119 research outputs found

    El suicidio en instituciones psiquiátricas: descripción de dos casos

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    ResumenSe ha estimado que cerca del 5% de los suicidios ocurre dentro de las instituciones psiquiátricas. Este reporte describe dos casos de suicidios en un hospital psiquiátrico, los cuales ilustran, por un lado, las características de riesgos suicida en el paciente psiquiátrico hospitalizado, y por otro, las limitaciones que aún impiden evitar el suicidio.[Jiménez A, Ibarra C, Peñalosa L, Díaz JL. El suicidio en instituciones psiquiátricas: descripción de dos casos. MedUNAB 2004; 7:140-3].Palabras clave: suicidio, hospital psiquiátrico y paciente psiquiátrico hospitalizado

    Spin Seebeck effect in insulating epitaxial ¿-Fe2O3 thin films

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    We report the fabrication of high crystal quality epitaxial thin films of maghemite (¿-Fe2O3), a classic ferrimagnetic insulating iron oxide. Spin Seebeck effect (SSE) measurements in ¿-Fe2O3/Pt bilayers as a function of sample preparation conditions and temperature yield a SSE coefficient of 0.5(1) µV/K at room temperature. Dependence on temperature allows us to estimate the magnon diffusion length in maghemite to be in the range of tens of nanometers, in good agreement with that of conducting iron oxide magnetite (Fe3O4), establishing the relevance of spin currents of magnonic origin in magnetic iron oxides

    The 1989 and 2015 outbursts of V404 Cygni: a global study of wind-related optical features

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    The black hole transient V404 Cygni exhibited a bright outburst in June 2015 that was intensively followed over a wide range of wavelengths. Our team obtained high time resolution optical spectroscopy (~90 s), which included a detailed coverage of the most active phase of the event. We present a database consisting of 651 optical spectra obtained during this event, that we combine with 58 spectra gathered during the fainter December 2015 sequel outburst, as well as with 57 spectra from the 1989 event. We previously reported the discovery of wind-related features (P-Cygni and broad-wing line profiles) during both 2015 outbursts. Here, we build diagnostic diagrams that enable us to study the evolution of typical emission line parameters, such as line fluxes and equivalent widths, and develop a technique to systematically detect outflow signatures. We find that these are present throughout the outburst, even at very low optical fluxes, and that both types of outflow features are observed simultaneously in some spectra, confirming the idea of a common origin. We also show that the nebular phases depict loop patterns in many diagnostic diagrams, while P-Cygni profiles are highly variable on time-scales of minutes. The comparison between the three outbursts reveals that the spectra obtained during June and December 2015 share many similarities, while those from 1989 exhibit narrower emission lines and lower wind terminal velocities. The diagnostic diagrams presented in this work have been produced using standard measurement techniques and thus may be applied to other active low-mass X-ray binaries.Comment: Accepted for publication in MNRAS. 23 pages paper, plus a 9 pages appendix with extra tables and figures. 18 figures are included in the paper and 8 in the appendi

    GHEP-ISFG collaborative exercise on mixture profiles of autosomal STRs (GHEP-MIX01, GHEP-MIX02 and GHEP-MIX03): results and evaluation

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    One of the main objectives of the Spanish and Portuguese-Speaking Group of the International Society for Forensic Genetics (GHEP-ISFG) is to promote and contribute to the development and dissemination of scientific knowledge in the area of forensic genetics. Due to this fact, GHEP-ISFG holds different working commissions that are set up to develop activities in scientific aspects of general interest. One of them, the Mixture Commission of GHEP-ISFG, has organized annually, since 2009, a collaborative exercise on analysis and interpretation of autosomal short tandem repeat (STR) mixture profiles. Until now, three exercises have been organized (GHEP-MIX01, GHEP-MIX02 and GHEP-MIX03), with 32, 24 and 17 participant laboratories respectively. The exercise aims to give a general vision by addressing, through the proposal of mock cases, aspects related to the edition of mixture profiles and the statistical treatment. The main conclusions obtained from these exercises may be summarized as follows. Firstly, the data show an increased tendency of the laboratories toward validation of DNA mixture profiles analysis following international recommendations (ISO/IEC 17025:2005). Secondly, the majority of discrepancies are mainly encountered in stutters positions (53.4%, 96.0% and 74.9%, respectively for the three editions). On the other hand, the results submitted reveal the importance of performing duplicate analysis by using different kits in order to reduce errors as much as possible. Regarding the statistical aspect (GHEP-MIX02 and 03), all participants employed the likelihood ratio (LR) parameter to evaluate the statistical compatibility and the formulas employed were quite similar. When the hypotheses to evaluate the LR value were locked by the coordinators (GHEP-MIX02) the results revealed a minor number of discrepancies that were mainly due to clerical reasons. However, the GHEP-MIX03 exercise allowed the participants to freely come up with their own hypotheses to calculate the LR value. In this situation the laboratories reported several options to explain the mock cases proposed and therefore significant differences between the final LR values were obtained. Complete information concerning the background of the criminal case is a critical aspect in order to select the adequate hypotheses to calculate the LR value. Although this should be a task for the judicial court to decide, it is important for the expert to account for the different possibilities and scenarios, and also offer this expertise to the judge. In addition, continuing education in the analysis and interpretation of mixture DNA profiles may also be a priority for the vast majority of forensic laboratories.Fil: Sala, Adriana Andrea. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Servicio de Huellas Digitales Genéticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Crespillo, M.. Instituto Nacional de Toxicología y Ciencias Forenses; EspañaFil: Barrio, P. A.. Instituto Nacional de Toxicología y Ciencias Forenses; EspañaFil: Luque, J. A.. Instituto Nacional de Toxicología y Ciencias Forenses; EspañaFil: Alves, Cíntia. Universidad de Porto; PortugalFil: Aler, M.. Servicio de Laboratorio. Sección de Genética Forense y Criminalística; EspañaFil: Alessandrini, F.. Università Politecnica delle Marche. Department of Biomedical Sciences and Public Health; ItaliaFil: Andrade, L.. Instituto Nacional de Medicina Legal e Ciências Forenses, Delegação do Centro. Serviço de Genética e Biologia Forenses; PortugalFil: Barretto, R. M.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Bofarull, A.. Instituto Nacional de Toxicología y Ciencias Forenses; EspañaFil: Costa, S.. Instituto Nacional de Medicina Legal y Ciencias Forenses; PortugalFil: García, M. A.. Servicio de Criminalística de la Guardia Civil. Laboratorio Central de Criminalística. Departamento de Biología; EspañaFil: García, O.. Basque Country Police. Forensic Genetics Section. Forensic Science Unit; EspañaFil: Gaviria, A.. Cruz Roja Ecuatoriana. Laboratorio de Genética Molecular; EcuadorFil: Gladys, A.. Corte Suprema de Justicia de la Nación; ArgentinaFil: Gorostiza, A.. Grupo Zeltia. Genomica S. A. U.. Laboratorio de Identificación Genética; EspañaFil: Hernández, A.. Instituto Nacional de Toxicología y Ciencias Forenses; EspañaFil: Herrera, M.. Laboratorio Genda S. A.; ArgentinaFil: Hombreiro, L.. Jefatura Superior de Policía de Galicia. Brigada de Policía Científica. Laboratorio Territorial de Biología – ADN; EspañaFil: Ibarra, A. A.. Universidad de Antioquia; ColombiaFil: Jiménez, M. J.. Policia de la Generalitat – Mossos d’Esquadra. Divisió de Policia Científica. Àrea Central de Criminalística. Unitat Central de Laboratori Biològic; EspañaFil: Luque, G. M.. Instituto Nacional de Toxicología y Ciencias Forenses; EspañaFil: Madero, P.. Centro de Análisis Genéticos; EspañaFil: Martínez Jarreta, B.. Universidad de Zaragoza; EspañaFil: Masciovecchio, M. Verónica. IACA Laboratorios; ArgentinaFil: Modesti, Nidia Maria. Provincia de Córdoba. Poder Judicial; ArgentinaFil: Moreno, F.. Servicio Médico Legal. Unidad de Genética Forense; ChileFil: Pagano, S.. Dirección Nacional de Policía Técnica. Laboratorio de Análisis de ADN para el CODIS; UruguayFil: Pedrosa, S.. Navarra de Servicios y Tecnologías S. A. U.; EspañaFil: Plaza, G.. Neodiagnostica S. L.; EspañaFil: Prat, E.. Comisaría General de Policía Científica. Laboratorio de ADN; EspañaFil: Puente, J.. Laboratorio de Genética Clínica S. L.; EspañaFil: Rendo, F.. Universidad del País Vasco; EspañaFil: Ribeiro, T.. Instituto Nacional de Medicina Legal e Ciências Forenses, Delegação Sul. Serviço de Genética e Biologia Forenses; PortugalFil: Santamaría, E.. Instituto Nacional de Toxicología y Ciencias Forenses; EspañaFil: Saragoni, V. G.. Servicio Médico Legal. Departamento de Laboratorios. Unidad de Genética Forense; ChileFil: Whittle, M. R.. Genomic Engenharia Molecular; Brasi

    Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms

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    [EN] The main aim of this study is to establish the effect of the Exploitation and Exploration; and the influence of these learning flows on the Innovative Outcome (IO). The Innovative Outcome refers to new products, services, processes (or improvements) that the organization has obtained as a result of an innovative process. For this purpose, a relationship model is defined, which is empirically contrasted, and can explains and predicts the cyclical dynamization of learning flows on innovative outcome in knowledge intensive firms. The quantitative test for this model use the data from entrepreneurial firms biotechnology sector. The statistical analysis applies a method based on variance using Partial Least Squares (PLS). Research results confirm the hypotheses, that is, they show a positive dynamic effect between the Exploration and the Innovative as outcomes. In the same vein, they results confirm the presence of the cyclic movement of innovative outcome with the Exploitation.In addition, this research is part of the Project ECO2015-71380-R funded by the Spanish Ministry of Economy, Industry and Competitiveness and the State Research Agency. Co-financed by the European Regional Development Fund (ERDF).Vargas-Mendoza, NY.; Lloria, MB.; Salazar Afanador, A.; Vergara Domínguez, L. (2018). Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms. International Entrepreneurship and Management Journal. 14(4):1053-1069. https://doi.org/10.1007/s11365-018-0496-5S10531069144Alegre, J., & Chiva, R. (2008). Assessing the impact of organizational learning capability on product innovation performance: an empirical test. Technovation, 28, 315–326.Amara, N., Landry, R., Becheikh, N., & Ouimet, M. (2008). Learning and novelty of innovation in established manufacturing SMEs. Technovation, 28, 450–463.Aragón-Mendoza, J., Pardo del Val, M., & Roig, S. (2016). The influence of institutions development in venture creation decision: a cognitive view. Journal of Business Research, 69(11), 4941–4946.Ardichvili, A. (2008). Learning and knowledge sharing in virtual communities of practice: motivators, barriers, and enablers. Advances in Developing Human Resources, 10(4), 541–554.Argyris, C., & Schön, D. (1978). Organizational learning: a theory of action perspective. Reading: Addison Wesley.Bagozzi, R. P., Yi, Y., & Singh, S. (1991). On the use of structural equation models in experimental designs: two extensions international. Journal of Research in Marketing, 8, 125–140.Belda, J., Vergara L., Salazar, A., & Safont G. (2018). Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs, accepted for publication in Signal Processing.Boland, R. J. J., & Tenkasi, R. V. (1995). Perspective making and perspective taking in communities of knowing. Organization Science, 6(4), 350–372.Bontis, N., (1998). Intellectual capital: an exploratory study that develops measures models. Management Decision, 36, 63–76.Bontis, N. (1999). Managing an organizational learning system by aligning stocks and flows of knowledge: an empirical examination of intellectual capital, knowledge management, and business performance. 1999. Management of Innovation and New Technology Research Centre, McMaster University.Bontis, N., Keow, W., & Richardson, S. (2000). Intellectual capital and the nature of business in Malaysia. Journal of Intellectual Capital, 1(1), 85–100Bontis, N., Hullan, J., & Crossan, M. (2002). Managing an organizational learning system by aligning stocks and flows. Journal of Management Studies, 39, 438–469.Brachos, D., Kostopulos, K., Sodersquist, K. E., & Prastacos, G. (2007). Knowledge effectiveness, social context and innovation. Journal of Knowledge Management, 11(5), 31–44.Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial Marketing Management, 31, 515–524.Chang, T. J., Yeh, S. P., & Yeh, I. J. (2007). The effects of joint rewards system in new product development. International Journal of Manpower, 28(3/4), 276–297.Chin, W. (1998). The partial least square approach to structural equation modeling. In G. A. Marcoulides (Ed.) (pp. 294–336). New Jersey: Lawrence Erlbaum Associates.Cho, N., Li, G., & Su, C. (2007). An empirical study on the effect of individual factors on knowledge sharing by knowledge type. Journal of Global Business and Technology, 3(2), 1–15.Cohen, W. M., & Levin, R. C. (1989). Empirical studies of innovation and market structure. In R. Schmalansee & R. D. Willing (Eds.), Handbook of industrial organization II. New York: Elsevier.Cohen, W. M., & Levinthal, D. A. (1990). Absorptive-capacity – a new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.Cooper, R. G. (2000). New product performance: what distinguishes the star products. Austrian Journal of Management, 25, 17–45.Crossan, M., & Berdrow, I. (2003). Organizational learning and strategic renewal. Strategic Management Journal, 24, 1087–1105.Crossan, M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: a systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191.Crossan, M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: from intuition to institution. Academy of Management Review, 24, 522–537.Damanpour, F., & Aravind, D. (2012). Managerial innovation: conceptions, processes, and antecedents. Management and Organization Review, 8(2), 423–454.Damanpour, F., & Shanthi, G. (2001). The dynamics of the adoption of products and process innovations in organizations. Journal of Management Studies, 38(1), 21–65.Decarolis, D. M., & Deeds, D. L. (1999). The impact of stock and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry. Strategic Management Journal, 20, 953–968.Demartini, C. (2015). Relationships between social and intellectual capital: empirical Evidence from IC statements. Knowledge and Process Management, 22(2), 99–111.Dupuy, F. (2004). Sharing knowledge: they why and how of organizational change. Hampshire: Palgrave Macmillan.Fornell, C., & Bookstein, F. I. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.Ganter, A., & Hecker, A. (2013). Deciphering antecedents of organizational innovation. Journal of Business Research, 66(5), 575–584.Ganter, A., & Hecker, A. (2014). Configurational paths to organizational innovation: qualitative comparative analyses of antecedents and contingencies. Journal of Business Research, 67, 1285–1292.Gopalakrishnan, S., & Damanpour, F. (1997). A review of innovation research in economics, sociology and technology management. International Journal of Management Science, 25, 15–28.Hedberg, B. (1981). How organizations learn and unlearn. In P. Nystrom & W. Starbuck (Eds.), Handbook of organizational design. New York: Oxford University.Hedlund, G., & Nonaka, I. (1993). Models of knowledge management in the west and Japan. In: P. Lorange, B. Chacravrarthy, J. Ross, and J. Van de ven (Eds.) Cambridge: Basil Blackwell.Henseler, J., Ringle, C.M., & Sinkovics, R.R. (2009). The use the partial least squares path modeling. In: R. Sinkovics and N. Pervez (Eds.) 277–319.Hsu, I. (2006). Enhancing employee tendencies to share knowledge-case studies on nine companies in Taiwan. International Journal of Information Management, 26(4), 326–338.Hsu, I. (2008). Knowledge sharing practices as a facilitating factor for improving organizational performance though human capital: a preliminary test. Expert Systems with Application, 35, 316–1326.Huang, Q., Davison, R., & Gu, J. (2008). Impact of personal and cultural factors on knowledge sharing in China. Asia Pacific Journal Management, 25(3), 451–471.Ibarra, H. (1993). Network centrality, power, and innovation involvement – determinants of technical and administrative roles. Academy of Management Journal, 36(3), 471–501.Iebra, I. L., Zegarra, P. S., & Zegarra, A. S. (2011). Learning for sharing: an empirical analysis of organizational learning and knowledge sharin. International Entrepreneurship Management Journal, 7, 509–518.Ipe, M. (2003). Knowledge sharing in organizations: a conceptual framework. Human Resource Development Review, 2(4), 337–359.Jenkin, T. (2013). Extending the 4I organizational learning model: information sources, foraging processes and tools. Administrative Sciences, 3, 96–109.Jiménez-Jiménez, D., & Sanz-Valle, R. (2011). Innovation, organizational learning, and performance. Journal of Business Research, 64, 408–417.Kane, G. C., & Alavi, M. (2007). Information technology and organizational learning: an investigation of exploration and exploitation processes. Organization Science, 18(5), 796–812.Kleinbaum, D. G., Kupper, N. N., Muller, K. E. (1988). Applied regression analysis and other Multivariable’s methods, PWS KENT.Klomp, L., & Van Leeuwen, G. (2001). Linking innovation and firm performance: a new approach. International Journal of the Economics of Business, 8(3), 343–364.Lansisalmi, H., Kivimaki, M., Aalto, P., & Ruoranen, R. (2006). Innovation in healthcare: a systematic review of recent research. Nursing Science Quarterly, 19(1), 66–72.Laperrière, A., & Spence, M. (2015). Enacting international opportunities: the role of organizational learning in knowledge-intensive business services. Journal of International Entrepreneurship, 13(3), 212–241.Levitt, B., & March, J. G. (1988). Organizational learning. Annual Review of Sociology, 14, 319–340.Lin, H. (2007). Knowledge sharing and firm innovation capability: an empirical study. International Journal of Manpower, 28(3/4), 315–332.Lloria, M. B., & Moreno-Luzón, M. D. (2014). Organizational learning: proposal of an integrative scale and research instrument. Journal of Business Research, 67, 692–697.March, J. G. (1991). Exploration and exploitation in organizational learning. Organizational Science, 2, 71–87.Matikainen, M., Terho, H., Parvinen, P., & Juppo, A. (2016). The role and impact of firm’s strategic orientations on launch performance: significance of relationship orientation. Journal of Business & Industrial Marketing, 31(5), 625–639.Mone, M. A., McKinley, W., & Barker, V. L. (1998). Organizational decline and innovation: a contingency framework. Academy of Management Review, 23, 115–132.Moreno-Luzón, M. D., & Lloria, B. (2008). The role of non-structural and informal mechanisms of integration and integration as forces in knowledge creation. British Journal of Management, 19, 250–276.Moskaliuk, J., Bokhorst, F., & Cress, U. (2016). Learning from others' experiences: how patterns foster interpersonal transfer of knowledge-in-use. Computers in Human Behavior, 55, 69–75.Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. How Japanese companies create the dynamics of innovation. New York: Oxford University Press.Nonaka, I., & von Krogh, G. (2009). Perspective tacit knowledge and knowledge conversion: controversy and advancement in organizational knowledge creation theory. Organization Science, 20(3), 635–652.Parida, V., Lahti, T., & Wincent, J. (2016). Exploration and exploitation and firm performance variability: a study of ambidexterity in entrepreneurial firms. International Entrepreneurship Management Journal, 12, 1147–1164.Pew, H., Plowman, D., & Hancock, P. (2008). The involving research on intellectual capital. Journal of Intellectual Capital, 9, 585–608.Potter, R. E., & Balthazard, P. A. (2004). The role of individual memory and attention processes during electronic brainstorming. MIS Quarterly, 28(4), 621–643.Ramadani, V., Hyrije, A. A., Léo-Paul, D., Gadaf, R., & Sadudin, I. (2017). The impact of knowledge spillovers and innovation on firm-performance: findings from the Balkans countries. International Entrepreneurship Management Journal, 13, 299–325.Ren, S., Shu, R., Bao, Y., & Chen, X. (2016). Linking network ties to entrepreneurial opportunity discovery and exploitation: the role of affective and cognitive trust. International Entrepreneurship and Management Journal, 12(2), 465–485.Ringle, C. M., Wende, S., & Will, A. (2005). Smart PLS 2.0 (M3) beta, Hamburg: http://www.smartpls.de .Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM. MIS Quarterly, 36(1), iii–xiv.Sanchez, R., & Heene, A. (1997). A competence perspective on strategic learning and knowledge management. En Sanchez, R. and Heene, A. (eds.) Strategic learning and knowledge management. John Wiley and Sons.Seidler-de Alwis, R., & Hartmann, E. (2008). The use of tacit knowledge within innovative companies: knowledge management in innovative enterprises. Journal of Knowledge Management, 12(1), 133–147.Shrivastava, P. (1983). A typology of organizational learning systems. Journal of Management Studies, 20, 7–28.Tansky, J., Ribeiro, D., & Roig, S. (2010). Linking entrepreneurship and human resources in globalization. Human Resource Management, 49(2), 217–223.Teece, D. (2012). Dynamic capabilities: routines versus entrepreneurial action. Journal of Management Studies, 49(8), 1395–1401.Tenenhaus, M., Vinzi, V., Chatelin, Y., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 49, 159–205.vande Vrande, V., de Jong, J., Vanhaverbeke, W., & Rochemont, M. (2009). Open innovation in SMEs: trends, motives and management challenges. Technovation, 29, 423–437.Vargas, N., & Lloria, M. B. (2014). Dynamizing intellectual capital through enablers and learning flows. Industrial Management and Data Systems, 114(1), 2–20.Vargas, N., & Lloria, M. B. (2017). Performance and intellectual capital: how enablers drive value creation in organisations. Knowledge and Process Management, 24(2), 114–124.Vargas, N., Lloria, M. B., & Roig-Dobón, S. (2016). Main drivers of human capital, learning and performance. The Journal of Technology Transfer, 41(5), 961–978.Vergara, L., Salazar, A., Belda, J., Safont, G., Moral, S., & Iglesias, S. (2017). Signal processing on graphs for improving automatic credit card fraud detection. Proceeding of 2017 I.E. 51st international Carnahan Conference on Security Technology (ICCST 2017), https://doi.org/10.1109/CCST.2017.8167820 , 23–26 Oct, 2017, Madrid, Spain.Wallin, M. W., & Von Krogh, G. (2010). Organizing for open innovation: focus o the integration of knowledge. Organizational Dynamics, 39(2), 145–154.Wang, C. L., & Ahmed, P. K. (2004). Linking innovation and firm performance: a new approach. European International Journal of Technology Management, 27, 674–688.Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometric models (pp. 47–74). Cambridge: Academic Press.Wold, H. (1985). Factors influencing the outcome of economic sanctions. In Sixto Ríos Honorary. Trabajos de Estadística and de Investigación Operativa, 36(3), 325–337

    Childhood acute leukemias are frequent in Mexico City: descriptive epidemiology

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    <p>Abstract</p> <p>Background</p> <p>Worldwide, acute leukemia is the most common type of childhood cancer. It is particularly common in the Hispanic populations residing in the United States, Costa Rica, and Mexico City. The objective of this study was to determine the incidence of acute leukemia in children who were diagnosed and treated in public hospitals in Mexico City.</p> <p>Methods</p> <p>Included in this study were those children, under 15 years of age and residents of Mexico City, who were diagnosed in 2006 and 2007 with leukemia, as determined by using the International Classification of Childhood Cancer. The average annual incidence rates (AAIR), and the standardized average annual incidence rates (SAAIR) per million children were calculated. We calculated crude, age- and sex-specific incidence rates and adjusted for age by the direct method with the world population as standard. We determined if there were a correlation between the incidence of acute leukemias in the various boroughs of Mexico City and either the number of agricultural hectares, the average number of persons per household, or the municipal human development index for Mexico (used as a reference of socio-economic level).</p> <p>Results</p> <p>Although a total of 610 new cases of leukemia were registered during 2006-2007, only 228 fit the criteria for inclusion in this study. The overall SAAIR was 57.6 per million children (95% CI, 46.9-68.3); acute lymphoblastic leukemia (ALL) was the most frequent type of leukemia, constituting 85.1% of the cases (SAAIR: 49.5 per million), followed by acute myeloblastic leukemia at 12.3% (SAAIR: 6.9 per million), and chronic myeloid leukemia at 1.7% (SAAIR: 0.9 per million). The 1-4 years age group had the highest SAAIR for ALL (77.7 per million). For cases of ALL, 73.2% had precursor B-cell immunophenotype (SAAIR: 35.8 per million) and 12.4% had T-cell immunophenotype (SAAIR 6.3 per million). The peak ages for ALL were 2-6 years and 8-10 years. More than half the children (58.8%) were classified as high risk. There was a positive correlation between the average number of persons per household and the incidence of the pre-B immunophenotype (Pearson's r, 0.789; P = 0.02).</p> <p>Conclusions</p> <p>The frequency of ALL in Mexico City is among the highest in the world, similar to those found for Hispanics in the United States and in Costa Rica.</p
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