Inequality in Cosmetic Services and Surgery among Iranian Households in 2019: A Decomposition Analysis.

Background: The demand for cosmetic surgery is on the rise worldwide, making it the common form of surgery globally while the use of cosmetic surgery being exponentially high in Iran. The aim of this study was to investigate inequality in the use of cosmetic services and surgery (CSS) among Iranian households concerning demographic and socio-economic characteristics. Methods: This study used data of 38960 Iranian household from the income-expenditure survey of the statistical center of Iran (SCI) in 2019. Concentration index (C) was used to measure inequalities in the use of CSS. Microsoft Excel sheet 2019 was used to extract the data, and the analysis was performed using Stata statistical package version 14.2. Results: Households with female head, with single head, households with 3 - 4 people, headed with undergraduate education person, households with insurance coverage, with higher socio-economic quintiles, rural households and residents of northwestern Iran were accounted for the highest use of CSS. Also, according to the decomposition analysis, wealth and education level are the two main factors in creating inequality, with wealth, having the highest positive share (88.11%) and education level having the most negative share (-5.26%) in creating measured inequality. Conclusion: The use of CSS is more concentrated in well-off households in Iran. As the resources of health system are limited, the government and the policy makers should have defined plans with regards to CSS use especially taking factors like socioeconomic status and education status of target groups in to account.

The effect of Cinnamomum zeylanicum bark water extract on memory performance in alloxan-induced diabetic mice.

Cinnamomum zeylanicum (cinnamon) has a wide range of beneficial effects including mild glucose lowering activity. The aim of the present study was to investigate whether cinnamon bark extract has the potential to improve memory performance and glucose profiles in diabetic mice. Memory was assessed by the novel object recognition task in male Balb/c mice. In this method, the difference between exploration time of a familiar object and a novel object was considered as an index of memory performance (recognition index, RI). The water extract was prepared by boiling cinnamon bark for 15 min. Alloxan induced diabetes in animals (serum glucose levels were 322 ± 7.5 mg/dL), and also impaired memory performance (RI= -3.3% ± 3.3) which differed significantly from control animals (RI = 32% ± 6.5). Although treatment with cinnamon only reduced fasting blood glucose level moderately but it improved memory performance remarkably (RI = 25.5% ± 5.6). Oxidative stress following administration of cinnamon extract was lower in diabetic mice. It was concluded that cinnamon water extract could be a useful alternative medicine in diabetic patients' daily regimen which not only reduces blood glucose levels but also improves memory performance and lipid peroxidation level.

Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.

Investigating the efficacy of an intelligent operation planning and support tool for acute healthcare contexts.

Nurses are the largest group of healthcare professionals in hospitals providing 24-hour care to patients. Hence, nurses are pivotal in coordinating and communicating patient care information in the complex network of healthcare professionals, services and other care processes. Yet, despite nurses' central role in health care delivery, intelligent systems have historically rarely been designed around nurses' operational needs. This could explain the poor integration of technologies into nursing work processes and consequent rejection by nursing professionals. The complex nature of acute care delivery in hospitals and the frequently interrupted patterns of nursing work suggest that nurses require flexible intelligent systems that can support and adapt to their variable workflow patterns. This study is designed to explore nurses' initial reactions to a new intelligent operational planning and support tool (IOPST) for acute healthcare. The following reports on the first stage of a longitudinal project to use an innovative approach involving nurses in the development of the IOPST; from conceptualization to implementation.