Sociodemographic characteristics of the clusters
To facilitate specific target group prevention measures, sociodemographic
indicators of the clusters were investigated and
set in relation to Cluster 1 (‘No Risk Behaviours’). The odds
ratios for cluster membership according to different social
factors are shown in table 2. The composition of the ‘healthaware’
Cluster 1 was as follows: 60.4% female, 46.3% aged 50–
59 and 71% in the middle or upper SES categories.
In contrast with the reference group, the ‘Physically
Inactives’ (Cluster 2) were significantly more likely to live
alone (OR = 1.60). The ‘Fruit and Vegetable Avoiders’ (Cluster
3) were also significantly more likely to be without a partner
(OR = 1.98). In particular, this group was comprised of
divorced or widowed men.
The ‘Smokers with Risk Behaviours’ (Cluster 4) were less
likely to be female (OR = 0.37), to be within the oldest age
group (OR = 0.37) and to have middle or high SES
(OR = 0.35). This cluster largely included younger men with
low SES, indicating an interaction effect between gender and
SES (OR = 3.41; 95% CI: 1.13–10.28). This group also was
more likely to live alone (OR = 2.62).
As with the previous two clusters (Clusters 3 and 4), the
‘Drinkers with Risk Behaviours’ were significantly less likely to
be female (OR = 0.60) and also showed an interaction effect
between gender and SES (OR = 2.95; 95% CI: 1.36–6.39).
The health behaviour clusters identified were plotted on
a two-dimensional matrix by gender and SES. Figure 2 shows
the size and position of the clusters, which was based on the
proportion of men and individuals in the upper social class.
Generally, the higher the percentage of men in a cluster, the
more unfavourable the health behaviour. The interaction effect
between gender and SES is clearly demonstrated for Cluster 4
(‘Smokers with Risk Behaviours’).
Sociodemographic characteristics of the clustersTo facilitate specific target group prevention measures, sociodemographicindicators of the clusters were investigated andset in relation to Cluster 1 (‘No Risk Behaviours’). The oddsratios for cluster membership according to different socialfactors are shown in table 2. The composition of the ‘healthaware’Cluster 1 was as follows: 60.4% female, 46.3% aged 50–59 and 71% in the middle or upper SES categories.In contrast with the reference group, the ‘PhysicallyInactives’ (Cluster 2) were significantly more likely to livealone (OR = 1.60). The ‘Fruit and Vegetable Avoiders’ (Cluster3) were also significantly more likely to be without a partner(OR = 1.98). In particular, this group was comprised ofdivorced or widowed men.The ‘Smokers with Risk Behaviours’ (Cluster 4) were lesslikely to be female (OR = 0.37), to be within the oldest agegroup (OR = 0.37) and to have middle or high SES(OR = 0.35). This cluster largely included younger men withlow SES, indicating an interaction effect between gender andSES (OR = 3.41; 95% CI: 1.13–10.28). This group also wasmore likely to live alone (OR = 2.62).As with the previous two clusters (Clusters 3 and 4), the‘Drinkers with Risk Behaviours’ were significantly less likely tobe female (OR = 0.60) and also showed an interaction effectbetween gender and SES (OR = 2.95; 95% CI: 1.36–6.39).The health behaviour clusters identified were plotted ona two-dimensional matrix by gender and SES. Figure 2 showsthe size and position of the clusters, which was based on theproportion of men and individuals in the upper social class.Generally, the higher the percentage of men in a cluster, themore unfavourable the health behaviour. The interaction effectbetween gender and SES is clearly demonstrated for Cluster 4(‘Smokers with Risk Behaviours’).
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