Factors influencing quality of life among the elderly: An approach using logistic regression.

BACKGROUND: Improving the physical, psychological, and social factors in the elderly significantly increases the QoL1 among them. This study aims to identify the crucial factors for predicting QoL among the elderly using statistical methods. MATERIALS AND METHODS: In this study, 980 samples related to the elderly with favorable and unfavorable QoL were investigated. The elderly's QoL was investigated using a qualitative and self-assessment questionnaire that measured the QoL among them by five Likert spectrum and independent factors. The Chi-square test and eta coefficient were used to determine the relationship between each predicting factor of the elderly's QoL in SPSS V 25 software. Finally, we used the Enter and Forward LR methods to determine the correlation of influential factors in the presence of other variables. RESULTS: The study showed that 20 variables gained a significant relationship with the quality of life of the elderly at P < 0.05. The study results showed that the degree of dependence (P = 0.03), diabetes mellitus (P = 0.03), formal and informal social relationships (P = 0.01 and P = 0.02), ability to play an emotional role (P = 0.03), physical performance (P = 0.01), heart diseases and arterial blood pressure (P = 0.02), and cancer (P = 0.01) have favorable predictive power in predicting the QoL among the elderly. CONCLUSION: Attempts to identify and modify the important factors affecting the elderly's QoL have a significant role in improving the QoL and life satisfaction in this age group people. This study showed that the statistical methods have a pleasant capability to discover the factors associated with the elderly's QoL with high performance in this regard.

Developing a prediction model for successful aging among the elderly using machine learning algorithms.

Objective: The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)1 concept. It is believed that the SA prediction model can increase the quality of life (QoL)2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA. Methods: The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB3, XG-Boost J-48, RF4, artificial neural network5, support vector machine6, and NB7 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)8, negative predictive value (NPV)9, sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC). Results: Comparing the machine learning10 model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA. Conclusions: Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

Quality and cost analysis of a wastewater treatment plant using GPS-X and CapdetWorks simulation programs.

Wastewater management is a significant challenge, especially for arid and semiarid countries due to the water scarcity crisis. Considering human health, environmental regulations, wastewater treatment plant expenses, and water reusability, this study aims to assist in designing an efficient and economical WWTP. Technical and economic simulation and modeling are important for designing, constructing, and predicting the requirements of wastewater treatment plant designs. Simulation of a project before its implementation, in the first place, reduces additional costs, and from another point of view, a project is examined and researched from different aspects. In this study, three wastewater treatment plants based on conventional activated sludge, contact stabilization, and step aeration were simulated and evaluated technically and economically using the Zargandeh treatment plant data in Tehran. In three scenarios, GPS-X software investigated the effect of raw wastewater variations between their minimum and maximum intervals on effluent quality, and because the range of inlet effluent was different, more precise results were obtained. Then, the economic costs of the project, including implementation, maintenance, and also energy use, were calculated by Capdetwork software, and contact stabilization was found to be more cost-effective.

Influences for Gender Disparity in Academic Family Medicine in North American Medical Schools.

Background Women physicians continue to comprise the minority of leadership roles in Academic Family Medicine (AFM) faculty across North American medical schools. Our study quantified the current state of gender disparity by analyzing academic position, leadership ranking, and research productivity. Methods We generated a database for 6,746 AFM faculty members. Gender and academic profiles were obtained for 2,892 academic ranks and 1,706 leadership roles by searching faculty listings enlisted in Fellowship and Residency Electronic Interactive Database (FREIDA) and Canadian Resident Matching Service (CaRMS). To measure research productivity, we obtained bibliometric data: h-index, citations, and tenure from 2,383 faculty members using Elsevier's SCOPUS archives. Data analysis and h-index were formulated using Stata version 14.2 (StataCorp LP, College Station, TX). Results Our results indicated that women hold 46.11% (3,110/6,746) of faculty positions. The proportional composition decreased with increasing academic ranking (49.84% assistant, 46.78% associate, and 41.5% full professor). The same decreasing trend was demonstrated with leadership rank (57.14% minor leadership, 47.65% second-in-command, and 36.61 first-in-command). Compared to their gender counterparts, women in AFM demonstrated lower publication productivity as measured by citation number (p=0.04) and years of study (p=0.008). The final prediction equation model after multivariable analyses included gender, publications, citations, country of graduation, and years of active research (p<0.05). Conclusions The composition of academic family medicine faculty members included in this study demonstrated gender disparity. Inclusivity initiatives and policies to tackle the issue of female retention, promotion, and recruitment need to be further explored.

Assessment priorities in cerebral palsy using ICF core set by Iranian occupational therapists.

BACKGROUND.: International Classification of Functioning, Disability and Health (ICF) core set for cerebral palsy allows for the description of the levels of functioning in cerebral palsy. It is not exactly clear which of these levels is more important for evaluation from the perspective of occupational therapists in Iran. By identifying these priorities, we can establish a better plan for intervention. PURPOSE.: This study defines assessment priorities in children with cerebral palsy (<6 years). METHOD.: Sixty-two Iranian occupational therapists studied the priorities of assessment based on the Iranian ICF core set. The therapists were asked to rate the code categories from 1 to 3. The results are presented as mean values. FINDINGS.: Occupational therapists first focus on body functions assessment, then activities/participation, and ultimately, environmental factors. IMPLICATIONS.: Occupational therapists in Iran have a bottom-up approach toward clients with cerebral palsy. It may be necessary to revise the educational curriculum, prepare a training course, and provide more supervision for practising occupational therapists.

Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining.

OBJECTIVES: The aim of this study was to examine a predictive model using features related to the diabetes type 2 risk factors. METHODS: The data were obtained from a database in a diabetes control system in Tabriz, Iran. The data included all people referred for diabetes screening between 2009 and 2011. The features considered as "Inputs" were: age, sex, systolic and diastolic blood pressure, family history of diabetes, and body mass index (BMI). Moreover, we used diagnosis as "Class". We applied the "Decision Tree" technique and "J48" algorithm in the WEKA (3.6.10 version) software to develop the model. RESULTS: After data preprocessing and preparation, we used 22,398 records for data mining. The model precision to identify patients was 0.717. The age factor was placed in the root node of the tree as a result of higher information gain. The ROC curve indicates the model function in identification of patients and those individuals who are healthy. The curve indicates high capability of the model, especially in identification of the healthy persons. CONCLUSIONS: We developed a model using the decision tree for screening T2DM which did not require laboratory tests for T2DM diagnosis.

The effects of organizational contextual factors on physicians' attitude toward adoption of Electronic Medical Records.

INTRODUCTION: Physicians' adoption seems to be a significant issue when comprehensive implementation of Electronic Medical Records (EMR) is considered. This study was conducted to determine the organizational contextual factors affecting physicians' adoption of EMR. METHODS: This was a descriptive-analytical study in which a sample of 330 physicians working in hospitals affiliated to Tehran University of Medical Sciences was selected. Physicians' attitudes toward EMRs' adoption have been assessed by a conceptual path model of Technology Acceptance model (TAM) and organizational context variables. Collected data were analyzed by SPSS16 using regression analysis. The final model was tested by structural equation modeling (SEM) and represented by SPSS-AMOS, structural equation modeling software. RESULTS: The results suggest that modified proposed conceptual path model explains about 56% of the variance of EMRs' adoption. The findings also evidenced that perceived usefulness (PU), perceived ease of use (PEOU), management support; physicians' involvement, physicians' autonomy, and the doctor-patient relationship have direct and significant effect on physicians' attitudes toward EMRs' adoption. However, training showed to have no significant effect on PU and PEOU. CONCLUSION: The present study acknowledged that considerable part of physicians' attitude toward EMRs' adoption is controlled by organizational contextual factors. These factors should be subsequently the major concern of health organizations and health policy makers.

An assessment of readiness for pre-implementation of electronic health record in Iran: a practical approach to implementation in general and teaching hospitals.

Readiness assessment provides a proper image of the existing conditions and an explanation of facilitated operational plans and functional approaches to successful implementation of electronic health record. Readiness assessment requires indices adjusted to particular conditions in each country. Therefore, the present study attempts to provide an acceptable model in Iran and to provide an assessment of public and teaching hospitals in medical education university. After reviewing related papers and descriptive study of five selected countries, the initial model was designed in the form of a questionnaire for analysis through Delphi and distributed among 30 experts nationwide. Along identification of components in the proposed method, a 7-point Likert scale was used to determine priority of each component. Then, all general -education hospitals at Tehran University of Medical Education were examined based on this model in terms of total, relative, or no readiness. The final model was designed in five dimensions: cultural, leadership and management, technical infrastructure, governance and operational dimensions; Educational Hospitals were evaluated in this dimension. 28.6 % of general--teaching hospitals are ready for pre-implementation. It seems to establish uniform strategic and executive team in Health center is essential for the preparation them in abovementioned area in the least possible time.

A framework of a health system responsiveness assessment information system for iran.

BACKGROUND: Responsiveness assessment of health system with the quality information is the key in effective evidence-based management of the health system. OBJECTIVES: This qualitative study defines the necessary components required for the health system responsiveness assessment information system (HS-RAIS). MATERIALS AND METHODS: This study was conducted based on mixed-methods approach and by using Delphi technique (29 participants in first round and 25 participants in second round) and semi-structured interviews in Iran 2013. The participant selection strikes a balance between being able to provide valid data, and increasing representative's leverage. The final framework for HS-RAIS was extracted from in-depth interviews with ten key informants. RESULTS: We followed these recommendations and developed a framework in 10 components including: minimum datasets, data sources, data gathering, data analysis, feedback and dissemination, legislative needs, objectives of health system responsiveness assessment, repetition period, executive committee and stewardship. CONCLUSIONS: This framework provides useful information for decision-making at all levels about assessment of health system.

Information technology governance domains in hospitals: a case study in Iran.

IT governance is a set of organizational structures ensuring decision-making rights and responsibilities with regard to the organization's IT assets. This qualitative study was carried out to identify the IT governance domains in teaching hospitals affiliated to Iran University of Medical Sciences. There were 10 heads of IT departments and 10 hospital directors. Semi structured interviews used for data collection. To analyze the data content analysis was applied. All the interviewees (100%) believed that decisions upon hospital software needs could be made in a decentralized fashion by the IT department of the university. Most of the interviewees (90%) believed that there were policies for logistics and maintenance of networks, purchase and maintenance, standards and general policies in the direction of the policies of the ministry of health and medical education. About 80% of the interviewees believed that the current emphasis of the hospital's IT unit and the hospital management for outsourcing of services were in the format of specialized contracts and under supervision of the university Statistic and IT department. A hospital strategic committee is an official organizational group consisting of hospital executives, heads of IT and multiple functional areas and business units in a hospital. In this committee, "the head of hospital" acts as the director of IT activities and ensures that IT strategies are alignment with the hospital business strategies.

Intensive care information system impacts.

INTRODUCTION: Today, intensive care needs to be increased with a prospect of an aging population and socioeconomic factors influencing health intervention, but there are some problems in the intensive care environments, it is essential to resolve. The intensive Care information system has the potential to solve many of ICU problems. The objective of the review was to establish the impact of intensive care information systems on the practitioners practice, patient outcomes and ICU performance. METHODS: Scientific databases and electronic journal citations was searched to identify articles that discussed the impacts of intensive care information system on the practices, patient outcomes and ICU performance. A total of 22 articles discussing ICIS outcomes was included in this study from 609 articles initially obtained from the searches. RESULTS: Pooling data across studies, we found that the median impact of ICIS on information management was 48.7%. The median impact of ICIS on user' outcomes was 36.4%, impact on saving tips by 24%, clinical decision support by a mean of 22.7%, clinical outcomes improved by a mean of 18.6%, and researches improved by 18%. CONCLUSION: The functionalities of ICIS are growing day by day and new functionalities are available with every major release. Better adoption of ICIS by the intensive care environments emphasizes the opportunity of better intensive care services through patient oriented intensive care clinical information systems. There is an immense need for developing guidelines for standardizing ICIS to to maximize the power of ICISs and to integrate with HISs. This will enable intensivists to use the systems in a more meaningful way for better patient care. This study provides a better understanding and greater insight into the effectiveness of ICIS in improving patient care and reducing health care expenses.

Use of data mining techniques to determine and predict length of stay of cardiac patients.

OBJECTIVES: Predicting the length of stay (LOS) of patients in a hospital is important in providing them with better services and higher satisfaction, as well as helping the hospital management plan and managing hospital resources as meticulously as possible. We propose applying data mining techniques to extract useful knowledge and draw an accurate model to predict the LOS of heart patients. METHODS: Data were collected from patients with coronary artery disease (CAD). The patient records of 4,948 patients who had suffered CAD were included in the analysis. The techniques used are classification with three algorithms, namely, decision tree, support vector machines (SVM), and artificial neural network (ANN). LOS is the target variable, and 36 input variables are used for prediction. A confusion matrix was obtained to calculate sensitivity, specificity, and accuracy. RESULTS: The overall accuracy of SVM was 96.4% in the training set. Most single patients (64.3%) had an LOS ≤5 days, whereas 41.2% of married patients had an LOS >10 days. Moreover, the study showed that comorbidity states, such as lung disorders and hemorrhage with drug consumption have an impact on long LOS. The presence of comorbidities, an ejection fraction <2, being a current smoker, and having social security type insurance in coronary artery patients led to longer LOS than other subjects. CONCLUSIONS: All three algorithms are able to predict LOS with various degrees of accuracy. The findings demonstrated that the SVM was the best fit. There was a significant tendency for LOS to be longer in patients with lung or respiratory disorders and high blood pressure.

Determining the most important evaluation indicators of health care information systems (HCIS) in Iran.

Accurate evaluation of healthcare information systems (HCIS) relies upon the choice of appropriate indicators. Iranian healthcare and health industry professionals were surveyed, by means of a descriptive cross sectional study, in order to identify the indicators they considered most relevant to the evaluation of healthcare information systems currently in use in Iran. It was concluded that effective evaluation of HCIS should encompass a variety of perspectives and methodologies (including qualitative methodologies), focus upon technical, economic and organisational concerns, and involve diversely constituted research teams.