Boletín MOMENTO ECONÓMICO, año 5, núm. 45, marzo-junio de 2015.

En este número de Momento Económico, se analizan las perspectivas de crecimiento para la economía mexicana al finalizar el 2015. Las actuales circunstancias de la economía internacional son una fuente probable de inestabilidad y lento crecimiento de la actividad económica en nuestro país para este año; no obstante, más allá del contexto mundial, las actuales políticas macroeconómicas (monetaria, cambiaria y fiscal) lejos de dirigir a la economía hacia un camino de desarrollo sostenido, propician sólo un crecimiento inercial que no respalda la generación de empleos mejor remunerados, así como una mayor y mejor seguridad social para la toda la población, ambos elementos fundamentales para una más equitativa distribución del ingreso. Así, las actuales políticas económicas reproducen, constantemente, la debilidad del mercado interno haciendo a la economía mexicana vulnerable ante los acontecimientos que ocurren fuera de nuestras fronteras

México y América Latina sujetos a la vulnerabilidad externa

El presente trabajo analiza cómo la globalización y las políticas macroeconómicas de estabilidad que la acompañan le han quitado el control del dinero a los gobiernos, y por ende, el manejo de la política para configurar condiciones endógenas de acumulación y crecimiento. Las economías han pasado a depender de las variables externas. Los países que tienen ventajas comparativas en el sector primario han mostrado mayor crecimiento que el resto, aunque todos están sujetos al comportamiento del contexto internacional. Se analizan los problemas de crecimiento que enfrentan los países desarrollados y sus efectos en América Latina, que no tiene una política contracíclica para encarar los embates externos. Se plantea la necesidad de retomar el control de la moneda, así como de regular el sector externo y el financiero para poder flexibilizar la política económica a favor del sector productivo y del empleo, y de colocar el mercado interno como motor de crecimiento

Boletín MOMENTO ECONÓMICO, año 2, núms. 25-26, Septiembre-Octubre 2012.

El regreso del Partido Revolucionario Institucional (PRI) a la Presidencia de la República, sin lugar a dudas, se trata de un cambio político que los expertos en la materia deberán precisar su magnitud e impacto; en cuanto a las expectativas del momento económico, el futuro inmediato y mediato de la economía mexicana, lo cual es materia de este Boletín (a principios de diciembre y antes de ser enviado el presupuesto) todavía es muy pronto para tener una evaluación completa y estricta, ya que se están planteando un conjunto de reformas con el objetivo de “mover al país”. En principio se habla de tres iniciativas (Reformas Educativa, de Telecomunicaciones y a la Ley Nacional de Responsabilidad Hacendaria y Deuda Pública para entidades federativas y municipios). Asimismo, se ha insistido en Reformas a PEMEX para su apertura a la inversión priva- da en refinación, petroquímica y transporte de hidrocarburos, además de la puesta en marcha de la Reforma Laboral, vigente a partir del pasado 1° de diciembre

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

[EN] Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient's electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.Huerta Herraiz, Á.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Quesada, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. Entropy. 22(7):1-17. https://doi.org/10.3390/e22070733S117227Lippi, G., Sanchis-Gomar, F., & Cervellin, G. (2020). Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. International Journal of Stroke, 16(2), 217-221. doi:10.1177/1747493019897870Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T., & Liu, X. (2013). Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population. The American Journal of Cardiology, 112(8), 1142-1147. doi:10.1016/j.amjcard.2013.05.063Khoo, C. W., Krishnamoorthy, S., Lim, H. S., & Lip, G. Y. H. (2012). Atrial fibrillation, arrhythmia burden and thrombogenesis. International Journal of Cardiology, 157(3), 318-323. doi:10.1016/j.ijcard.2011.06.088Warmus, P., Niedziela, N., Huć, M., Wierzbicki, K., & Adamczyk-Sowa, M. (2020). Assessment of the manifestations of atrial fibrillation in patients with acute cerebral stroke – a single-center study based on 998 patients. Neurological Research, 42(6), 471-476. doi:10.1080/01616412.2020.1746508Sposato, L. A., Cipriano, L. E., Saposnik, G., Vargas, E. R., Riccio, P. M., & Hachinski, V. (2015). Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. The Lancet Neurology, 14(4), 377-387. doi:10.1016/s1474-4422(15)70027-xSchotten, U., Dobrev, D., Platonov, P. G., Kottkamp, H., & Hindricks, G. (2016). Current controversies in determining the main mechanisms of atrial fibrillation. Journal of Internal Medicine, 279(5), 428-438. doi:10.1111/joim.12492Ferrari, R., Bertini, M., Blomstrom-Lundqvist, C., Dobrev, D., Kirchhof, P., Pappone, C., … Vicedomini, G. G. (2016). An update on atrial fibrillation in 2014: From pathophysiology to treatment. International Journal of Cardiology, 203, 22-29. doi:10.1016/j.ijcard.2015.10.089Meyre, P., Blum, S., Berger, S., Aeschbacher, S., Schoepfer, H., Briel, M., … Conen, D. (2019). Risk of Hospital Admissions in Patients With Atrial Fibrillation: A Systematic Review and Meta-analysis. Canadian Journal of Cardiology, 35(10), 1332-1343. doi:10.1016/j.cjca.2019.05.024Van Wagoner, D. R., Piccini, J. P., Albert, C. M., Anderson, M. E., Benjamin, E. J., Brundel, B., … Wehrens, X. H. T. (2015). Progress toward the prevention and treatment of atrial fibrillation: A summary of the Heart Rhythm Society Research Forum on the Treatment and Prevention of Atrial Fibrillation, Washington, DC, December 9–10, 2013. Heart Rhythm, 12(1), e5-e29. doi:10.1016/j.hrthm.2014.11.011De Vos, C. B., Pisters, R., Nieuwlaat, R., Prins, M. H., Tieleman, R. G., Coelen, R.-J. S., … Crijns, H. J. G. M. (2010). Progression From Paroxysmal to Persistent Atrial Fibrillation. Journal of the American College of Cardiology, 55(8), 725-731. doi:10.1016/j.jacc.2009.11.040SCHUCHERT, A., BEHRENS, G., & MEINERTZ, T. (1999). Impact of Long-Term ECG Recording on the Detection of Paroxysmal Atrial Fibrillation in Patients After an Acute Ischemic Stroke. Pacing and Clinical Electrophysiology, 22(7), 1082-1084. doi:10.1111/j.1540-8159.1999.tb00574.xPagola, J., Juega, J., Francisco-Pascual, J., Moya, A., Sanchis, M., Bustamante, A., … Arenillas, J. F. (2018). Yield of atrial fibrillation detection with Textile Wearable Holter from the acute phase of stroke: Pilot study of Crypto-AF registry. International Journal of Cardiology, 251, 45-50. doi:10.1016/j.ijcard.2017.10.063Luong, D. T., Ha, N. T., & Thuan, N. D. (2019). Android Smart Phones Application in Tele-monitoring Electrocardiogram (ECG). American Journal of Biomedical Sciences, 15-21. doi:10.5099/aj190100015Haverkamp, H. T., Fosse, S. O., & Schuster, P. (2019). Accuracy and usability of single-lead ECG from smartphones - A clinical study. Indian Pacing and Electrophysiology Journal, 19(4), 145-149. doi:10.1016/j.ipej.2019.02.006Nagai, S., Anzai, D., & Wang, J. (2017). Motion artefact removals for wearable ECG using stationary wavelet transform. Healthcare Technology Letters, 4(4), 138-141. doi:10.1049/htl.2016.0100Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment. IEEE Reviews in Biomedical Engineering, 11, 36-52. doi:10.1109/rbme.2018.2810957Aboukhalil, A., Nielsen, L., Saeed, M., Mark, R. G., & Clifford, G. D. (2008). Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. Journal of Biomedical Informatics, 41(3), 442-451. doi:10.1016/j.jbi.2008.03.003Bashar, S. K., Ding, E., Walkey, A. J., McManus, D. D., & Chon, K. H. (2019). Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients. IEEE Access, 7, 88357-88368. doi:10.1109/access.2019.2926199Yoon, D., Lim, H. S., Jung, K., Kim, T. Y., & Lee, S. (2019). Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model. Healthcare Informatics Research, 25(3), 201. doi:10.4258/hir.2019.25.3.201Oster, J., Behar, J., Sayadi, O., Nemati, S., Johnson, A. E. W., & Clifford, G. D. (2015). Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters. IEEE Transactions on Biomedical Engineering, 62(9), 2125-2134. doi:10.1109/tbme.2015.2402236Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: a review of the subtraction procedure. BioMedical Engineering OnLine, 4(1). doi:10.1186/1475-925x-4-50Luo, S., & Johnston, P. (2010). A review of electrocardiogram filtering. Journal of Electrocardiology, 43(6), 486-496. doi:10.1016/j.jelectrocard.2010.07.007Martínez, A., Alcaraz, R., & Rieta, J. J. (2010). Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiological Measurement, 31(11), 1467-1485. doi:10.1088/0967-3334/31/11/005Manikandan, M. S., & Ramkumar, B. (2014). Straightforward and robust QRS detection algorithm for wearable cardiac monitor. Healthcare Technology Letters, 1(1), 40-44. doi:10.1049/htl.2013.0019Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). An automated ECG signal quality assessment method for unsupervised diagnostic systems. Biocybernetics and Biomedical Engineering, 38(1), 54-70. doi:10.1016/j.bbe.2017.10.002Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring. IEEE Journal of Biomedical and Health Informatics, 22(3), 722-732. doi:10.1109/jbhi.2017.2686436Zhang, Q., Fu, L., & Gu, L. (2019). A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG. Computational and Mathematical Methods in Medicine, 2019, 1-12. doi:10.1155/2019/7095137Xu, X., Wei, S., Ma, C., Luo, K., Zhang, L., & Liu, C. (2018). Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. Journal of Healthcare Engineering, 2018, 1-8. doi:10.1155/2018/2102918Al Rahhal, M. M., Bazi, Y., Al Zuair, M., Othman, E., & BenJdira, B. (2018). Convolutional Neural Networks for Electrocardiogram Classification. Journal of Medical and Biological Engineering, 38(6), 1014-1025. doi:10.1007/s40846-018-0389-7He, R., Wang, K., Zhao, N., Liu, Y., Yuan, Y., Li, Q., & Zhang, H. (2018). Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01206Yildirim, O., Talo, M., Ay, B., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019). Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Computers in Biology and Medicine, 113, 103387. doi:10.1016/j.compbiomed.2019.103387SINGH, S. A., & MAJUMDER, S. (2019). A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK. Journal of Mechanics in Medicine and Biology, 19(04), 1950026. doi:10.1142/s021951941950026xByeon, Y.-H., Pan, S.-B., & Kwak, K.-C. (2019). Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics. Sensors, 19(4), 935. doi:10.3390/s19040935Clifford, G., Liu, C., Moody, B., Lehman, L., Silva, I., Li, Q., … Mark, R. (2017). AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017. 2017 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2017.065-469Redmond, S. J., Xie, Y., Chang, D., Basilakis, J., & Lovell, N. H. (2012). Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiological Measurement, 33(9), 1517-1533. doi:10.1088/0967-3334/33/9/1517Li, T., & Zhou, M. (2016). ECG Classification Using Wavelet Packet Entropy and Random Forests. Entropy, 18(8), 285. doi:10.3390/e18080285Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751-5757. doi:10.1016/j.eswa.2010.02.033Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P., & Rodriguez, B. (2018). Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of The Royal Society Interface, 15(138), 20170821. doi:10.1098/rsif.2017.0821Mincholé, A., & Rodriguez, B. (2019). Artificial intelligence for the electrocardiogram. Nature Medicine, 25(1), 22-23. doi:10.1038/s41591-018-0306-1Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48. doi:10.1016/j.neucom.2015.09.116Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002Zhao, Z., & Zhang, Y. (2018). SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00727Moeyersons, J., Smets, E., Morales, J., Villa, A., De Raedt, W., Testelmans, D., … Varon, C. (2019). Artefact detection and quality assessment of ambulatory ECG signals. Computer Methods and Programs in Biomedicine, 182, 105050. doi:10.1016/j.cmpb.2019.105050Clifford, G. D., Behar, J., Li, Q., & Rezek, I. (2012). Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiological Measurement, 33(9), 1419-1433. doi:10.1088/0967-3334/33/9/1419Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., & Tarassenko, L. (2014). Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2014.2338351Hayn, D., Jammerbund, B., & Schreier, G. (2012). QRS detection based ECG quality assessment. Physiological Measurement, 33(9), 1449-1461. doi:10.1088/0967-3334/33/9/1449Casey, S., Avalos, G., & Dowling, M. (2018). Critical care nurses’ knowledge of alarm fatigue and practices towards alarms: A multicentre study. Intensive and Critical Care Nursing, 48, 36-41. doi:10.1016/j.iccn.2018.05.004Nattel, S., Guasch, E., Savelieva, I., Cosio, F. G., Valverde, I., Halperin, J. L., … Camm, A. J. (2014). Early management of atrial fibrillation to prevent cardiovascular complications. European Heart Journal, 35(22), 1448-1456. doi:10.1093/eurheartj/ehu028Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B.-S., & Li, J. (2019). Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. IEEE Access, 7, 34060-34067. doi:10.1109/access.2019.2900719Petrėnas, A., Marozas, V., & Sörnmo, L. (2015). Low-complexity detection of atrial fibrillation in continuous long-term monitoring. Computers in Biology and Medicine, 65, 184-191. doi:10.1016/j.compbiomed.2015.01.01

Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots

[EN] Current wearable electrocardiogram (ECG) recording systems have great potential to revolutionize early diagnosis of paroxysmal atrial fibrillation (AF). They are able to continuously acquire an ECG signal for long weeks and then increase the probability of detecting first brief, intermittent signs of the arrhythmia. However, the recorded signal is often broadly corrupted by noise and artifacts, and accurate assessment of its quality to avoid automated misdiagnosis and false alarms of AF is still an unsolved challenge. In this context, the present work is pioneer in exploring the usefulness of transforming the single-lead ECG signal into two common phase space (PS) representations, such as the Poincare plot and the first order difference graph, for evaluation of its quality. Several machine and deep learning models fed with features and images derived from these PS portraits reported a better performance than well-known previous methods, even when they were trained and validated on two separate databases. Indeed, in binary classification of high- and low-quality ECG excerpts, the generated PS-based algorithms reported a discriminant power greater than 85%, misclassifying less than 20% of high-quality AF episodes and non -normal rhythms as noisy excerpts. Moreover, because both PS reconstructions do not require any mathematical transformation, these algorithms also spent much less time in classifying each ECG excerpt in validation and testing stages than previous methods. As a consequence, ECG transformation to both PS portraits enables novel, simple, effective, and computational low-cost techniques, based both on machine and deep learning classifiers, for ECG quality assessment.This research has received financial support from Daiichi Sankyo SLU and from public grants PID2021-00X128525-IV0, PID2021-12380 4OB-I00, and TED2021-130935B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU) , SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, Spain, and AICO/2021/286 from Generalitat Valenciana.Huerta, A.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Ayo-Martin, O.; Rieta, JJ.; Alcaraz, R. (2024). Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots. Biomedical Signal Processing and Control. 91. https://doi.org/10.1016/j.bspc.2023.1059209

Desempeño agronómico y fisiológico de variedades nativas de tomate mexicano sometidas a deficiencias de agua y nutrientes

El agua y los nutrimentos minerales son factores esenciales para el crecimiento vegetal y la producción agrícola. El objetivo de este trabajo fue comparar la respuesta a reducción combinada de agua y de nutrientes (25%) de cuatro poblaciones nativas de tomate y de un híbrido comercial, en comparación con un régimen de riego y nutrición suficiente (100%). Las principales variables evaluadas durante el ciclo de cultivo fueron: área foliar, biomasa, rendimiento, tamaño y número de frutos por planta, número de lóculos por fruto, firmeza, sólidos solubles totales, tasa fotosintética y eficiencia en el uso del agua (EUA). Se encontró que el híbrido comercial superó a los tomates nativos en área foliar, biomasa total, y en rendimiento de fruto, con y sin déficit hídrico. Entre los tomates nativos (que no han sido sometido al mejoramiento genético formal) sobresalió OAX por su alto potencial de rendimiento de fruto (estadísticamente similar al del híbrido) y por su alta EUA, tanto en ambiente favorable como en estrés hídrico-nutrimental. La var. EMX destacó por su tolerancia al estrés expresada en rendimiento de fruto y en tasa de fotosíntesis. La var. PUE mostró tolerancia al estrés en área foliar y en biomasa total, así como buen rendimiento. Por su parte la var. CAM tuvo el más alto contenido de sólidos solubles totales, tanto con y sin estrés. Estos resultados evidencian el potencial de los tomates nativos en productividad y calidad de fruto, que puede ser aprovechada directamente para producción comercial y como donadores de genes para formar nuevas variedades mejoradas. https://doi.org/10.54167/tecnociencia.v16i1.88

Cuatro productos ancestrales y su importancia en la gastronomía Mexicana

La presente investigación, es un estudio bibliográfico, descriptivo de la historia de la dieta alimentaria del mexicano, así como significados e importancia del maíz, frijol, calabaza y chile, sus variedades y evolución

Do case-only designs yield consistent results across design and different databases? A case study of hip fractures and benzodiazepines.

BACKGROUND: The case-crossover (CXO) and self-controlled case series (SCCS) designs are increasingly used in pharmacoepidemiology. In both, relative risk estimates are obtained within persons, implicitly controlling for time-fixed confounding variables. OBJECTIVES: To examine the consistency of relative risk estimates of hip/femur fractures (HFF) associated with the use of benzodiazepines (BZD) across case-only designs in two databases (DBs), when a common protocol was applied. METHODS: CXO and SCCS studies were conducted in BIFAP (Spain) and CPRD (UK). Exposure to BZD was divided into non-use, current, recent and past use. For CXO, odds ratios (OR; 95%CI) of current use versus non-use/past were estimated using conditional logistic regression adjusted for co-medications (AOR). For the SCCS, conditional Poisson regression was used to estimate incidence rate ratios (IRR; 95%CI) of current use versus non/past-use, adjusted for age. To investigate possible event-exposure dependence the relative risk in the 30 days prior to first BZD exposure was also evaluated. RESULTS: In the CXO current use of BZD was associated with an increased risk of HFF in both DBs, AORBIFAP = 1.47 (1.29-1.67) and AORCPRD = 1.55 (1.41-1.70). In the SCCS, IRRs for current exposure was 0.79 (0.72-0.86) in BIFAP and 1.21 (1.13-1.30) in CPRD. However, when we considered separately the 30-day pre-exposure period, the IRR for current period was 1.43 (1.31-1.57) in BIFAP and 1.37 (1.27-1.47) in CPRD. CONCLUSIONS: CXO designs yielded consistent results across DBs, while initial SCCS analyses did not. Accounting for event-exposure dependence, estimates derived from SCCS were more consistent across DBs and designs

Crisis económica y migración ¿Impactos temporales o estructurales?

La reciente crisis global, que para algunos autores ha sido tan o más profunda que la de los años treinta del siglo pasado extendida a un importante conjunto de países en el mundo, podría marcar el inicio de un nuevo patrón de acumulación, y como consecuencia un nuevo patrón migratorio que respondería a las exigencias de los nuevos mercados laborales, conceptos que se encuentran a debate entre los autores del presente libro. Destaca un análisis histórico que sirve de marco para conocer cómo Estados Unidos se desarrolló hasta ser una potencia hegemónica y el papel que desempeñaron los países periféricos como exportadores de materias primas, así como la forma como se articularon los flujos migratorios al responder a los intereses de las corporaciones trasnacionales. Se observa el estudio de la relación México-Estados Unidos en un escenario de crisis bajo la posición diversa de los autores lo que enriquece la discusión acerca de sus causas y posibles formas de enfrentarla, así como sus efectos sobre los mercados de trabajo y los flujos migratorios a los que se encuentran articulados internacionalmente. Se discuten también las repercusiones que ha tenido la crisis sobre la migración internacional de trabajadores, en México, en España y en latinoamérica. Finalmente, se destaca la importancia de la educación y los problemas que enfrentan algunos países ante la falta de absorción de sus profesionales en los mercados laborales internos, situación que genera graves desequilibrios e incrementa la participación de los migrantes con mayores niveles de calificación en respuesta a las nuevas exigencias de los mercados laborales internacionales

Healthcare-Associated Pneumonia: Don't Forget About Respiratory Viruses!

Introduction: Healthcare-associated infections are an important cause of morbidity and mortality, are among the most common adverse events in healthcare, and of them, pneumonia is the most commonly reported. Our objective was to evaluate the incidence and clinical outcome of respiratory viruses in hospital-acquired pneumonia (HAP).Methods: This was a prospective cohort study, include patients aged between 0 and 18 who fulfilled Centers for Diseases Control and Prevention (CDC) criteria for HAP. Demographic and clinical data were obtained, and a nasopharyngeal swab specimen was taken for the detection of respiratory viruses. All included patients were monitored until discharge to collect data on the need for mechanical ventilation, intensive care unit (ICU) admission, and mortality. All-cause 30-day mortality was also ascertained.Results: Four thousand three hundred twenty-seven patients were followed for 42,658 patient-days and 5,150 ventilator-days. Eighty-eight patients (2.03%) met the CDC criteria for HAP, 63 patients were included, and clinical and epidemiological characteristics showed no statistically significant differences between patients with virus associated healthcare-associated pneumonia (VAHAP) and those with non-viral healthcare-associated pneumonia (NVHAP). At least one respiratory virus was detected in 65% [95% CI (53–77)] of episodes of HAP, with a single viral pathogen observed in 53.9% and coinfection with 2 viruses in 11.1% of cases. The outcome in terms of ICU admission, mechanical ventilation and the 30-day mortality did not show a significant difference between groups.Conclusions: In two-thirds of the patients a respiratory virus was identified. There was no difference in mortality or the rest of the clinical outcome variables. About half of the patients required mechanical ventilation and 10% died, which emphasizes the importance of considering these pathogens in nosocomial infections, since their identification can influence the decrease in hospital costs and be taken into account in infection control policies