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Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster-correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

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The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

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International guidelines have pointed out the importance of the physical environment of health care facilities in preventing and controlling infection. We aimed to describe the physical environment of dental care facilities in Brazil in 2014, focusing on characteristics designed to control infections. Exactly 16,202 dental offices in the Brazilian Unified National Health System (SUS) participated in this survey. Trained researchers extracted information about the infection control characteristics of health facilities by using a structured instrument. We used data from 12 dichotomous questions that evaluated the wall, floor, sink and tap conditions, and the presence and condition of sterilization equipment. We calculated a score by summing the number of characteristics handled appropriately for infection control, which could range from 0 to 12. Hierarchical cluster analyses were developed. None of the 12 criteria were met by all the oral health teams. Only 208 (1.3%) dental offices correctly performed all 12-infection control practices. Two clusters, with different frequencies of structure for infection control in dental offices, were identified. South and Southeast regions had the highest frequencies for Cluster 1, with better structure of infection control in dental offices. Dental care facilities of oral health teams were not typically meeting the infection control guidelines regarding clinic design and equipment. Adherence to the guidelines varied among the Brazilian geographic regions.

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International guidelines have pointed out the importance of the physical environment of health care facilities in preventing and controlling infection. We aimed to describe the physical environment of dental care facilities in Brazil in 2014, focusing on characteristics designed to control infections. Exactly 16,202 dental offices in the Brazilian Unified National Health System (SUS) participated in this survey. Trained researchers extracted information about the infection control characteristics of health facilities by using a structured instrument. We used data from 12 dichotomous questions that evaluated the wall, floor, sink and tap conditions, and the presence and condition of sterilization equipment. We calculated a score by summing the number of characteristics handled appropriately for infection control, which could range from 0 to 12. Hierarchical cluster analyses were developed. None of the 12 criteria were met by all the oral health teams. Only 208 (1.3%) dental offices correctly performed all 12-infection control practices. Two clusters, with different frequencies of structure for infection control in dental offices, were identified. South and Southeast regions had the highest frequencies for Cluster 1, with better structure of infection control in dental offices. Dental care facilities of oral health teams were not typically meeting the infection control guidelines regarding clinic design and equipment. Adherence to the guidelines varied among the Brazilian geographic regions.

As diretrizes internacionais destacam a importância do ambiente físico dos serviços de saúde para prevenir e controlar as infecções. Procuramos descrever o ambiente físico em serviços de saúde bucal no Brasil em 2014, com enfoque nas características programadas para controlar as infecções. Precisamente 16.202 consultórios odontológicos no Sistema Único de Saúde (SUS) participaram na pesquisa. Pesquisadores treinados coletaram informações sobre as características do controle de infecções nesses serviços de saúde, utilizando um instrumento padronizado. Utilizamos dados de 12 perguntas dicotômicas que avaliavam as condições das paredes, piso, pia e torneira e a presença e as condições do equipamento de esterilização. Calculamos um escore pela soma do número de características administradas adequadamente para o controle de infecções, variando de 0 a 12. Foram desenvolvidas análises hierárquicas de clusters. Nenhum dos 12 critérios foi atendido por todas as equipes de saúde bucal. Apenas 208 (1,3%) dos consultórios odontológicos realizavam todas as 12 práticas de controle de infecções. Foram identificados dois clusters com distintas frequências de estruturas para controle de infecções nos consultórios odontológicos. As regiões Sul e Sudeste mostraram as maiores frequências no Cluster 1, com melhor estrutura de controle de infecções nos consultórios odontológicos. De maneira geral os serviços de saúde bucal não atendiam as diretrizes para o controle de infecções, referentes à planta física e equipamento dos consultórios. A aderência às diretrizes variava de acordo com a região do país.

Las directrices internacionales destacan la importancia del ambiente físico de los servicios de salud para prevenir y controlar infecciones. Procuramos describir el ambiente físico en servicios de salud bucal en Brasil en 2014, centrándonos en las características programadas para controlar las infecciones. Precisamente 16.202 consultorios odontológicos del Sistema Único de Salud (SUS) participaron en la investigación. Investigadores entrenados recogieron información sobre las características del control de infecciones en esos servicios de salud, utilizando un instrumento estandarizado. Utilizamos los datos procedentes de 12 preguntas dicotómicas que evaluaban las condiciones de las paredes, suelo, fregadero y grifo, además de la existencia y condiciones del equipamiento de esterilización. Calculamos una puntuación para la suma del número de características administradas adecuadamente para el control de infecciones, variando de 0 a 12. Se desarrollaron análisis jerárquicos de clúster. Ninguno de los 12 criterios fue observado por todos los equipos de salud bucal. Solamente 208 (1,3%) de los consultorios odontológicos realizaban las 12 prácticas de control de infecciones al completo. Se identificaron dos clústeres con distintas frecuencias de estructuras para el control de infecciones en los consultorios odontológicos. Las regiones Sur y Sudeste mostraron las mayores frecuencias en el Clúster 1, con una mejor estructura de control de infecciones en los consultorios odontológicos. De manera general, los servicios de salud bucal no atendían a las directrices para el control de infecciones, referentes a las instalaciones físicas y equipamiento de los consultorios. La adherencia a las directrices variaba de acuerdo con la región del país.

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BACKGROUND: The birth prevalence of cleft lip with or without cleft palate (CL/P) in Brazil increased between the years from 1975 to 1994 but has not been evaluated for temporal trend since then. METHODS: We used data from the Brazilian National Health Information System for the years 2000 through 2013. We calculated the reported CL/P birth prevalence each year per 10,000 live births and estimated the average increase in reported prevalence per year (and 95% confidence interval [CI]) by fitting a negative binomial regression model. We also estimated the temporal trend in each of the five Brazilian regions for this time period. RESULTS: The overall reported birth prevalence was 4.85 (95% CI, 4.78-4.91) per 10,000 live births. The reported birth prevalence of CL/P increased over this time period, from 3.94 (95% CI, 3.73-4.17) per 10,000 in 2000 to 5.46 (95% CI, 5.20-5.74) per 10,000 in 2013. The temporal trend differed for different Brazilian geographic regions, being confined primarily to the Northeast (4.7% per year; 95% CI, 4.0%-5.5%), North (3.3% per year; 95% CI, 1.8%-4.7%), and Central (2.9% per year; 95% CI, 0.9%-4.9%) regions. CONCLUSION: In recent years, there appears to be an upward trend in the reported prevalence of CL/P in Brazil, confined to the less developed regions of the country. The increase likely reflects improved surveillance; whether it also reflects etiologic differences is unknown. Birth Defects Research (Part A) 106:789-792, 2016. © 2016 Wiley Periodicals, Inc.

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