Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Feb 24;35(7):e57.
doi: 10.3346/jkms.2020.35.e57.

Big Data Statistical Analysis of Facial Fractures in Korea

Cheol Heum Park  1 , Kyu Jin Chung  1 , Tae Gon Kim  1 , Jun Ho Lee  1 , Il Kug Kim  1 , Yong Ha Kim  2
Affiliations

Big Data Statistical Analysis of Facial Fractures in Korea

Cheol Heum Park et al. J Korean Med Sci. .

Abstract

Background: The big data provided by Health Insurance Review and Assessment (HIRA) contains data from nearly all Korean populations enrolled in the National Health Insurance Service. We aimed to identify the incidence of facial fractures and its trends in Korea using this big data from HIRA.

Methods: We used the Korean Standard Classification of Disease and Cause of Death 6, 7 for diagnosis codes. A total of 582,318 patients were included in the final analysis. All statistical analyses were performed using SAS software and SPSS software.

Results: The incidence of facial fractures consistently declined, from 107,695 cases in 2011 to 87,306 cases in 2016. The incidence of facial fractures was the highest in June 2011 (n = 26,423) and lowest in January 2014 (n = 10,282). Nasal bone fractures were the most common, followed by orbit and frontal sinus fractures. The percentage of nasal bone fractures declined, whereas those of orbital fractures increased from 2011 to 2016 (P < 0.001). Among orbital fractures, inferior wall fractures were the most common, followed by medial wall fractures. Among mandibular fractures, angle fractures were the most common, followed by condylar process and symphysis fractures. Although it was difficult to predict the most common type of zygomatic and maxilla fractures, their incidence consistently declined since 2011.

Conclusion: We observed trends in facial fractures in Korea using big data including information for nearly all nations in Korea. Therefore, it is possible to predict the incidence of facial fractures. This study is meaningful in that it is the first study that investigated the incidence of facial fractures by specific type.

Keywords: Big Data; Bone Fractures; Facial Bones.

PubMed Disclaimer

Conflict of interest statement

The authors have no potential conflicts of interest to disclose.

Figures

Fig. 1
Fig. 1. Flow chart of screening the number of patients with diagnosed facial fractures.
HIRA = Health Insurance Review and Assessment, KCD = Korean Standard Classification of Disease and Cause of Death, NEO = nasoorbitoethmoidal fracture.
Fig. 2
Fig. 2. Number of patients by year.
Fig. 3
Fig. 3. Number of patients by month.
Fig. 4
Fig. 4. Number of the facial fractures by season.
Fig. 5
Fig. 5. Number of the patients by age groups.
Fig. 6
Fig. 6. Incidence per 100,000 population by age groups. Number of patients in a given year/total population of the age group in given year × 100,000.
Fig. 7
Fig. 7. Number of patients among youth.
Fig. 8
Fig. 8. Incidence per 100,000 population among youth. Number of patients in a given year/total population of the age group in given year × 100,000.
Fig. 9
Fig. 9. Comparison of gender-related differences in the number of patients by year.
Fig. 10
Fig. 10. Comparison of facial fractures prevalence among facial areas by year.
NEO = nasoorbitoethmoidal fracture.
Fig. 11
Fig. 11. Number of orbital fractures by subtypes.
Fig. 12
Fig. 12. Number of mandibular fractures by subtypes.
Fig. 13
Fig. 13. Number of zygomatic and maxilla fractures by subtypes.
See this image and copyright information in PMC

References

    1. Kim JA, Yoon S, Kim LY, Kim DS. Towards actualizing the value potential of Korea Health Insurance Review and Assessment (HIRA) data as a resource for health research: Strengths, limitations, applications, and strategies for optimal use of HIRA data. J Korean Med Sci. 2017;32(5):718–728. - PMC - PubMed
    1. Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science. 2014;343(6176):1203–1205. - PubMed
    1. Yoo JH. Has the incidence of acute pyelonephritis increased in Korea? – Big Data as a double-edged sword. J Korean Med Sci. 2018;33(49):e317. - PMC - PubMed
    1. Kim JW, Lee JH, Kim TG, Kim YH, Chung KJ. Breast reconstruction statistics in Korea from the big data hub of the Health Insurance Review and Assessment service. Arch Plast Surg. 2018;45(5):441–448. - PMC - PubMed
    1. Levine E, Degutis L, Pruzinsky T, Shin J, Persing JA. Quality of life and facial trauma: psychological and body image effects. Ann Plast Surg. 2005;54(5):502–510. - PubMed