Introduction and Objectives
Cluster analysis is a statistical method for categorizing groups of individuals with similar properties or characteristics.
This method is useful for examining the relationship between complex urological problems and other health conditions or lifestyle factors. The method has previously been shown to differentiate men with mixed patterns of voiding and storage symptoms in a large Canadian sample.1 Assessing symptom co-occurrence can increase understanding of disease etiology and has patient management implications. We used this method to classify men with storage, voiding and post-voiding symptoms in the Boston Area Community Health (BACH) Survey.2
Methods
The BACH Survey is a racially and ethnically diverse sample (n=2301 men) of community-dwelling residents of Boston, Massachusetts aged 30-79. Fourteen urologic symptoms measured by participant self-report were used in this analysis. Cluster analyses were conducted using hierarchical and nonhierarchical (k-means) methods. Within clusters, demographic characteristics and risk factors for urologic symptoms, severity (measured by frequency of occurrence), and the interference of symptoms with daily activities were also assessed.
Results
Seventy percent (70%) of men in the BACH sample reported one or more urologic symptoms. Among these symptomatic men, five statistically distinct symptom clusters were identified. Approximately half of the symptomatic men were designated as Cluster 1, which included individuals with a low prevalence and frequency of urologic symptoms and a correspondingly low level of interference with usual activities. Intermediate levels of symptom frequency and prevalence were noted in Clusters 2-4, which were characterized by mixed patterns of voiding, storage and post-voiding symptoms. Cluster 5 consisted of predominantly older men (mean age = 58.9), with a high prevalence and frequency of urologic symptoms (mean no. of symptoms = 9.9 + 2.1) from all 3 categories, and with markedly elevated levels of psychosocial distress and comorbid cardiovascular disease (p < 0.0001). These men also had higher rates of kidney and bladder infections and prior urologic surgery. Men with increased waist circumference and more sedentary lifestyles were overrepresented in the more symptomatic clusters.
Conclusions
Five clusters of urologic symptoms were identified among symptomatic men in the BACH Survey. Clusters showed consistent co-occurrence of storage, voiding and post-micturition symptoms. Increasing frequency and severity of urologic symptoms was associated with psychosocial and cardiovascular comorbidities. These results are consistent with previous findings by Norman et al.,1 and provide an empirically-based classification for examining etiologic mechanisms, risk factors and comorbidities associated with common urologic symptoms in aging men. Clinical implications include the need for a more comprehensive approach to assessment and treatment of men with common urological symptoms.
Keywords
Lower Urinary Tract Symptoms (LUTS), Cluster Analysis, Heart Disease, Diabetes, Hypertension
References
1 Norman RW et al, Br J Urol 1994; 74:542-50.
2 McKinlay JB, Link CL. Measuring the Urologic Iceberg: design and
implementation of the Boston Area Community Health (BACH) Survey. Eur Urol 2007;52:389-396.
Supported by: Grant DK 56842 from the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK). Additional funding support by Pfizer, Inc.