do healthy and unhealthy behaviours cluster in new zealand?

2007 vol. 31 no. 2 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 155© 2007 The Authors. Journal Compilation © 2007 Public Health Association of Australia

The role of health-related behaviours

as risk and protective factors in the

causation and prevention of chronic

disease is widely recognised.1 Yet few studies

internationally,2-9 and to our knowledge

none in New Zealand, have described the

population distribution of clustering of

health-related behaviours within individuals.

Such information about the distribution of

‘lifestyles’ within the population may be

helpful in designing and evaluating health

promotion policies and programs.

The relatively few published studies2-9 have

varied in their selection of behaviours, their

measure of clustering, and their analytical

approach. Some have reported only the

number of co-occurring behaviours rather

than specific behavioural patterns. Others

have privileged one behaviour over others

and defined clustering only in relation to this

‘central’ behaviour (most usually tobacco

use). Most studies have focused exclusively

on unhealthy behaviours to the neglect of

healthy behaviours.

The objective of the current study is

to describe the clustering of both healthy

and unhealthy behaviours in the New

Zealand population today using a symmetric

analytical approach. Since human behaviour

does not occur in isolation from its social

and cultural context, we examine how the

clustering of behaviours (co-occurrence of

Do healthy and unhealthy behaviours

cluster in New Zealand?

Martin TobiasPublic Health Intelligence, New Zealand Ministry of Health

Gary JacksonCounties Manukau District Health Board, New Zealand

Li-Chia Yeh, Ken HuangPublic Health Intelligence, New Zealand Ministry of Health

Abstract

Objective: To describe the co-occurrence

and clustering of healthy and unhealthy

behaviours in New Zealand.

Method: Data were sourced from the

2002/03 New Zealand Health Survey.

Behaviours selected for analysis were

tobacco use, quantity and pattern of alcohol

consumption, level of physical activity, and

intake of fruit and vegetables. Clustering

was defined as co-prevalence of behaviours

greater than that expected based on the

laws of probability. Co-occurrence was

examined using multiple logistic regression

modelling, while clustering was examined in

a stratified analysis using age and (where

appropriate) ethnic standardisation for

confounding control.

Results: Approximately 29% of adults

enjoyed a healthy lifestyle characterised

by non-use of tobacco, non- or safe use

of alcohol, sufficient physical activity and

adequate fruit and vegetable intake. This

is only slightly greater than the prevalence

expected if all four behaviours were

independently distributed through the

population i.e. little clustering of healthy

behaviours was found. By contrast, 1.5%

of adults exhibited all four unhealthy

behaviours and 13% exhibited any

combination of three of the four unhealthy

behaviours. Unhealthy behaviours were

more clustered than healthy behaviours,

yet Maori exhibited less clustering of

unhealthy behaviours than other ethnic

groups and no deprivation gradient was

seen in clustering.

Discussion: The relative lack of clustering

of healthy behaviours supports single issue

universal health promotion strategies at the

population level. Our results also support

targeted interventions at the clinical level

for the 15% with ‘unhealthy lifestyles’. Our

finding of only limited clustering of unhealthy

behaviours among Maori and no deprivation

gradient suggests that clustering does not

contribute to the greater burden of disease

experienced by these groups.

Key words: Behaviour, clustering, lifestyle,

health promotion.

(Aust N Z J Public Health. 2007; 31:155-63)

doi:10.1111/j.1753-6405.2007.00034.x

Submitted: September 2006 Revision requested: January 2007 Accepted: February 2007

Correspondence to: Dr Martin Tobias, Ministry of Health, PO Box 5013, Wellington, New Zealand. Fax: +64 4 495 4401; e-mail: [email protected]

behaviours in the same individual greater

than would be expected by chance) varies

across socio-demographic groups.

MethodsNew Zealand Health Survey 2002/03

The New Zealand Health Survey (NZHS)

was conducted from August 2002 to October

2003. The target population was the usually

resident, non-institutionalised civilian

adult population (aged 15 years and over)

living in permanent private dwellings. A

stratified multistage cluster sample design

was employed using an area-based sampling

frame. Details of the survey design and

analysis, including calculation of integrated

survey weights and standard errors for

estimates, are reported elsewhere.10

For the present analysis, survey participants

self-reporting any present or past cardio-

vascular disease or cancer (any type) were

excluded, since diagnosis of such diseases

may lead to behaviour change (e.g. smoking

cessation). This left 10,241 participants

for analysis, comprising 3,416 Maori, 821

Pacific and 6,005 European/Others.

Selection of behavioursHealth-related behaviours established as

major risk or protective factors for chronic

disease, in particular cardiovascular disease

Article Surveys

156 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2007 vol. 31 no. 2© 2007 The Authors. Journal Compilation © 2007 Public Health Association of Australia

and cancer, include tobacco use, alcohol use, diet and physical

activity.11

The healthy and unhealthy behaviours included in this study,

together with their definitions and abbreviations, are summarised

in Table 1. For the purposes of this paper we define a ‘healthy

lifestyle’ as one that incorporates all four healthy behaviours, and

an ‘unhealthy lifestyle’ as one that features any three of the four

unhealthy behaviours listed or all four of these behaviours.

The definition of ‘current smoking’ excludes non-daily smokers

because the self-reporting of occasional or non-daily use is

less robust than that of daily use.12 The Alcohol Use Disorders

Identification Test (AUDIT) is a widely used eight-item scale

identifying potentially hazardous drinking.13 A score of eight or

more is generally accepted as indicative of a harmful quantity or

pattern of drinking, although some investigators prefer a cut-point

of seven for females.13 The threshold for physical (in)activity of

150 minute per week of activity equivalised to moderate intensity

is widely accepted as indicating sufficient physical activity for

health.14 Five or more servings of fruit plus vegetables per day is

used in the New Zealand Health Survey as a proxy measure for

a healthy diet and correlates reasonably well with more compre-

hensive dietary assessments such as the Healthy Eating Index.15

Measurement of clusteringCorrelation between risk factors does not necessarily imply

clustering.3 Rather, clustering exists when the observed distribution

of the risk factors differs from that expected assuming the risk

factors to be independent of each other.3

The expected joint prevalence of any set of risk factors is

simply the product of the individual risk factor prevalences.3 For

example, if the prevalence of smoking is 20% and the prevalence

of drinking is 30%, then by the laws of probability the expected

joint prevalence of [smoking + drinking] is 6%. If the observed

prevalence exceeds the expected prevalence, clustering is said

to occur.

Analytical approachFirst, the observed prevalence of each singular behaviour and of

all possible behavioural patterns (co-occurrences of behaviours)

is briefly described.

Second, the association of each behavioural pattern with socio-

demographic variables is analysed by multiple logistic regression

modelling. The model adjusts for age (in four categories: 15-24,

25-44, 45-64, 65+), sex, ethnicity (three categories as indicated

Table 1: Selected health-related behaviours.

Healthy behaviours Definitions Unhealthy Definitions behavioursNon-smoking Never smoker, ex-smoker, Smoking One or more cigarettes per day or non-daily smoker

Healthy drinking Abstainer or AUDIT score 7 or less Unhealthy drinking AUDIT score 8 or above (both sexes)

Physically active 150 or more mins per week of activity Inactive <150 mins per week of activity equivalised equivalised to moderate intensity to moderate intensity (~3 METs)

Healthy diet 5 or more servings of fruit plus Unhealthy diet <5 servings of fruit plus vegetables per day vegetables per day

Healthy lifestyle All 4 of the above Unhealthy lifestyle All 4, or any combination of 3 of the above

above) and deprivation as a measure of socio-economic position

(five categories: quintiles of the NZDep2001 deprivation index,

1 being least deprived, 5 being most deprived). NZDep2001 is a

Census-based small area index of deprivation derived by principal

component analysis of nine socio-economic variables included in

the 2001 Census.16 The reference group for the regression models

is 25-44 years of age, male, European, deprivation quintile 5. While

age is adjusted for in all reported odds ratios, the effect of age

is not itself reported as this is biased by the exclusion of survey

respondents with diagnosed cardiovascular disease or cancer.

Finally, clustering (Observed/Expected prevalence ratio)

is analysed for each behavioural pattern separately by socio-

demographic subgroup (i.e. a stratified analysis). Confidence

intervals for the O/E or ‘clustering’ ratios were estimated by

standard parametric methods for Poisson random variables.

For all analyses, the prevalence or clustering of both healthy

and unhealthy behaviours is reported. These are not mirror images

of each other, because unhealthy behaviours are typically less

prevalent than their healthy counterparts – so higher degrees of

clustering are typically found for the former (since clustering

is defined as a ratio measure). While this could be corrected

by normalisation (re-scaling), this has not been done since it is

the actual degree of clustering that is important rather than the

comparison of clustering of healthy compared with clustering of

unhealthy behaviours.

Prevalence rates have been age standardised to the World Health

Organization (WHO) World Population17 by the direct method,

and ethnicity standardised to the New Zealand 2001 Census

population, where relevant. Ethnic standardisation is intended to

enable comparison of degrees of clustering by deprivation level

unconfounded by ethnicity. By contrast, comparison of clustering

by ethnicity does not require standardisation for deprivation

because deprivation is a mediator, not a confounder, of the

ethnicity/outcome relationship.

ResultsDescriptive epidemiologyHealthy behaviours

Table 2 shows the observed prevalence of single healthy

behaviours in the New Zealand adult (15+) population, as well

as the prevalence of a ‘healthy lifestyle’. Details of all healthy

behavioural patterns are shown in Table 6.

Tobias et al. Article


Table 2: Healthy behaviour prevalence rates.

Numbera Prevalence (%) Non-smoking Healthy Physically Healthy Healthy drinking active diet lifestyleb

All (crude) 10,223 76.3 81.1 75.3 54.0 29.4

Age

15-24 1,517 73.2 67.1 76.1 44.9 20.5

25-44 4,664 72.4 80.5 74.5 51.9 26.2

45-64 2,918 80.0 87.9 78.6 60.0 36.8

65+ 1,124 90.2 94.7 66.8 66.7 41.9

Sex

Malec 3,961 75.1 70.9 80.4 44.3 22.4

Femalec 6,262 77.1 87.6 71.1 61.9 34.5

Ethnicity

Maoric 3,406 50.4 71.2 77.0 49.5 18.6

Pacificc 819 67.3 80.8 67.2 38.4 15.9

Otherc 5,998 79.8 80.5 75.9 54.7 30.6

SES

Dep Q1d 1,389 83.6 82.0 79.4 57.2 34.1

Dep Q2d 1,300 81.4 79.6 77.0 59.3 33.2

Dep Q3d 1,518 79.0 80.6 76.4 55.6 30.3

Dep Q4d 1,950 72.4 78.7 74.1 49.1 23.8

Dep Q5d 4,066 64.0 76.7 71.0 45.8 22.4Notes:(a) Number of respondents included in analysis.(b) Healthy lifestyle = all four behaviours reported.(c) Age standardised.(d) Age and ethnicity standardised.

Table 3: Unhealthy behaviour prevalence rates.

Numbera Prevalence (%) Smoking Unhealthy Insufficiently Unhealthy Unhealthy Unhealthy drinking active diet lifestyle – lifestyle – all 4 any 3 of 4 behaviours behavioursAll (crude) 10,223 23.7 18.9 24.7 46.0 1.4 13.6

Age

15-24 1,517 26.8 32.9 23.9 55.1 3.3 22.9

25-44 4,664 27.6 19.5 25.5 48.1 1.4 14.8

45-64 2,918 20.0 12.1 21.4 40.0 0.5 7.9

65+ 1,124 9.8 5.3 33.2 33.3

Sex

Maleb 3,961 24.9 29.1 19.6 55.7 1.8 17.2

Femaleb 6,262 22.9 12.4 28.9 38.1 1.4 11.8

Ethnicity

Maorib 3,406 49.6 28.8 23.0 50.5 2.4 26.3

Pacificb 819 32.7 19.2 32.8 61.6 4.0 28.1

Otherb 5,998 20.2 19.5 24.1 45.3 1.4 12.1

SES

DepQ1c 1,389 16.4 18.0 20.6 42.8 d d

DepQ2c 1,300 18.6 20.4 23.0 40.7 1.0 11.5

DepQ3c 1,518 21.0 19.4 23.6 44.4 0.7 10.0

DepQ4c 1,950 27.6 21.3 25.9 50.9 1.3 15.1

DepQ5c 4,066 36.0 23.3 29.0 54.2 3.3 25.9Notes:(a) Number of respondents included in analysis.(b) Age standardised.(c) Age and ethnicity standardised.(d) Count suppressed (<10).

Surveys Do healthy and unhealthy behaviours cluster?


Overall, 76% of New Zealand adults are non-smokers, 81%

have a healthy drinking pattern, 75% are sufficiently physically

active for health, and 54% consume a healthy diet (as indexed

by sufficient fruit and vegetable intake). However, this overall

assessment disguises substantial variations between population

subgroups, with older people more likely to exhibit healthy

behaviours (except for physical activity) than youth; females

than males (again except for physical activity); European/Others

than Maori or Pacific people; and deprivation quintile 1 (the least

deprived) than quintile 5 (the most deprived).

Almost 30% of adults report a healthy lifestyle (as defined).

However, this again varies with age, from 20% in youth to more

than 40% in older people. It also varies with sex, being higher in

females (35%) than males (22%) after standardising for age. Maori

and Pacific ethnic groups have lower prevalence of the healthy

lifestyle (19% and 16% respectively) than European/Others (31%),

adjusting for age but not deprivation. A socio-economic gradient

was found, with the prevalence of the healthy lifestyle varying from

34% in deprivation quintile 1 (least deprived) to 22% in quintile

5 (most deprived), adjusting for age and ethnicity.

Unhealthy behaviours

Table 3 shows the observed prevalence of single unhealthy

behaviours in the New Zealand adult (15+) population, as well as

the prevalence of unhealthy lifestyle. Detailed prevalences of all

unhealthy behavioural patterns are shown in Table 7.

Overall, 24% of New Zealanders currently smoke cigarettes,

19% exhibit a hazardous drinking pattern, 25% are insufficiently

physically active for health, and 46% consume an unhealthy diet

(as indexed by inadequate intake of fruit and vegetables). As with

the corresponding healthy behaviours, differences are seen with

age, sex, ethnicity and deprivation as shown in Table 3.

Turning to the unhealthy lifestyle construct, approximately 15%

of the population may be characterised as having an unhealthy

lifestyle (defined as any three or all four unhealthy behaviours),

although only 1.5% of the population acknowledge all four

unhealthy behaviours. The prevalence of an unhealthy lifestyle

varies with age (26% in youth, 8% in middle-aged adults), sex

Table 4: Odds ratios and 95% CIs for healthy behaviour patterns.

S-A-P-D A-P-D S-P-D S-A-D S-A-PFemale 1.83 1.91 1.47 2.39 1.18 (1.60-2.09) (1.69-2.16) (1.30-1.67) (2.11-2.71) (1.05-1.34)Maori 0.57 0.89 0.62 0.47 0.47 (0.44-0.73) (0.72-1.09) (0.48-0.79) (0.37-0.59) (0.39-0.57)Pacific 0.52 0.65 0.49 0.50 0.86 (0.39-0.71) (0.48-0.88) (0.36-0.67) (0.38-0.67) (0.64-1.15)Q1 1.83 1.71 1.76 1.81 1.74 (1.41-2.38) (1.34-2.18) (1.35-2.29) (1.41-2.33) (1.39-2.17)Q2 1.84 1.75 1.80 1.84 1.50 (1.42-2.38) (1.37-2.24) (1.38-2.35) (1.42-2.38) (1.20-1.88)Q3 1.55 1.47 1.48 1.63 1.32 (1.21-1.99) (1.16-1.86) (1.16-1.90) (1.29-2.07) (1.05-1.67)Q4 1.10 1.10 1.09 1.17 1.11 (0.85-1.41) (0.88-1.38) (0.86-1.38) (0.91-1.50) (0.89-1.37)Notes:Reference group is male, European/Other, Deprivation Quintile 5.S=non-smoking; A=healthy drinking; P= sufficient physical activity; D=healthy diet.Two-behaviour patterns not shown (available from first author on request).

(19% in males, 13% in females, age adjusted), ethnicity (29%

in Maori, 32% in Pacific people, 13.5% in Others, adjusting for

age) and deprivation quintile (from 13% in quintile 2 to 29% in

quintile 5, adjusting for age and ethnicity).

Co-occurrence: regression analysisHealthy behaviours

Table 4 summarises the model output for all healthy behaviour

patterns (i.e. co-occurrence, not clustering); two-behaviour patterns

are not shown but are available from the first author on request.

Females are almost twice as likely as males to enjoy a healthy

lifestyle (odds ratio 1.8). Females also have signif icantly

higher prevalences of all three and two-behaviour patterns (co-

occurrences) except for the [non-smoking + sufficient activity]

pattern that occurs 20% less often than in males, a difference that

is also statistically significant at the 95% level.

Maori are less likely than European/Others to exhibit healthy

lifestyles and all three- and two-behaviour patterns except for

[physically active + healthy diet], for which there is no difference.

The results for Pacific people are essentially the same, although

not all differences are statistically significant – perhaps due to the

relatively smaller numbers in the sample.

A clear socio-economic gradient in the prevalence of healthy

lifestyles and all three- and two-behaviour patterns is evident from

the deprivation analysis, adjusting for age, sex and ethnicity.


Table 5 summarises the model output for all unhealthy behaviour

patterns (i.e. co-occurrence, not clustering); two-behaviour

patterns are not shown but are available from the first author on

request.

Females are less likely than males to show an unhealthy lifestyle,

although not all differences are statistically significant. Females are

significantly less likely than males to show all of the two-behaviour

patterns except [smoking + insufficient physical activity] (which

they are significantly more likely to exhibit) and [unhealthy diet

+ insufficient physical activity] for which the gender difference

is not statistically significant.



Table 5: Odds ratios and 95% CIs for unhealthy behaviour patterns.

S-A-P-D A-P-D S-P-D S-A-D S-A-PFemale 0.67 0.42 1.18 0.42 0.71 (0.42-1.08) (0.29-0.62) (0.86-1.62) (0.32-0.55) (0.48-1.05)Maori 1.48 1.27 1.80 2.44 1.68 (0.67-3.29) (0.69-2.33) (1.17-2.78) (1.68-3.55) (0.84-3.36)Pacific 1.91 1.56 1.53 1.65 2.10 (0.83-4.38) (0.83-2.94) (0.90-2.61) (0.87-3.12) (0.97-4.54)Q1 0.55 0.60 0.31 0.50 0.47 (0.21-1.42) (0.31-1.20) (0.16-0.60) (0.27-0.92) (0.18-1.23)Q2 0.39 0.70 0.31 0.54 0.48 (0.15-1.03) (0.36-1.36) (0.18-0.54) (0.31-0.96) (0.21-1.08)Q3 0.26 0.34 0.31 0.67 0.30 (0.09-0.76) (0.15-0.75) (0.19-0.52) (0.39-1.17) (0.12-0.74)Q4 0.46 0.71 0.56 0.68 0.70 (0.21-1.02) (0.40-1.26) (0.36-0.86) (0.43-1.09) (0.34-1.45)Notes:Reference group is male, European/Other, Deprivation Quintile 5. S=current daily smoking; A=unhealthy drinking; P=insufficient activity for health; D=unhealthy diet (inadequate fruit and vegetable intake).Two behaviour patterns not shown (available from first author on request).

Confidence intervals for Maori and Pacific ethnic groups are

wide, making it difficult to demonstrate significant differences.

However, the results are suggestive of higher prevalences for all

unhealthy behaviour patterns. Unlike the situation for healthy

behaviours, there is less of a clear gradient in unhealthy behaviour

patterns across the deprivation quintiles after adjustment for age,

sex and ethnicity. There is a tendency, however, for quintiles 1-4, and

especially quintiles 1-3, to have lower (sometimes significantly lower)

prevalences of all unhealthy behaviour patterns than quintile 5.

Clustering: stratified analysisHealthy behaviours

Table 6 shows the observed and expected prevalences for all

healthy behaviour patterns (except for the two-behaviour patterns

– available from the first author on request), and the corresponding

O/E ratios (‘clustering ratios’). Confidence intervals for the

ratios are not shown for clarity (available from the first author on

request), but those with p values <0.05 are identified.

In the total New Zealand population, a small but statistically

significant degree of clustering is seen for most healthy behaviour

patterns (i.e. the prevalence of co-occurrence is typically greater

than would be expected if the component behaviours were

independent). The healthy lifestyle pattern shows the highest

degree of clustering (a ratio of 1.17, 95% confidence interval

1.13-1.21). That is, the proportion of individuals exhibiting all

four behaviours was 29%, which is 17% higher than the 25%

that would have been expected had the four component healthy

behaviours been independently distributed across the population.

All three-behaviour patterns show clustering ratios ranging from

1.05 to 1.15. Less clustering is seen for the two-behaviour patterns,

with [non-smoking + sufficient activity] and [healthy drinking +

sufficient activity] showing no clustering at all.

Clustering varies with age, with younger age groups showing

more clustering than older age groups. Indeed, older people

(65+) show no clustering at all except for overall healthy lifestyle

(clustering ratio 1.10, 95% CI 1.01-1.20). Few significant gender

differences were found after standardising for age.

Maori demonstrate higher degrees of clustering of healthy

behaviours than do European/Others. Healthy lifestyle has an O/E

ratio of 1.36 (CI 1.26-1.46) in Maori compared with only 1.15 (CI

1.09-1.20) in European/Others. Other major differences include

[non-smoking + healthy drinking + healthy diet]: 1.27 versus 1.14;

[non-smoking + sufficiently active + healthy diet]: 1.21 versus 1.08;

[non-smoking + healthy drinking]: 1.10 versus 1.05; and [non-

smoking + healthy diet]: 1.11 versus 1.05 respectively.

Little if any difference in the nature or extent of clustering of

healthy behaviours is seen across deprivation quintiles 1-4, but

quintile 5 displays higher degrees of clustering for some patterns,

including the healthy lifestyle pattern itself (1.25 versus 1.11 for

quintile 1). Compared with quintile 1, quintile 5 also exhibits

substantial clustering in regard to [non-smoking + sufficiently

active + healthy diet]: 1.17 versus 1.05; and [non-smoking +

healthy drinking]: 1.10 versus none (1.00).


Table 7 shows the observed and expected prevalences for

all unhealthy behaviour patterns (other than the two-behaviour

patterns, which are available from the first author on request), and

the corresponding O/E (‘clustering’) ratios. Confidence intervals

for the ratios are not shown for clarity (available from the first

author on request), but those with p values <0.05 are identified.

For the total New Zealand population, moderate or high

degrees of clustering are seen for some patterns (especially the

four- and three-behaviour patterns), with slight or no clustering

for most of the two-behaviour patterns. The ‘four behaviour

unhealthy lifestyle’ pattern is highly clustered (O/E ratio 2.75,

CI 2.29-3.21). Almost as much clustering is found for one of the

three-behaviour unhealthy lifestyle patterns, namely [smoking +

unhealthy drinking + unhealthy diet] (clustering ratio 2.43, CI

2.19-2.66). The remaining three-behaviour unhealthy lifestyle

patterns show moderate to slight clustering. One two-behaviour

pattern – [smoking + unhealthy drinking] – also shows a high

degree of clustering (ratio 1.79, CI 1.65-1.92).

Males and females show the same overall pattern, but the degree

of clustering of unhealthy behaviours (when present) is always



greater for females than males. For example, the four-behaviour

unhealthy lifestyle pattern has a clustering ratio of 4.48 in females

compared with 2.28 in males, after adjusting for age. Other major

gender differences are seen with the three-behaviour pattern

[smoking + unhealthy drinking + unhealthy diet]: 3.24 versus 1.81;

and the two-behaviour pattern [smoking + unhealthy drinking]:

2.11 versus 1.57 respectively.

There are few differences in the extent or nature of clustering

with age, at least to 64 years. Not all behaviour patterns could be

assessed in the 65+ age group because of small numbers in some

Table 6: Clustering of healthy behaviours.

S-A-P-D S-A-P S-A-D S-P-D A-P-DAll (crude) Observed 29.4 48.7 38.5 34.0 35.0 Expected 25.2 46.6 33.4 31.0 33.0 Ratio 1.17a 1.05a 1.15a 1.10a 1.06a

15-24 years Observed 20.5 40.3 25.7 28.0 24.7 Expected 16.8 37.4 22.1 25.0 22.9 Ratio 1.22a 1.08a 1.17a 1.12a 1.0825-44 years Observed 26.2 45.4 35.5 30.2 32.6 Expected 22.5 43.4 30.2 28.0 31.1 Ratio 1.16a 1.05a 1.17a 1.08a 1.05a

45-64 years Observed 36.8 57.1 45.9 41.1 42.9 Expected 33.2 55.3 42.2 37.7 41.5 Ratio 1.11a 1.03 1.09a 1.09a 1.0365+ years Observed 41.9 57.9 59.0 43.6 45.4 Expected 38.1 57.1 57.0 40.2 42.2 Ratio 1.10a 1.01 1.04 1.08 1.08Maleb Observed 22.4 45.8 27.4 29.0 26.7 Expected 19.0 42.8 23.6 26.7 25.3 Ratio 1.18a 1.07a 1.16a 1.08a 1.06Femaleb Observed 34.5 50.0 46.6 37.9 40.9 Expected 29.7 48.0 41.8 33.9 38.6 Ratio 1.16a 1.04a 1.11a 1.12a 1.06a

Maorib Observed 18.6 30.6 22.6 23.2 30.2 Expected 13.7 27.6 17.8 19.2 27.1 Ratio 1.36a 1.11a 1.27a 1.21a 1.11a

Pacificb Observed 15.9 42.1 22.9 17.9 22.0 Expected 14.0 36.5 20.9 17.4 20.9 Ratio 1.13 1.15a 1.10 1.03 1.06Otherb Observed 30.6 50.4 39.9 35.8 35.2 Expected 26.7 48.8 35.1 33.1 33.4 Ratio 1.15a 1.03a 1.14a 1.08a 1.05a

Quintile 1c Observed 34.1 55.1 42.4 39.1 38.5 Expected 29.4 52.2 37.2 36.5 35.9 Ratio 1.15a 1.03 1.11a 1.07 1.06Quintile 2c Observed 33.2 51.0 42.6 38.1 38.7 Expected 28.5 49.9 37.3 35.8 35.5 Ratio 1.15a 1.04 1.13a 1.06 1.09Quintile 3c Observed 30.3 48.4 39.9 35.4 35.6 Expected 26.5 47.9 34.7 33.1 33.8 Ratio 1.14a 1.01 1.14a 1.07 1.05Quintile 4c Observed 23.8 44.3 32.3 28.9 29.0 Expected 20.8 42.3 28.0 26.5 28.6 Ratio 1.14a 1.04 1.15a 1.09a 1.01Quintile 5c Observed 22.4 40.5 29.3 27.4 28.0 Expected 17.1 35.5 23.9 22.7 26.0 Ratio 1.31a 1.14a 1.22a 1.21a 1.08a

Notes:(a) p value <0.05. (b) Age standardised.(c) Age and ethnicity standardised.S=non smoking; A=healthy drinking; P=sufficient physical activity; D=healthy diet.

cells. However, it does appear that clustering of the two-behaviour

pattern [smoking + unhealthy drinking] does increase with age,

from a clustering ratio of 1.58 in youth to 1.93 in older people.

Pacific people show much the same pattern and extent of

clustering as European/Others. However, Maori consistently

show less clustering of unhealthy behaviours than the other ethnic

groups (despite showing more clustering of healthy behaviours).

For example, the four-behaviour unhealthy lifestyle is clustered to

an O/E ratio of only 1.45 in Maori versus 3.15 in Pacific people

and 3.26 in European/Others. As another example, the [smoking



+ unhealthy drinking] pattern has a clustering ratio of only 1.26

in Maori (although this is still statistically significant) compared

with 1.85 in both of the other ethnic groups.

There is very little if any evidence for a gradient in clustering

across deprivation quintiles. If anything, for several unhealthy

behaviour patterns the degree of clustering tends to be slightly

greater in quintiles 1-2 than in the other quintiles. For example, the

important two-behaviour pattern [smoking + unhealthy drinking]

has a clustering ratio of 2.26 in Q1, 1.79 in Q2, 1.79 in Q3, 1.67

in Q4 and 1.59 in Q5.

Table 7: Clustering of unhealthy behaviours.

S-A-P-D S-A-P S-A-D S-P-D A-P-DAll (crude) Observed 1.4 2.0 5.0 4.0 2.6 Expected 0.5 1.1 2.1 2.7 2.1 Ratio 2.75a 1.81a 2.43a 1.49a 1.21a

15-24 years Observed 3.3 4.2 8.8 5.5 4.4 Expected 1.2 2.1 4.9 3.5 4.3 Ratio 2.84a 1.99a 1.81a 1.56a 1.0225-44 years Observed 1.4 2.0 5.5 4.4 2.9 Expected 0.7 1.4 2.6 3.4 2.4 Ratio 2.12a 1.46a 2.12a 1.30a 1.21a

45-64 years Observed 0.5 0.9 2.9 2.9 1.2 Expected 0.2 0.5 1.0 1.7 1.0 Ratio 2.41 1.74a 3.00a 1.69a 1.1665+ years Observed – – – 1.7 1.3 Expected – – – 1.1 0.6 Ratio – – – 1.57 2.22a

Maleb Observed 1.8 2.4 7.3 3.7 3.8 Expected 0.8 1.4 4.0 2.7 3.2 Ratio 2.28a 1.69a 1.81a 1.36a 1.20a

Femaleb Observed 1.4 2.0 3.5 4.5 1.8 Expected 0.3 0.8 1.1 2.5 1.4 Ratio 4.48a 2.44a 3.24a 1.78a 1.32a

Maorib Observed 2.4 3.8 10.9 8.0 3.6 Expected 1.7 3.3 7.2 5.8 3.3 Ratio 1.45a 1.16 1.51a 1.39a 1.08Pacificb Observed 4.0 5.2 8.4 8.7 5.4 Expected 1.3 2.1 3.9 6.9 3.9 Ratio 3.15a 2.53a 2.17a 1.32a 1.39a

Otherb Observed 1.4 1.8 4.5 3.3 2.5 Expected 0.4 0.9 1.8 2.2 2.1 Ratio 3.26a 1.90a 2.52a 1.50a 1.17a

Quintile 1c Observed – 1.8 4.8 2.8 2.9 Expected – 0.7 1.5 1.6 1.8 Ratio – 2.57a 3.20a 1.75a 1.64a

Quintile 2c Observed 1.2 1.7 4.2 2.8 2.8 Expected 0.4 0.9 1.7 1.9 2.0 Ratio 3.10a 1.83a 2.46a 1.41a 1.41a

Quintile 3c Observed 0.7 1.0 5.2 2.4 1.4 Expected 0.5 1.0 1.9 2.2 2.1 Ratio 1.40 1.04 2.76a 1.09 0.70Quintile 4c Observed 1.3 2.3 5.3 4.5 3.0 Expected 0.8 1.5 3.0 3.6 2.8 Ratio 1.68 1.51a 1.80a 1.24 1.03Quintile 5c Observed 3.3 4.3 8.5 8.3 4.8 Expected 1.2 2.4 4.4 5.0 3.6 Ratio 2.75a 1.77a 1.94a 1.66a 1.33a

Notes:(a) p value <0.05.(b) Age standardised.(c) Age and ethnicity standardised.S=smoking; A=unhealthy drinking; P=insufficient physical activity; D=unhealthy diet.Rates were not calculated when counts less than 10.

DiscussionConcept of clustering

The concept of clustering is not clearly defined in the literature,

and different authors have theorised and operationalised this

construct in different ways. An important contribution of this study

has been to clarify the definition of clustering as co-occurrence

greater than that expected by chance. Clustering thus implies that

the distribution of the risk factors concerned is not independent of

each other, but instead reflects a common, more distal, determinant.



Thus finding clustering – or not – has both policy and research

implications, some of which are elaborated below.

Healthy behavioursOur study finds that, in 2002/03, approximately 29% of adult

New Zealanders enjoyed a healthy lifestyle characterised by non-

use of tobacco, safe or non-use of alcohol, sufficient physical

activity for health, and a healthy diet as indexed by adequate

fruit and vegetable consumption. This is very similar to rates

reported for Australia,5 the United Kingdom3 and Finland,2 despite

differences between studies in the definitions and thresholds for

these health-related behaviours. It is higher than rates reported

for the United States,6-8 with better physical activity and fruit and

vegetable consumption rates being reported for New Zealand, but

again differing definitions make the comparison inexact. While

in line with at least some other developed countries, and perhaps

higher than many public health workers might have predicted,

our results imply that more than two-thirds of adults are still

not meeting even modest thresholds for healthy living. Or more

positively, this finding reveals the great scope still available for

health gain via improvements in lifestyle.

Of even more interest, we found that healthy behaviours show

little clustering within individuals. For example, independent

distribution of all four health-related behaviours selected for

study would have yielded a joint prevalence of 25% rather than

the 29% actually observed – indicating only 17% clustering

(i.e. a clustering or observed/expected ratio of 1.17). Even less

clustering was found for all the three-behaviour and two-behaviour

patterns. Such low levels of clustering of healthy behaviours has

also been found in other studies.6,8 This finding has implications,

for example, for research into the health benefits of fruit and

vegetable consumption. Such research has often been criticised

on the grounds that fruit and vegetable consumption is merely

a marker of a healthy lifestyle, so any association between such

consumption and health outcomes may be subject to residual

confounding by other dimensions of a healthy lifestyle (e.g. non-

smoking, healthy drinking, sufficient physical activity) rather

than reflecting a true dietary impact. Our analysis, finding little

or no clustering of healthy behaviours within individuals, refutes

this hypothesis.

Unhealthy behavioursOur study found that, in 2002/03, less than 1.5% of adult New

Zealanders exhibited all four of the selected unhealthy behaviours

(i.e. daily tobacco use, potentially hazardous alcohol consumption

in terms of volume or drinking pattern, insufficient physical

activity for health, and inadequate intake of fruit and vegetables

as an indicator of wider dietary pattern). Again, this is similar to

the rate found in some other developed countries,2-5 especially

if allowance is made for differences in variable definition,

behavioural thresholds, and study design. Using a wider definition

of unhealthy lifestyle to include any three as well as all four

unhealthy behaviours, then 15% of the adult population would

be so categorised. Youth (23%), males (17%), people living in

the most deprived 20% of small areas (26%), Maori (26%) and

Pacific people (28%) exhibited higher-than-average prevalences

of these behaviour patterns. For the latter three socio-demographic

groups the proportion with an unhealthy lifestyle was higher than

the corresponding proportion with a healthy lifestyle.

Turning to clustering, even the low prevalence of the ‘four-

behaviour unhealthy lifestyle’ observed (approximately 1.5%)

is almost three times higher than the 0.5% prevalence expected

given independent risk factor distributions. The three-behaviour

unhealthy lifestyles show lesser but still substantial degrees of

clustering. The two-behaviour patterns show progressively less

clustering than the three-behaviour patterns with the exception of

the co-occurrence of tobacco and potentially hazardous alcohol

consumption, which co-occurs almost twice as often as expected,

a finding that has been reported previously in other countries.2,4,5,7,9

Interestingly, insufficient physical activity and unhealthy diet

(insufficient fruit and vegetable intake) were not found to cluster

to more than a very minor degree – contrary to what might have

been hypothesised if physical inactivity contributed substantively

to the obesity epidemic.

Unlike the situation with healthy behaviours, Maori exhibited

less clustering than other ethnic groups for most unhealthy

behaviour patterns. While Maori experience higher prevalences of

most of the individual risk factors,10 recent research suggests that

unhealthy behaviours – including tobacco use – make a smaller

contribution to the ethnic disparity in health than previously

estimated.18 Our study suggests that clustering of unhealthy

behaviours makes no contribution to the Maori/non-Maori health

disparity at all.

Analogously, it is generally the case that more disadvantaged

or lower status groups have higher risk factor prevalences than

their more powerful and privileged counterparts, yet differences

in the distribution of singular unhealthy behaviours explain

relatively little of the socio-economic gradient in ischaemic heart

disease.19 It has been hypothesised that greater clustering of risk

factors might explain more of the gradient,3 yet our results do not

support the notion of a social gradient in clustering of unhealthy

behaviours.

Health system Our findings on clustering (or lack thereof) of both healthy

and unhealthy behaviours are new (for New Zealand) and have

implications for health promotion policy and practice both

in New Zealand and abroad. Conventional population-based

health promotion programs have tended to focus on single

issues, while individual-based (clinical) services tend towards

the case management approach of dealing with multiple factors

simultaneously (e.g. absolute cardiovascular risk). The current

work supports both these approaches. The relative lack of

clustering of healthy behaviours shows that single issue initiatives

(e.g. separately promoting fruit and vegetable consumption and

walking) are generally appropriate for most universal strategies

(because the lack of substantial clustering means that little spin-

off can be expected from one dimension of health to another).

Conversely, the clustering of multiple unhealthy behaviours in



a relatively small number of people justifies a case management

approach to ‘high-risk’ strategies. Targeted chronic care

management and integrated care initiatives emanating from the

personal health sector will thus meet – and reinforce – universal

public health initiatives coming the other way.

Yet three caveats should be borne in mind in using our results

for planning either clinical or population-based preventive

interventions.

First, exclusion of survey participants with a history of

cardiovascular disease or cancer may have introduced bias,

especially for older males. It is unlikely, however, that this could

have been sufficient to affect our conclusions regarding the extent

of clustering.

Second, different results may be obtained if different

behaviours are selected for study, if those behaviours are defined

and operationalised differently, and if different thresholds

for categorising the behaviours are chosen. Indeed, our

dichotomisation of behaviours represents a major simplification,

and a more nuanced approach that recognised varying degrees of

behavioural expression (e.g. light, moderate and heavy smoking)

might be preferable. Yet contrasts between groups and trends over

time in the extent of clustering may still be validly estimated,

provided the method is consistently applied.

Some analyses have included overweight/obesity (defined via

body mass index or waist circumference) in behaviour clustering

studies. Given that obesity is not a behaviour per se, we prefer to

use measures of physical activity and nutrition directly.

Finally, a policy focus on behavioural clustering risks being

counterproductive if it decontextualises human behaviour and

neglects the social forces that shape behavioural repertoires

and their expression in different settings. The lifestyle construct

as applied in this study does not imply free and unconstrained

behavioural choices by individuals or families. Provided this is

understood, we conclude that measurement and monitoring of

behavioural clustering can be a useful tool for health promotion,

helping to identify intervention points (‘over-represented’

behavioural patterns within socio-demographic and geographic

subgroups) and evaluate outcomes relating to such intervention

points.

We have shown that 29% of adults in New Zealand live

a relatively healthy lifestyle, being non-smokers, eating 5+

vegetables and fruit a day, being moderately active, and indulging

in non-harmful use (or non-use) of alcohol. Conversely, 15%

live a relatively unhealthy lifestyle, with any three or all four

unhealthy behaviours. There is clearly still much scope for

health gain via health promotion. Clustering tendencies were not

strong, especially for healthy behaviours, and did not display any

social gradient or ethnic disparity. Hence clustering of unhealthy

behaviours cannot explain any of the well-established ethnic

or socio-economic disparities in the burden of chronic disease.

Moreover, while a clinical case management approach seems

appropriate for the 15% of the population with unhealthy lifestyles,

promotion of healthy lifestyles via population-based programs will

need to focus on single issues and little spin-off from one issue to

another can be expected.

AcknowledgementsWe thank the 13,000 New Zealanders who freely gave of their

time to participate in the 2002/03 New Zealand Health Survey.

This report is published with the approval of the Deputy Director-

General (Public Health). However, opinions expressed are those of

the authors and do not necessarily reflect the views of the Ministry

of Health or the Counties Manukau District Health Board.

References1. Oppenheimer GM. Profiling risk: the emergence of coronary heart disease

epidemiology in the United States 1947-70. Int J Epidemiol. 2006;35:720-30.

2. Laaksonen M, Prattala R, Karisto A. Patterns of unhealthy behaviour in Finland. Eur J Public Health. 2001;11:294-300.

3. Ebrahim S, Montaner D, Lawler D. Clustering of risk factors and social class in childhood and adulthood. Br Med J. 2004;328:861-4.

4. Klein-Getlink J, Choi B, Fry R. Multiple exposures to smoking, alcohol, physical inactivity and overweight: Prevalences according to the Canadian Community Health Survey Cycle 1.1. Chronic Dis Canada. 2006;27:25-33.

5. Australian Institute of Health and Welfare. Living Dangerously: Australians with Multiple Risk Factors for Cardiovascular Disease. Canberra (AUST): AIHW; 2005.

6. Newsom J, McFarland B, Kaplan M, et al. The health consciousness myth: implications of the near independence of major health behaviours in the North American population. Soc Sci Med. 2004;60:433-7.

7. Fine L, Philogene G, Gramling R. Prevalence of multiple chronic disease risk factors: 2001 National Health Interview Survey. Am J Prev Med. 2004;27:18-24.

8. Reeves M, Rafferty A. Healthy lifestyle characteristics among adults in the United States, 2000. Arch Intern Med. 2005;165:854-7.

9. Riyami A, Afifi M. Clustering of cardiovascular risk factors among Omani adults. East Mediterr Health J. 2003;9:893-903.

10. Ministry of Health. A Portrait of Health. Key Results of the 2002/03 New Zealand Health Survey. Wellington (NZ): The Ministry; 2004.

11. Tobias M, Turley M, Paul S, et al. Debunking the ‘only 50%’ myth: prevalence of established risk factors in New Zealanders with self-reported ischaemic heart disease. Aust N Z J Public Health. 2005;29:405-11.

12. World Health Organization. Guidelines for Controlling and Monitoring the Tobacco Epidemic. Geneva (CHE): WHO; 1998.

13. Saunders JB, Aasland OG, Babor TF, et al. Development of the Alcohol Use Disorders Screening Test (AUDIT). WHO collaborative project on early detection of persons with harmful alcohol consumption. Addiction. 1993;88:791-804.

14. Department of Health and Human Services. 1996. The Report of the Surgeon General on Physical Activity and Health. Washington (DC): United States DHHS.

15. Weinstein S, Vogt TM, Gerrior SA. Healthy eating index scores are associated with blood nutrient concentration in the Third National Health and Nutrition Examination Survey. J Am Diet Assoc. 2004;104:576-84.

16. Crampton P, Salmond C, Kirkpatrick B. Degrees of Deprivation. 2nd ed. Wellington (NZ): David Bateman; 2004.

17. World Health Organization. Age Standardisation of Rates: A New WHO Standard. Geneva (CHE): WHO; 2000. GPE Discussion Paper No.: 31.

18. Blakely T, Fawcett J, Hunt D, et al. What is the contribution of smoking and socio-economic position to ethnic inequalities in mortality in New Zealand? Lancet. 2006;368(9529):4-6.

19. Marmot M, Wilkinson RG. Social Determinants of Health. Oxford (UK): Oxford University Press; 2005.


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