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Socioeconomic inequalities in dental health services in Sao Paulo, Brazil, 2003–2008
No full textAbstract\ud
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Background\ud
Access to, and use of, dental health services in Brazil have improved since 2003. The increase of private health care plans and the implementation of the “Smiling Brazil” Program, the largest public oral health care program in the world, could have influenced this increase in access. However, we do not yet know if inequalities in the use of dental health services persist after the improvement in access. The aims of this study are to analyze socioeconomic differences for dental health service use between 2003 and 2008 in São Paulo and to examine changes in these associations since the implementation of the Smiling Brazil program in 2003.\ud
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Method\ud
Data was obtained via two household health surveys (ISA-Capital 2003 and ISA-Capital 2008) which investigated living conditions, lifestyle, health status and use of health care services. Logistic regression was used to analyze associations between socioeconomic factors and dental services use. Additionally, trends from 2003 to 2008 regarding socioeconomic characteristics and dental health service use were explored.\ud
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Results\ud
Overall, dental health service use increased between 2003 and 2008 and was at both time points more common among those who had higher income, better education, better housing conditions, private health care plans and were Caucasian. Inequalities in use of dental health care did not decrease over time. Among the reasons for not seeking dental care, not having teeth and financial difficulty were more common in lower socioeconomic groups, while thinking it was unnecessary was more common in higher socioeconomic groups.\ud
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Conclusions\ud
The Brazilian oral health policy is still in a period of expansion and seems to have contributed slightly to increased dental health service use, but has not influenced socioeconomic inequalities in the use of these services. Acquiring deeper knowledge about inequalities in dental health service use will contribute to better understanding of potential barriers to reducing them.São Paulo Research Foundation-FAPESP. Process 2012/214153-0 and\ud
2013/26687-2.\ud
National Council for Scientific and Technological Development (CNPq).\ud
The surveys were financed by the Municipal Health Secretary of São Paulo.\ud
Researchers from three universities in São Paulo state (University of Sao\ud
Paulo, University of Campinas and University of the State of São Paulo)\ud
participated in administering the surveys
Regeneração no coral invasor Tubastraea coccinea (Cnidaria, Anthozoa, Scleractinia)
No full textFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) - Proc. 2012/21583-
Feature selection for multi-label learning
No full textFeature Selection plays an important role in machine learning and data mining, and it is often applied as a data pre-processing step. This task can speed up learning algorithms and sometimes improve their performance. In multi-label learning, label dependence is considered another aspect that can contribute to improve learning performance. A replicable and wide systematic review performed by us corroborates this idea. Based on this information, it is believed that considering label dependence during feature selection can lead to better learning performance. The hypothesis of this work is that multi-label feature selection algorithms that consider label dependence will perform better than the ones that disregard it. To this end, we propose multi-label feature selection algorithms that take into account label relations. These algorithms were experimentally compared to the standard approach for feature selection, showing good performance in terms of feature reduction and predictability of the classifiers built using the selected features.São Paulo Research Foundation (FAPESP) (grant 2011/02393-4
Anatomia comparada do sistema nervoso de quatro espécies do gênero Okenia (Mollusca: Nudibranchia) e a descoberta de um novo par de gânglios
No full textCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) - Proc. 2013/08425-
The log-Weibull-negative-binomial regression model under latent failure causes and presence of randomized activation schemes
No full textThe purpose of this paper is to develop a Bayesian approach for the log-Weibull-negative-binomial regression\ud
model under latent failure causes and presence of a randomized activation mechanism. We assume\ud
the number of competing causes of the event of interest follows a negative binomial distribution while\ud
the latent lifetimes are assumed to follows a Weibull distribution. Markov chain Monte Carlo methods\ud
are used to develop a Bayesian approach. Model selection to compare the fitted models is discussed.\ud
Moreover, we develop case deletion influence diagnostics for the joint posterior distribution based on the\ud
ψ-divergence, which has several divergence measures as particular cases. The developed procedures are\ud
illustrated on artificial and real data sets.FAPESPCNP
Comparing the topological properties of real and artificially generated scientific manuscripts
No full textRecent years have witnessed the increase of competition in science. While promoting the quality of research in many cases, an intense competition among scientists can also trigger unethical scientific behaviors. To increase the total number of published papers, some authors even resort to software tools that are able to produce grammatical, but meaningless scientific manuscripts. Because automatically generated papers can be misunderstood as real papers, it becomes of paramount importance to develop means to identify these scientific frauds. In this paper, I devise a methodology to distinguish real manuscripts from those generated with SCIGen, an automatic paper generator. Upon modeling texts as complex networks (CN), it was possible to discriminate real from fake papers with at least 89 % of accuracy. A systematic analysis of features relevance revealed that the accessibility and betweenness were useful in particular cases, even though the relevance depended upon the dataset. The successful application of the methods described here show, as a proof of principle, that network features can be used to identify scientific gibberish papers. In addition, the CN-based approach can be combined in a straightforward fashion with traditional statistical language processing methods to improve the performance in identifying artificially generated papers.São Paulo Research Foundation (FAPESP) (grant number 14/20830-0