Mulheres que fizeram aborto no Município do Rio de Janeiro, Brasil: aplicação de um modelo hierárquico bayesiano / Mujeres que abortaron en el Municipio de Río de Janeiro, Brasil: aplicación de un modelo jerárquico bayesiano / Women who have undergone abortion in the city of Rio de Janeiro, Brazil: application of a Bayesian hierarchical model

Resumo: Estimativas de mulheres que fizeram aborto provocado em localidades cujas leis são restritivas ainda são escassas na literatura científica, e a não coincidência de estimativas oriundas dos métodos hoje em uso clama pela aplicação de métodos inovadores, como novos métodos indiretos. Tal necessidade é especialmente aguda nas áreas mais densamente povoadas, como as capitais brasileiras, dada a magnitude do fenômeno e os danos e riscos daí decorrentes. O artigo objetiva estimar o número de mulheres que fez aborto provocado no Município do Rio de Janeiro, Brasil, em 2011, por meio de um modelo hierárquico bayesiano. Ele foi aplicado aos dados de um inquérito domiciliar que subsidiou a utilização do método network scale-up, no Município do Rio de Janeiro, um modelo hierárquico bayesiano utilizando as informações indiretas baseadas na rede de contatos dos participantes selecionados de forma aleatória da população. Das 1.758.145 mulheres de 15-49 anos residentes no Município do Rio de Janeiro (13.025; ICr95%: 10.635; 15.748) mulheres fizeram aborto provocado em 2011, resultando numa incidência acumulada média de 7,41 (ICr95%: 6,05; 8,96) para cada 1.000 mulheres de 15-49 anos. O estudo de autovalidação do modelo permitiu identificar padrões de subestimação em subpopulações estigmatizadas com baixa visibilidade social, como mulheres fizeram aborto provocado. O abortamento provocado é uma prática recorrente entre as mulheres no Município do Rio de Janeiro. Novos métodos de estimação indireta podem contribuir para a apreensão mais precisa do evento, considerando o contexto de ilegalidade, e contribuir para formulação de políticas de saúde.

Resumen: Las estimaciones de mujeres que tuvieron un aborto provocado en localidades cuyas leyes son restrictivas todavía son escasas en la literatura científica, y la no coincidencia de las estimaciones procedentes de los métodos hoy en uso reclama urgentemente la aplicación de métodos innovadores, como los nuevos métodos indirectos. Tal necesidad es especialmente acuciante en las áreas más densamente pobladas, como las capitales brasileñas, dada la magnitud del fenómeno y los daños y riesgos derivados de allí. El artículo tiene como objetivo estimar el número de mujeres que realizaron un aborto provocado en el Municipio de Río de Janeiro, Brasil, en 2011, a partir de un modelo jerárquico bayesiano. Este se aplicó a los datos de una encuesta domiciliaria que fomentó la utilización del método network scale-up, en el Municipio de Río de Janeiro, un modelo jerárquico bayesiano utilizando información indirecta, basada en la red de contactos de los participantes seleccionados de forma aleatoria en la población. De las 1.758.145 mujeres de 15-49 años, residentes en el Municipio de Río de Janeiro, 13.025 (ICr95%: 10.635; 15.748) mujeres tuvieron un aborto provocado en 2011, resultando en una incidencia acumulada media de 7,41 (ICr95%: 6,05; 8,96) para cada 1.000 mujeres de 15-49 años. El estudio de autovalidación del modelo permitió identificar patrones de subestimación en subpoblaciones estigmatizadas con baja visibilidad social, como las mujeres que tuvieron un aborto provocado. El aborto provocado es una práctica recurrente entre mujeres en el municipio de Río de Janeiro. Nuevos métodos de estimación indirecta pueden contribuir a la aprehensión más precisa de este evento, considerando el contexto de ilegalidad, y contribuir a la formulación de políticas de salud.

Abstract: Estimates of number of women who have undergone induced abortion in jurisdictions with restrictive abortion laws are still scarce in the scientific literature, and the disparate estimates from currently used methods call for the application of innovative estimation techniques such as new indirect methods. This need is especially acute in more densely populated areas, such as Brazil's state capitals, given the magnitude of unsafe abortions and the resulting risks and harms. The article aims to estimate the number of women who had induced abortions in the city of Rio de Janeiro in 2011, based on a Bayesian hierarchical model. The model was applied to data from a household survey that supported the use of the network scale-up method in the city of Rio de Janeiro, a Bayesian hierarchical model using indirect information based on the contact networks of randomly selected participants from the general population. Among the 1,758,145 women 15-49 years of age living in the city of Rio de Janeiro, 13,025 women (95%CrI: 10,635; 15,748) had induced abortions in 2011, resulting in a mean cumulative incidence of 7.41 (95%CrI: 6.05; 8.96) for every 1,000 women 15-49 years of age. The model's self-validation process identified patterns of underestimation in stigmatized subpopulations with low social visibility, such as women who have undergone induced abortion. Induced abortion is a common practice among women in the city of Rio de Janeiro. New indirect estimation methods can contribute to more precise measurement of this event, considering the context of illegality, and thereby contribute to appropriate health policies.

Texture Analysis of Torn Rotator Cuff on Preoperative Magnetic Resonance Arthrography as a Predictor of Postoperative Tendon Status

OBJECTIVE: To evaluate texture data of the torn supraspinatus tendon (SST) on preoperative T2-weighted magnetic resonance arthrography (MRA) using the gray-level co-occurrence matrix (GLCM) for prediction of post-operative tendon state. MATERIALS AND METHODS: Fifty patients who underwent arthroscopic rotator cuff repair for full-thickness tears of the SST were included in this retrospective study. Based on 1-year follow-up, magnetic resonance imaging showed that 30 patients had intact SSTs, and 20 had rotator cuff retears. Using GLCM, two radiologists measured independantly the highest signal intensity area of the distal end of the torn SST on preoperative T2-weighted MRA, which were compared between two groups.The relationships with other well-known prognostic factors, including age, tear size (anteroposterior dimension), retraction size (mediolateral tear length), grade of fatty degeneration of the SST and infraspinatus tendon, and arthroscopic fixation technique (single or double row), also were evaluated. RESULTS: Of all the GLCM features, the retear group showed significantly higher entropy (p < 0.001 and p = 0.001), variance (p = 0.030 and 0.011), and contrast (p = 0.033 and 0.012), but lower angular second moment (p < 0.001 and p = 0.002) and inverse difference moment (p = 0.027 and 0.027), as well as larger tear size (p = 0.001) and retraction size (p = 0.002) than the intact group. Retraction size (odds ratio [OR] = 3.053) and entropy (OR = 17.095) were significant predictors. CONCLUSION: Texture analysis of torn SSTs on preoperative T2-weighted MRA using the GLCM may be helpful to predict postoperative tendon state after rotator cuff repair.

Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System / 대한의료정보학회지

OBJECTIVES: In this paper, we proposed an algorithm for recognizing a rotator cuff supraspinatus tendon tear using a texture analysis based on a histogram, gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM). METHODS: First, we applied a total of 57 features (5 first order descriptors, 40 GLCM features, and 12 GLRLM features) to each rotator cuff region of interest. Our results show that first order statistics (mean, skewness, entropy, energy, smoothness), GLCM (correlation, contrast, energy, entropy, difference entropy, homogeneity, maximum probability, sum average, sum entropy), and GLRLM features are helpful to distinguish a normal supraspinatus tendon and an abnormal supraspinatus tendon. The statistical significance of these features is verified using a t-test. The support vector machine classification showed accuracy using feature combinations. Support Vector Machine offers good performance with a small amount of training data. Sensitivity, specificity, and accuracy are used to evaluate performance of a classification test. RESULTS: From the results, first order statics features and GLCM and GLRLM features afford 95%, 85%, and 100% accuracy, respectively. First order statistics and GLCM and GLRLM features in combination provided 100% accuracy. Combinations that include GLRLM features had high accuracy. GLRLM features were confirmed as highly accurate features for classified normal and abnormal. CONCLUSIONS: This algorithm will be helpful to diagnose supraspinatus tendon tear on ultrasound images.