Mahmoodlomholt5135

Background The prevalence of metabolic syndrome (MS) is rapidly increasing in the world. Thus, the aim of the present study was to identify the latent subgroups of Iranian male adults based on MS components and investigate the effect of abnormal alanine aminotransferase (ALT) and aspartate aminotransferase (AST), high total cholesterol (TC), and low-density lipoprotein (LDL) on the odds of membership in each class. Methods In the present study, we used the data of a population-based screening program conducted on 823 urban adult men aged 25 years and older in city of Qom in 2014. Abdominal obesity, fasting blood sugar (FBS), blood pressure, and serum lipid profile were measured in participants after for at least 8 hours. MS was defined according to the Adults Treatment Panel III criteria. Latent class analysis was used to achieve the aims of study. Analyses were conducted using PROC LCA in SAS 9.2 software. In all analysis, p value less then 0.05 was considered statistically significant. Results There were 3 different latent classes among participants. Latent class 1, non-MS, 55.1%; latent lass 2, at risk, 21.3%; and finally latent class 3, MS, with 23.6% of the participants. Age (OR=0.98, 95% CI 0.98-0.99, high LDL (OR=0.27, 95% CI 0.13-0.56), high TC (OR=8.12, 95% CI 4.40-15.00), and abnormal ALT (OR=2.25, 95% CI 1.49-3.41) were associated with at risk class. Also, only age (OR=1.02, 95% CI 1.01-1.04) was associated with MS class. The most prevalent components among the participants were having low HDL (34.0%) and high WC (33.9%). INX-315 supplier Conclusion Notable percent of samples fell in "at risk" and "MS" classes, which stress the necessity of designing preventive interventions for these specific stratums of population.Background The 2019 coronavirus (COVID-19) is a highly contagious disease associated with a high morbidity and mortality worldwide. The accumulation of data through a prospective clinical registry enables public health authorities to make informed decisions based on real evidence obtained from surveillance of COVID-19. This registry is also fundamental to providing robust infrastructure for future research surveys. The purpose of this study was to design a registry and its minimum data set (MDS), as a valid and reliable data source for reporting and benchmarking COVID-19. Methods This cross sectional and descriptive study provides a template for the required MDS to be included in COVID-19 registry. This was done by an extensive literature review and 2 round Delphi survey to validate the content, which resulted in a web-based registry created by Visual Studio 2019 and a database designed by Structured Query Language (SQL). Results The MDS of COVID-19 registry was categorized into the administrative part with 3 sections, including 30 data elements, and the clinical part with 4 sections, including 26 data elements. Furthermore, a web-based registry with modular and layered architecture was designed based on final data classes and elements. Conclusion To the best of our knowledge, COVID-19 registry is the first designed instrument from information management perspectives in Iran and can become a homogenous and reliable infrastructure for collecting data on COVID-19. We hope this approach will facilitate epidemiological surveys and support policymakers to better plan for monitoring patients with COVID-19.Background Spinal cord injury (SCI) has serious impacts on the patient's function. Therefore, their participation is important as one of the major indicators of the quality of life. This study reviews instruments that evaluate participation among people with spinal cord injury. Methods Four electronic databases (WebofScience, Scopus, MEDLINE/PubMed, SID) were searched for studies published in the English language between 2000 and 2019 in one or more peer-reviewed journals on the measurement properties (reliability, validity and/or responsiveness) in all populations including adults with SCI. Instruments assessed based on special criteria designed for disability outcome measures. Results Six instruments were included Incontinence - Activity Participation Scale, Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P), World Health Organization's disability assessment tool-II (WHODAS-II), ICF Measure of Participation and ACTivities Screener (IMPACT-S), Impact on Participation and Autonomy (IPA) , Participation measure for Post-Acute care (PM-PAC). Evidence related to the reliability and validity was reported for all of the instruments. Only WHODAS-II, USER-P, and IMPACT were compared with each other in recent publications. Responsiveness was not obtained for any of the instruments. Conclusion As the underlying structure of every instrument is different, the concept of the evaluated participation varies between instruments. The proper instrument for examining participation of the patients with SCI should be selected based on a thorough analysis of the individual's condition and context. Innovative models of disability should be the basis of emerging instruments for evaluation of participation, as well as empirical studies and modern measurement technologies that fill the gap between the perceived participation of the individual and the research's record.Background Investigating the spatial aspects of the disease can help decision-makers and researchers better understand the pattern of the disease, and is also very important in the implementation of the disease control programs. Given the vast area of Iran, as well as the diverse geographical and climate conditions of the country, using the geographical information system (GIS) is a suitable method for the study of influenza. In this study, we provide a clear picture of the distribution of the influenza-like illness (ILI) in Iranian provinces through the years from 2011 to 2016 by applying a spatio-temporal analysis, using geographic information system (GIS). Disease rates by location and year are estimated, and then hot spots and cold spots are distinguished. Methods This study was conducted using the ILI incidence rate data recorded in the Iranian Influenza Surveillance System from August 2011 to August 2016. The Choropleth map method and the various equal interval and natural break classifications were used.