Kwon J, et al. J Korean Med Sci. 2022 Nov;37(50):e349. https://doi.org/10.3346/jkms.2022.37.e349

Background: Trauma is a major cause of mortality, disability, and health care costs worldwide. The establishment of a trauma system is known to solve these problems, but few studies have objectively evaluated the impact of a trauma system on outcomes. Since 2012, South Korea has established a national trauma system based on the implementation of 17 regional trauma centers nationwide and the improvement of the prehospital transfer system. This study aimed to measure the changes in performance and outcome according to the established national trauma system. Material and Methods: In this national cohort-based, retrospective follow-up observational study, the authors calculated the preventable trauma death rate (PTDR) by conducting a multipanel review of patients who died in 2015, 2017, and 2019. Furthermore, the authors constructed a risk-adjusted mortality prediction model of 4 767 876 patients between 2015 and 2019 using the extended-International Classification of Disease Injury Severity Scores to compare outcomes. Results: The PTDR was lower in 2019 than in 2015 (15.7 vs. 30.5, P <0.001) and 2017 (15.7 vs. 19.9%, P<0.001) representing 1247 additional lives saved in 2019 compared to that in 2015. In the risk-adjusted model, total trauma mortality was highest in 2015 at 0.56%, followed by that in 2016 and 2017 (0.50%), 2018 (0.51%), and 2019 (0.48%), revealing a significant decrease in mortality over the years (P<0.001 for trend), representing nearly 800 additional lives saved. The number of deaths for more severe patients with a probability of survival less than 0.25 significantly decreased from 81.50% in 2015 to 66.17% in 2019 (P<0.001). Conclusions: The authors observed a significant reduction in the PTDR and risk-adjusted trauma mortality in the 5-year follow-up since 2015 when the national trauma system was established. These findings could serve as a model for low-income and middle-income countries, where trauma systems are not yet established.

Background: Health insurers and policymakers are trying to prevent and reduce cardiovascular diseases due to obesity. A smart belt that monitors activity and waist circumference is a new concept for conquering obesity and may be a promising new strategy for health insurers and policymakers. Objective: This preliminary study evaluated whether the use of a smart belt was associated with a decrease in waist circumference. Methods: In the manufacturer’s database, there were data on a total of 427 men at baseline. A total of 223, 81, and 27 users kept using the smart belt for 4, 8, and 12 weeks, respectively. Paired t tests and repeated measures analysis of variance (ANOVA) were used to identify the change in waist circumference at specified time intervals (at 4, 8, and 12 weeks). In addition, a linear mixed model was used to incorporate all users’ waist circumference data at each time point. Preexisting data on waist circumference and self-reported demographics were obtained from the manufacturer of the smart belt (WELT Corporation, South Korea). Results: Compared with baseline, the waist circumference (cm) decreased significantly at all time points: –0.270 for week 4, –0.761 for week 8, and –1.972 for week 12 (all P<.01). Although each paired t test had a different sample size because of loss to follow-up, the differences between baseline and each subsequent week increased. Equal continuous reduction in waist circumference was observed with the ANOVA and mixed model analysis (beta=–0.158 every week). Conclusions: The smart belt is a newly developed, wearable device that measures real-time steps, sedentary time, and waist circumference. In this study, we showed that wearing the smart belt was associated with reducing waist circumference over 12 weeks. This direct-to-consumer smart health device may contribute toward reducing the risk of obesity and related conditions and controlling increasing health costs for health insurers.

Sub-cohort sampling designs, such as nested case-control (NCC) and case-cohort (CC) studies, have been widely used to estimate biomarker-disease associations because of their cost effectiveness. These designs have been well studied and shown to maintain relatively high efficiency compared to full-cohort designs, but their performance of building risk prediction models has been less studied. Moreover, sub-cohort sampling designs often use matching (or stratifying) to further control for confounders or to reduce measurement error. Their predictive performance depends on both the design and matching procedures. Based on a dataset from the NYU Women's Health Study (NYUWHS), we performed Monte Carlo simulations to systematically evaluate risk prediction performance under NCC, CC, and full-cohort studies. Our simulations demonstrate that sub-cohort sampling designs can have predictive accuracy (i.e. discrimination and calibration) similar to that of the full-cohort design, but could be sensitive to the matching procedure used. Our results suggest that researchers can have the option of performing NCC and CC studies with huge potential benefits in cost and resources, but need to pay particular attention to the matching procedure when developing a risk prediction model in biomarker studies.

Spatial pattern detection can be a useful tool for understanding the geographical distribution of cancer mortality. This study compared the results from several special analysis methods to examine the geographical variations in Korean major cancer mortality during 2010 to 2013. The spatial scan statistic and Tango’s method were used to detect spatial clusters with unusually high or low rates. The local indicator of spatial association was used for cluster detection with accounting for spatial autocorrelation among adjacent areas. A nonparametric approach was used to identify local districts with significantly higher or lower rates, compared to the average national rate. The different methods revealed similar results in the overall patterns, but slightly different specific results. These techniques can provide complementary information and should be used based on the distinguishing features of each method. It may be better to use several different methods for these analyses rather than a single method.