1. 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

2. 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

3. 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

4. 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

5. 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]

6. But can population health surveys [epidemiological model] inform this process in some ways ?

7. 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

8. Meanings of “healthy living” Healthy weight Active Living Heritage / Sport tobacco Workplace Health Social capital Sense of community Mental health stress Healthy eating Indigenous Canadians Other Special populations Physical activity

9. This project To use some population health data to inform the HL process of campaign development, especially through audience segmentation

10. 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

11. 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”

12. 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

13. 1. CCHS data 2. analysis 3. Interaction with Social marketing team 4. More (qual) research Further reflection Optimal campaign CONCEPTUAL MODEL Strategic Plans HL

14. 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

15. Data used in these analyses Demographic Healthy living variables Mental health variables Change and intention variables

16. Beha HL Variables of interest Demographic characteristics. Age Gender Education Housing Country of birth (Canada, Asian, Europe &Nth America / other) Cultural group/language Identifies as indigenous Work pattern – and unemployed and looking for work Income Food insecurity Province, local health region General health.

18. Mental Health. On medication Feeling happy (single item) Work stress Self esteem Mastery Social support scale (4 d.v.’s) Spirituality Contact 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

19. 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)

20. 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

21. 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 6476 11081 26607 36721 25762 24233 130880 - 23.5 32.4 22.7 21.4 100 Gender: [2 categories] male [1] female [2 ] Total 60514 70366 130880 46.2 53.8 100 Education: [4 categories] <secondary [1] secondary graduate [2] other post secondary [3] post secondary graduate [4] Not stated Total 44338 22860 9832 52586 1264 130880 34.2 17.6 7.6 40.6 - 100

22. Demographic variables: Category [codes from variables]Raw data n% for analysis categories Food insecurity: [2 categ ] (1)Yes [1] (2)No [2] Not stated total 20635 108830 1415 130880 15.9 84.1 - 100 Housing: [2 categories] owned by a member of household [1] not own by a member of household [2] Not applicable Don't know/refusal/not stated Total 93464 36968 88 360 130880 71.7 28.3 - 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”

24. Behavioral HL variables used 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 19286 111348 246 130880 14.8 85.2 - 100 Alcohol dependence scale (1)“Scale” (did not have 5 or more drinks) (2)“Scale” 1-7 Not stated Total 120104 9430 1346 130880 92.7 7.3 - 100

25. 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 ( 64) Not stated Total 6040 35404 29688 15042 42866 1840 130880 7.0 41.1 34.5 17.5 - 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<36 Not stated Total Weighted mean=4.41, median=3.9 (male); mean=4.98, median=4.6 (female) 23311 26353 25811 25339 28084 1982 130880 18.1 20.4 20.0 19.7 21.8 - 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 stated total 35598 50331 44601 350 130880 27.3 38.6 34.2 - 100

26. 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 < 1.5 Not stated Total 29147 29168 64104 8461 130880 23.8 52.4 - 100

27. 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 4674 13715 36037 46442 29953 59 130880 3.6 10.5 27.5 35.5 22.9 - 100 Self-perceived belonging/local community [5 categories] (1)very strong (2)somewhat strong (3)somewhat weak (4)very weak Don't know/refusal/not stated [7,8,9] total 24083 52497 30868 14249 9183 130880 19.8 43.1 25.4 11.7 - 100 Has a chronic condition? (1) Yes [1] (2) No [2] Not stated [9] total 87138 43508 234 130880 66.7 33.3 - 100

28. 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 applicable Don't know/refusal/not stated [7,8,9] Total 44850 39943 40173 5914 130880 52.9 47.1 - 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 24687 6389 5214 3293 391 2148 1054 1633 80116 5955 130880 55.1 14.3 11.6 7.3 0.9 4.8 2.4 3.6 - 100

29. 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 51827 32723 40173 6157 130880 61.3 38.7 - 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 22484 7401 7995 10304 405 3174 72896 6221 130880 43.4 14.3 15.4 19.9 0.8 6.1 - 100

30. 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 applicable Don't know/refusal/not stated [7,8,9] Total 24115 27644 72896 6225 130880 46.6 53.4 - 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 applicable Don'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] 9210 14888 100540 6242 130880 7779 16319 923 23175 800 23298 964 23134 1626 22472 2036 22062 2781 21317 38.2 61.8 - 100 32.3 67.7 3.8 96.2 3.3 96.7 4.0 96.0 6.7 93.3 8.4 91.6 11.5 88.5

31. 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 applicable Don't know/refusal/not stated [7,8,9] total 36137 15309 72896 6538 130880 70.2 29.8 - 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 applicable Don'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] 22087 14010 88205 6578 130880 6073 30024 6406 29691 5294 30803 461 35636 1035 35062 1211 34886 699 35398 2006 34091 61.2 38.8 - 100 16.8 83.2 17.7 82.3 14.7 85.3 1.3 98.7 2.9 97.1 3.4 96.6 1.9 98.1 5.6 94.4

32. Mental health variables Unweighted (%) Weighted (%) Feel happy with life 23.924.4 Take any (MH) medication (antidepressant, tranquilliser, sedative) Yes (%)11.49.4 Distress scale (score 1-2) (score 3-4) (score 5-10) (score 11-24) 0123401234 31.9 20.8 17.1 24.2 5.9 30.3 21.3 17.9 24.5 5.9 Mood scale Negative scale (Score 5) (Score 6) (Score 7) (Score 8-9) (Score 10-15) 0123401234 19.7 19.9 19.7 27.0 13.6 20.0 20.2 19.6 27.0 13.2 Positive scale (Score <10) (score 10-11) (score 12-13) (score 14-15) 01230123 17.9 38.0 26.0 18.0 18.3 37.6 25.8 18.3 Ever had depression Yes (%)10.610.1

33. Mental health variables Unweighted (%) Weighted (%) Feel happy with life 23.924.4 Social support Affection (yes) 83.385.0 Emotional/info support (score <24) (score 24-28) (score 29-31) (score  32) 01230123 23.3 24.9 14.9 37.0 22.3 24.6 15.0 38.1 Positive social interaction (score <12) (score 12-15) (score 16 +) 012012 19.7 34.2 45.9 17.8 34.1 48.1 Tangible social support (score <12) (score 12-15) (score 16+) 012012 23.3 36.8 39.9 20.8 37.0 42.2 Mastery Score Category (score <17) (score 17-19) (score 20-21) (score 22-30) 01230123 19.2 25.7 30.0 25.1 18.1 25.0 29.5 27.4 Self Esteem category (score <18) (score 18) (score 19-21) (score 22-30) 01230123 14.3 33.7 29.3 22.8 13.7 31.0 30.2 25.1 Work Stress Scale (score <16) (score 16-18) (score 19-21) (score 22-30) 01230123 19.3 23.8 24.5 32.4 21.5 23.5 23.4 31.6

34. 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

35. Health Description correlates

36. Health Description correlates

37. Health Description correlates

38. Not all associations consistent… Self compared health

39. sense of belonging to the community

41. Healthy lifestyle variables and demographic correlates

42. BODY MASS INDEX

43. ALCOHOL REGULAR DRINKER

44. ALCOHOL REGULAR DRINKER

45. ALCOHOL DEPENDENCE

46. ALCOHOL DEPENDENCE

47. Fruit & Vege- tables

48. Fruit & Vegetables

49. Tobacco

50. Risk groups

51. Tobacco Risk groups Tobacco use by food insecurity

52. Physical activity

53. Physical activity

54. Other types Of PA

59. Specific groups: country of birth COBCanadaAsiaEurope/N Amer Other Overweight / obese (%) 50255242 Alcohol dependent8232 Fruit/veg lowest Quintile 21201517 Current smoker28102017 Physically active >3KKD 22131916

60. Sub groups: indigenous Canadians IndigenousNo %Yes % Food insecurity1431 Unemployed49 Lowest income group1123 HL variables Overweight / obese (%)4855 Alcohol dependent713 Fruit/veg lowest Quintile 2029 Current smoker2546 Physically active >3KKD2329

61. Sub groups: unemployed UnemployedNo %Yes % Food insecurity1430 Lowest income group1025 HL variables Overweight / obese (%)4843 Alcohol dependent814 Fruit/veg lowest Quintile2125 Current smoker2740 Physically active >3KKD2231

62. Considering or intending to make health improvements

63. Did something To improve health past 12 months

64. Most important Change to improve Health

65. Intention To improve Health

66. Intention To exercise Or lose weight

67. Intend to Change eating Habits or to quit smoking

68. Change and intention by sub group UnemployedIndigenous NO %YES %NO %YES % Have made changes 55585460 Should make healthy lifestyle changes 66726374 Intend to make changes 71767077 Barriers to change 49434849

69. Concept of statistically adjusted analyses – odds ratios [likelihood of being at risk] for (un)healthy lifestyle attribute

70. See data analytic models

71. Examples H. Living Behaviors : obesity & overweight

73. 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

74. 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

75. 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

76. 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 !

77. 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

78. 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

79. 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

80. 1. CCHS data 2. analysis 3. Interaction with Social marketing team 4. More (qual) research Further reflection Optimal campaign Analysis is not finished Until it iterates through a process Like this with relevant HC groups Strategic Plans HL

81. 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