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We aimed to develop a risk prediction model for gestational diabetes mellitus (GDM) based on the common maternal demographics and routine clinical variables in Chinese population.Individual information was collected from December 2018 to October 2019 by a pretested questionnaire on demographics, medical and family history, and lifestyle factors. Multivariable logistic regression was performed to establish a predictive model for GDM by variables in pre- and early pregnancy. The consistency and discriminative validity of the model were evaluated by Hosmer-Lemeshow goodness-of-fit testing and ROC curve analysis. Internal validation was appraised by fivefold cross-validation. Clinical utility was assessed by decision curve analysis.Total 3263 pregnant women were included with 17.2% prevalence of GDM. The model equation was: LogitP = -11.432 + 0.065 × maternal age (years) + 0.061 × pre-pregnancy BMI (kg/mThe predictive model of GDM exhibited well acceptable predictive ability, discriminative performance, and clinical utilities. The project was registered in clinicaltrial.gov.com with identifier of NCT03922087.

Background Reproductive tract infections (RTIs) have become major but silent public health problems devastating women’s lives in Bangladesh. Accurately and precisely identifying high-risk areas of RTIs through high-resolution risk maps is meaningful for resource-limited settings. Methods We obtained data reported with RTI symptoms by women of childbearing age in the years 2007, 2011 and 2014 from Bangladesh Demographic and Health Survey. High-spatial Environmental, socio-economic and demographic layers were downloaded from different open-access data sources. We applied Bayesian spatial-temporal models to identify important influencing factors and to estimate the infection risk at 5 km spatial resolution across survey years in Bangladesh. Results We estimated that in Bangladesh, there were approximate 11.1% (95% Bayesian credible interval, BCI: 10.5–11.7%), 13.9% (95% BCI: 13.3–14.5%) and 13.4% (95% BCI: 12.8–14.0%) of women of childbearing age reported with RTI symptoms in 2007, 2011 and 2014, respectively. The risk of most areas shows an obvious increase from 2007 to 2011, then became stable between 2011 and 2014. High risk areas were identified in the southern coastal areas, the western Rajshahi Division, the middle of Khulna Division, and the southwestern Chittagong Division in 2014. The prevalence of Rajshahi and Nawabganj District were increasing during all the survey years. Conclusion The high-resolution risk maps of RTIs we produced can guide the control strategies targeted to priority areas cost-effectively. More than one eighth of women of childbearing age reported symptoms suggesting RTIs and the risk of RTIs varies in different geographical area, urging the government to pay more attention to the worrying situation of female RTIs in the country.

Background: Diarrhea in children under five years of age remains a challenge in reducing child mortality in Nepal. Understanding the spatiotemporal patterns and influencing factors of the disease is important for control and intervention. Methods: Data regarding diarrhea prevalence and its potential influencing factors were extracted from the Demographic and Health Surveys in Nepal and other open-access databases. A Bayesian logistic regression model with district-specific spatio-temporal random effects was applied to explore the space and time patterns of diarrhea risk, as well as the relationships between the risk and the potential influencing factors. Results: Both the observed prevalence and the estimated spatiotemporal effects show a decreasing diarrhea risk trend from 2006 to 2016 in most districts of Nepal, with a few exceptions, such as Achham and Rasuwa. The disease risk decreased with mothers&rsquo, years of education (OR 0.93, 95% Bayesian Credible Interval (BCI) 0.87, 0.997). Compared to spring, autumn and winter had lower risks of diarrhea. The risk firstly increased and then decreased with age and children under 12&ndash, 24 months old were the highest risk group (OR 1.20, 95% BCI 1.04, 1.38). Boys had higher risk than girls (OR 1.24, 95% BCI 1.13, 1.39). Even though improved sanitation wasn&rsquo, t found significant within a 95% BCI, there was 93.2% of chance of it being a protective factor. There were no obvious spatiotemporal clusters among districts and each district tended to have its own spatiotemporal diarrhea prevalence pattern. Conclusions: The important risk factors identified by our Bayesian spatial-temporal modeling provide insights for control and intervention on children diarrhea in Nepal. Special attention should be paid to high risk groups of children and high risk seasons, as well as districts with high risk or increased trend of risk. Effective actions should be implemented to improve sanitation and women&rsquo, s education level. District-specific control planning is recommended for local governments for effective control of children diarrhea in Nepal.

Background: Hand, foot, and mouth disease (HFMD) is a common infectious disease among children. Guangdong Province is one of the most severely affected provinces in south China. This study aims to identify the spatiotemporal distribution characteristics and potential predictors of HFMD in Guangdong Province and provide a theoretical basis for the disease control and prevention. Methods: Case-based HFMD surveillance data from 2009 to 2012 was obtained from the China Center for Disease Control and Prevention (China CDC). The Bayesian spatiotemporal model was used to evaluate the spatiotemporal variations of HFMD and identify the potential association with meteorological and socioeconomic factors. Results: Spatially, areas with higher relative risk (RR) of HFMD tended to be clustered around the Pearl River Delta region (the mid-east of the province). Temporally, we observed that the risk of HFMD peaked from April to July and October to December each year and detected an upward trend between 2009 and 2012. There was positive nonlinear enhancement between spatial and temporal effects, and the distribution of relative risk in space was not fixed, which had an irregular fluctuating trend in each month. The risk of HFMD was significantly associated with monthly average relative humidity (RR: 1.015, 95% CI: 1.006&ndash, 1.024), monthly average temperature (RR: 1.045, 95% CI: 1.021&ndash, 1.069), and monthly average rainfall (RR: 1.004, 95% CI: 1.001&ndash, 1.008), but not significantly associated with average GDP. Conclusions: The risk of HFMD in Guangdong showed significant spatiotemporal heterogeneity. There was spatiotemporal interaction in the relative risk of HFMD. Adding a spatiotemporal interaction term could well explain the change of spatial effect with time, thus increasing the goodness of fit of the model. Meteorological factors, such as monthly average relative humidity, monthly average temperature, and monthly average rainfall, might be the driving factors of HFMD.