Paper
Monday, November 14, 2005
This presentation is part of : Health Education for Youth
Technology and Practice: Are Point-of-Care Blood Tests Useful Tools in Identifying At-Risk Youth?
Ruth C. McGillis Bindler, RNC, PhD and Margaret A. Bruya, DNSc, ARNP, FAAN. College of Nursing, Washington State University, Spokane, WA, USA
Learning Objective #1: Interpret data comparing point-of-care technology with “gold standard” laboratory measures for at-risk youth
Learning Objective #2: Apply research findings to identify youth at risk of overweight and chronic disease in clinical nursing settings

Type 2 diabetes and cardiovascular disease are serious, debilitating, and increasingly common. Their roots are linked with current lifestyles, and risks can be identified in youth. This study applied Urie Bronfenbrenner's bioecological model to evaluate ethnically diverse/socioeconomically-challenged youth in clinic/school settings. The study's purpose was to describe the incidence of risk factors in the children, and to evaluate the effectiveness of point-of-care analysis. A non-experimental descriptive design examined children from 5-18 years in nurse-practitioner managed clinic/CareMobile/school sites from October 2002-June 2003 in an inland northwest urban setting. Measurements included height, weight, body mass index (BMI), acanthosis nigricans, blood pressure, family history, and point-of-care finger-stick analysis of glucose/lipids. Children =/> 85th percentile for BMI with two additional risk factors were invited to return for fasting blood draw, dietary analysis, and exercise testing. A total of 118 children were included in initial analysis, 42 met high risk criteria, and 23 returned for fasting serum blood analysis. Of the initial sample, 48% (n=57) had BMI =/> 85th percentile and 36% (n=42) had high BMI and 2 other risk factors. Significant correlations were found for point-of-care vs. fasting HDL (r=.591, p=.003) and triglyceride (r=.534, p=.010), while nonsignificant results were found for total cholesterol (r=.373, p=.087), LDL (r=.301, p=.185), and glucose (r=-.056, p=.804). Descriptive data on the sample and further correlations among study variables will be provided. Researchers concluded that a high risk of chronic disease was present in this vulnerable population. Point-of-care machines had limited usefulness in expanding identification of risk over usual assessments. The researchers reiterate the importance of evaluating height, weight, blood pressure, and family history in settings with youth. Careful analysis of results to identify youth in need of additional testing and intervention will be emphasized by the presenters, enabling session participants to apply these evidence-based findings in care of youth.