In China, great differences in economy, social characteristics and hygiene exist between developing and developed regions. A comparative study of infectious diarrhea between two regions was needed. Three groups of diarrheal patients were collected: children ≤5 year-olds from Beijing (developed region) and Henan Province (developing region), and adults over 18 year-olds from Beijing. A questionnaire was used to survey and feces samples were examined for 16 enteropathogens. We enrolled 1422 children and 1047 adults from developed region and 755 children from developing region. Virus positive rates were 32.98% for children and 23.67% for adults in developed region. The most prevalent pathogen for children was rotavirus whereas for adults was norovirus. Bacterial isolation rates were 13.92% for children from developed region, while 29.14% for children from the developing regions. For the greatest difference, Shigella accounted for 50.79% and was the dominant pathogen in the developing region, whereas in the developed region it was only 1.45%. There was no significant relationship between the local levels of development with diarrheogenic Escherichia coli (DEC) categories. But it was seen the notable differences between the population with different age: enteropathogenic E.coli (EPEC) and enteroaggregative E.coli (EAggEC) were the primary classes of DEC in children from both regions, whereas it was enterotoxigenic E.coli (ETEC) in adults. The symptoms of Shigella and Salmonella infection, such as bloody stools, white blood cells (WBC) and red blood cells (RBC) positivity and fever were similar in children, which may lead to the misidentification. Yersinia enterocolitica and shiga toxin-producing E.coli (STEC) infections were firstly reported in Beijing. There was a large difference in etiology of bacterial diarrhea between children in developing and developed regions of China.

Many large‐scale surveys collect both discrete and continuous variables. Small‐area estimates may be desired for means of continuous variables, proportions in each level of a categorical variable, or for domain means defined as the mean of the continuous variable for each level of the categorical variable. In this paper, we introduce a conditionally specified bivariate mixed‐effects model for small‐area estimation, and provide a necessary and sufficient condition under which the conditional distributions render a valid joint distribution. The conditional specification allows better model interpretation. We use the valid joint distribution to calculate empirical Bayes predictors and use the parametric bootstrap to estimate the mean squared error. Simulation studies demonstrate the superior performance of the bivariate mixed‐effects model relative to univariate model estimators. We apply the bivariate mixed‐effects model to construct estimates for small watersheds using data from the Conservation Effects Assessment Project, a survey developed to quantify the environmental impacts of conservation efforts. We construct predictors of mean sediment loss, the proportion of land where the soil loss tolerance is exceeded, and the average sediment loss on land where the soil loss tolerance is exceeded. In the data analysis, the bivariate mixed‐effects model leads to more scientifically interpretable estimates of domain means than those based on two independent univariate models.

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An adaptive treatment length strategy is a sequential stage‐wise treatment strategy where a subject's treatment begins at baseline and one chooses to stop or continue treatment at each stage provided the subject has been continuously treated. The effects of treatment are assumed to be cumulative and, therefore, the effect of treatment length on clinical endpoint, measured at the end of the study, is of primary scientific interest. At the same time, adverse treatment‐terminating events may occur during the course of treatment that require treatment be stopped immediately. Because the presence of a treatment‐terminating event may be strongly associated with the study outcome, the treatment‐terminating event is informative. In observational studies, decisions to stop or continue treatment depend on covariate history that confounds the relationship between treatment length on outcome. We propose a new risk‐set weighted estimator of the mean potential outcome under the condition that time‐dependent covariates update at a set of common landmarks. We show that our proposed estimator is asymptotically linear given mild assumptions and correctly specified working models. Specifically, we study the theoretical properties of our estimator when the nuisance parameters are modeled using either parametric or semiparametric methods. The finite sample performance and theoretical results of the proposed estimator are evaluated through simulation studies and demonstrated by application to the Enhanced Suppression of the Platelet Receptor IIb/IIIa with Integrilin Therapy (ESPRIT) infusion trial data.