The Scalding Truth: Geospatial Analysis Identifies Communities at Risk for Pediatric Scald Burns.

INTRODUCTION: Pediatric scald burns account for 12% of all U.S. burn center admissions and are the most common type of burn in children. We hypothesized that geospatial analysis of burn registry data could identify specific geographic areas and risk factors to focus injury prevention efforts. METHODS: The burn registry of a U.S. regional burn center was used to retrospectively identify pediatric scald burn patients ages 0-17, from January 2018 to June 2023. Geocoding of patient home addresses with census tract data was performed. Area Deprivation Index (ADI) was assigned to patients at the census block group level. Burn incident hot spot analysis to identify statistically significant burn incident clusters was done using the Getis Ord Gi∗ statistic. RESULTS: There were 950 pediatric scald burn patients meeting study criteria. The cohort was 52% male and 36% White, with median age of 3 y and median total body surface area of 1.5%; 23.8% required hospital admission. On multivariable logistic regression, increased child poverty levels (P = 0.004) and children living in single-parent households (P = 0.009) were associated with increased scald burn incidence. Geospatial analysis identified burn hot spots, which were associated with higher ADI (P < 0.001). Black patients were more likely to undergo admission compared to White patients. CONCLUSIONS: Geospatial analysis of burn registry data identified geographic areas at high risk of pediatric scald burn. ADI, poverty, and children in single-parent households were the greatest predictors of injury. Addressing these inequalities requires targeted injury prevention education, enhanced outpatient support systems and more robust community resources.

Characterizing robotic surgical expertise: An exploratory study of neural activation during mental imagery of robotic suturing.

BACKGROUND: Mental imagery (MI) aids skill acquisition, however, it is unclear to what extend MI is used by experienced surgeons. The purpose of this study was to assess differences in MI of participants with varying surgical expertise in robotic surgery. METHODS: Students, residents, and surgeons completed the Mental Imagery Questionnaire to assess MI for robotic suturing. Participants then completed robotic simulator tasks, and imagined performing robotic suturing while being assessed with electroencephalogram (EEG). RESULTS: Attending surgeons reported higher MI for robotic suturing, and EEG revealed higher neural activation during imagery of robotic suturing than other groups. CONCLUSIONS: Experienced surgeons displayed higher MI ability for robotic suturing, and displayed higher cortical activity in the frontal and parietal areas of the brain, which is associated with more advanced motor imagery. MI appears to be a component of robotic surgery expertise.