Trend and change analysis in an Australian surveillance system Associate Professor Anne Taylor South...

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Trend and change analysis in an Australian

surveillance system

Associate Professor Anne TaylorSouth Australian Department of Health

University of Adelaide

Eleonora Dal Grande, Tiffany Gill, Zumin Shi Population Research & Outcome Studies, SA Health

Michele HerriotHealth Promotion, SA Health

2

Background

• The importance of evidence to Health Promotion– Range of sources– Health surveys ► risk factor surveillance

• Flexibility• Addition of time• Seasonal trends• Trends over time

• Difference between surveys and surveillance - The ways things were vs the way things are changing

3

Outline of presentation

• Surveillance in Australia• Examples/results from South

Australia• What challenges we face

4

History of surveillance in Australia

South Australia

Northern Territory

Western Australia

Queensland

New South Wales

← Victoria

TasmaniaSurveillance systemNo surveillance systemAdaptation

5

Surveillance in Australia

• COAG (Council of Australian Governments)– “Laying the foundations for healthy

behaviours in the daily lives of Australians”– ($A448.1m over 4 yrs) – reward payments

• National Partnership Agreement on Preventive Health

6

Surveillance in Australia

• CATI infrastructure ($A10m over 4 yrs)– National consistency

• Questions• Measurement• Sampling frame

– Minimum sample sizes

• Indicators (adults & children)- Fruit & vegetables- Physical activity- Healthy weight- Smoking

7

South Australian Monitoring & Surveillance System (SAMSS)

• Commenced July 2002– Continuous chronic disease and risk

factor surveillance system– CATI (Computer Assisted Telephone

Interviews)

– n = 600 per month– Random selection of South Australians

of all ages (0+ years)

8

Sampling

• Australia– Electronic White Pages

• 2004 privacy legislation

– Random Digit Dialling

• All telephone numbers included in IPND (Integrated Public Number Database)– (fixed line, mobile, public/private payphone,

freecall) included in 1 database (listed and unlisted)

9

Data use

•Prevalence, change in estimates, trends

•Description of at risk populations•Geographic distribution of

illness/risk factors•Detecting epidemics•Generating hypotheses•Facilitating planning

10

Data use

•Importance of continuous data collection– Provide trends– Timeliness– Aggregation over time

11

Fruit and vegetable consumption

12

Mean Serves of Mean serves of fruit per day – Adults aged 18 years and over

Data Source: SAMSS 2003-2009

.00

.25

.50

.75

1.00

1.25

1.50

1.75

2.00

2.25

2.50

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Mea

n S

erve

s

13

Mean serves of fruit per day – Children aged 5 to 17 years

Data Source: SAMSS 2003-2009

.00

.25

.50

.75

1.00

1.25

1.50

1.75

2.00

2.25

2.50

2.75

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Me

an

Se

rve

s

14

Mean Serves of Mean serves of vegetables per day – Adults aged 18 years and over

Data Source: SAMSS 2003-2009

.00

.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Mea

n S

erve

s

15

Mean serves of vegetables per day – Children aged 5 to 17 years

Data Source: SAMSS 2003-2009

.00

.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Me

an

Se

rve

s

16

Fruit and vegetable consumption campaign

• Go for 2&5 Campaign®

– Awareness raising and educating– Comprehensive media campaign May-

June 2005 – National and State based activities – $A100,000 in SA; Nationally $A4.75

million– “Go for 2&5 Fruit and Vegetable man”

events

17

Fruit & vegetable consumption campaign

18

Proportion eating 5+ serves vegetables/day (pre and post

campaign)

Data Source: SAMSS 2002-2010

05

10

15

20

P

reva

len

ce (

%)

Month

19

Proportion eating 5+ serves vegetables/day

(pre and post campaign) by gender

Data Source: SAMSS 2002-2010

05

10

15

20

Male Female

P

reva

len

ce (

%)

MonthGraphs by sex

20

Proportion eating 5+ serves vegetables/day

(pre and post campaign) by BMI

Data Source: SAMSS 2002-2010

05

10

15

20

Underweight/normal Overweight/obesity

P

reva

len

ce (

%)

Month

21

Proportion eating 2+ serves fruit/day(pre and post campaign)

Data Source: SAMSS 2002-2010

01

02

03

04

05

06

0 P

reva

len

ce (

%)

Month

22

Proportion eating 2+ serves fruit/day(pre and post campaign) by gender

Data Source: SAMSS 2002-2010

01

02

03

04

05

06

0

Male Female

P

reva

len

ce (

%)

MonthGraphs by sex

23

Proportion eating 2+ serves fruit/day(pre and post campaign) by BMI

Data Source: SAMSS 2002-2010

01

02

03

04

05

06

0

Underweight/normal Overweight/obesity

P

reva

len

ce (

%)

Month

24

Physical Activity

25

Sufficient physical activity – Adults 18 years and over

0

10

20

30

40

50

60

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

(%)

Data source: SAMSS, age 18 years and over 2003 - 2009

26

60 minutes of physical activity per day –

Children 5 to 15 years

0

2

4

6

8

10

12

14

16

18

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

(%)

Data source: SAMSS, age 15 - 15 years 2003 - 2009

27

Proportion undertaking sufficient physical activity (adults)

Data Source: SAMSS 2003-2010

01

02

03

04

05

06

07

0 P

reva

len

ce (

%)

Month

28

Proportion undertaking sufficient physical activity by BMI (adults)

Data Source: SAMSS 2003-2010

01

02

03

04

05

06

07

0

Underweight/normal Overweight/obesity

P

reva

len

ce (

%)

Month

29

Proportion undertaking sufficient physical activity by gender (adults)

Data source: SAMSS, age 16 years and over 2003 - 2010

01

02

03

04

05

06

07

0

Male Female

P

reva

len

ce (

%)

Month

30

Proportion undertaking sufficient physical activity by SEIFA (adults)

Data source: SAMSS, age 16 years and over 2003 - 2010

01

02

03

04

05

06

07

0

lowest/low/middle high/highest

P

reva

len

ce (

%)

Month

31

Proportion undertaking sufficient physical activity by overall health

status (adults)

Data source: SAMSS, age 16 years and over 2003 - 2010

01

02

03

04

05

06

07

0

Fair/Poor Good/Very good/Excellent

P

reva

len

ce (

%)

Month

32

Proportion undertaking sufficient physical activity by smoking status

(adults)

Data source: SAMSS, age 16 years and over 2003 - 2010

01

02

03

04

05

06

07

0

Non-/Ex-smoker Current smoker

P

reva

len

ce (

%)

Month

33

Smoking

34

Data source: SAMSS, age 16 years and over

Smoking – Adults aged 16 years and over

0

5

10

15

20

25

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

(%)

35

Proportion of adults smoking

Data source: SAMSS, age 16 years and over 2002 - 2010

05

10

15

20

25

P

reva

len

ce (

%)

Month

36

Proportion of adult smokers by gender

Data source: SAMSS, age 16 years and over 2002 to 2010

05

10

15

20

25

Male Female

P

reva

len

ce (

%)

MonthGraphs by sex

37

Smoking policy and legislation

• Dec 2004– Smoke-free workplaces and public areas

except licensed hospitality venues

• May 2007 – Ban on smoking in cars with children under

16 years

• Nov 2007 – All public areas smoke-free, including

hospitality venues– Current policy targeting retail sales displays

38

Proportion of adults reporting smoking undertaken in the home

Data source: SAMSS, age 16 years and over

05

10

15

20

25

P

reva

len

ce (

%)

Month

39

Healthy Weight

40

Data source: SAMSS, age 18 years and over

Unhealthy weight – Adults aged 18 years and over

0

10

20

30

40

50

60

70

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

(%)

41

Unhealthy weight – Children aged 5 -17 years

Data source: SAMSS, age 5-17 years

0

5

10

15

20

25

30

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

(%)

42

Proportion of adults reporting overweight/obese (BMI >25)

Data source: SAMSS, age 16 years and over

20

30

40

50

60

70

80

Pre

vale

nce

(%

)

July

2003

Janu

ary 2

004

July

2004

Janu

ary 2

005

July

2005

Janu

ary 2

006

July

2006

Janu

ary 2

007

July

2007

Janu

ary 2

008

July

2008

Janu

ary 2

009

July

2009

Janu

ary 2

010

Month

Actual Predicted 95% CI

43

Proportion of adults reporting overweight/obese (BMI > 25) by

gender

Data source: SAMSS, age 16 years and over

20

30

40

50

60

70

80

July

2003

Janu

ary 2

004

July

2004

Janu

ary 2

005

July

2005

Janu

ary 2

006

July

2006

Janu

ary 2

007

July

2007

Janu

ary 2

008

July

2008

Janu

ary 2

009

July

2009

Janu

ary 2

010

July

2003

Janu

ary 2

004

July

2004

Janu

ary 2

005

July

2005

Janu

ary 2

006

July

2006

Janu

ary 2

007

July

2007

Janu

ary 2

008

July

2008

Janu

ary 2

009

July

2009

Janu

ary 2

010

Male Female

Actual Predicted 95% CI

Pre

vale

nce

(%

)

Month

Graphs by sex

44

Proportion of adults reporting overweight/obese (BMI > 25) by age

Data source: SAMSS, age 16 years and over

2030

4050

6070

80

July

2003

July

2004

July

2005

July

2006

July

2007

July

2008

July

2009

July

2003

July

2004

July

2005

July

2006

July

2007

July

2008

July

2009

July

2003

July

2004

July

2005

July

2006

July

2007

July

2008

July

2009

<40 40-59 >=60

Actual Predicted 95% CI

Pre

vale

nce

(%

)

Month

Graphs by age

45

Proportion of adults reporting overweight/obese (BMI > 25) by

income

Data source: SAMSS, age 16 years and over

2030

4050

6070

80

July

2003

July

2004

July

2005

July

2006

July

2007

July

2008

July

2009

July

2003

July

2004

July

2005

July

2006

July

2007

July

2008

July

2009

July

2003

July

2004

July

2005

July

2006

July

2007

July

2008

July

2009

High Low Not stated

Actual Predicted 95% CI

Pre

vale

nce

(%

)

Month

Graphs by income

46

What challenges do we face?

• In Australia - continued harmonization– State-based system– Conflicting goals

• Aim of all surveillance systems– Improvement on health outcomes– Value for money– Use of data

An effective risk factor surveillance system will provide the evidence for change

47

Challenges - Sampling

•Scientific•Known probability of selection•Random•Power•System approach•Population framework

•Limited by:– Needs/resources available

48

Challenges - Questions

•Standards•Best practice guidelines•Question development

– Cognitive testing– Field testing

•Use of modules

•Flexibility but consistency•Reliability/validity

49

Challenges

•In Australia -Too many different systems

•Harmonization-Questions (SNAPS)

•No national conference

•No governing committee-Informal vs formal

50

Challenges - Data collection

•Telephones•Mixed mode•Regular & sustained•Issues

– Response rates– Cultural differences

•Importance of quality assurance in all aspects

51

Challenges

•Dissemination– We have room for improvement– The use of the media & relationship

with media– Explore more options

•The power of collaboration– Partners

52

Challenges - Sustainability/continuity

•Long term commitment - cost•Show a difference/make a

difference•Be useful as an evidence provider

53

Contact Details

Anne Taylor

Population Research & Outcome Studies (PROS)

South Australian Department of Health

University of Adelaide

PROS Website:

http://www.health.sa.gov.au/PROS/