1. A TIME SERIES ANALYSIS OF GOUT PATIENTS IN A GRADE A TERTIARY HOSPITAL IN QINGDAO, CHINA
- Author
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GUO Jianguo, SUN Mengzhu, ZHOU Xiaobin
- Subjects
gout ,forecasting ,models, statistical ,time factors ,health education ,prevention and control ,ambulatory care ,Medicine - Abstract
Objective To investigate the rules of gout patients attending the hospital by analyzing their time series data, and to provide a reference for the prevention and treatment of gout in medical and health departments. Methods Time series data were collected from the gout patients who attended a grade A tertiary hospital in Qingdao from 2013 to 2018, and a descriptive analysis was performed for the data including the time of patients attending the hospital, age, and sex. An Autoregressive Integra-ted Moving Average (ARIMA) model was established based on the time series data of gout patients in 2013—2018, and the data of gout patients from January to October, 2019, were used for the extrapolation validation of this model. The X-11 method was used to analyze seasonal factors, long-term trends, and random fluctuations. Results Among the gout patients attending the hospital in 2013—2018, male patients accounted for 94.68% and female patients accounted for 5.32%; in terms of age composition, the patients aged 30-39 years, 40-49 years, 50-59 years, and >60 years accounted for 21.50%, 21.67%, 19.74%, and 23.32%, respectively. The ARIMA model established based on time series data of the gout patients in 2013—2018 was ARIMA(0,1,1)×(0,1,1)12, with an AIC value of 674.89 and an SBC value of 679.05, and the extrapolation verification of the predictive effect of this model showed an MAE value of 86.28 and an MAPE value of 7.64%. The X-11 method for seasonal stability (F=27.81,P0.05) showed that the time series of gout patients had stable seasonality and was not affected by time. The number of patients attending the hospital in July and August each year was higher than the mean value, and that in February was lower than the mean value; the number of patients was stable in the other months. Conclusion The ARIMA model is effective with stable prediction results, and combined with the seasonal factors and long-term trend extracted by the X-11 method, it can better explain the rules of gout patients attending the hospital and thus provide a reference for health authorities and medical institutions to formulate the prevention and control policies for gout, conduct health education, and allocate human resources.
- Published
- 2023
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