Background: Disaster identification and alert systems can be processed in dif-ferent ways. An early warning system is designed to detect impending danger and send appropriate and clear signals to at risk communities and organizations at the right time and in an unambiguous way. This study aimed to determine early warning system for disaster within health organization in Iran.
Methods: This article presents the findings of a mixed-methods study of early warning systems for disaster management within the health organizations in Iran. During the years 2011 to 2012, a sample of 230 health managers was surveyed using a questionnaire and 65 semi-structured interviews were conducted with public health and therapeutic affairs managers who were responsible for disaster management.
Results: A range of problems were identified. Although there is a multi-agency alert system within the health organizations, other indicators of early warning system are not satisfactory. Furthermore, standard messages which are used to alert organizations are not used under the current system.
Conclusion: Some activities such as memorandum of understanding among different stakeholders of disaster response and education of staff and communities could improve the response to disasters within the health organizations.
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http://dx.doi.org/10.5681/hpp.2013.030 | DOI Listing |
Sci Rep
January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
View Article and Find Full Text PDFViruses
January 2025
Antiguo Hospital Civil de Guadalajara, "Fray Antonio Alcalde", Guadalajara 44280, Mexico.
This study investigates the relationship between SARS-CoV-2 RT-PCR cycle threshold (Ct) values and key COVID-19 transmission and outcome metrics across five years of the pandemic in Jalisco, Mexico. Utilizing a comprehensive time-series analysis, we evaluated weekly median Ct values as proxies for viral load and their temporal associations with positivity rates, reproduction numbers (Rt), hospitalizations, and mortality. Cross-correlation and lagged regression analyses revealed significant lead-lag relationships, with declining Ct values consistently preceding surges in positivity rates and hospitalizations, particularly during the early phases of the pandemic.
View Article and Find Full Text PDFViruses
December 2024
Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Crimean-Congo hemorrhagic fever (CCHF) is a serious tick-borne disease with a wide geographical distribution. Classified as a level 4 biosecurity risk pathogen, CCHF can be transmitted cross-species due to its aerosol infectivity and ability to cause severe hemorrhagic fever outbreaks with high morbidity and mortality. However, current methods for detecting anti-CCHFV antibodies are limited.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model.
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