Association of gestational anemia with pregnancy conditions and outcomes: A nested case-control study.

BACKGROUND: Gestational anemia is a serious public health problem that affects pregnant women worldwide. Pregnancy conditions and outcomes might be associated with the presence of gestational anemia. This study investigated the association of pregnancy characteristics with anemia, exploring the potential etiology of the disease. AIM: To assess the association of pregnancy parameters with gestational anemia. METHODS: A nested case-control study was conducted based on the Chinese Pregnant Women Cohort Study-Peking Union Medical College Project (CPWCS-PUMC). A total of 3172 women were included. Patient characteristics and gestational anemia occurrence were extracted, and univariable and multivariable logistic regression models were used to analyze the association of pregnancy parameters with gestational anemia. RESULTS: Among the 3172 women, 14.0% were anemic, 46.4% were 25-30 years of age, 21.9% resided in eastern, 15.7% in middle, 12.4% in western 18.0% in southern and 32.0% in northern regions of China. Most women (65.0%) had a normal prepregnancy body mass index. Multivariable analysis found that the occurrence of gestational anemia was lower in the middle and western regions than that in the eastern region [odds ratio (OR) = 0.406, 95% confidence interval (CI): 0.309-0.533, P < 0.001)], higher in the northern than in the southern region (OR = 7.169, 95%CI: 5.139-10.003, P < 0.001), lower in full-term than in premature births (OR = 0.491, 95%CI: 0.316-0.763, P = 0.002), and higher in cases with premature membrane rupture (OR=1.404, 95%CI: 1.051-1.876, P = 0.02). CONCLUSION: Gestational anemia continues to be a health problem in China, and geographical factors may contribute to the situation. Premature birth and premature membrane rupture may be associated with gestational anemia. Therefore, we should vigorously promote local policy reformation to adapt to the demographic characteristics of at-risk pregnant women, which would potentially reduce the occurrence of gestational anemia.

Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study.

OBJECTIVES: Haemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China. DESIGN: Time-series study. SETTING: The People's Republic of China. METHODS: Autoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models' performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models' effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series. RESULTS: The monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12 model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684). CONCLUSIONS: The hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.

Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China.

BACKGROUND: Hepatitis B virus (HBV) infection is a major public health threat in China for China has a hepatitis B prevalence of more than one million people in 2017 year. Disease incidence prediction may help hepatitis B prevention and control. This study intends to build and compare 2 forecasting models for hepatitis B incidence in China. METHODS: Autoregressive integrated moving average (ARIMA) model and grey model GM(1,1) were adopted to fit the monthly incidence of hepatitis B in China from March 2010 to October 2017. The fitting and forecasting performances of the 2 models were evaluated. The better one was adopted to predict the incidence from November 2017 to March 2018. Database was built by Excel 2016 and statistical analysis was completed using R 3.4.3 software. RESULTS: Descriptive analysis showed that the incidence of hepatitis B in China has seasonal variation and has shown a downward trend from 2010 to 2017. We selected the ARIMA (3,1,1) (0,1,2)12 model among all the ARIMA models for it has the lowest AIC value. Model expression of GM (1,1) was X(1) (k + 1) = 3386876.7478e0.0249k - 3289206.7428. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA(3,1,1)(0,1,2)12 model were lower than GM(1,1) model on fitting part and forecasting part. According to the forecast results, the incidence may have a slight fluctuation during the following months. CONCLUSIONS: The ARIMA model showed better hepatitis B fitting and forecasting performance than GM(1,1) model. It is a potential decision supportive tool for controlling hepatitis B in China before a predictive hepatitis B outbreak.