El objetivo del estudio es analizar las características demográficas, comorbilidad y condición médica de las personas infectadas y no-infectadas en México. La información de artículos especializados, noticias científicas, reportes de investigación y notas de agencias de noticias es articulada selectivamente para generar hipótesis para el estudio de caso. La selección de estas hipótesis depende en gran medida de la información disponible y el objetivo de la investigación. Los resultados confirman que los hombres mueren más que las mujeres y que las comorbilidades principales de la población son la hipertensión, la obesidad y la diabetes. El análisis confirma que el tabaquismo no es relevante para la admisión en la Unidad de Cuidados Intensivos (UCI), pero es un factor asociado al fallecimiento de los infectados. El embarazo no está asociado a la gravedad de la infección, medida por admisión en la UCI. Finalmente, el análisis también muestra que la infección del virus no es más aguda en la población indígena infectada. El estudio presenta resultados y sugiere una ruta metodológica que pudiera ser útil para la toma de decisiones en materia de salud. Exploraciones en el buscador Google Scholar muestran que el estudio actual es el primer análisis formal de
Background The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico. Methods We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters. Results DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence. Conclusions Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.
Background: Conventional contact tracing approaches have not kept pace with the scale of the coronavirus disease 2019 (COVID-19) pandemic and the highly anticipated smartphone applications for digital contact tracing efforts are plagued by low adoption rates attributed to privacy concerns; therefore, innovation is needed in this public health capability. Methods: This study involved a cross-sectional, nonrepresentative, online survey in the United States of individuals tested for COVID-19. Testing survey items measured the performance of conventional contact tracing programs, quantified the stigma related to the notification of COVID-19 close contacts, and assessed the acceptability of a website service for digital contact tracing. Results: A sample of 668 (19.9%) individuals met the inclusion criteria and consented to participation. Among the 95 participants with COVID-19, results were received after a median of 2 days, 63.2% interacted with a contact tracing program a median of 2 days after receiving test results, 62.1% had close contacts, and 37.1% of participants with COVID-19 and close contacts did not disclose their results to all close contacts. Among all participants, 17% had downloaded a mobile application and 40.3% reported interest in a website service. One hundred and nine participants perceived stigma with the disclosure of COVID-19 test results; of these, 58.7% reported that a website service for close contact notification would decrease this stigma. Discussion: Conventional contact tracing programs did not comprehensively contact individuals who tested positive for COVID-19 nor did so within a meaningful time frame. Digital contact tracing innovations may address these shortcomings; however, the low penetration of mobile application services in the United States indicates that a suite of digital contact tracing tools, including website services, are warranted for a more exhaustive coverage of the population. Conclusions: Public health officials should develop a complementary toolkit of digital contact tracing strategies to enable effective pandemic containment strategies.
The purpose of the study is to identify areas that are possibly gentrified or in the process of being gentrified, through a localized typology of two components: youthification and an increase in the quality of life. This typology can be applied in similar investigations. Thisd paper addresses the case study of the Metropolitan Center of the City of Monterrey (CMM), Nuevo León, Mexico. The current urban regeneration plans and the increase of housing density in the CMM have caused a vertical real estate “boom” of apartment buildings and have strengthened the emergence of gentrification in the area, understood here as the decrease in social backwardness (increase in the quality of life) over time, with an increase in young adults (25 to 34 years-old), compared to older adults (60+ years-old). This article suggests a procedure to measure gentrification by overlapping the Index of Social Backwardness (ISB) at the Basic Geostatistical Area (AGEB) level, with a youthification index at the electoral section level between the 2010-2020 period. Both the decline of social backwardness (2010-2020) and youthification (2010-2020), are analytically articulated for successive census years, to generate a localized typology of the gentrification process.
Background Pregnancy increases a woman’s risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico. Method Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status. Results Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype. Conclusion The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico.
Los investigadores de índices compuestos han detectado y tratado de eliminar, con poco éxito hasta ahora, la ponderación implícita de los datos porque generan índices distorsionados. El origen de esta distorsión suele adjudicarse a las variables con distinto rango de valores, al valor máximo y mínimo desigual entre ellas o a la de asimetría de cada variable. El procedimiento para abordar este triple reto es la estandarización de los datos. A la fecha no hay un procedimiento de este tipo que corrija simultáneamente este triple problema y, con ello, la ponderación implícita. El igual máximo y mínimo entre variables resuelve el problema de rangos desiguales, pero no corrige la asimetría en cada una de ellas. El rango igual entre variables no implica igual máximo y mínimo entre ellas ni garantiza simetría en cada variable. Finalmente, la corrección de la asimetría en cada variable no implica igual máximo y mínimo ni igual rango entre variables. El objetivo del presente estudio es sugerir e ilustrar un procedimiento de estandarización de variables que elimina la ponderación implícita de cada variable. Las estandarizaciones revisadas en este trabajo para elaborar índices compuestos resuelven solo de forma parcial el problema de la ponderación implícita por lo que este trabajo propone un procedimiento de estandarización que resuelve simultáneamente el triple reto impuesto por los rangos disparejos, los máximos y mínimos desiguales y la asimetría. El nuevo procedimiento propuesto, la estandarización balanceada, soluciona el problema de la ponderación implícita en el tiempo y el espacio, en la agregación transversal y longitudinal de las variables de las distintas unidades de observación. Este trabajo ilustra el procedimiento de estandarización propuesto con las variables del rezago educativo en los estados de México. La metodología propuesta es aplicable a cualquier tema donde la estandarización espacio temporal sea necesaria.
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