Data Matthew James The central role that peoples health and well - - PowerPoint PPT Presentation

Health and Welfare Data

Matthew James

The central role that people’s health and well-being play in social cohesion and community change

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Aboriginal and Torres Strait Islander Health Performance Framework

Proportion of the adjusted health gap explained by differences in social determinants and health risk factors between Indigenous and non-Indigenous Australians, 2011–13

Gap due to other factors 46.8% (unexplained component)

Social determinants 34.4% Overlap between social determinants & health risk factors (10.8%) Healh risk factors 18.8%

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Life expectancy increases with socioeconomic position

Source: Health-adjusted life expectancy in Australia: expected years lived in full health 2011. Australian Burden of Disease Study series no.16. BOD 17. Canberra: AIHW. IHW 2017

77.3 78.5 79.8 81.6 83 82.7 83.5 84.4 85.3 86 70 72 74 76 78 80 82 84 86 88 Q1 (lowest) Q2 Q3 Q4 Q5 (highest)

Life expectancy (years) Socioeconomic group

Life expectancy at birth, by sex, by socioeconomic group (SEIFA), 2011

Males Females 17.7 18.2 18.9 20.1 20.9 21.2 21.5 22.2 22.6 22.9 15 20 25 Q1 (lowest) Q2 Q3 Q4 Q5 (highest)

Life expectancy (years) Socioeconomic group

Life expectancy at age 65, by sex, by socioeconomic group (SEIFA), 2011

Males Females

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Lowest socioeconomic group (SEIFA) has highest all-cause mortality rate

Source:AIHW 2017. Mortality Over Regions and Time (MORT) books. Socioeconomic group, 2011-2015.

100 200 300 400 500 600 700 800 Quintile 1 (lowest) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (highest)

per 100,000 Socioeconomic group

Age-standardised mortality rate, all causes, by sex, 2015

Females Males

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Life expectancy at birth, by PHN, 2014-2016

Low: Northern Territory 77.7 years High: Northern Sydney 85.7 years

Source: Source: AIHW analysis of the Australian Bureau of Statistics Life Tables, 2014–2016.

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Life expectancy at birth, by PHN, 2014-2016: Sydney

High: Northern Sydney 85.7 years (1st in Australia) Low: Nepean Blue Mountains 81.9 years (18th in Australia)

Source: Source: AIHW analysis of the Australian Bureau of Statistics (ABS) Life Tables, 2014–2016.

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Mortality rates, by LGA, 2016

High: Derby-West Kimberley 1068.8 Low: Nedlands 345.1 The mortality rate for Derby-West Kimberley is 3.1 times the rate for Nedlands

Source: AIHW Mortality Over Regions and Time (MORT) books, 2017. Data by LGA.

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Mortality rates, by LGA, 2016: Sydney

Low: Ku-ring-gai 352.6 (395th in Australia) High: Campbelltown 618.7 (110th in Australia) The mortality rate for Campbelltown is 1.8 times the rate for Ku-ring-gai

Source: AIHW Mortality Over Regions and Time (MORT) books, 2017. Data by LGA.

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Kimberley region North western NSW and Northern Tablelands regions Northern and eastern Tasmania

Source: AIHW Mortality Over Regions and Time (MORT) books, 2017. Data by LGA.

Broader regional areas with consistently high mortality rates 2011-2016

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Potentially avoidable mortality rates are decreasing over time

2016 50 100 150 200 250 300 1997 2002 2007 2012

Deaths per 100,000 population Year

Age-standardised death rates for potentially avoidable deaths, by sex, 1997 to 2016

Males Females Source: National Mortality Database

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Potentially avoidable deaths, by LGA, 2016

Low: Burnside 42.3 High: Derby-West Kimberley 365.4 The potentially avoidable deaths rate for Derby- West Kimberley is 8.6 times the rate for Burnside

Source: AIHW Mortality Over Regions and Time (MORT) books, 2017. Data by LGA.

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Potentially avoidable deaths, by LGA, 2016: Sydney

Low: Waverley 46.8 (278th in Australia) High: Campbelltown 141.7 (64th in Australia) The rate of potentially avoidable deaths for Campbelltown is 3 times the rate for Waverley

Source: AIHW Mortality Over Regions and Time (MORT) books, 2017. Data by LGA.

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Chart from Case and Deaton for the US (Case and Deaton 2017)

White non-Hispanics high school or less Black non-Hispanics White non-Hispanics (all) Hispanics

300 500 700 900 2000 2005 2010 2015 Survey year

All-cause mortality by race and ethnicity, ages 50-54

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Employment rate by sex, age 15–64, February 1978 to June 2018 trend data (per cent)

ABS Labour Force Survey

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 Male Female

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Employment rate non-Indigenous men aged 20-64, left school at 14 or below

Unpublished census data 86% 54% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 1976 1981 1986 1991 1996 2001 2006 2011

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Unpublished census data

Employment rate, non-Indigenous men aged 20-64, with and without a post school qualification (cert III and above)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1971 1976 1981 1986 1991 1996 2001 2006 2011 With post school Without post school

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Unpublished data from the ABS Survey of Education and Work

Employment rate, for men and women with a year 10 or below level of education, May 2008 to May 2017 (age 15-64)

67.4% 65.5% 65.3% 65.4% 64.9% 63.3% 62.6% 60.6% 60.7% 60.0%

51.1% 51.6% 48.7% 48.4% 49.2% 49.4% 48.8% 48.5% 46.5% 48.2%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Male Female

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Unpublished data from the ABS Survey of Education and Work

Full-time, employment rate, for men and women with a year 10 or below level of education, May 2008 to May 2017 (age 15-64)

55.4% 53.4% 52.8% 52.2% 51.7% 49.9% 48.6% 48.1% 46.5% 46.7% 22.7% 23.5% 21.2% 20.1% 21.7% 21.1% 21.6% 20.6% 18.9% 19.6%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Male Female

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Proportion of persons whose self-assessed health is excellent by highest level of educational attainment, age 15–64, 2014-15

Clear gradient by highest level of education

ABS 2016. National Health Survey (2014–15), Expanded Confidentialised Unit Record File (CURF), DataLab. Findings based on use of ABS Microdata.

31.7 27.8 18.4 20.1 17.8 17.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0

Postgrad degree/Graduate Dip]/Graduate Cert Bachelor Degree Advanced Dip/Diploma/Cert III/IV Year 12 Year 11/Year 10/Cert I/II Year 9 and below/Never attend school and no non-school qual

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Proportion of persons whose self-assessed health is excellent by highest level of educational attainment, age 50-59, 2014-15

Clear gradient by highest level of education

ABS 2016. National Health Survey (2014–15), Expanded Confidentialised Unit Record File (CURF), DataLab. Findings based on use of ABS Microdata.

37.1 30.3 15.8 12.7 12.6 10.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0

Postgrad degree/Graduate Dip/Graduate Cert Bachelor Degree Advanced Dipl/Diploma/Cert III/IV Year 12 Year 11/Year 10/Cert I/II Year 9 and below/No school and no non- school qual

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Six Questions about Swedish Healthcare, Swedish Board of Health and Welfare

40 42 44 46 48 50 52 54 56 58 60

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Pre-upper-secondary, women Upper-secondary, women Post-secondary, women Pre-upper-secondary, men Upper-secondary, men Post-secondary, men

Source: Sweden Population Statistics, Statistics Sweden..

Average life expectancy at 30-years of age (only for people born in Sweden).

• Education. Average life expectancy (at 30-years of age)

Year

Education, gender

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Six Questions about Swedish Healthcare, Swedish Board of Health and Welfare

20 40 60 80 100 120 140 160

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Pre-upper-secondary, women Upper-secondary, women Post-secondary, women Pre-upper-secondary, men Upper-secondary, men Post-secondary, men

Healthcare-related avoidable mortality, Sweden

Healthcare-related avoidable mortality – number of deaths per 100 000 inhabitants aged 35-79 years. Age-standardised statistics. Per 100 000 inhabitants

Education, gender

Source: Cause of Death Register, National Board of Health and Welfare.

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Six Questions about Swedish Healthcare, Swedish Board of Health and Welfare

50 100 150 200 250 300 350

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Pre-upper-secondary, women Upper-secondary, women Post-secondary, women Pre-upper-secondary, men Upper-secondary, men Post-secondary, men

• Education. Avoidable deaths from ischaemic heart disease, Sweden

Avoidable deaths from ischaemic heart disease per 100 000 inhabitants age 35–79, Age-standardised.

Per 100 000 inhabitants

Education

Source: Cause of Death Register, National Board of Health and Welfare.

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Daily smoking by socioeconomic area, age 18+

Clear socioeconomic gradient. Rates declined across all groups but second highest and highest area declining at fastest rate than the lowest group.

AIHW customise analysis of the NDSHS

23.9 22.4 20.3 20.2 14.1 18.7 15.2 12.4 10.7 6.9 5 10 15 20 25 30

Lowest 2 3 4 Highest

2001 2004 2007 2010 2013 2016 Per cent

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Daily smoking by education level (per cent), age 18+

• Smoking has decline across all education levels.
• Bachelor degree or higher – consistently the lowest smoking rates over time and made the greatest improvement (% change).
• People who had only completed up to grade 11 and people with a Certificate III or IV made the least improvement over time and

have the highest smoking rates.

AIHW customise analysis of the NDSHS

25 18 24 18 10 10 23 18 22 15 9 7 23 17 23 15 8 7 22 15 19 14 7 5 20 11 18 10 6 6 19 12 19 11 5 4 5 10 15 20 25 30 Year 11 or below (includes certificate I and II) Year 12 Certificate III or IV Diploma Bachelor Degree Post-graduate or doctorate 2001 2004 2007 2010 2013 2016 Per cent

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Alcohol risk by socioeconomic status, age 18+, 2016

No clear socioeconomic gradient for alcohol risk.

AIHW customise analysis of the 2016 NDSHS

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18 27 18 26 19 29 19 27

5 10 15 20 25 30 35 Lifetime risk Single occasion risk Per cent Lowest 2 3 4 Highest

Lifetime risk: >2 drinks per day (on average) Single occasion risk: >4 drinks on one occasion (at least monthly)

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Alcohol risk by education level, age 18+, 2016

People with post-graduate qualification was slightly less likely to exceed the guidelines while people with a Certificate III or IV were generally the most likely group to consume alcohol in risky quantities.

AIHW customise analysis of the 2016 NDSHS

17 21 19 32 23 35 18 26 16 26 15 22

5 10 15 20 25 30 35 40 Lifetime risk Single occasion risk (monthly) Year 11 or below (includes certificate I and II) Year 12 Certificate III or IV Diploma Bachelor Degree Post-graduate or doctorate Per cent

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Recent use of any illicit drug by socioeconomic area, age 18+, 2016

No clear pattern

AIHW customise analysis of the 2016 NDSHS

Per cent 17 16 15 17 15 2 4 6 8 10 12 14 16 18 20

Lowest 2 3 4 Highest

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Use of any illicit drug by level of education, age 18-50, 2016

Lower for adults with degrees

AIHW customise analysis of the 2016 NDSHS

24 23 25 18 18 13

5 10 15 20 25 30

Year 11 or below (includes Cert I and II) Year 12 Certificate III or IV Diploma Bachelor Degree Post-graduate or doctorate

Per cent

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Pharmaceutical misuse by education level (per cent), 2016

In 2016, misuse of pharmaceuticals was lower for those with a bachelor degree or higher

5.5 4.7 5.8 4.7 3.5 3.5

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 Year 11 or below (includes certificate I and II) Year 12 Certificate III or IV Diploma Bachelor Degree Post-graduate or doctorate

AIHW customise analysis of the 2016 NDSHS

Per cent

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Latest findings from the SHSC, 2016–17

Specialist homelessness agencies and clients, 2016–17

Source: SHSC 2016–17

→ 1 in 84 Australians were assisted by SHS agencies (288,000 clients) → Over 940,000 Australians have been supported by SHS agencies since the collection began in 2011–12 → 56% at risk of homelessness → 44% homeless → 6 in 10 were female → 1 in 4 were Indigenous → 4 in 10 were experiencing domestic and family violence → Clients housed, but at risk of homelessness: agencies assisted 9 in 10 clients to maintain housing → Clients homeless: agencies assisted 4 in 10 into housing → 27% of clients in 2016-17 were experiencing mental health issues

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Rate of specialist homelessness services use, by sex

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 2011–12 2012–13 2013–14 2014–15 2015–16 2016–17

Rate (per 10,000 population) Year

Males Females All clients

Specialist Homelessness Services clients, 2011–12 to 2016–17

Source: Specialist Homelessness Services Collection, 2011–12 to 2016–17

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Cohort analysis of adult homeless and at risk clients: patterns of service use, 2011–12 to 2014–15

13% 43% 44% 0% 10% 20% 30% 40% 50% Client (as % of all homeless clients) Persistent service users Service cyclers Transitory service users 10% 40% 50% 0% 10% 20% 30% 40% 50% 60% Client (as % of all at risk clients) Persistent service users Service cyclers Transitory service users

Homeless clients At Risk clients

Persistent service users—clients who accessed services every financial year from 2011–12 to 2014–15. Service cyclers—clients who accessed services in 2011–12 and at least in one other financial year to 2014–15. Transitory service users—clients who accessed services in 2011–12 only, and not again in the study period, to 30 June 2015. Source: SHSC 2011–12 to 2014–15; Cohort includes adults (18 and over) and young people presenting alone aged 15–17. Percentages may not add due to rounding.

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Social Housing Share in Australia

Social housing dwellings per 100 households, 2007–08 to 2016–17

Source: AIHW National Housing Assistance Data Repository 2007–08 to 2016–17, ABS 3236.0 Household and Family Projections, Australia, 2011–2036. See AIHW Housing Assistance in Australia 2018

5.1 5.0 4.9 5.0 5.0 4.9 4.8 4.7 4.7 4.6

0.0 1.0 2.0 3.0 4.0 5.0 6.0 2007–08 2008–09 2009–10 2010–11 2011–12 2012–13 2013–14 2014–15 2015–16 2016–17 Rate (per 100 households)

Year

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Home Ownership in Australia by age

Home ownership (aggregate) rates (excluding not stated), by selected age groups, 1971 to 2016

Source: Australian Bureau of Statistics, Census of Population and Housing, 1971 to 2016, AIHW customised data report

10 20 30 40 50 60 70 80 90 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016 Per cent Year

20–24 years 25–29 years 30–34 years 35–39 years 40–44 years 45–49 years 50–54 years 55–59 years

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Sources: AIHW Child Protection Collections 2012–13 to 2016–17; Child protection Australia 2016-17, Table S62.

Children in substantiations by Indigenous status, 2012–2013 to 2016– 2017 (rate)

38.1 38.8 39.8 43.6 46.0 5.7 5.7 5.9 6.4 6.8 7.8 7.8 8.0 8.5 9.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 2012–13 2013–14 2014–15 2015–16 2016–17 Number per 1,000

Year

Indigenous children Non-Indigenous children All children

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Children in out-of-home care by Indigenous status, at 30 June, 2013 to 2017 (rate)

Sources: AIHW Child Protection Collections 2012–13 to 2016–17; Child protection Australia 2016-17, Table S62.

48.2 51.4 52.5 57.5 58.7 5.3 5.6 5.5 5.8 5.8 7.7 8.1 8.1 8.6 8.7 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 2013 2014 2015 2016 2017 Number per 1,000 Year Indigenous children Non-Indigenous children All children

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Data linkage at AIHW

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• Data integration not that new – the AIHW has been linking cancer

data with the national death index since 1990

• All proposals have to be approved by our Ethics Committee.

Incidentally this does not delay projects unduly — a much bigger challenge, at times, is gaining approval from data custodians.

→Have been undertaking around 50 data linkage projects a year for around 5-6 years and we are dealing with around 80 requests every year. →The linkages are becoming more complex and they are getting larger so it is not just a simple numbers game.

Data linkage at the AIHW

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The AIHW is working with the Commonwealth Department of Health and state and territory health authorities to create the National Integrated Health Services Information (NIHSI) Analysis Asset (AA).

The (NIHSI) Analysis Asset (AA)

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Data Linkage: Exploring drug treatment and homelessness in Australia: 1 July 2011 to 30 June 2014

Source: SHSC 2011–12 to 2013–14, AODTS NMDS 2012–13 to 2013–14

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Data Linkage: Vulnerable young people: interactions across homelessness, youth justice and child protection

Source: SHSC 2011–12 to 2013–14, Child Protection NMDS 2013–14, Juvenile Justice NMDS 2011–12 to 2013–14

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Young people under youth justice supervision and detention who received an alcohol and other drug treatment service compared to the young Australian population,1 July 2012 to 30 June 2016

Source: AIHW 2018. Overlap between youth justice supervision and alcohol and other drug treatment services 2012–13 to 2016–17, tables S1, S2 and S4.

5 10 15 20 25 30 35 40 45 YJ—All supervision YJ—Detention Young Australian population Population type Per cent

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Data linkage example - Veteran’s suicide prevalence

(b) All causes (a) Suicide

* A statistically significant difference between the ADF population group and Australian men of the same age.

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Compared to all Australian men, suicide rates were:

• 53% lower for men currently serving
• 49% lower for men in the reserve
• 14% higher for ex-serving men (difference not

significant). Ex-serving men aged 18-24 at particular risk (2 X suicide rate of Australian men of the same age)

Key findings: comparisons to Australian population

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Prevalence of common mental disorders in Australia, by sex, 2007

10.8 5.3 7.0 17.6 17.9 7.1 3.3 22.3

5 10 15 20 25

Per cent

Males Females

Anxiety Affective Substance Any 12 month disorders disorders use disorders mental disorder Source: 2007 National Survey of Mental Health and Wellbeing,ABS 2008.

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Mental health data: an overview

Services provided

2.4 million people

Medicare-subsidised mental health-specific services (2016–17)

276,954 presentations

mental health-related emergency departments admissions (2016–17)

414,176 people

9.4 million community mental health care service contacts (2015–16)

244,934 hospitalisations

• vernight mental health-related hospital care (2015–16)

5,840 people

residential mental health care (2015–16)

100,939 people

psychiatric disability receiving disability support services (2016–17)

77,569 clients

received mental health related specialist homelessness services (2016–17)

4.0 million patients

mental health-related prescriptions (2016–17)

→ Mental Health Services in Australia

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Government mental health-related expenditure as a per cent

• f Government health expenditure, 1992–93 to 2015–16

7.3% 7.7%

0.0 2.0 4.0 6.0 8.0

(%) of total

7.5% 7.2% 7.1% 7.5% 7.7%

Source: AIHW analysis; www.aihw.gov.au/mhsa

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Australia’s top 5 burden of disease groups, 2011

18.5 14.6 12.1 11.6 8.8

2 4 6 8 10 12 14 16 18 20

Cancer Cardiovascular Mental Musculoskeletal Injuries

Per cent

The main causes of non-fatal burden in 2011 were anxiety disorders (27%), depressive disorders (24%) and alcohol use disorders (11%).

Source: Australian Burden of Disease Study: impact and causes of illness and death in Australia 2011

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Mental health condition by comorbidity of selected long-term health condition, all ages, 2014-15

1.5 4.5 13.4 4.2 9.4 2.4 8.3 24.3 7.6 17.7 0.0 5.0 10.0 15.0 20.0 25.0 30.0

Cancer Diabetes Arthritis Cardiovascular disease Asthma

Per cent

No mental health condition All mental health conditions

Sources: National Health Survey 2014-15

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Six Questions about Swedish Healthcare, Swedish Board of Health and Welfare

5 10 15 20 25 30 35 40 45 50 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Pre-upper-secondary, women Upper-secondary, women Post-secondary, women Pre-upper-secondary, men Upper-secondary, men Post-secondary, men

Education, gender

Per 100 000 inhabitants Source: Cause of Death Register, National Board of Health and Welfare.

• Education. Suicide among the general population, Sweden

Number of suicides and deaths with undetermined intent per 100 000 inhabitants aged 35-79 years. Age-standardised.

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