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:


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


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


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]


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



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



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


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


Health description

%

< than secondary Secondary grad.

Other post-sec. Post-sec. Grad.

0

10

20

30

40


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


%


SB5.Sense of belonging by COB SB6. Sense of belonging by language

0

20

40

60


%

Canada Other

0

20

40

60

80


%

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


%

Low income Lower middle incomeUpper middle income High income

0

20

40

60


%

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


%

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


%


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

%


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

%


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


%


Figure TU3. Tobacco use by education

3135 35

32 34 3530

3437

21

3841

0

10

20

30

40

50


%


Tobacco

Risk groups

0

10

20

30

40


%

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


%


Figure PA3. Physical activity by education

1318

68

18 20

63

22 23

55

2125

55

0

10

20

30

40

50

60

70


%


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


20 29

Current smoker 25 46


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


21 25

Current smoker 27 40


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

Using population data sets to inform the development of social marketing initiatives around Healthy...

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