Begg S & Guthridge S 2013. Decomposing Indigenous life expectancy gap by risk factors: a life table analysis. Population Health Metrics 11:1–9.

5.9 Health of Australians with disability

One of six priority outcomes of the National Disability Strategy 2010–2020 is 'People with disability attain the highest possible health and wellbeing outcomes throughout their lives' (DSS 2012). The Australian Bureau of Statistics (ABS) short disability module was first included in the ABS 2007–08 National Health Survey (NHS) and again in the 2011–12 NHS collection. This snapshot focuses on people aged under 65 years, as disability prevalence among older people is under-reported in the NHS due to the exclusion of institutional care settings.

Disability and self-assessment of health

Due to a range of factors—some of which may be directly related to a person's disability—people with disability, as a group, experience significantly poorer health than those without disability.

Based on survey data, in 2011–12, half (51%) of people aged 15–64 with severe or profound core activity limitation (that is, 'sometimes or always needing help with activities of self-care, mobility or communication') self-assessed their health as 'poor' or 'fair, compared with 5.6% for people without disability. The gap in self-assessed health between the two population groups remained large between 2007–08 and 2011–12 (Figure 5.9.1).

Figure 5.9.1: Self-assessed health status, people aged 15–64, by disability status, 2007–08 and 2011–12

Stacked column graph showing the proportion of people aged 15-64 who self-assessed their health to be excellent/very good, good, or fair/poor in 2007-08 and 2011-12, by disability status. Fewer people rated their health as excellent/very good in 2011-12 than in 2007-08. Most people with a severe or profound core activity limitation rated their health as fair/poor (around 40-50%25).

Note: Percentages have been age-standardised to the Australian population as at 30 June 2001.

Sources: AIHW analysis of ABS 2007–08 and 2011–12 National Health Survey confidentialised unit record files.

Long-term health conditions

In 2011–12, people aged under 65 with severe or profound core activity limitation had a higher prevalence of various types of long-term health conditions and were 3.3 times as likely as people without disability to have three or more long-term health conditions (74% versus 23%).

  • Half (50%) of people aged under 65 with severe or profound core activity limitation had mental health conditions, compared with 7.7% of people without disability.
  • One in 5 (21%) people aged under 65 with severe or profound core activity limitation had arthritis—3.9 times the rate for people without disability (5.3%).
  • Other conditions more commonly reported by people with severe or profound core activity limitation were back problems (27%), deafness (22%), cardiovascular diseases (18%), asthma (18%) and migraine (16%).

Health risk factors and behaviours

In 2011–12, a higher proportion of adults aged 18–64 with severe or profound activity limitation were overweight or obese, compared with people without disability (70% versus 60%). People with severe or profound core activity limitation were 1.7 times as likely as those without disability to be obese (43% versus 25%).

Almost half (46%) of people aged 15–64 with severe or profound disability did no exercise, compared with 31% of people without disability. Between 2007–08 and 2011–12, the difference in the proportions doing no exercise increased by 6 percentage points.

People aged 15–64 with severe or profound disability were twice as likely as those without disability to be current daily smokers (31% versus 15%) and 1.8 times as likely to start daily smoking before the age of 18 (41% versus 23%).

Adults aged 18–64 with severe or profound core activity limitation were 18 times as likely as those without disability to have a very high level of psychological distress (22% versus 1.2%).

What is missing from the picture?

As discussed earlier, because the NHS excludes institutional care settings and therefore underestimates disability prevalence among older people, the associated analysis of health status and risks of people with disability is limited to younger people. Similarly, the experiences of older people (with and without disability) in their encounters with the health care system are restricted to people living in the community (see 'Chapter 6.17 Health care use by older Australians').

There is also limited information on the relationship between health status and other priority outcomes of the National Disability Strategy, such as employment and social participation.

Where do I go for more information?

The report Health status and risk factors of Australians with disability 200708 and 201112 is available for free download on the AIHW website. More information on the health of Australians with disability based on analysis of national population survey data is available for free download on the AIHW website: Health of Australians with disability: health status and risk factorsThe use of health services among Australians with disability, and Access to health services by Australians with disability 2012.

Reference

DSS (Department of Social Services) 2012. National Disability Strategy 2010–2020. Canberra: DSS.

5.10 Health of prisoners in Australia

There were over 36,000 people in prisons in Australia on 30 June 2015, and more than 50,000 people were in prison at some time during 2014. With thousands of people leaving prison and returning to the community each year, the health of prisoners is also a health care issue for the general community. This snapshot presents an overview of prisoner health issues followed by a closer look at an area of current policy debate in Australia, that is, smoking among prisoners and the impact of prison smoking bans.

The National Prisoner Health Data Collection (NPHDC) is the main source of national data about the health of prisoners in Australia. In 2015, data were collected from 1,011 prisoners entering prison (prison entrants), 437 prisoners expecting to be released from prison in the following 4 weeks (prison dischargees), over 9,500 prisoners who visited the prison health clinic, and about 9,400 prisoners taking medication.

People entering prison in 2015 often came from socioeconomically disadvantaged backgrounds:

Graphic indicating 2 students out of 3. 2 in 3 had not studied past Year 10 schooling

Graphic indicating 1 person out of 2. 1 in 2 were unemployed in the 30 days before entering prison

Graphic indicating that 1 person out of 4 did not have a home. 1 in 4 were homeless or in insecure accommodation in the 4 weeks before entering prison

Health issues faced by prison entrants in 2015 included:

Graphic indicating 1 sick person out of 3. 1 in 3 had a chronic health condition (most commonly asthma)

Graphic indicating that 2 out of 3 people used drugs. 2 in 3 used illicit drugs in last 12 months, more than 2–3 times the rate in the general population for most drug types

Graphic indicating that 2 out of 5 people were heavy drinkers. 2 in 5 drank alcohol at risky levels, and unlike those in the general community, their risky drinking persists with age

Graphic indicating that 1 out of 4 people were on medication for mental health issues. 1 in 4 were receiving medication for mental health issues

Graphic indicating 1 person in 3. 1 in 3 had limitations to daily activities or restrictions in education or employment—more than twice the rate in the general population

Most prisoners are male (92%) and relatively young—68% are aged under 40 compared with 38% of the general adult population (ABS 2015a, 2015b). Aboriginal and Torres Strait Islander people are over-represented in the prison system: Indigenous Australians were imprisoned at an age-standardised rate of 1,951 per 100,000 of the adult population, 13 times that of the non-Indigenous population (153 per 100,000) (ABS 2015b).

Tobacco smoking among prisoners

Smoking rates among prison entrants are high, with 74% being current smokers; almost all of whom (93%) did so daily—69% of all entrants. A further 9.4% were ex-smokers, and 13% had never smoked. The falls in smoking rates that have occurred in recent decades in the general community have not occurred in the prison population. Data on those who have never smoked indicate that fewer young people are taking up smoking in the general community, but not for prison entrants. Among younger prison entrants (aged 18–24), the proportion of never smokers remains low (11% non-Indigenous and 8.2% Indigenous). In comparison, in the general community, never smokers now make up the majority of younger people (68% non-Indigenous and 42% Indigenous) (Figure 5.10.1). Prison entrants share many characteristics of those in the general community who are most likely to be smokers, including being socially and economically disadvantaged (AIHW 2013). For more information on smoking in the general population, see 'Chapter 4.7 Tobacco smoking'.

Figure 5.10.1: Proportion of never smokers, general community and prison entrants, people aged 18–44, by Indigenous status, 2011–13 and 2015

Bar chart comparing the proportion of prison entrants who have never smoked with the general community, by age and Indigenous status, in 2011-13 and 2015. Only around 10%25 of prison entrants of any age had never smoked, compared to around 50%25 of the non-Indigenous general community and 30%25 of the non-Indigenous community.

Sources: ABS 2014, Table 10.3; Entrants form 2015 NPHDC.

Note: Prison entrants data are based on the total 1,011 prison entrants in the NPHDC and in this figure exclude entrants with unknown Indigenous status. General community data are for 2011–13; prison entrants data is for 2015.

Smoking bans in prisons

One of the policy changes that accompanied the reduction in smoking in the general community over the last 20 years has been the introduction of bans on smoking in public places. Until recently, this policy has not been reflected in prisons in Australia.

Beginning in the Northern Territory in July 2013, smoking bans have been implemented in prisons across Australia and are currently in place also in New South Wales, Victoria, Queensland and Tasmania. In 2015:

  • almost 1 in 5 (18%) dischargees in prisons with smoking bans considered themselves to be current smokers, compared with 74% of dischargees in prisons without bans
  • of people who smoked on entry to prison, dischargees from prisons with smoking bans were less likely to intend to smoke after release than dischargees from prisons in which smoking is allowed (59% and 73%, respectively) (Figure 5.10.2)
  • two-thirds (67%) of dischargees reported that smoking cessation assistance was available in their prison, and dischargees in prisons with smoking bans were more likely than others to use available assistance to quit (26% compared with 10%).

Figure 5.10.2: Smoking intentions on release, prison dischargees who smoked on entry to prison, by prison smoking ban status, 2015

Column graph comparing the smoking intentions of prison dischargees on release who smoked on entry to prison in 2011. Rates of those who did not intend to smoke were slightly higher (around 20%25) for dischargees from prisons that banned smoking.

Notes

  1. Excludes New South Wales, as data were not provided for dischargees.
  2. Only includes dischargees who smoked on entry to prison.

Source: Dischargee form, NPHDC 2015.

What is missing from the picture?

The NPHDC has yet to achieve full participation. In 2015, data for New South Wales were collected from entrants only. About 84% of all prisons in Australia participated in the collection, including 49% of prison entrants and 42% of sentenced dischargees. The AIHW is working with states and territories to improve the coverage of this collection.

The collection provides data from prison entrants, the main population of prisoners in custody, and dischargees. Currently there is little information regarding the health and health risks of recently released prisoners. This means, for example, that information is currently lacking on whether the reductions in smoking from the smoking bans in prisons are maintained after release.

Where do I go for more information?

More information on the health of prisoners in Australia is available at Prisoner health. The report The health of Australia's prisoners 2015 is also available for free download.

References

ABS (Australian Bureau of Statistics) 2014. Australian Aboriginal and Torres Strait Islander Health Survey: updated results, 2012–13. ABS cat. no. 4727.0.55.006. Canberra: ABS.

ABS 2015a. Population by age and sex, regions of Australia, 2014. ABS cat. no. 3235.0. Canberra: ABS.

ABS 2015b. Prisoners in Australia, 2015. ABS cat. no. 4517.0. Canberra: ABS.

AIHW (Australian Institute of Health and Welfare) 2013. Smoking and quitting smoking among prisoners 2012. Bulletin no. 119. Cat. no. AUS 176. Canberra: AIHW.

5.11 Rural and remote health

In 2013, 29% of the Australian population lived in rural and remote areas: 18% in Inner regional areas, 8.9% in Outer regional areas, 1.4% in Remote areas and 0.9% in Very remote areas.

In this snapshot, the term 'rural and remote' encompasses all areas outside Australia's Major cities. Using the Australian Standard Geographical Classification System, these areas are classified as Inner regional, Outer regional, Remote or Very remote. In many instances, the term 'rural and remote' is used interchangeably with the classification terms 'regional and remote'.

Australians living in rural and remote areas tend to have lower life expectancy, higher rates of disease and injury, and poorer access to and use of health services than people living in Major cities.

Poorer health outcomes in rural and remote areas may reflect a range of social and other factors that are detrimental to health, including a level of disadvantage related to educational and employment opportunities, income, and access to health services. People living in rural and remote areas may face more occupational and physical risks, for example, from farming or mining work and transport-related accidents, and experience higher rates of other risk factors associated with poorer health, such as tobacco smoking and alcohol misuse.

Despite poorer health outcomes for some, the Household, Income and Labour Dynamics in Australia (HILDA) survey found that people living in small towns (fewer than 1,000 people) and non-urban areas often experienced greater life satisfaction than those living in Major cities (Wilkins 2015).

Health status

In 2009–2011, people living in Remote and Very remote areas had mortality rates 1.4 times as high as people living in Major cities. For nearly all causes of death, rates were higher for people living outside Major cities, with people in Remote and Very remote areas faring the worst.

  • Coronary heart disease was the leading cause of death for all areas, and mortality rates were between 1.2 and 1.5 times as high in rural and remote areas as in Major cities.
  • In Remote and Very remote areas, the rate of dying due to a land transport accident was more than 4 times as high as in Major cities.
  • In Remote and Very remote areas, death rates due to diabetes were between 2.5 and 4 times as high, and, for suicide, between 1.8 and 2.2 times as high as in Major cities.

Disease prevalence is generally higher in rural and remote areas of Australia than in Major cities. Based on self-reported data from the 2014–15 National Health Survey (NHS) (ABS 2015), compared with people living in Major cities, people living in Inner regional and in Outer regional/Remote areas of Australia had higher rates of:

 
Disease type A major city.
Major cities
An inner regional area.
Inner regional
An outer regional area.
Outer regional/
Remote
A person with arthritis.Arthritis 14% 20% 18%
A person with back pain and problems. Back pain and problems 16% 18% 16%
A person with asthma. Asthma 10% 12% 12%
A person with COPD. COPD 2.4% 3.4% 2.7%
An eye. Blindness 0.5% 0.9% 0.8%
An ear that is not working properly. Deafness 9.8% 15% 14%
A finger with a drop of blood coming from it. Diabetes 4.7% 6.0% 6.7%
A person with CVD. CVD 4.7% 6.7% 5.8%
A person with cancer. Cancer 1.6% 1.7% 1.8%
A person with a mental health problem. Mental health problems 17% 19% 19%

Notes

  1. '%' represents prevalence of chronic diseases in each region (excluding Very remote areas of Australia).
  2. Proportions are not age-standardised, and in some instances higher prevalence may reflect the older age profiles in Inner regional and Outer regional/Remote areas.
  3. 'COPD' refers to chronic obstructive pulmonary disease.
  4. 'Blindness' includes partial and complete blindness.
  5. 'CVD' refers to heart, stroke and vascular disease.

Health behaviours and risk factors

People living in rural and remote areas generally have higher rates of health risk factors. The rates among adults in Major cities, Inner regional and Outer regional/Remote areas, based on self-reported data from the 2014–15 NHS (ABS 2015), were:

 
Health risk factors A major city.
Major cities
An inner regional area.
Inner regional
An outer regional area.
Outer regional/
Remote
A cigarette. Current daily smoker 13% 17% 21%
A set of scales. Overweight or obese 61% 69% 69%
A person using a remote control.No/low levels of exercise 64% 70% 72%
Two alcoholic drinks. Exceed lifetime alcohol risk guideline 16% 18% 23%
A blood pressure cuff. High blood pressure 22% 27% 24%

Notes

  1. '%' represents prevalence of risk factor in each region (excluding Very remote areas of Australia).
  2. 'Proportions' are not age-standardised and, in some instances, higher prevalence may reflect the older age profiles in Inner regional and Outer regional/Remote areas.

Health care

People living in Remote and Very remote areas generally have poorer access to, and use of, health care services than people in regional areas and Major cities. They also have lower rates of breast and bowel cancer screening (see 'Chapter 6.2 Cancer screening'), higher rates of potentially avoidable hospitalisations, and lower access to selected hospital procedures (see 'Chapter 6 Preventing and treating ill health').

In 2014, the full-time equivalent (based on total weekly hours worked) rate of employed general practitioners (GPs) per 100,000 population was higher in Remote and Very remote areas (137) than in Major cities (109); however:

  • the overall rate of employed medical practitioners (including specialists) was lower (253 per 100,000 population compared with 409)
  • the number of GP services provided per person in Very remote areas during 2010–11 was about half that of Major cities (Duckett et al. 2013).

People living in remote areas of Australia may need to travel long distances or relocate to attend health services or receive specialised treatment. For example, based on combined data for 2005–2010, 57% of people with end-stage kidney disease who lived in Very remote areas at the start of their treatment moved to less remote areas within 1 year.

In 2013–14, the rate for emergency hospital admissions involving surgery was highest for people living in Very remote areas (22 per 1,000) and fell with decreasing remoteness to be lowest among people living in Major cities (12 per 1,000).

What is missing from the picture?

It can be difficult to assess the implications of remoteness for health due to:

  • the interactions between remoteness, low socioeconomic position and the higher proportion of Indigenous Australians in many of these areas compared with Major cities
  • the variability in the distribution of disadvantage and of Indigenous Australians across all areas—for example, levels of disadvantage on the fringe of Major cities can be more akin to those in rural/remote areas than to inner-city areas
  • gaps in the availability and coverage of health data in rural and remote areas, and in information available at the local area level.

It is also difficult to measure whether there is adequate supply of medical services because of the influence of factors such as varying health-seeking behaviours, professional scope of practice, and health system efficiency across remoteness areas.

Where do I go for more information?

Information is presented by remoteness categories for a range of risk factors, health conditions and health care settings in various AIHW reports, including Chronic kidney disease: regional variation in AustraliaMortality inequalities in Australia 20092011, and Health workforce. These reports can be viewed and downloaded for free.

References

ABS (Australian Bureau of Statistics) 2015. National Health Survey: first results, 2014–15. ABS cat. no. 4364.0.55.001. Canberra: ABS.

Duckett S, Breadon P & Ginnivan L 2013. Access all areas: new solutions for GP shortages in rural Australia. Melbourne: Grattan Institute.

Wilkins R 2015. The Household, Income and Labour Dynamics in Australia Survey: selected findings from waves 1 to 12. Melbourne: Melbourne Institute of Applied Economic and Social Research.