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Background: Administrative health databases are a valuable research tool to assess health care utilization at the population level. However, their use in obesity research limited due to the lack of data on body weight. A potential workaround is to use the ICD code of obesity to identify obese individuals. The objective of the current study was to investigate the sensitivity and specificity of an ICD code-based diagnosis of obesity from administrative health data relative to the gold standard measured BMI. Methods: Linkage of a population-based survey with anthropometric measures in elementary school children in 2003 with longitudinal administrative health data (physician visits and hospital discharges 1992-2006) from the Canadian province of Nova Scotia. Measured obesity was defined based on the CDC cut-offs applied to the measured BMI. An ICD code-based diagnosis obesity was defined as one or more ICD-9 (278) or ICD-10 code (E66-E68) of obesity from a physician visit or a hospital stay. Sensitivity and specificity were calculated and health care cost estimates based on measured obesity and ICD-based obesity were compared. Results: The sensitivity of an ICD code-based obesity diagnosis was 7.4% using ICD codes between 2002 and 2004. Those correctly identified had a higher BMI and had higher health care utilization and costs. Conclusions: An ICD diagnosis of obesity in Canadian administrative health data grossly underestimates the true prevalence of childhood obesity and overestimates the health care cost differential between obese and non-obese children.

Background: The Simulation Effectiveness Tool (SET) frequently is used to assess perceived learning and confidence in simulation. However, few studies have reported the validity of the tool. This study assessed the reliability and validity of the SET. Method: This retrospective analysis evaluated the tool using 568 cases conducted at three nursing schools. Results: A two-factor model showed reasonable fit indices. The fit statistics for the two-factor structure were: χ 2 , 152.98 ( df = 53, p < .001); comparative fit index, 0.94; root mean square error of approximation, 0.05 (range, 0.04 to 0.06); and standardized root mean square residual, 0.04. In addition, weak convergence was identified between the confidence in the SET and responding in the Lasater rubric. Conclusion: The psychometric properties of the study indicate the SET has demonstrated acceptable evidence of validity and reliability to measure simulation effectiveness in Korean nursing students. The use of this instrument for brief simulation education is recommended. [ J Nurs Educ . 2020;59(4):186–193.]

Aim To develop a comprehensive, hands‐on assessment tool for assessing health in children under five in underserved regions. Design Methodological study design and usability testing were used. Sample Eight nurses working at two health posts and 261 children under five living in the migrant villages participated in the study. Measurement The developed tool was evaluated using 10 items of a questionnaire based on the honeycomb model of Morville (2004). Community nurses administered the questionnaire then followed with a focus group interview after completing a child health exam using the developed tool. Data were collected during July 2017. Results The Hands‐on Assessment Tool for Child Health is composed of developmental screening, identification of risk factors and clinical signs, growth measurement, diagnostic tests and interpretation of each subdomain, and final impressions. Management strategies include parent education, resource networking, referral to a paediatrician, and follow‐up plans. Usability testing revealed high scores on the facets “valuable,” “useful,” “desirable,” and “findable.” Conclusions Considering the demand for hands‐on tools in underserved regions, the developed tool can provide nurses with resources for competent management of child assessment, interpretation, and nurses' intervention strategies, fortified with clinical judgement processes.