Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data.

Objectives: Injury prevention can be achieved through various interventions, but it faces challenges due to its comprehensive nature and susceptibility to external environmental factors, making it difficult to detect risk signals. Moreover, the reliance on standardized systems leads to the construction and statistical analysis of numerous injury surveillance data, resulting in significant temporal delays before being utilized in policy formulation. This study was conducted to quickly identify substantive injury risk problems by employing text mining analysis on national emergency response data, which have been underutilized so far. Methods: With emerging issue and topic analyses, commonly used in science and technology, we detected problematic situations and signs by deriving injury keywords and analyzing time-series changes. Results: In total, 65 injury keywords were identified, categorized into hazardous, noteworthy, and diffusion accidents. Semantic network analysis on hazardous accident terms refined the injury risk issues. Conclusion: An increased risk of winter epidemic fractures due to extreme weather, self-harm due to depression (especially drug overdose and self-mutilation), and falls was observed in older adults. Thus, establishing effective injury prevention strategies through inter-ministerial and interagency cooperation is necessary.

Impact of Government Intervention in Response to Coronavirus Disease 2019.

Coronavirus disease 2019 (COVID-19) led to the loss of lives and had serious social and economic effects. Countries implemented various quarantine policies to reduce the effects. The countries were divided into low- and high-risk groups based on the differences in quarantine policies and their levels of infection. Quarantine policies that significantly contributed to risk reduction were determined by analyzing 11 quarantine indicators for reducing the spread of COVID-19. The cross-tabulation and Chi-square tests were used to compare the quarantine policies by the groups. Multivariate logistic regression was used to determine the useful quarantine policies implemented by the low-risk group to verify quarantine policies for minimizing the negative effects. The analysis showed that the low- and medium-risk groups showed significant differences for 9 of the 11 indicators, and 4 of these differentiated the low- from the medium-risk group. Countries with strict quarantine policies related to workplace closure and staying at home were more likely to be included in the low-risk group. These policies had a significant impact in the low-risk countries and could contribute to reducing the spread and effects of COVID-19 in countries included in the high-risk group.