RESUMO
Objective: To verify the accuracy of the autoregressive integrated moving average (ARIMA) in predicting the incidence of occupational pneumoconiosis (hereinafter referred as pneumoconiosis) and to predict the incidence of pneumoconiosis in Guangdong Province in the next five years. Methods: A follow-up survey was performed to collect data on pneumoconiosis patients reported in Guangdong Province from 1956 to 2021. Collected data from 1956 to 2016 were used as the training set to build an ARIMA model. Collected data from 2017 to 2021 were used as the prediction set to evaluate the predicting result of the ARIMA model. The ARIMA model was used to predict the incidence of pneumoconiosis in Guangdong Province in next five years. Results: The ARIMA (1,1,2) model was set up after model identification and order estimation. The model was used to predict the prediction set, and its result was good. The ARIMA result and actual values in 2021 were 213 and 210 cases, respectively, with a difference of only three cases. The number of pneumoconiosis cases predicted using the ARIMA model in Guangdong Province from 2022 to 2026 was 214, 204, 202, 194, and 191 cases, respectively, showing a trend of low-level prevalence. Conclusion: The ARIMA model demonstrates high accuracy in predicting pneumoconiosis incidence over a long period of time and with large sample sizes. The forecast results of the ARIMA(1,1,2) model indicate that the incidence of pneumoconiosis in Guangdong Province will be around 200 cases in the next five years, indicating a low-level prevalence.
RESUMO
Objective: To study the survival time and its risk factors of patients with occupational pneumoconiosis. Methods: A total of 11 011 newly diagnosed occupational pneumoconiosis patients in Guangdong Province from 1980 to 2019 were selected as study subjects. The life table method was used for survival analysis. The influencing factors of survival time of occupational pneumoconiosis patients were analyzed using the WilCoxon (Gehan) test and Cox proportional hazards regression model. Results: The median survival time of pneumoconiosis patients was 26.0 years. The median survival period of stage Ⅰpatients was 3.5 years longer than that of stage Ⅱ patients and 10.1 years longer than that of stage Ⅲ patients. The median survival time of patients with an initial diagnosis age under 40.0 years old was 34.8 years longer than that of patients with an initial diagnosis age over 60.0 years old. The median survival time of patients with dust exposure duration under 25.0 years old was 13.6 years longer than patients with dust exposure duration age over 45.0 years old. The results of the Cox proportional hazards regression model showed that the initial diagnosis stage, initial diagnosis age, dust exposure duration, and medical insurance were risk factors of the survival time of occupational pneumoconiosis patients (all P<0.01). The risk of reduced survival time for patients with stage Ⅱ and stage Ⅲ as the initial diagnosis stage was 1.15 and 2.04 times higher, respectively, compared with stage Ⅰ patients (both P<0.01). The risk of reduced survival time for patients without medical insurance was 60.22 times higher than those with medical insurance (P<0.01). Conclusion: The risk factors of the survival time of occupational pneumoconiosis patients in Guangdong Province are initial diagnosis stage, initial diagnosis age, the dust exposure age, and medical insurance. Earlier detection, earlier diagnosis, and improvement of medical insurance coverage for patients can effectively improve the survival time of occupational pneumoconiosis patients.
