Abstract
Child safety remains a paramount concern, constituting 20% of all safety incidents. Notably, accidents linked to children's product usage constitute a significant subset of these occurrences. However, comprehensive research elucidating the specific typologies and determinants of safety accidents originating from the utilization of children's products is deficient.
In this study, we categorized children of various age groups infants (0 to 12 months), toddlers (12 months to 36 months), and children (36 months to 13 years) according to age-appropriate product classifications. We procured and analyzed a dataset encompassing 22,200 instances of child injuries attributable to children's products over a span of five years until 2022. The analysis scrutinized seven key accident factors, namely age, gender, year, certification status, location, season, and behavior.
The result of logistic regression analysis unveiled several noteworthy findings. Across all age model, age, certification information, behavior, and season were statistically significant factors correlating with severe accidents, whereas place, year, and gender exhibited no such significance. Within the baby(~12month) model, solely behavior emerged as a statistically significant factor. Meanwhile, among infant(12~36month) and children(36month~13age), factors such as location, certification information, behavior, and season demonstrated statistical significance.
Through the relationship between various accident factors, it was confirmed that logistic regression analysis is a useful model for identifying the causes of serious accidents related to children's products. The results of this study can be used to establish policies to improve the safety of children's product use. These policies include designing product usage precautions to prevent serious accidents, promoting prevention education, and comprehensive risk assessment. Accordingly, the results of this study are expected to play an important role in providing a scientific basis for various policies and initiatives for the safety of children's product use.
In this study, we categorized children of various age groups infants (0 to 12 months), toddlers (12 months to 36 months), and children (36 months to 13 years) according to age-appropriate product classifications. We procured and analyzed a dataset encompassing 22,200 instances of child injuries attributable to children's products over a span of five years until 2022. The analysis scrutinized seven key accident factors, namely age, gender, year, certification status, location, season, and behavior.
The result of logistic regression analysis unveiled several noteworthy findings. Across all age model, age, certification information, behavior, and season were statistically significant factors correlating with severe accidents, whereas place, year, and gender exhibited no such significance. Within the baby(~12month) model, solely behavior emerged as a statistically significant factor. Meanwhile, among infant(12~36month) and children(36month~13age), factors such as location, certification information, behavior, and season demonstrated statistical significance.
Through the relationship between various accident factors, it was confirmed that logistic regression analysis is a useful model for identifying the causes of serious accidents related to children's products. The results of this study can be used to establish policies to improve the safety of children's product use. These policies include designing product usage precautions to prevent serious accidents, promoting prevention education, and comprehensive risk assessment. Accordingly, the results of this study are expected to play an important role in providing a scientific basis for various policies and initiatives for the safety of children's product use.
| Translated title of the contribution | A study of Factors Influencing Severe Accidents Associated with Children’s Product Usage through Logistic Regression Analysis |
|---|---|
| Original language | Korean |
| Pages (from-to) | 103-122 |
| Number of pages | 20 |
| Journal | 표준인증안전학회지 |
| Volume | 13 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2023 |