Decision Tree Model vs Traditional Measures to Identify Patterns of Sun-Protective Behaviors and Sun Sensitivity Associated With Sunburn.

Importance: Understanding patterns of sun-protective behaviors and their association with sunburn can provide important insight into measurement approaches and intervention targets. Objective: To assess whether decision-based modeling can be used to identify patterns of sun-protective behaviors associated with the likelihood of sunburn and to compare the predictive value of this method with traditional (ie, composite score) measurement approaches. Design, Setting, and Participants: This cross-sectional study used a nationally representative sample of 31 162 US adults from the 2015 National Health Interview Survey, consisting of household interviews conducted in person and completed by telephone when necessary. Participants included civilian noninstitutionalized US adults. Data were collected from January 1 through December 31, 2015. Main Outcomes and Measures: The associations among sun sensitivity, multiple sun-protective behaviors (ie, using sunscreen, seeking shade, wearing a hat, and wearing protective clothing), and sunburn were examined using a χ2 automatic interaction detection method for decision tree analysis. Results were compared with a composite score approach.
Results: In our study population of 28 558 respondents with complete data (54.1% women; mean [SD] age, 49.0 [18.0] years), 20 patterns of sun protection were identified. Among 15 992 sun-sensitive individuals, those who used only sunscreen had the highest likelihood of sunburn (62.4%). The group with the lowest likelihood of sunburn did not report using sunscreen but engaged in the other 3 protective behaviors (24.3% likelihood of sunburn). Among 12 566 non-sun-sensitive individuals, those who engaged in all 4 protective behaviors had the lowest likelihood of sunburn (6.6%). The highest likelihood of sunburn was among those who only reported sunscreen use (26.2%). The decision tree model and the composite score approach correctly classified a similar number of cases; however, the decision tree model was superior in classifying cases with sunburn (44.3% correctly classified in the decision tree vs 25.9% with the composite score). Conclusions and Relevance: This innovative application of a decision tree analytic approach demonstrates the interactive and sometimes counterintuitive effects of multiple sun-protective behaviors on likelihood of sunburn. These data show where traditional measurement approaches of behavior may fall short and highlight the importance of linking behavior to a clinically relevant outcome. Given the scope of those affected and enormous associated health care costs, improving efforts in skin cancer prevention has the potential for a significant effect on public health.