The burden of typhoid fever in low- and middle-income countries: A meta-regression approach.

BACKGROUND: Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data. METHODS: We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model. RESULTS: We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9-48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2-4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models. CONCLUSIONS: Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases

An Ensemble Method: Case-Based Reasoning and the Inverse Problems in Investigating Financial Bubbles

This paper presents an ensemble approach and model; IPCBR, that leverages the capabilities of Case based Reasoning (CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) in time series datasets from historical stock market prices. The framework proposes to use a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem; Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns. The technique brings a novel perspective to the problem of asset bubbles predictability. Conventional research practice uses traditional forward approaches to predict abnormal fluctuations in financial time series; conversely, this work proposes a formative strategy aimed to determine the causes of behaviour, rather than predict future time series points. This suggests a deviation from the existing research trend

A copula model for joint modeling of longitudinal and time‐invariant mixed outcomes

Motivated by a preclinical study in a mouse model of breast cancer, we suggest a joint modeling framework for outcomes of mixed type and measurement structures (longitudinal versus single time/time-invariant). We present an approach based on the time-varying copula models, which is used to jointly model longitudinal outcomes of mixed types via a time-varying copula, and extend the scope of these models to handle outcomes with mixed measurement structures. Our framework allows the parameters corresponding to the longitudinal outcome to be time varying and thereby enabling researchers to investigate how the response-predictor relationships change with time. We investigate the finite sample performance of this new approach via a Monte Carlo simulation study and illustrate its usefulness by an empirical analysis of the motivating preclinical study, comparing the effect of various treatments on tumor volume (longitudinal continuous response) and the number of days until tumor volume triples (time-invariant count response). Through the real-life application and the simulation study, we demonstrate that, compared with marginal modeling, the joint modeling framework offers more precision in the estimation of model parameters

Histomorpholofty of the Brunner's glands in the Angora rabbit

The study was aimed to demonstrate the distribution, morphological and histochemical properties of BrunnerTs glands in the small intestine of the Angora rabbit. The duodenum of It) healthy animals of both sexes constituted the material of the study. The glands were composed of acini densely packed within the submucosa. The Brunner's glands contained two types of cells, namely, serous and mucous cells. Hi biochemical examination revealed that the mucous glands and secretory ducts did not react with the Periodic Acid-Schiff(PAS) stain, while serous glands were weakly PAS-positive. Furthermore, mucous glands reacted positively with alcian blue pH 2.5. When applied the combined aldehyde fuchsin-alcian blue pH 2.5. staining procedure, mucous glands were determined to be aldehyde fuchsin (-) and alcian blue (+). These results showed that while a limited amount of neutral carbohydrates was secreted in serous glands, the secretion of the ducts and mucous cells of the duodenal glands in the Angora rabbit was composed of acidic carbohydrates with this acidity being due to the presence of carboxyl groups. Males and females did not differ in the hisiochemical staining properties of the duodenal secretion. Electron microscopic examination revealed the cytoplasm of mucous gland cells to be filled with electron light secretion granules. Fewer electron dense granules were determined to be present among these electron light granules. The electron dense granules were found within the apical cytoplasm of serous glands. © Medwell Journals, 2010

Multilevel joint modeling of hospitalization and survival in patients on dialysis

More than 720,000 patients with end-stage renal disease in the United States require life-sustaining dialysis treatment. In this population of typically older patients with a high morbidity burden, hospitalization is frequent at a rate of about twice per patient-year. Aside from frequent hospitalizations, which is a major source of death risk, overall mortality in dialysis patients is higher than other comparable populations, including Medicare patients with cancer. Thus, understanding patient- and facility-level risk factors that jointly contribute to longitudinal hospitalizations and mortality is of interest. Towards this objective, we propose a novel methodology to jointly model hospitalization, a binary longitudinal outcome, and survival, based on multilevel data from the United States Renal Data System (USRDS), with repeated observations over time nested in patients and patients nested in dialysis facilities. In our approach, the outcomes are modeled through a common set of multilevel random effects. In order to accommodate the USRDS data structure, we depart from the literature on joint modeling of longitudinal and survival data by including multilevel random effects and multilevel covariates, at both the patient and facility levels. An approximate Expectation-Maximization algorithm is developed for estimation and inference where fully exponential Laplace approximations are utilized to address computational challenges

Observed versus predicted age-specific incidence rates.

<p>Sites are labeled by location and year, and plots are ordered by decreasing overall model-predicted incidence. The red line and regions represent the model fits—median and 95% credible interval of the expected incidence estimated by the joint posterior distribution of model parameter (excluding study specific random effects and the impact of the observation process). The black symbols are the observed incidence with the 95% credible intervals after adjusting for the observation process: surveillance type (active/augmented passive versus passive surveillance), the participation rate, and blood culture sensitivity. Only studies that reported age-specific incidence are featured here.</p

Map of the location of studies in our dataset.

<p>Studies used in the estimation sample are depicted in red and the studies used in the validation sample are depicted in blue. The studies in the validation sample come from the Typhoid Fever Surveillance in Africa Program (TSAP).</p

Uncertainty in incidence estimates for the Global Burden of Disease regions and subregions made up of low- and middle-income countries.

<p>Uncertainty in incidence estimates for the Global Burden of Disease regions and subregions made up of low- and middle-income countries.</p