Typical patterns of modifiable health risk factors (MHRFs) in elderly women in Germany: results from the cross-sectional German Health Update (GEDA) study, 2009 and 2010

Abstract

BackgroundModifiable health risk factors (MHRFs) significantly affect morbidity and mortality rates and frequently occur in specific combinations or risk clusters. Using five MHRFs (smoking, high-risk alcohol consumption, physical inactivity, low intake of fruits and vegetables, and obesity) this study investigates the extent to which risk clusters are observed in a representative sample of women aged 65 and older in Germany. Additionally, the structural composition of the clusters is systematically compared with data and findings from other countries.MethodsA pooled data set of Germany’s representative cross-sectional surveys GEDA09 and GEDA10 was used. The cohort comprised 4,617 women aged 65 and older. Specific risk clusters based on five MHRFs are identified, using hierarchical cluster analysis. The MHRFs were defined as current smoking (daily or occasionally), risk alcohol consumption (according to the Alcohol Use Disorders Identification Test, a sum score of 4 or more points), physical inactivity (less active than 5 days per week for at least 30 min and lack of sports-related activity in the last three months), low intake of fruits and vegetables (less than one serving of fruits and one of vegetables per day), and obesity (a body mass index equal to or greater than 30). A total of 4,292 cases with full information on these factors are included in the cluster analysis. Extended analyses were also performed to include the number of chronic diseases by age and socioeconomic status of group members.ResultsA total of seven risk clusters were identified. In a comparison with data from international studies, the seven risk clusters were found to be stable with a high degree of structural equivalency.ConclusionEvidence of the stability of risk clusters across various study populations provides a useful starting point for long-term targeted health interventions. The structural clusters provide information through which various MHRFs can be evaluated simultaneously.

Similarities and differences in health‐related behavior clustering among older adults in Eastern and Western countries: A latent class analysis of global aging cohorts

“…The present study contributes to the literature by directly demonstrating the general similarities alongside variations in HRB clustering across Western and Eastern aging cohorts. The three common cluster patterns found here largely match the HRB structural compositions identified in recent reviews, particularly regarding the clustering of multiple positive HRB, and the clustering of smoking and heavy alcohol consumption . The present findings showed sex‐ and cohort‐specific patterns of smoking and alcohol consumption.…”

“…Smoking, alcohol consumption and physical activity were the most frequently studied HRB. The clustering of smoking with alcohol consumption, and the absence or presence of all HRB have been identified fairly consistently in Europe, the UK and the US, but evidence in Asian countries is scarce …”

“…Just eight out of the 56 studies on HRB clustering (till 2014) reviewed by Noble et al . focused on adults aged ≥50 years, besides a few recent updates . Some of these studies suggest that older adults tend to adopt more positive HRB (e.g.…”

Patterns of risk behaviors in Brazilian older adults: A latent class analysis

“…Likewise, engaging extensively in sedentary activities, such as watching television, has been linked to an increased risk of metabolic syndrome, and of premature morbidities and mortality among older adults . There is evidence that these modifiable behaviors do not occur in isolation, but occur simultaneously with a specific clustering …”

“…11 There is evidence that these modifiable behaviors do not occur in isolation, but occur simultaneously with a specific clustering. 3,[12][13][14] Clusters of risk behaviors can illustrate protective and healthy lifestyle patterns, as well as identify population subgroups at a higher risk of the synergistic effects of two or more risk factors.…”