RESUMO
Air pollution has been linked to poor olfactory function in human adults. Among pollutants, particulate matter (PM) is especially relevant, as it may contain toxic metal ions that can reach the brain via olfactory pathways. Our purpose was to investigate the relation between atmospheric PM and olfactory identification performance in children. Using a validated method, we tested the olfactory identification performance of 120 children, 6-12 years old, from two locations in Mexico City: a focal group (n = 60) from a region with high PM levels and a control group of equal size and similar socioeconomic level from a region with markedly lower PM concentrations. Groups were matched for age and sex. Concentrations of manganese and lead in the hair of participants were determined as biomarkers of exposure. Daily outdoor PM levels were obtained from official records, and indoor PM levels were measured in the children's classrooms. Official records confirmed higher levels of outdoor PM in the focal region during the days of testing. We also found higher classroom PM concentrations at the focal site. Children from the focal site had on average significantly lower olfactory identification scores than controls, and hair analysis showed significantly higher levels of manganese for the focal children but no difference in lead. Children appear to be vulnerable to the effects of air pollution on olfactory identification performance, and metal-containing particles likely play a role in this. Olfactory tests provide a sensitive, noninvasive means to assess central nervous function in populations facing poor air quality.
Assuntos
Chumbo/efeitos adversos , Manganês/efeitos adversos , Transtornos do Olfato/induzido quimicamente , Material Particulado/efeitos adversos , Atmosfera/química , Biomarcadores/análise , Criança , Feminino , Cabelo/química , Humanos , Chumbo/análise , Masculino , Manganês/análise , México , Transtornos do Olfato/fisiopatologia , Tamanho da Partícula , Material Particulado/análiseRESUMO
Resumen: Introducción: El diferimiento quirúrgico desde el inicio de la pandemia COVID-19 representa un riesgo para la salud de los pacientes y un reto para las instituciones sanitarias. La métrica de indicadores de productividad en quirófano, como la tasa de diferimiento quirúrgico, nos brindará información para planear estrategias basadas en evidencia científica que nos permitan mejorar la calidad de la atención. Objetivo: Determinar la tasa de diferimiento quirúrgico durante la pandemia COVID-19. Material y métodos: Se realizó un estudio, retrospectivo, descriptivo y transversal en el que se analizaron 297 pacientes cuyas cirugías fueron diferidas durante el período comprendido del 11 de marzo de 2020 al 10 de marzo de 2021. Para el análisis de variables se efectuó estadística descriptiva, utilizando medidas de tendencia central y dispersión, frecuencias simples y proporciones. Adicionalmente se realizó un diagrama de Pareto. La información fue procesada con SPSS v-25.0. Resultados: La proporción de diferimiento fue de 7.34%; 20.92% de las causas fueron atribuidas al paciente, 31.65% a causas médicas y 47.49% fueron logístico-administrativas. Conclusión: Las causas de diferimiento quirúrgico establecidas son evitables y revelan fracasos en los procesos logístico-administrativos que requieren planes de mejora continua.
Abstract: Introduction: Surgical deferral since the beginning of the COVID-19 pandemic represents a risk to the health of patients and a challenge for health institutions. The metric of productivity indicators in the operating room, such as the surgical deferral rate, will provide us with information to plan strategies based on scientific evidence that allow us to improve the quality of care. Objective: To determine the surgical deferral rate during the COVID-19 pandemic. Material and methods: A retrospective, descriptive and cross-sectional study was carried out in which 297 patients whose surgeries were deferred during the period from March 11, 2020 to March 10, 2021 were analyzed. Descriptive statistics were employed, using measures of central tendency and dispersion, simple frequencies and proportions. Additionally, a Pareto diagram was made. The information was processed with SPSS v-25.0. Results: The deferral rate was 7.34%. 20.92% of the causes were attributed to the patient, 31.65% to medical causes and 47.49% logistic-administrative. Conclusion: The established causes of surgical deferral are avoidable and reveal failures in the logistical-administrative processes that require continuous improvement plans.