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Objective. Apply and assess a Bayesian approach to projecting cancer mortality rates by fitting age-period-cohort (APC) models. Methods. The Bayesian estimation method was applied to bladder cancer mortality data in Argentina. A second-order autoregressive model was adopted for a priori specification of APC model coefficients. The estimates obtained were compared with all available information and excluding age groups with low mortality, to assess behavior of the approach in light of scattered data. Mortality was projected for two successive periods following the ones observed. Results. Robustness of the method was verified, which avoids excluding age groups with null or low mortality. Observed rates all fall within the credibility bands and confirm the model's goodness of fit. An overall downward trend in bladder cancer mortality was observed. Estimates and projections of these rates are more precise in age groups that have greater incidence of mortality. Conclusions. The Bayesian formulation used herein makes it possible to reduce random variation between adjacent estimates by specifying that the effects of each scale depend on the immediately preceding ones. It was demonstrated that the approach has the capacity to handle low frequencies and obtain reliable mortality estimates, as well as precise projections, without the need for making additional assumptions, as happens in classical APC model fitting. [ABSTRACT FROM AUTHOR]

Topic recognition technology has been commonly applied to identify different categories of news topics from the vast amount of web information, which has a wide application prospect in the field of online public opinion monitoring, news recommendation, and so on. However, it is very challenging to effectively utilize key feature information such as syntax and semantics in the text to improve topic recognition accuracy. Some researchers proposed to combine the topic model with the word embedding model, whose results had shown that this approach could enrich text representation and benefit natural language processing downstream tasks. However, for the topic recognition problem of news texts, there is currently no standard way of combining topic model and word embedding model. Besides, some existing similar approaches were more complex and did not consider the fusion between topic distribution of different granularity and word embedding information. Therefore, this paper proposes a novel text representation method based on word embedding enhancement and further forms a full-process topic recognition framework for news text. In contrast to traditional topic recognition methods, this framework is designed to use the probabilistic topic model LDA, the word embedding models Word2vec and Glove to fully extract and integrate the topic distribution, semantic knowledge, and syntactic relationship of the text, and then use popular classifiers to automatically recognize the topic categories of news based on the obtained text representation vectors. As a result, the proposed framework can take advantage of the relationship between document and topic and the context information, which improves the expressive ability and reduces the dimensionality. Based on the two benchmark datasets of 20NewsGroup and BBC News, the experimental results verify the effectiveness and superiority of the proposed method based on word embedding enhancement for the news topic recognition problem., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Qiming Du et al.)

Objective. To propose a method for the interpolation of yearly local-level covariates of health status that is suitable for panel data analysis of the effect of health services. Methods. The proposed method distributes the yearly rate of growth of covariates at the regional level (e.g., state) from household survey data, and applies it to interpolate yearly data at the local level (e.g., municipality) between two consecutive census surveys. The method was applied to municipal-level socioeconomic covariates of health status in Brazil for every year between 2001 and 2009. The data was tested on a previously validated analysis of the effects of the Family Health Program on post-neonatal infant mortality in Brazil. Results. A total of 895 628 values were generated for 20 socioeconomic predictors of health status. Valid data were obtained for 5 057 municipalities in the Northeast, Southeast, South, and Center-West regions of Brazil, from 2001 to 2009, covering 98.89% of the municipalities in these regions and 90.87% of municipalities in the country. A supplemental annex includes the interpolated data from 2001 to 2009, plus the 2000 and 2010 census data, for all 5 057 municipalities. An application on a fixed-effect regression model suggested that, compared to linear interpolation, the proposed method reduced multi-collinearity and improved the precision of the estimates of the effects of health services. Conclusions. The advantages of the proposed interpolation method suggest that it is a feasible solution for panel data analysis of health services at the local level in Brazil and other countries. [ABSTRACT FROM AUTHOR]

Objective. Design and implement a surveillance method for locating Aedes aegypti infestation foci with the use of larvae traps and spatial interpolation techniques, which facilitate the ongoing estimation of vector abundance in the area by counting the individuals collected in the study area. Methods. A total of 228 larvae traps were installed--at a rate of one per block--in the most densely populated area of commune five of Villavicencio (Meta). With regionalized information on larvae abundance, spatial interpolations were conducted with the Voronoi polygon, ordinary kriging, and inverse distance weighting techniques. Results. An alternative method for the surveillance of dengue vectors is presented. This method is based on the use of larvae traps and spatial interpolation techniques to obtain area maps supported by specific observations. Conclusions. The results show that this strategy is better than the indices normally used, since it facilitates continuous visualization of the level of vector infestation and consequently, the risk of dengue transmission, based on the extent of A. aegypti infestation. Implementation of this strategy is expected to contribute to more effective planning, optimization, and evaluation of prevention and control activities. [ABSTRACT FROM AUTHOR]

A new comorbidity index predicts the risk of mortality in patients with end-stage renal disease (eSrD). This index, which was developed using data from a large US database, is an important tool for observational research in patients with eSrD, but its value in patient populations other than dialysis populations in the US needs to be validated.

The survival advantages associated with renal replacement therapy might be counterbalanced by its negative effects on quality of life. Cohen et al. have proposed a simple mortality prediction model to help physicians discuss prognosis and individualized palliative care with patients on maintenance hemodialysis, to enable patients and their families make informed treatment decisions.