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 Taylor South Australian Department of Health University of Adelaide Eleonora Dal Grande, Tiffany Gill, Zumin Shi Population Research & Outcome Studies, SA Health Michele Herriot Health Promotion, SA Health
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Transcript of Trend and change analysis in an Australian surveillance system Associate Professor Anne Taylor South...
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/
