Topologia do risco de acidentes do trabalho em Piracicaba, SP Spatial distribution of risks for work-related injuries in a city of Southeastern e Brazil

OBJETIVO: Analisar a distribuição espacial do risco de acidente do trabalho controlado por variáveis nutricionais e outras co-variáveis. MÉTODOS: Estudo caso-controle espacial de base hospitalar, tendo como variável de interesse a localização espacial dos acidentes do trabalho. Foram amostrados 794 trabalhadores, no período de maio a outubro de 2004. Os critérios de inclusão para casos (N=263) foram: ser trabalhador acidentado do trabalho, morador de Piracicaba, com idade entre 15 e 60 anos, e atendido em centro de ortopedia e traumatologia. Os controles (N=531) tiveram o mesmo critério de idade e residência na cidade, exceto que o acidente não era do trabalho, tendo sido considerandos também trabalhadores acompanhantes dos casos. A distribuição espacial da estimativa baseou-se no ajuste do modelo aditivo generalizado, tendo as coordenadas geográficas dos casos e controles como componente espacial não linear e as demais co-variáveis como componente linear. RESULTADOS: A variação da estimativa do risco espacial de acidentes do trabalho, controlada por sexo (OR=1,87; p<0,001), escolaridade (OR=0,85; p<0,0001), ser autônomo (OR=0,36; p<0,01) e circunferência de cintura (OR=0,98; p=0,05), mostra que as regiões de maior risco foram a centro-leste e a área que forma um "corredor" entre as regiões sul-norte. CONCLUSÕES: O uso de ferramentas de geoprocessamento e a consideração de variáveis nutricionais fornecem elementos para a compreensão das relações que compõem o risco de acidentes do trabalho, sendo oportuna a continuidade de investigações que contemplem esses fatores.<br>OBJECTIVE: To assess spatial distribution of risks for work-related injuries controlled for nutritional variables and other covariables. METHODS: Hospital-based spatial case-control study with work-related injuries spatial distribution as the main variable of interest. A total of 794 workers were selected between May and October 2004. Inclusion criteria for cases (N=263) were: worker with work-related injury; living in Piracicaba (Southeastern Brazil); age between 15 and 60 years old; and cared at an orthopedics and trauma center. Controls (N=531) met the same criteria for age and residence, but had non-work-related injuries and workers accompanying cases were included as well. Spatial distribution was estimated by adjusting a generalized additive model with geographical coordinates of cases and controls as spatial non-linear component and the remaining covariables as linear components. RESULTS: The variation of estimated spatial risks for work-related injuries controlled for gender (OR=1.87, p<0.001), schooling (OR=0.85, p<0.0001), self-employed (OR=0.36, p<0.01), and waist circumference (OR=0.98, p=0.05) showed that the mideastern area and north-to-south "strip" have higher risk for injuries. CONCLUSIONS: The use of geoprocessing tools and nutritional variables can provide input for understanding the universe of risks for work-related injuries. Further investigation exploring these factors is needed

Fast and Accurate Learning When Making Discrete Numerical Estimates

Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates. Author Summary: Studies of human perception and decision making have traditionally focused on scenarios where participants have to make estimates about continuous variables. However discrete variables are also common in our environment, potentially requiring different theoretical models. We describe ways to model such scenarios within the statistical framework of Bayesian inference and explain how aspects of such models can be teased apart experimentally. Using two experimental setups, a numerosity task and an area estimation task, we show that human participants do indeed rely on combinations of specific model components. Specifically we show that human learning in discrete tasks can be surprisingly fast and that participants can use the learned information in a way that is either optimal or near-optimal