Application value of SARIMA model in forecasting and analyzing inpatient cases of pediatric limb fractures_Chinese Journal of Evidence-Based Medicine

Authors：

WANG Lufei ¹ , NIE Yijing ¹ ,  YANG Xiaodong ²

1. West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, P.R.China;
2. Department of Pediatric Trauma, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;

Corresponding?author：

YANG Xiaodong, Email: yxd2260483@126.com

Keywords：

Limb fracture; Time series analysis; Seasonal auto-regressive moving average model; Prediction

DOI：

10.7507/1672-2531.201906049

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To establish a forecasting model for inpatient cases of pediatric limb fractures and predict the trend of its variation.Methods According to inpatient cases of pediatric limb fractures from January 2013 to December 2018, this paper analyzed its characteristics and established the seasonal auto-regressive integrated moving average (SARIMA) model to make a short-term quantitative forecast.Results A total of 4 451 patients, involving 2 861 males and 1 590 females were included. The ratio of males to females was 1.8 to 1, and the average age was 5.655. There was a significant difference in age distribution between males and females (χ²=44.363, P<0.001). The inpatient cases of pediatric limb fractures were recorded monthly, with predominant peak annually, from April to June and September to October, respectively. Using the data of the training set from January 2013 to May 2018, a SARIMA model of SARIMA (0,1,1)(0,1,1)₁₂ model (white noise test, P>0.05) was identified to make short-term forecast for the prediction set from June 2018 to November 2018, with RMSE=8.110, MAPE=9.386, and the relative error between the predicted value and the actual value ranged from 1.61% to 8.06%.Conclusions Compared with the actual cases, the SARIMA model fits well with good short-term prediction accuracy, and it can help provide reliable data support for a scientific forecast for the inpatient cases of pediatric limb fractures.

Citation： WANG Lufei, NIE Yijing, YANG Xiaodong. Application value of SARIMA model in forecasting and analyzing inpatient cases of pediatric limb fractures. Chinese Journal of Evidence-Based Medicine, 2020, 20(6): 651-655. doi: 10.7507/1672-2531.201906049 Copy

1.	Hedstr?m EM, Svensson O, Bergstr?m U, et al. Epidemiology of fractures in children and adolescents. Acta Orthop, 2010, 81(1): 148-153.
2.	Kang HM, Kim MG, Hong SM, et al. Comparison of temporal bone fractures in children and adults. Acta Oto-Laryngologica, 2013, 133(5): 469-474.
3.	王海波, 李明. 小兒骨折的流行病學特點及相關影響因素研究進展. 現代醫藥衛生, 2016, 32(15): 2333-2336.
4.	Naranje SM, Erali RA, Warner WC. Epidemiology of pediatric fractures presenting to emergency departments in the United States. J Pediatr Orthop, 2016, 36(4): e45-e48.
5.	Feng H, Duan G, Zhang R, et al. Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors. PLoS One, 2014, 9(1): e87916.
6.	李小升, 馬春柳, 雷海科, 等. SARIMA 模型在醫院門診量預測中的應用. 中國病案, 2013, 14(3): 37-40.
7.	Leslie WD, Morin SN, Lix LM, et al. Comparison of treatment strategies and thresholds for optimizing fracture prevention in Canada: a simulation analysis. Arch Osteoporos, 2019, 15(1): 4.
8.	Merlijn T, Swart KMA, van der Horst HE, et al. Fracture prevention by screening for high fracture risk: a systematic review and meta-analysis. Osteoporos Int, 2019, Epub ahead of print.
9.	Hadley-Miller N, Carry PM, Brazell CJ, et al. Trends in Trampoline Fractures: 2008-2017. Pediatrics, 2020, 145(1): e20190889.
10.	Clark EM. The epidemiology of fractures in otherwise healthy children. Curr Osteoporos Rep, 2014, 12(3): 272-278.
11.	劉曉光, 郭源, 呂艷偉. 北京地區 10 977 例兒童骨折分析. 中國矯形外科雜志, 2014, 22(24): 2233-2237.
12.	李秋燕, 王津雨. 應用 SARIMA 模型預測醫院出院人次. 中國病案, 2016, 17(7): 32-35.
13.	Wren TA, Shepherd JA, Kalkwarf HJ, et al. Racial disparity in fracture risk between white and nonwhite children in the United States. J Pediatr, 2012, 161(6): 1035-1040.
14.	Brudvik C, Hove LM. Childhood fractures in Bergen, Norway: identifying high-risk groups and activities. J Pediatr Orthop, 2003, 23(5): 629-634.
15.	Rennie L, Court-Brown CM, Mok JY, et al. The epidemiology of fractures in children. Injury, 2007, 38(8): 913-922.
16.	Masterson E, Borton D, O'Brien T. Victims of our climate. Injury, 1993, 24(4): 247-248.
17.	廉恒麗, 俞劍琴, 傅映暉. ARIMA 與 SARIMA 模型在醫院門診人次與出院人次預測中的應用. 中國醫院統計, 2017, 24(2): 81-84.

1. Hedstr?m EM, Svensson O, Bergstr?m U, et al. Epidemiology of fractures in children and adolescents. Acta Orthop, 2010, 81(1): 148-153.
2. Kang HM, Kim MG, Hong SM, et al. Comparison of temporal bone fractures in children and adults. Acta Oto-Laryngologica, 2013, 133(5): 469-474.
3. 王海波, 李明. 小兒骨折的流行病學特點及相關影響因素研究進展. 現代醫藥衛生, 2016, 32(15): 2333-2336.
4. Naranje SM, Erali RA, Warner WC. Epidemiology of pediatric fractures presenting to emergency departments in the United States. J Pediatr Orthop, 2016, 36(4): e45-e48.
5. Feng H, Duan G, Zhang R, et al. Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors. PLoS One, 2014, 9(1): e87916.
6. 李小升, 馬春柳, 雷海科, 等. SARIMA 模型在醫院門診量預測中的應用. 中國病案, 2013, 14(3): 37-40.
7. Leslie WD, Morin SN, Lix LM, et al. Comparison of treatment strategies and thresholds for optimizing fracture prevention in Canada: a simulation analysis. Arch Osteoporos, 2019, 15(1): 4.
8. Merlijn T, Swart KMA, van der Horst HE, et al. Fracture prevention by screening for high fracture risk: a systematic review and meta-analysis. Osteoporos Int, 2019, Epub ahead of print.
9. Hadley-Miller N, Carry PM, Brazell CJ, et al. Trends in Trampoline Fractures: 2008-2017. Pediatrics, 2020, 145(1): e20190889.
10. Clark EM. The epidemiology of fractures in otherwise healthy children. Curr Osteoporos Rep, 2014, 12(3): 272-278.
11. 劉曉光, 郭源, 呂艷偉. 北京地區 10 977 例兒童骨折分析. 中國矯形外科雜志, 2014, 22(24): 2233-2237.
12. 李秋燕, 王津雨. 應用 SARIMA 模型預測醫院出院人次. 中國病案, 2016, 17(7): 32-35.
13. Wren TA, Shepherd JA, Kalkwarf HJ, et al. Racial disparity in fracture risk between white and nonwhite children in the United States. J Pediatr, 2012, 161(6): 1035-1040.
14. Brudvik C, Hove LM. Childhood fractures in Bergen, Norway: identifying high-risk groups and activities. J Pediatr Orthop, 2003, 23(5): 629-634.
15. Rennie L, Court-Brown CM, Mok JY, et al. The epidemiology of fractures in children. Injury, 2007, 38(8): 913-922.
16. Masterson E, Borton D, O'Brien T. Victims of our climate. Injury, 1993, 24(4): 247-248.
17. 廉恒麗, 俞劍琴, 傅映暉. ARIMA 與 SARIMA 模型在醫院門診人次與出院人次預測中的應用. 中國醫院統計, 2017, 24(2): 81-84.

Chinese Journal of Evidence-Based Medicine

Application value of SARIMA model in forecasting and analyzing inpatient cases of pediatric limb fractures

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