Abstract :
Background This secondary analysis
describes the frequency of self-reported psychosocial
risk factors in a geographically defined population of
adolescents and quantifies the extent of multiple risks.
Cluster analysis is used to develop three empirically
distinct psychosocial risk clusters. Methods High
school students in grades 9–13 from all seven public
and two catholic high schools in the study area
completed a class-administered survey. The analysis
is based on 3540 surveys reflecting approximately a
71% response. Cumulative risk was calculated by
summing the number of times students exceeded a
pre-defined threshold on a series of global rating
scales. Risk clusters were created using a non-hierarchical
cluster analysis technique for binary data.
Clusters were partially validated by examining differences
in socio-demographic and health utilization
patterns. Reliability was assessed by examining two,
three, and four-group solutions across gender and
grade strata. Results Multiple symptoms of emotional
distress were reported by 37% of the sample, multiple
stressors by 62% of the sample, and poly-drug use by
33%. In addition, three empirically distinct clusters
were derived. Normals, 21% of the sample, did not
report excessive stress or distress, and did not use
substances. The Stressed (45%) reported excessive
stress and distress predominantly related to schoolwork,
parents, and facing problems. Virtually none
used drugs. Substance Users (34%) reported excessive
stress, distress, and high levels of substance use:
smoking, drinking, and use of illicit drugs. Clusters
were significantly different with respect to most sociodemographic
factors, self-reported general health, and
most aspects of health service utilization suggesting
that they have some validity for targeting programs.
Conclusions This study highlights the importance of
focusing on multi-morbidities and illustrates the use
of cluster analysis to identify risk profiles that may be
amenable to school-based health promotion and
prevention programs
