Objective: To compare the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) model, the Prophet model, and the Bayesian structural time series (BSTS) model in forecasting the incidence of hand, foot, and mouth disease (HFMD) , so as to provide a basis for optimizing the early warning system of this disease. Methods: Weekly incidence data of HFMD in Longgang District, Shenzhen City from 2014 to 2024 were collected. The HFMD incidence data from 2014-2019 and 2023 were used as the training set to construct SARIMA, Prophet, and BSTS models, while the data from 2024 were used as the test set to compare and evaluate the predictive performance of the three models. The technique for order preference by similarity to ideal solution (TOPSIS) method was employed to calculate the C-value. This approach integrates multiple evaluation metrics, such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE), to comprehensively assess model performance. Results: A total of 150 111 cases of HFMD were reported in Longgang District from 2014 to 2024, with an average annual incidence of 400.72/105. The weekly incidence fluctuated between 0 and 63.78/105, exhibiting a bimodal seasonal pattern characterized by a primary peak from May to July and a secondary peak from September to October. In the training set, all three models demonstrated a good fit to the bimodal epidemic trend of HFMD, with the BSTS model achieving the best fit. The BSTS model yielded performance metrics as follows: MAE=0.124, MSE=0.050, RMSE=0.223, SMAPE=0.021, and a C-value of 1.000. In the test set, all three models, including SARIMA, Prophet, and BSTS, performed well for short-term predictions (≤16 weeks), with the Prophet model showing relatively superior predictive performance. However, the prediction accuracy of all models declined as the forecast horizon extended. During the primary peak period (May-July), the Prophet model exhibited better predictive performance, whereas the BSTS model performed relatively better during the secondary peak period (September-October). Conclusions For the short-term forecasting of weekly HFMD incidence, the Prophet model outperformed both the SARIMA and BSTS models. During the primary peak period, the Prophet model demonstrated superior predictive performance, whereas the BSTS model exhibited better accuracy in forecasting the secondary peak period.

Objective To explore the application value of distortion product otoacoustic emission (DPOAE) and speech in noise(SIN) testing in occupational health surveillance of noise-exposed workers. Methods A total of 220 noise-exposed workers was selected as the study subjects using the convenient sampling method. The study subjects participated questionnaire survey, personal noise exposure assessment, acoustic immittance testing, pure tone audiometry (PTA), DPOAE and SIN testing. According to PTA results, workers were enrolled into a high-frequency hearing loss (HFHL) group and a non-HFHL group. Results The detection rate of HFHL among the study subjects was 41.4%, and the detection rate of speech-frequency hearing loss was 15.9%. Workers′ bilateral DPOAE response amplitudes and signal-to-noise ratios at frequencies of 2.0-8.0 kHz in the HFHL group were lower than those in the non-HFHL group (all P<0.05). The DPOAE amplitudes at frequencies of 1.0-8.0 kHz in both ears of the study subjects were negatively correlated with the PTA threshold (all P<0.01), and were negatively correlated with age (all P<0.01). The signal-to-noise ratio loss score was higher among worker in the HFHL group than in the non-HFHL group (P<0.01) and was positively correlated with PTA thresholds (P<0.05). Conclusion DPOAE and SIN testing can detect early cochlear outer hair cell impairment and reduction of noise-related speech recognition ability in noise-exposed workers and may serve as an effective supplementary tool to routine PTA in occupational hearing surveillance.

OBJECTIVE: To explore the effect of ultra-high frequency radiation on psychomotor neurological behavior in workers with exposure. METHODS: A total of 85 workers who exposed to 40.68 MHz radiofrequency were recruited as the exposure group by judgment sampling method. A group of 121 workers without occupational EMR exposure were recruited as the control group. Workers in both groups were from the same shoe factory. The electric field intensity(EFI) of ultra-high frequency radiation of workplace in the exposure group was measured. The computerized neurobehavioral evaluation system in Chinese version 3 was used to evaluate the psychomotor neurobehavioral function which included the neurobehavioral ability index(NAI) of simple visual reaction time(SVRT), digital screening and fit curve and the general NAI(GNAI) of the above 3 indexes. RESULTS: The median of the workplace EFI of ultra-high frequency radiation in the exposure group was 119.0 V/m, and all of them exceeded the national occupational exposure limit. NAI of digital screening in exposure group was lower than that in the control group(P<0.05). There is no statistically significant difference in the NAI of SVRT, fit curve and GNAI(P>0.05). Meanwhile, there is no statistically significant difference in abnormal rate of NAI of SVRT, digital screening, fit curve and GNAI(P>0.05). The results of multiple linear regression analysis showed that the ultra-high frequency radiation EFI exposure was negatively correlated with NAI of digital screening(P<0.05) after eliminating the influence of confounding factors such as age, working age, gender, education level, smoking, drinking and staying up late. CONCLUSION: The digital screening of psychomotor neurobehavioral function in the exposure workers was adversely affected by the ultra-high frequency radiation. The neural behavioral ability of eye-hand coordination and precise movement may be the specific performance.