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Using population data sets to inform the development of social marketing initiatives around Healthy...
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Transcript of Using population data sets to inform the development of social marketing initiatives around Healthy...
Using population data sets to inform the development
of social marketing initiatives around Healthy
Living
Adrian Bauman [1], Sharyn Lymer [1] ,Tien Chey [1] and Cora Craig [2]
1 University of NSW, Sydney, Australia 2 Canadian Fitness and Lifestyle Research Institute,
Ottawa
Background Social marketing uses a variety of research methods to
understand its audience
Qualitative techniques: focus groups, stakeholder interviews, Audience response analysis
Traditionally, social marketing has also commissioned quantitative market research data collections
Existing health agency population data sets seldom used
Healthstyles® (with CDC) in USA provided some of this kind of psychographic information from population sample surveys
This presentation Using existing HC population health survey
data to inform “Healthy Living” social marketing initiatives
Not just data for its own sake …… goal is to provide “food for thought” for HL campaign planners
information of interest to specific HC groups
Technical contribution to the HL development
Iterative process needs to happen sequentially before campaigns, in conjunction with traditional formative message development
Social marketing often includes:
elements of audience segmentation Specific needs, aspirations of
subgroups Messaging, brand specificity ‘Price’ of the actions to individuals,
location/place, promotion, exchange required, and sustained intervention(s) across multiple levels
Developing social marketing
Therefore, from public health sciences, some of the formative elements are:
to identify risk groups to clarify values of those groups to develop targeted messages
[audience segmentation]
But can population health surveys [epidemiological model] inform this process in some ways ?
This context : Healthy living initiative
What does it mean ? Informing campaigns Use of HC population data sets to
inform the planning of campaigns Examples from CCHS data 2000/1
Meanings of “healthy living”
Healthy weight
Active LivingHeritage / Sport
tobacco
Workplace Health
Social capitalSense of community
Mental healthstress
Healthy eating
Indigenous Canadians
Other Special
populations
Physical activity
This project
To use some population health data to inform the HL process of campaign development, especially through audience segmentation
CCHS [Canadian Community Health Survey ]
CCHS : population representative sample [CATI administered] telephone survey of Canadians
Statistics Canada auspiced
target population : residents >= 12 years , all provinces /territories *
* This analysis confined to 20 years and older
CCHS [Canadian Community Health Survey ]
Sample of 130 000 randomly sampled Canadians 2000-2001
response rate 84.7 %
Purpose
“CCHS captures information about those Canadians who are healthy (the majority) and have not needed to interact with health system”
• Breastfeeding• Chronic conditions• Contacts with health professionals• Health care utilization• Injuries• Mammography• PAP smear test• PSA test• Restriction of activities• Two-week disability• Household composition / housing• Income• Alcohol• Alcohol dependence / abuse• Blood pressure check
• Labour force• Socio-demographic characteristics
• Smoking
• Food insecurity
• Fruit & vegetable consumption
• General health
• physical activity
• Mental health dimensions
• Height / weight [obesity]
CCHS: Possible “Healthy living” related variables
1. CCHS data 2. analysis 3. Interaction with
Social marketing team
4. More (qual) researchFurther
reflection
Optimal campaign
CONCEPTUAL MODEL
Strategic Plans HL
Some issues about CCHS ‘public use data files’ and analyses
Not all demographic variables used in public data files – reanalysis August 03 in HC
Some derived variables reconstructed differently to Stats Canada
Not cluster adjusted and not always weighted analyses - this is irrelevant for hypothesis generation – but would be important for parameter estimation
Data used in these analyses Demographic Healthy living variables Mental health variables Change and intention variables
Beha
HL Variables of interest
Demographic characteristics.AgeGenderEducationHousing Country of birth (Canada, Asian, Europe &Nth America / other) Cultural group/language Identifies as indigenous Work pattern – and unemployed and looking for workIncomeFood insecurity Province, local health region
General health.
Behavioral HL variables.
1. Weight/height (BMI) usual cutpoints
2. reported hypertension (but level not known)
3. Alcohol – several measures- alc dependence used
4. Nutrition (daily F + V - use quintiles )
5. Smoking : current smoking
6. Physical activity
i. PA summary categories
ii. Walk to work/school/errands
iii. Bicycle to work/school/errands
iv. PA at work
Mental Health.
On medicationFeeling happy (single item)Work stressSelf esteemMasterySocial support scale (4 d.v.’s)SpiritualityContact with MH professionals Mood scale (d.v.’s positive affect, negative affect)Distress scale (distress:, d.v.) (chronic:, d.v.)Depression (C1D1) d.v.Suicidal thoughts
Changes to improve health/intention
Q1 made changes to improve health past 12 months
(Q2 – specific changes): exercise, weight, diet, smoking, alcohol
Q3 should do (anything) else to improve health
(Q4 – most important: exercise, weight, diet, smoking, vitamins)
Q5 barriers to improvement(Q6 – list of barriers)
Q7 intend to make change in next year
(Q8 – what change (intended): exercise, lose weight, diet quit smoking, stress)
Phase 1
• assess socio demographic correlates of each derived behave variable
• assess changes, importance , intention in relation to each outcome variable
Phase 2
analyses profiles of specific groups as risk of healthy living or unhealthy living, and examine protective factors and resilience within the data and those groups, using the population data available.
Healthy Living’ focused analysis of CCHS 2000
Demographic variables:
Category [codes from variables] Raw data
n % for analysis
categories
Age:[4 categories used, ages >20]
12-14 [1]15-19 [2]20-34 [3,4,5]35-49 [6,7,8]50-64 [9,10,11]65+ [12,13,14,15]total
64761108126607367212576224233
130880
--
23.532.422.721.4100
Gender:[2 categories]
male [1]female [2]Total
6051470366
130880
46.253.8100
Education:[4 categories]
<secondary [1]secondary graduate [2]other post secondary [3]post secondary graduate [4]Not statedTotal
44338228609832
525861264
130880
34.217.67.6
40.6-
100
Demographic variables:
Category [codes from variables] Raw data
n % for analysis
categories
Food insecurity:[2 categ ]
(1)Yes [1](2)No [2]Not statedtotal
20635108830
1415130880
15.984.1
-100
Housing:[2 categories]
owned by a member of household [1]not own by a member of household [2]Not applicableDon't know/refusal/not statedTotal
9346436968
88360
130880
71.728.3
--
100
Food insecurity question:
“In the past 12 months, how often did you or anyone else in your household:… worry that there would not be enough to eat because of a lack of money”
Raw Data Variable Categories (codes) n % for
analysis Country of Birth
Canada Asia European/Nth America Other
108508 2628 8445 5361
86.85 2.10 6.76 4.29
Cultural / Racial B’ground (multiresponse:)
White Asian Indigenous Other
114381 5132 3672 2774
91.49 4.09 2.92 2.21
Employment Has a job Unemployed Not seeking work
71116 4571 33931
64.9 4.1 30.9
Absenteeism n=5052
Vacation Illness/disability
1893 1214
37.47 24.03
Working more than 1 job
multiple jobs Currently has 1 job Currently no job
6912 64229 38440
6.31 58.61 35.08
Behavioral HL variablesused for analysis]
Categories Raw data
n % for analysis categories
Hypertension:Self report
(1) Yes [1](2) No [2]Don't know/refusal/not stated [6,7,8,9]Total
19286111348
246130880
14.885.2
-100
Alcohol dependence scale
(1)“Scale” (did not have 5 or more drinks)(2)“Scale” 1-7Not statedTotal
12010494301346
130880
92.77.3
-100
Behavioral HL variables
Number of category [codes from variables] Raw data
n % for analysis
categories
Weight/height (BMI)[4 categories]
(1)Under wt:
[5<=hwtagbmi<20](2)Acceptable:
[20<=hwtagbmi<25](3)Over wt:
[25<=hwtagbmi<30](4)Obese:
[30<=hwtagbmi<65] Not applicable (<20 and >64)Not statedTotal
6040354042968815042428661840
130880
7.041.134.517.5
--
100
Nutrition[5 categories] (Total daily consumption F + V times/day, gender specific quintiles
Male Female (5) 0<=FVCADTOT<2.5 0<=FVCADTOT<3.0(4) 2.5<=FVCADTOT<3.5 3.0<=FVCADTOT<4.0(3) 3.5<=FVCADTOT<4.5 4.0<=FVCADTOT<5.1(2) 4.5<=FVCADTOT<6.1 5.1<=FVCADTOT<6.8(1) 6.1<=FVCADTOT<81 6.8<=FVCADTOT<36Not statedTotalWeighted mean=4.41, median=3.9 (male);mean=4.98, median=4.6 (female)
23311263532581125339280841982
130880
18.120.420.019.721.8
-100
Current Smoking [3 categories]
(1)Current smoker (daily, occasional, always occasional) [1,2,3](2)ex-smoker (former daily, former occasional) [4,5](3)non-smoker [6]Not statedtotal
355985033144601350
130880
27.338.634.2
-100
Behavioral HL variables
Number of category [codes from variables] Raw data
n % for analysis
categories
Physical activity[3 categories] based on (total energy xpenditure, kcal/kg/day, MET)
(1)Active - PA index 1: > 3.0 kkd(2)Moderate - PA index 2: kkd 1.5 – 2.99(3)Inactive - PA index 3: kkd < 1.5Not statedTotal
2914729168641048461130880
23.823.852.4
-100
General Health variables
Category [codes from variables] Raw data
n % for analysis categories
self-perceived general health
(1)poor [0](2)fair [1](3)good [2] (4)very good [3](5)excellent [4]Not stated [9]Total
467413715360374644229953
59130880
3.610.527.535.522.9
-100
Self-perceived belonging/local community[5 categories]
(1)very strong(2)somewhat strong(3)somewhat weak(4)very weakDon't know/refusal/not stated [7,8,9]total
24083524973086814249
9183130880
19.843.125.411.7
-100
Has a chronic condition?
(1) Yes [1](2) No [2]Not stated [9]total
8713843508
234130880
66.733.3
-100
Changes to improve health/intention
Category [codes from variables] Raw data
n % for analysis
categories
Did something to improve health?
(1) Yes [1](2) No [2]Not applicableDon't know/refusal/not stated [7,8,9]Total
4485039943401735914
130880
52.947.1
--
100
Most important change to improve health[8 categories]CIHA_2
(1)more exercise [1](2)lost weight [2](3)eating habits [3](4)smoke less/stop [4](5)less alcohol [5](6)medical treat [6](7)took vitamins [7](8)other [8]Not applicable [96]Don't know/refusal/not stated [97,98,99]total
24687638952143293391
214810541633
801165955
130880
55.114.311.6
7.30.94.82.43.6
--
100
Changes to improve health/intention variables[categories used for analysis]
Category [codes from variables] Raw data
n % for analysis
categories
Thinks should do something - to improve health
(1) Yes [1](2) No [2]Not applicable [6]Don't know/refusal/not stated [7,8,9]Total
5182732723401736157
130880
61.338.7
--
100
[6 categories]CIHA_4
(1)more exercise [1](2)lost weight [2](3)eating habits [3](4)quit smoking [4](5)take vitamins [5](6)other [6]Not applicable [96]Don't know/refusal/not stated [97,98,99]total
2248474017995
10304405
3174728966221
130880
43.414.315.419.9
0.86.1
--
100
Changes to improve health/intention variables[categories used for analysis]
Category [codes from variables] Raw data
n % for analysis
categories
Barriers to improving health
(1) Yes [1](2) No [2]Not applicableDon't know/refusal/not stated [7,8,9]Total
2411527644728966225
130880
46.653.4
--
100
List of barriers[2 categories]
_6A Lack will power
_6B Lack of time
_6C Too tired
_6D Too difficult
_6E Too costly
_6F Too stressed
_6G Disabled/health problem_6H Other
(1) Yes [1](2) No [2]Not applicableDon't know/refusal/not stated [7,8,9]total(1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2]
921014888
1005406242
1308807779
16319923
23175800
23298964
231341626
224722036
220622781
21317
38.261.8
--
10032.367.73.8
96.23.3
96.74.0
96.06.7
93.38.4
91.611.588.5
Changes to improve health/intention variables[categories used for analysis]
Category [codes from variables] Raw data
n % for analysis
categories
Intending to improve health - next year
(1) Yes [1](2) No [2]Not applicableDon't know/refusal/not stated [7,8,9]total
3613715309728966538
130880
70.229.8
--
100
List of intention for health improvement[2 categories]CIHA_8A to _8I
_8A More exercise
_8B Lose weight
_8C Eating habits
_8D Quit smoking
_8E Smoke reduction
_8F Manage stress
_8G Reduce stress
_8H Take vitamins
(1) Yes [1](2) No [2]Not applicableDon't know/refusal/not stated [7,8,9]total (1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2](1) Yes [1](2) No [2]
2208714010882056578
1308806073
300246406
296915294
30803461
356361035
350621211
34886699
353982006
34091
61.238.8
--
10016.883.217.782.314.785.31.3
98.72.9
97.13.4
96.61.9
98.15.6
94.4
Examples of the analysis provided
Full report will be available
This presentation is to illustrate examples
of using HC population data in the
understanding population HL variables
Figure HI1. Health description index by gender Figure HI2. Health description by age
0
10
20
30
40
Poor Fair Good Very good Excellent
Health description
%
Male Female
0
10
20
30
40
50
Poor Fair Good Very good Excellent
Health description
%
20-34 years 35-49 years 50-64 years 65+ years
Figure HI3. Health description by education Figure HI4. Health description by home
0
10
20
30
40
50
Poor Fair Good Very good Excellent
Health description
%
< than secondary Secondary grad.
Other post-sec. Post-sec. Grad.
0
10
20
30
40
Poor Fair Good Very good Excellent
Health description
%
Not own home Does own home
Health Descriptioncorrelates
SB2.Sense of belonging by age group SB3. Sense of belonging by education
0
20
40
60
Very/somewhat strong Very/somewhat weak
%
< than secondary Secondary grad.Other post-sec. Post-sec. Grad.
0
20
40
60
80
Very/somewhat strong Very/somewhat weak
%
20-34 years 35-49 years 50-64 years 65+ years
SB5.Sense of belonging by COB SB6. Sense of belonging by language
0
20
40
60
Very/somewhat strong Very/somewhat weak
%
Canada Other
0
20
40
60
80
Very/somewhat strong Very/somewhat weak
%
English French Both Neither
sense of belonging to the community
SB9. Sense of belonging by income SB10. Sense of belonging by food insecurity
0
20
40
60
Very/somewhat strong Very/somewhat weak
%
Low income Lower middle incomeUpper middle income High income
0
20
40
60
Very/somewhat strong Very/somewhat weak
%
No Yes
Healthy lifestyle variables and demographic correlates
Body mass index by gender Body mass index by age group
5
43
38
1415
47
25
13
0
10
20
30
40
50
Low Normal Overweight Obese
%
Male Female
15
50
26
109
44
34
14
4
39 39
18
0
10
20
30
40
50
Low Normal Overweight Obese
%
20-34 years 35-49 years 50-64 years
Body mass index by education
8
40
34
19
10
44
31
1514
45
30
119
47
32
12
0
10
20
30
40
50
Low Normal Overweight Obese
%
< than secondary Secondary grad.Other post-sec. Post-sec. Grad.
BODYMASSINDEX
Alcohol dependence by gender Alcohol dependence by age group
0
2
4
6
8
10
Male Female
%
0
5
10
15
20
20-34years
35-49years
50-64years
65+ years
%
Alcohol dependence by education
0
5
10
15
20
< thansecondary
Secondarygrad.
Other post-sec.
Post-sec.Grad.
%
ALCOHOLDEPENDENCE
Alcohol dependence by income Alcohol dependence by food insecurity
0
2
4
6
8
10
Low income Lowermiddleincome
Uppermiddleincome
High income
%
0
2
4
6
8
10
No Yes
%
ALCOHOLDEPENDENCE
Fruit & Vege-tables
Figure QFVI1. Quintile of fruit & vegetable Figure QFVI2. Quintile of fruit & vegetable intake intake by gender by age group
0
10
20
30
high low
%
Male Female
0
10
20
30
high low
%
20-34 years 35-49 years 50-64 years 65+ years
Figure QFVI3. Quintile of fruit & vegetable Figure QFVI4. Quintile of fruit & vegetable intake intake by education by home ownership
0
10
20
30
high low
%
< than secondary Secondary grad.Other post-sec. Post-sec. Grad.
0
10
20
30
high low
%
Not own home Does own home
Fruit & Vegetables
Quintile of fruit & vegetable Quintile of fruit & vegetable intake intake by income by food insecurity
0
10
20
30
high low
%
Low income Lower middle incomeUpper middle income High income
0
10
20
30
high low
%
No Yes
Tobacco
Figure TU1. Tobacco use by gender Figure TU2. Tobacco use by age group
29
39
32
22
32
45
0
10
20
30
40
50
Current Ex-smoker Never
%
Male Female
31
26
43
29
35 36
21
46
33
11
49
41
0
10
20
30
40
50
Current Ex-smoker Never
%
20-34 years 35-49 years 50-64 years 65+ years
Figure TU3. Tobacco use by education
3135 35
32 34 3530
3437
21
3841
0
10
20
30
40
50
Current Ex-smoker Never
%
< than secondary Secondary grad.Other post-sec. Post-sec. Grad.
Tobacco
Risk groups
0
10
20
30
40
Current Ex-smoker Never
%
No Yes
Tobacco use by food insecurity
Figure PA1. Physical activity by gender Figure PA2. Physical activity by age group
22 23
56
1622
62
0
10
20
30
40
50
60
70
Active Moderate Inactive
%
Male Female
24 23
53
1622
61
1722
61
1721
62
0
10
20
30
40
50
60
70
Active Moderate Inactive
%
20-34 years 35-49 years 50-64 years 65+ years
Figure PA3. Physical activity by education
1318
68
18 20
63
22 23
55
2125
55
0
10
20
30
40
50
60
70
Active Moderate Inactive
%
< than secondary Secondary grad.Other post-sec. Post-sec. Grad.
Physicalactivity
Specific groups: country of birth
COB Canada
Asia Europe/N Amer
Other
Overweight / obese (%)
50 25 52 42
Alcohol dependent 8 2 3 2
Fruit/veg lowest Quintile
21 20 15 17
Current smoker 28 10 20 17
Physically active >3KKD
22 13 19 16
Sub groups: indigenous Canadians
Indigenous No % Yes %Food insecurity 14 31
Unemployed 4 9
Lowest income group 11 23
HL variablesOverweight / obese (%)
48 55
Alcohol dependent 7 13
Fruit/veg lowest Quintile
20 29
Current smoker 25 46
Physically active >3KKD
23 29
Sub groups: unemployed
Unemployed No % Yes %Food insecurity 14 30
Lowest income group 10 25
HL variablesOverweight / obese (%)
48 43
Alcohol dependent 8 14
Fruit/veg lowest Quintile
21 25
Current smoker 27 40
Physically active >3KKD
22 31
Considering or intending to make health improvements
DSIH1.Did something to improve health by gender DSIH2.Did something to improve health by age
50
56
0
20
40
60
Male Female
%
5955 53
38
0
20
40
60
80
20-34years
35-49years
50-64years
65+ years
%
DSIH3. Did something to improve health by educ
41
5259 57
0
20
40
60
< thansecondary
Secondarygrad.
Other post-sec.
Post-sec.Grad.
%
Did somethingTo improve health past 12 months
Most important change to health by gender. Most important change to improve health by age
0
20
40
60% Male
Female
0
20
40
60% 20-34 years
35-49 years
50-64 years
65+ years
Figure MICIH3. Most important change to improve health by education
0
20
40
60% < than secondary
Secondary grad.
Other post-sec.
Post-sec. Grad.
Most important Change to improveHealth
Intend to improve health next year by gender Intend to improve health next year by age
6873
0
20
40
60
80
Male Female
% 74 7269
55
0
20
40
60
80
20-34years
35-49years
50-64years
65+ years
%
education income
62
69 71 73
0
20
40
60
80
< thansecondary
Secondarygrad.
Other post-sec.
Post-sec.Grad.
%68 68
71 72
0
20
40
60
80
Low income Lowermiddleincome
Uppermiddleincome
High income
%
IntentionTo improveHealth
Intend to exercise more by gender Intend to exercise more by age group
61 63
0
20
40
60
80
Male Female
%
63 62 6164
0
20
40
60
80
20-34years
35-49years
50-64years
65+ years
%
Intend to lose weight by gender Intend to lose weight by age group
14
19
0
5
10
15
20
Male Female
%
12
17
23
20
0
10
20
30
20-34years
35-49years
50-64years
65+ years
%
IntentionTo exerciseOr lose weight
Intend to change eating habits by gender Intend to change eating habits by age
0
5
10
15
20
Male Female
%
0
10
20
30
20-34years
35-49years
50-64years
65+ years
%
Intend to quit smoking by gender. Intend to quit smoking by age group
0
5
10
15
20
Male Female
%
0
5
10
15
20
20-34years
35-49years
50-64years
65+ years
%
Intend to Change eatingHabits or to quit smoking
Change and intention by sub group
Unemployed
Indigenous
NO % YES % NO % YES %
Have made changes
55 58 54 60
Should make healthy lifestyle changes
66 72 63 74
Intend to make changes
71 76 70 77
Barriers to change
49 43 48 49
0.5
0.70.8
0.91
1.11.2
1.4
1.72
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Siz
e o
f o
dd
s ra
tio
Concept of statistically adjusted analyses – odds ratios [likelihood of beingat risk] for (un)healthy lifestyle attribute
See data analytic models
Uses of this approach to these data
Analyses inform social marketing efforts and could be part of formative development of HL initiatives
Would need to be supplemented by other methods ands sources of information, but these could build on existing information bases
Data for subgroups, here modelled for indigenous Canadians, show high levels of unhealthy HL characteristics, but similar demographic correlates as for non indigenous samples
Further issues Other use of these data include describing
associations between HL variables and mental health, social environments and communities
Methodological issues
these data are better than much survey information [in terms of measurement, representativeness]
Other statistical techniques possible here, cluster analysis, and possibly conjoint analysis but less clear intepretations obtained
Uses of these data Correlates are not causal – just explain
cross sectional associations within data – but do provide some ‘groupings’ and both define segments, and in some cases show that segmentation not useful
concept that population groups can be defined from HC population data is an innovative approach
Main findings Gender – males at substantial risk for most
unhealthy living and less interested in change Indigenous Canadians – at markedly increased risk,
but similar demographic correlates [at risk sub groups]
socio economic and educational differentials in HL variables especially food insecurity > unemployment > income
Food insecurity independently associated with most poor outcomes
Change potential – younger, more educated, indigenous, food insecurity – lots of groups want to change !
Main findings of clustering analyses – from 2 to 5 unhealthy behaviors
Males - consistently increased risk for HL groupings Sense of community may be somewhat protective Marriage increases risk, but this decreases in five HL
clusters Correlation increases with mental health across
increasing unhealthy lifestyle Self rated health is poor in unhealthy groups Generally age increases risk, education protective Indigenous and food insecurity strong correlates
especially of multiple [5] unhealthy living variables These multiple risk groups havent changed, but still
report they want to
Main findings- clustering analyses by audience segment [at risk groups]
Audience segments show increased risk, but different correlates for different HL behaviors
Suggests some degree of behavior specific marketing, rather than specific group targeting – corroborated by similar correlates for specific behaviors
The common factors suggest some degree of mass messaging may be supported by these data
Conclusions Many other ‘combinations’ are possible from this
data exploratory exercise; these are the end points of this initial project
Ideally, a central research and formative evaluation function within HL initiative would interact with findings
If a clear social marketing initiative is planned, then this kind of use of HC data can augment the early formative evaluation stages
1. CCHS data 2. analysis3. Interaction with
Social marketing team
4. More (qual) research
Further reflection
Optimal campaign
Analysis is not finished Until it iterates through a processLike this with relevant HC groups
Strategic Plans HL
The next steps
other elements of a comprehensive formative evaluation would be included, to lead to clear campaign identity, objectives, goals and timelines
Communication objectives and strategies for the integrated elements of the HL initiative would build on this and other formative components