More than 10,000 pre-Columbian earthworks are still hidden throughout Amazonia

Indigenous societies are known to have occupied the Amazon basin for more than 12,000 years, but the scale of their influence on Amazonian forests remains uncertain. We report the discovery, using LIDAR (light detection and ranging) information from across the basin, of 24 previously undetected pre-Columbian earthworks beneath the forest canopy. Modeled distribution and abundance of large-scale archaeological sites across Amazonia suggest that between 10,272 and 23,648 sites remain to be discovered and that most will be found in the southwest. We also identified 53 domesticated tree species significantly associated with earthwork occurrence probability, likely suggesting past management practices. Closed-canopy forests across Amazonia are likely to contain thousands of undiscovered archaeological sites around which pre-Columbian societies actively modified forests, a discovery that opens opportunities for better understanding the magnitude of ancient human influence on Amazonia and its current state.

Bayesian analysis of extreme events with threshold estimation

The aim of this paper is to analyse extremal events using generalized Pareto distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters on the basis of data beyond it, discarding all the information available below the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold included. Prior distributions for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov chain Monte Carlo methods. Simulations are carried out in order to analyse the performance of our proposed model under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financial market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling extensions.

Point pattern analysis with spatially varying covariate effects, applied to the study of cerebrovascular deaths

This article proposes a modeling approach for handling spatial heterogeneity present in the study of the geographical pattern of deaths due to cerebrovascular disease.The framework involvesa point pattern analysis with components exhibiting spatial variation. Preliminary studies indicate that mortality of this disease and the effect of relevant covariates do not exhibit uniform geographic distribution. Our model extends a previously proposed model in the literature that uses spatial and non-spatial variables by allowing for spatial variation of the effect of non-spatial covariates. A number of relative risk indicators are derived by comparing different covariate levels, different geographic locations, or both. The methodology is applied to the study of the geographical death pattern of cerebrovascular deaths in the city of Rio de Janeiro. The results compare well against existing alternatives, including fixed covariate effects. Our model is able to capture and highlight important data information that would not be noticed otherwise, providing information that is required for appropriate health decision-making.

[A Bayesian model to investigate excess mortality during the dengue epidemic in Greater Metropolitan Rio de Janeiro, Brazil, in 2007-2008]

The aim of this study was to investigate excess mortality from dengue in Greater Metropolitan Rio de Janeiro, Brazil, during an epidemic in 2007-2008. A Poisson dynamic model was tested to predict the number of deaths during these epidemic years. Inference was conducted with a Bayesian approach. Excess mortality was detected in March 2008 in children < 15 years. In addition, the highest number of reported dengue cases in Rio de Janeiro was in March and April 2008. Since the increase in mortality should be preceded by an increase in morbidity, one can hypothesize that there was excess mortality from dengue in children during the epidemic in Greater Metropolitan Rio de Janeiro in March 2008.

A default Bayesian approach for regression on extremes

A default Bayesian approach to predict extreme events in the presence of explanatory variables is presented. In the prediction model, covariates are introduced, using a non-homogenous Poisson-Generalized Pareto Distribution (GPD) point process, which allows for variation in the tail behaviour. The prior distribution proposed is based on a Jeffreys’ rule for regression parameters, extending the results previously obtained for an independent and identically distributed random sample drawn from the GPD. Special attention is given to mean return levels as an important summarizer. Inference is performed approximately via Markov chain Monte Carlo methods and the posterior distribution turns out to be relatively easy to be computed. The model is applied to two real datasets from meteorological applications.

Transfer functions in dynamic generalized linear models

In a time series analysis it is sometimes necessary to assume that the effect of a regressor does not have only immediate impact on the mean response, but that its effects somehow propagate to future times. We adopt, in this work, transfer functions to model such impacts, represented by structural blocks present in dynamic generalized linear models. All the inference is carried under the Bayesian paradigm. Two sources of difficulties emerge for the analytical derivation of posterior distributions: non-Gaussian nature of the response, associated to non-conjugate priors and also non-linearity of the predictor on auto regressive parameters present in transfer functions. The purpose of this work is to produce full Bayesian inference on dynamic generalized linear models with transfer functions, using Markov chain Monte Carlo methods to build samples of the posterior joint distribution of the parameters involved in such models. Several transfer structures are specified, associated to Poisson, Binomial, Gamma and inverse Gaussian responses. Simulated data are analyzed under the resulting models in order to assess their performance. Finally, two applications to real data concerning environmental sciences are made under different model formulations.

Bayesian dynamic models for survival data with a cure fraction

In this paper, we propose a new class of semi-parametric cure rate models. Specifically, we construct dynamic models for piecewise hazard functions over a finite partition of the time axis. Allowing the size of partition and the levels of baseline hazard to be random, our proposed models provide a great flexibility in controlling the degree of parametricity in the right tail of the survival distribution and the amount of correlations among the log-baseline hazard levels. Several properties of the proposed models are derived, and propriety of the implied posteriors with improper noninformative priors for regression coefficients based on the proposed models is established for the fixed partition of the time axis. In addition, an efficient reversible jump computational algorithm is developed for carrying out posterior computation. A real data set from a melanoma clinical trial is analyzed in detail to further demonstrate the proposed methodology.

Dynamic survival models with spatial frailty

In many survival studies, covariates effects are time-varying and there is presence of spatial effects. Dynamic models can be used to cope with the variations of the effects and spatial components are introduced to handle spatial variation. This paper proposes a methodology to simultaneously introduce these components into the model. A number of specifications for the spatial components are considered. Estimation is performed via a Bayesian approach through Markov chain Monte Carlo methods. Models are compared to assess relevance of their components. Analysis of a real data set is performed, showing the relevance of both time-varying covariate effects and spatial components. Extensions to the methodology are proposed along with concluding remarks.

Desigualdade no tratamento à fratura proximal de fêmur no Rio de Janeiro

OBJETIVO: O objetivo deste trabalho é analisar a existência de variações na indicação terapêutica a pacientes com fratura proximal de fêmur entre os hospitais conveniados com o SUS e entre pacientes socialmente distintos. MÉTODO: Foram analisados os dados do SIH-SUS dos hospitais do município do Rio de Janeiro, 1994-1995. RESULTADO: A análise multivariada mostrou que as chances de cirurgia foram maiores para as mulheres (OR=1,53, IC95%1,18-1,99); menores para os hospitais federais (OR = 0,21, IC95% 0,10-0,41), estaduais (OR =0,07, IC95% 0,04-0,12) e municipais (OR=0,11, IC95% 0,07-0,18), em comparação com o hospital privado contratado pelo SUS; foram menores nas emergências (OR=0,31, IC95% 0,19-0,48); e foram maiores nos hospitais localizados em áreas mais privilegiadas (OR=1,68, IC95% 1,52-1,86). CONCLUSÃO: A configuração dos mercados variou com o perfil dos hospitais e pacientes, e a indicação de cirurgia foi associada a fatores não relacionados com a necessidade, mostrando diferenças no acesso ao tratamento adequado.

[Urban hospital markets: a methodological approach]

This study developed a method for the construction of hospital markets in a metropolitan area, focusing on users of the Unified Health System (SUS) with hip fractures and admitted to municipal hospitals in Rio de Janeiro in 1994-1995. The study used a spatial smoothing technique based on a Kernel (quartic) estimate for constructing areas of care for each hospital and subsequently for hospital markets. Areas of the city were presented where there was a market domain and a secondary domain for treating patients with hip fractures. Hospital market analysis can help health planners organize resources in the health care system.